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Bloomberg Chief Washington Correspondent Joe Mathieu delivers insight and analysis on the latest headlines from the White House and Capitol Hill, including conversations with influential lawmakers and key figures in politics and policy.
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STARKVILLE, Miss.—High school students and librarians from around the state were among recent visitors at Mississippi State for the third annual “MSU, The Library Experience,” which included an in-depth tour of the university’s Mitchell Memorial Library.
Dean of Libraries Lis Pankl, along with Associate Vice President for Academic Affairs Jim Dunne and Executive Vice Provost and Dean of the Graduate School Peter Ryan, welcomed students from Holly Springs, Houston, Northeast Lauderdale, Oxford, Shannon and Water Valley high schools.
Organizing Committee Chair LaDonne Delgado said “The day-long event was designed for high school librarians and students to increase awareness of what university libraries have to offer, entice students to consider attending MSU, introduce them to the library profession, and connect and collaborate with supporters of libraries and information science.”
Students toured Mitchell Memorial’s Charles H. Templeton Sr. Music Museum, John Grisham Room, Ulysses S. Grant Presidential Library, as well as the Frank and Virginia Williams Collection of Lincolniana.
During a session in the MaxxSouth Digital Media Center, students were introduced to the library’s Center for Advanced Vehicular Systems Mixed Reality Studio, a makerspace and multimedia lab with 3D and screen-printing capabilities, Cricut and Silhouette die-cutting machines, and audio, recording and television broadcast studios.
Participating schools included:
Holly Springs High School

Pictured left to right: Holly Springs High School Librarian Barbara Humphrey; HSHS students Amari Jefferson, Brandon Lester and Martavious Mays; MSU Professor and Dean of Libraries Lis Pankl; and Executive Vice Provost and Dean of the Graduate School Peter Ryan. 
 
Houston High School

Pictured left to right: Houston High School Librarian Miriam Garner; HHS students Ivette Gonzalez, Kaylee Miller and Greta Tate; MSU Professor and Dean of Libraries Lis Pankl; and Executive Vice Provost and Dean of the Graduate School Peter Ryan. 
 
Northeast Lauderdale High School

Pictured left to right: MSU Professor and Dean of Libraries Lis Pankl; Northeast Lauderdale High School Librarian Casey Clarke; NEHS students Emma Crocker, Talabria Lockhart and Elizabeth Robbins; and Associate Vice President for Academic Affairs Jim Dunne.
 
Oxford High School

Pictured left to right: MSU Professor and Dean of Libraries Lis Pankl; Oxford High School Librarian Amanda Osborne; OHS students Nick Gordon and Tahana McNeil; and Associate Vice President for Academic Affairs Jim Dunne.
 
Shannon High School

Pictured left to right: MSU Professor and Dean of Libraries Lis Pankl; Shannon High School Librarian Jennifer King; SHS students Becca Ausbon, Daniel Carnathan and Canton Forster; and Associate Vice President for Academic Affairs Jim Dunne. 
 
Water Valley High School

Pictured left to right: Water Valley High School Librarian Jennifer Rotkiewicz; WVHS students Addison Sossaman and Madalyn Stevens; MSU Professor and Dean of Libraries Lis Pankl; and Executive Vice Provost and Dean of the Graduate School Peter Ryan. 
Learn more about MSU Libraries at www.lib.msstate.edu. For more information on “MSU, The Library Experience” program, visit www.library.msstate.edu/libraryexperience.
MSU is Mississippi’s leading university, available online at www.msstate.edu.
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It looks like there are body parts everywhere. That’s how Steve Butler knows he has come to the right place.
There are at least three noses on the counter. There’s a stack of eyes, and a box full of ears and fingers. In the corner, there’s the lower half of a man’s face, complete with a moustache.
“It’s like something out of a movie,” Steve says, as he looks around.
The room is a mix of a laboratory and workshop. It’s spotless. Everything has a place.
In the lobby, there are stacks of photo albums of past visitors who are just like him: people who lost a visible part of their identity, people who had nowhere else to turn.
The last time Steve went looking for help, he ended up spending a lot of money, only to be left disappointed and dejected.
Read more: Man accused of being Russian spy in Norway attended universities in Ottawa, Calgary
Out of desperation, he started searching Google. He landed on this place, run by a man who appears to be the master of a very singular skill.
So Steve and his wife drove from West Virginia to the suburbs of Washington, D.C., and now he’s sitting in an oversized barber’s chair, surrounded by all these apparent body parts, while an 80-year-old ex-spy looks at his face — specifically, his amputated left ear, and neck tissue, which were lost to a rare glandular cancer.
“A lot of my family said ‘you look fine without it,’” he explains, “but you kind of don’t feel that way.”
A large skin graft covers the area where surgeons worked so desperately to save his life. Steve hopes the man who is currently prodding at his visible wounds can give him back what he lost.
“It’s hard to be around people sometimes,” he says, fighting back tears.
 
To watch Robert Barron is to see a master artist at work.
His gifted hands move with remarkable precision. Each brush stroke is calculated. Each piece he sculpts is meticulously crafted, and sometimes re-crafted, to reach perfection.
Some of the tools he uses seem wildly out of place: two pasta rollers, orange peels, and dozens of large-gauge syringes filled with fluids of various colours. That’s what it takes to make this art imitate life.
Barron’s medium of choice is silicone.
His finished pieces will be worn as facial prosthetics by people who have been visibly disfigured through birth defect, disease or trauma — people like Steve Butler.
“I mastered the technique of making silicone look like skin,” explains Barron as he picks up the half-face with the moustache.
Barron’s modest Virginia office, and unassuming company name — Custom Prosthetic Designs Inc. — downplay the miracle of his work.
When people walk through the door, they’re often desperate. Some have told him they are suicidal. By the time they leave, their physical differences are practically invisible to the outside world.
“It’s a great psychological benefit to their mental, physical and social well-being,” Barron says. “When I go to work, I know I’m going to make a difference in someone’s life.”
John Hill drove in from Milwaukee for that exact reason.
He wanted a set of custom-made ears to hide a genetic abnormality called microtia, that had prevented his outer ears from fully forming.
He’s 56 years old, and like Steve, his attempt at getting prosthetics elsewhere was disheartening.
They didn’t fit. They didn’t match his skin. They just didn’t look right.
“Not even close, is probably the nicest thing I could say.”
He came to see Barron, hoping that for the first time in his life his body would appear whole.
“His work is fantastic,” he says, while waiting for his ears to be finished.  “Seeing it in person, I mean it’s amazing.”
Some artists want fame. But if anyone spotted Robert Barron’s work, he’d consider that a failure.
That’s because his skill and attention to detail were honed in places where getting noticed could get you killed: Barron was a real-life master of disguise in the Central Intelligence Agency.
He travelled the world concealing identities at the height of the Cold War — often while concealing his own face.
He did a lot of things he still can’t talk about because they’re still classified, but what he can tell you about his life explains why virtually no one else can do what he does.
“Realism!” he proclaims, when asked what sets his work apart.
As a young man, he knew he had a special talent for replication.
He explains he could mimic songs on the piano by ear. No need for sheet music.
Read more: Tony Mendez, former CIA officer and inspiration for ‘Argo’, dies at 78 (Jan. 20, 2019)
He once entered an oil painting into an art competition, only to find that the judges had mistaken it for a photograph and put it in the wrong exhibit. “Everything I did had to be realistic,” he says emphatically as he recalls those early days.
The walls of Barron’s office are covered in memorabilia from his remarkable career.
Above the reception desk are the works he crafted as art director for several military magazines at the Pentagon, beginning in 1967, after a stint in the Marines.
There’s a twinkle in Barron’s eye that hints at his mischievous side, which set him on the path to where he is today.
At the Pentagon, he grew tired of the daily trek to and from his car across the sprawling parking lots, and so he took up an independent art project: he stole a parking permit from a car in a prime spot, and made a perfect replica of it, right down to the punch-holes.
He never doubted his ability to forge an official document at one of the most heavily guarded buildings in the world.
“That’s a federal offence,” Barron notes, with a hint of pride. “Forging government property is a federal offence!”
It took more than a year for anyone to catch on. Barron paid a small fine, and says the presiding judge quietly congratulated him on the quality of the forgery.
A man with that kind of talent has value. Soon Barron was hauled before the brass, who presented him with an offer: a job as a senior forger with the CIA. By the early 1970s, he was posted overseas, and gradually moved into work that involved disguising American agents and informants.
What he found was primitive, to put it politely — not much better than Halloween costuming.
“We found out that we just weren’t getting enough information with beards and moustaches,” he explains.
His ability to capture reality crept back in, and he began working to build disguises that were good enough to allow agents and cooperators to change their appearance entirely, and move through foreign countries undetected.
“The disguises had to pass the closest scrutiny, just 6 to 12 inches, and if they didn’t, their life would be in jeopardy.”
Back at CIA headquarters in Langley, Barron asked to be transferred to the disguise unit permanently. He developed full face masks that could completely change someone’s appearance, right down to their ethnicity, in seconds — a process known as an identity transfer.
“They could put it on in three seconds,” he recalls, “and take it off in three seconds.”
It sounds unbelievable, but Barron laughs at comparisons to Tom Cruise in Mission: Impossible. “How about Mission: Posssible?” he quips.
There were other tricks of the spy trade that Barron worked on.
The “jack-in-the-box” was an inflatable full-body double that could be carried in a briefcase. An agent could hop out of the passenger seat of a car as it turned the corner, the dummy would inflate, and anyone tailing the car would think the passenger was still inside.
“One of the most sophisticated items we ever had,” Barron recalls.
Barron and the Agency were always looking to upgrade their techniques.
He went to California to learn from legends like John Chambers, the artist who developed the masks and makeup for Planet of the Apes. But Robert quickly found many of his techniques were already ahead of Hollywood’s.
In the early 1980s, the Agency asked him to attend a conference of biomedical sculptors—undercover, of course—to see if there were new techniques he could learn. That’s where he discovered a hidden world that would chart the course of the rest of his life. “I saw disfigured people that they were working on,” he recalls. “Burn survivors, people with no ears.”
He realized his skill for capturing reality could change lives.
“I said to myself, ‘If you can put people in hiding, you can bring people out of hiding.’”
That was nine years before he retired from the agency, when he was awarded a Career Intelligence medal.
Hundreds of people have since walked through the doors of Custom Prosthetic Designs.
Barron can tell stories that would make you cringe, and some that would make you cry.
There was the woman from Pakistan, whose jealous husband, a barber, took his straight razor to her face, after accusing her of looking at another man.
There’s the young girl who was attacked by a raccoon as she slept in her crib. The raccoon took both of her ears. Barron made her two new ones.
And then there was Tim Dunaway.
Dunaway’s face was crushed in a boating accident, after he struck a sandbar. The injuries and subsequent infection cost him his nose and part of his forehead. He was devastated and despondent.
One afternoon in 2003, Dunaway’s sister was watching T.V. when she saw a former spy-turned-prosthetics master on The Oprah Winfrey Show.
It was Robert Barron.
She picked up the phone to call her brother. The phone rang seven times. Dunaway answered.
“Before he answered the phone, he was in the bathtub, with a .357 magnum,” explains Barron, as he mimes putting a pistol to his head. “He almost committed suicide. When I talked to him, his voice was quivering.”
Barron talked him out of it, and built him a flawless prosthetic.
Today, Dunaway is alive, married, and still friends with the ex-spy who saved him.
 
When you ask Barron if he plans to retire, he always has the same answer: “How can I?”
He is booked months in advance. He still loses sleep over some of his tougher cases — including Steve Butler’s — a rare combination of prosthetic skin flap and a full ear.
Barron’s life partner Ramona works alongside him. She guides patients through every step of their journey — from their first phone call, to their first appointment in that room full of ears and noses. She maintains an extensive catalogue of before and after photos, as curator of this rare art.
Yet Barron remains uncharacteristically modest for a man of his talents and accomplishments.
“It’s not over yet,” he says, when asked to reflect back on his life and career.
He still has the passion and the commitment to realism that’s defined his art since he was a child.
“That’s the way I’m going to be until I drop,” he laughs.
Steve Butler is back at Custom Prosthetic Designs.
The previous day, he was fitted with the prosthetic, but it was still unfinished, just a beige silicone ear and flap to cover his missing skin. Still, it sat flawlessly on his face. The shape of the fake ear matched his remaining ear perfectly.
“Unbelievable,” he kept saying.
The real magic started when the master artist pulled out his paintbrushes.
Barron spent hours wearing magnifying goggles, working directly on Steve’s face — a process called “tinting” — so that the silicone would blend in with the surrounding skin. That’s what it takes to make a prosthetic appear invisible.
“Got to get the right colour,” Barron explained as he worked away.
The painted silicone was cured overnight in a specialized oven. Then the prosthetic was treated with some of Barron’s trade secrets to make it perfectly match Steve’s natural sheen and skin tone.
Now Steve sits patiently as the finished piece is covered with a medical adhesive before it’s carefully attached to the side of his face for the very first time.
In a routine Barron has done with more than 1,000 patients before, the two men stand up and walk silently from the barber’s chair, past the long counter of ears, noses, and eyes, to a large mirror in the corner of the room.
Steve takes a first glance, and struggles to find his words.
“It looks just like it did before,” he says, his voice trembling.
The man looking back at him in the mirror looks nothing like the man who endured multiple surgeries, months of treatment, and questions about his own existence.
The man looking back is his former self.
“Words don’t express it,” he says. “I got my life back.”

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Each year, the School of Music faculty selects a rising fourth-year student as the recipient of the Presser Undergraduate Scholar Award, which is designed to encourage and support the education of a music student who exemplifies high academic and musical accomplishment, leadership and citizenship.
The 2022 recipient is Jake Anderson – a fourth-year undergraduate student from Boise, Idaho, who appreciates the cello and the outdoors with seemingly equal versatility and passion. An accomplished musician with a taste for chamber music, Anderson also loves the outdoors. During summers, he works as a river raft guide; during fall break, he spent four days biking 409 miles of North Carolina’s Blue Ridge Parkway with friends. On weekends, he will sometimes head to local attractions such as Pilot Mountain or Hanging Rock state parks for hiking or trail running.

UNCSA was on Anderson’s list when looking at music programs because his high school cello teacher earned a doctorate with UNCSA cello professor Brooks Whitehouse. His audition experience cemented his decision to attend.
“I had by far the best experience here,” Anderson recalls. “I felt really welcomed by a lot of students, and Brooks is just really a wonderful professor. It would have been hard for me to go anywhere else.”
To Whitehouse, Anderson was a perfect fit for the unique UNCSA environment. 
Jake came to UNCSA as a fairly sophisticated musician already. All he needed to do was hone his craft to perform at the level of that sophistication. I have seen him do that in remarkable ways here.
“Jake came to UNCSA as a fairly sophisticated musician already,” Whitehouse said. “All he needed to do was hone his craft to perform at the level of that sophistication. I have seen him do that in remarkable ways here. Through his participation in the symphony orchestra, role in leading the ‘Nutcracker’ orchestra and involvement in chamber music, Jake has brought his cello game to a new level.”
Luca Kevorkian, violin; Marta Ðorovic, violin; Jake Anderson, cello; Xue Mei, viola; and Paul Neubauer, viola perform “String Quintet in B Flat Major, K. 174” by Wolfgang Amadeus Mozart at the Chamber Music Festival March 15-19, 2022, in School of Music Watson Hall
Anderson said performing twice in UNCSA Chamber Music festivals is a highlight of his artistic experience. He was part of a quintet that performed the Piano Quintet in F minor by Cezar Franck, which he learned primarily through self-study.
“It’s like this tremendous piece of music that I really wanted to sound good,” he said. “I was playing with professors I’ve been watching, listening to and admiring for the last four years. Now I’m sort of collaborating with them. It was a great opportunity!”
Playing in the annual production of “The Nutcracker” is something that Anderson looks forward to every year.
“The audience for ‘The Nutcracker’ is very different from the proper classical music concert,” Anderson says. “People are cheering and laughing, and you are down in the pit every night with all of your friends. It’s really fun.” 
Campus Night at “The Nutcracker” means the orchestra dons its favorite holiday attire.
Outside of his personal studies, Anderson also served as a teaching assistant for Music History Professor Michael Dodds, who teaches a class covering 400 years of music. 
“He talks about the story of the pieces and really brings music to life in that way,” Anderson says. “It’s not just some notes on a page, there is a life behind them. His class was so good I just wanted to experience it again. I think being a TA was really impactful on my education.”
In addition, Anderson served as a resident assistant for two years, which allowed him to meet non-music students. He said he enjoyed seeing them during performances: working backstage or in a spotlight during a dance performance. And, if one didn’t think that was enough campus involvement, he was also the virtual tour guide for the newest residence hall, Artist Village. 
“I believe that Jake has professorial promise,” Whitehouse said. “He delivers some of the most insightful and cogent verbal program notes from the stage of any student I’ve ever had.”
The dream in life would be to do everything I love: music, biking, kayaking, without needing to sacrifice any of them.
Anderson said he won’t immediately be going into graduate school for music. Growing up on the Payette River in Idaho, he wants to explore being a professional whitewater kayaker or a pro cyclist.
“The dream in life,” Anderson said, “would be to do everything I love: music, biking, kayaking, without needing to sacrifice any of them.”
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Funded by a Presser Foundation grant, the annual Presser Award recipient is one who has grown individually and who has contributed earnestly to the success of the School of Music. The scholarship is $4,000.
By Elizabeth White
November 10, 2022
filter: music
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According to this latest study, In 2022 the growth of DAW Software Market is projected to reach Multimillion USD by 2028, In comparison to 2021, Over the next Seven years the DAW Software Market will register a magnificent spike in CAGR in terms of revenue, In this study, 2021 has been considered as the base year and 2022 to 2028 as the forecast period to estimate the market size for DAW Software.
“DAW Software Market” research provides significant and in-depth information on the global market. This research provides an in-depth analysis and trustworthy statistics. Included are the firm’s description, key characteristics, and overall revenue. The DAW Software market is characterized in terms of market size, market coverage, market exchange rate, segmentation, and market share. This report analyses the current competitive environment in addition to offering key details like market statistics and product releases from leading companies in this area. Additionally, it highlights the strategic planning needed by enterprises to handle COVID-19’s consequences.
The global DAW Software market size is projected to reach Multimillion USD by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028.
DAW Software Market Research Report is spread wide in terms of pages and provides exclusive data, information, vital statistics with tables and figures, trends, and competitive landscape details in this niche sector.
COVID-19 IMPACT ON MARKET
The outbreak of COVID-19 has severely impacted the overall supply chain of the DAW Software market. The halt in production and end use sector operations have affected the DAW Software market. The pandemic has affected the overall growth of the industry In 2020 and at the start of 2021, Sudden outbreak of the COVID-19 pandemic had led to the implementation of stringent lockdown regulations across several nations resulting in disruptions in import and export activities of DAW Software.
COVID-19 can affect the global economy in three main ways: by directly affecting production and demand, by creating supply chain and market disruption, and by its financial impact on firms and financial markets. Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.
Considering the economic change due to COVID-19 and Russia-Ukraine War Influence, DAW Software, which accounted for % of the global market of DAW Software in 2021
Final Report will add the analysis of the impact of COVID-19 on this industry.
TO KNOW HOW COVID-19 PANDEMIC AND RUSSIA UKRAINE WAR WILL IMPACT THIS MARKET – REQUEST SAMPLE
The report covers the major players operating in theDAW Software market. In terms of market share, the companies in the global DAW Software market do not have a considerable amount of market share, as the market is highly competitive and fragmented.
Some of the leading companies around the world are :
Short Description About DAW Software Market:
The Global DAW Software Market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2028. In 2020, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.
This report focuses on global and United States DAW Software market, also covers the segmentation data of other regions in regional level and county level.
Due to the COVID-19 pandemic, the global DAW Software market size is estimated to be worth USD million in 2022 and is forecast to a readjusted size of USD million by 2028 with a Impressive CAGR during the review period. Fully considering the economic change by this health crisis, by Type, DAW Software accounting for % of the DAW Software global market in 2021, is projected to value USD million by 2028, growing at a revised % CAGR in the post-COVID-19 period. While by Application, DAW Software was the leading segment, accounting for over percent market share in 2021, and altered to an % CAGR throughout this forecast period.
Highlights
The global DAW Software market is projected to reach USD million by 2028 from an estimated USD million in 2022, at a magnificent CAGR during 2023 and 2028.
Get a Sample PDF of report –https://www.360researchreports.com/enquiry/request-sample/19929857
Report Scope
This report aims to provide a comprehensive presentation of the global market for DAW Software, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding DAW Software.
The DAW Software market size, estimations, and forecasts are provided in terms of output/shipments (K Units) and revenue (USD millions), considering 2021 as the base year, with history and forecast data for the period from 2017 to 2028. This report segments the global DAW Software market comprehensively. Regional market sizes, concerning products by types, by application, and by players, are also provided. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the DAW Software manufacturers, new entrants, and industry chain related companies in this market with information on the revenues, production, and average price for the overall market and the sub-segments across the different segments, by company, product type, application, and regions.
Key Companies and Market Share Insights
In this section, the readers will gain an understanding of the key players competing. This report has studied the key growth strategies, such as innovative trends and developments, intensification of product portfolio, mergers and acquisitions, collaborations, new product innovation, and geographical expansion, undertaken by these participants to maintain their presence. Apart from business strategies, the study includes current developments and key financials. The readers will also get access to the data related to global revenue, price, and sales by manufacturers for the period 2017-2022. This all-inclusive report will certainly serve the clients to stay updated and make effective decisions in their businesses.
Get a Sample Copy of the DAW Software Market Report 2022
DAW Software Market 2022 is segmented as per type of product and application. Each segment is carefully analyzed for exploring its market potential. All of the segments are studied in detail on the basis of market size, CAGR, market share, consumption, revenue and other vital factors.
Global DAW Software Market Revenue Led By Product Type Segment:
Global DAW Software Market Leading End-Use Segment:
DAW Software Market is further classified on the basis of region as follows:
This DAW Software Market Research/Analysis Report Contains Answers to your following Questions
Customization of the Report
Our research analysts will help you to get customized details for your report, which can be modified in terms of a specific region, application or any statistical details. In addition, we are always willing to comply with the study, which triangulated with your own data to make the market research more comprehensive in your perspective.
Inquire more and share questions if any before the purchase on this report at –https://www.360researchreports.com/enquiry/pre-order-enquiry/19929857
Detailed TOC of Global DAW Software Market Insights and Forecast to 2028
1 DAW Software Market Overview
1.1 Product Overview and Scope of DAW Software
1.2 DAW Software Segment by Type
1.3 DAW Software Segment by Application
1.4 Global Market Growth Prospects
1.4.1 Global DAW Software Revenue Estimates and Forecasts (2017-2028)
1.4.2 Global DAW Software Production Capacity Estimates and Forecasts (2017-2028)
1.4.3 Global DAW Software Production Estimates and Forecasts (2017-2028)
1.5 Global Market Size by Region
1.5.1 Global DAW Software Market Size Estimates and Forecasts by Region: 2017 VS 2021 VS 2028
1.5.2 North America DAW Software Estimates and Forecasts (2017-2028)
1.5.3 Europe DAW Software Estimates and Forecasts (2017-2028)
1.5.4 China DAW Software Estimates and Forecasts (2017-2028)
1.5.5 Japan DAW Software Estimates and Forecasts (2017-2028)
2 Market Competition by Manufacturers
2.1 Global DAW Software Production Capacity Market Share by Manufacturers (2017-2022)
2.2 Global DAW Software Revenue Market Share by Manufacturers (2017-2022)
2.3 DAW Software Market Share by Company Type (Tier 1, Tier 2 and Tier 3)
2.4 Global DAW Software Average Price by Manufacturers (2017-2022)
2.5 Manufacturers DAW Software Production Sites, Area Served, Product Types
2.6 DAW Software Market Competitive Situation and Trends
2.6.1 DAW Software Market Concentration Rate
2.6.2 Global 5 and 10 Largest DAW Software Players Market Share by Revenue
2.6.3 Mergers and Acquisitions, Expansion
3 Production Capacity by Region
3.1 Global Production Capacity of DAW Software Market Share by Region (2017-2022)
3.2 Global DAW Software Revenue Market Share by Region (2017-2022)
3.3 Global DAW Software Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.4 North America DAW Software Production
3.4.1 North America DAW Software Production Growth Rate (2017-2022)
3.4.2 North America DAW Software Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.5 Europe DAW Software Production
3.5.1 Europe DAW Software Production Growth Rate (2017-2022)
3.5.2 Europe DAW Software Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.6 China DAW Software Production
3.6.1 China DAW Software Production Growth Rate (2017-2022)
3.6.2 China DAW Software Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.7 Japan DAW Software Production
3.7.1 Japan DAW Software Production Growth Rate (2017-2022)
3.7.2 Japan DAW Software Production Capacity, Revenue, Price and Gross Margin (2017-2022)
4 Global DAW Software Consumption by Region
4.1 Global DAW Software Consumption by Region
4.1.1 Global DAW Software Consumption by Region
4.1.2 Global DAW Software Consumption Market Share by Region
4.2 North America
4.2.1 North America DAW Software Consumption by Country
4.2.2 United States
4.2.3 Canada
4.3 Europe
4.3.1 Europe DAW Software Consumption by Country
4.3.2 Germany
4.3.3 France
4.3.4 U.K.
4.3.5 Italy
4.3.6 Russia
4.4 Asia Pacific
4.4.1 Asia Pacific DAW Software Consumption by Region
4.4.2 China
4.4.3 Japan
4.4.4 South Korea
4.4.5 China Taiwan
4.4.6 Southeast Asia
4.4.7 India
4.4.8 Australia
4.5 Latin America
4.5.1 Latin America DAW Software Consumption by Country
4.5.2 Mexico
4.5.3 Brazil
5 Segment by Type
5.1 Global DAW Software Production Market Share by Type (2017-2022)
5.2 Global DAW Software Revenue Market Share by Type (2017-2022)
5.3 Global DAW Software Price by Type (2017-2022)
6 Segment by Application
6.1 Global DAW Software Production Market Share by Application (2017-2022)
6.2 Global DAW Software Revenue Market Share by Application (2017-2022)
6.3 Global DAW Software Price by Application (2017-2022)
7 Key Companies Profiled
7.1 Company
7.1.1 DAW Software Corporation Information
7.1.2 DAW Software Product Portfolio
7.1.3 DAW Software Production Capacity, Revenue, Price and Gross Margin (2017-2022)
7.1.4 Main Business and Markets Served
7.1.5 Recent Developments/Updates
8 DAW Software Manufacturing Cost Analysis
8.1 DAW Software Key Raw Materials Analysis
8.1.1 Key Raw Materials
8.1.2 Key Suppliers of Raw Materials
8.2 Proportion of Manufacturing Cost Structure
8.3 Manufacturing Process Analysis of DAW Software
8.4 DAW Software Industrial Chain Analysis
9 Marketing Channel, Distributors and Customers
9.1 Marketing Channel
9.2 DAW Software Distributors List
9.3 DAW Software Customers
10 Market Dynamics
10.1 DAW Software Industry Trends
10.2 DAW Software Market Drivers
10.3 DAW Software Market Challenges
10.4 DAW Software Market Restraints
11 Production and Supply Forecast
11.1 Global Forecasted Production of DAW Software by Region (2023-2028)
11.2 North America DAW Software Production, Revenue Forecast (2023-2028)
11.3 Europe DAW Software Production, Revenue Forecast (2023-2028)
11.4 China DAW Software Production, Revenue Forecast (2023-2028)
11.5 Japan DAW Software Production, Revenue Forecast (2023-2028)
12 Consumption and Demand Forecast
12.1 Global Forecasted Demand Analysis of DAW Software
12.2 North America Forecasted Consumption of DAW Software by Country
12.3 Europe Market Forecasted Consumption of DAW Software by Country
12.4 Asia Pacific Market Forecasted Consumption of DAW Software by Region
12.5 Latin America Forecasted Consumption of DAW Software by Country
13 Forecast by Type and by Application (2023-2028)
13.1 Global Production, Revenue and Price Forecast by Type (2023-2028)
13.1.1 Global Forecasted Production of DAW Software by Type (2023-2028)
13.1.2 Global Forecasted Revenue of DAW Software by Type (2023-2028)
13.1.3 Global Forecasted Price of DAW Software by Type (2023-2028)
13.2 Global Forecasted Consumption of DAW Software by Application (2023-2028)
13.2.1 Global Forecasted Production of DAW Software by Application (2023-2028)
13.2.2 Global Forecasted Revenue of DAW Software by Application (2023-2028)
13.2.3 Global Forecasted Price of DAW Software by Application (2023-2028)
14 Research Finding and Conclusion
15 Methodology and Data Source
15.1 Methodology/Research Approach
15.1.1 Research Programs/Design
15.1.2 Market Size Estimation
15.1.3 Market Breakdown and Data Triangulation
15.2 Data Source
15.2.1 Secondary Sources
15.2.2 Primary Sources
15.3 Author List
15.4 Disclaimer
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Scientific Reports volume 12, Article number: 19057 (2022) Cite this article
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Intrinsically disordered proteins (IDP) are at the center of numerous biological processes, and attract consequently extreme interest in structural biology. Numerous approaches have been developed for generating sets of IDP conformations verifying a given set of experimental measurements. We propose here to perform a systematic enumeration of protein conformations, carried out using the TAiBP approach based on distance geometry. This enumeration was performed on two proteins, Sic1 and pSic1, corresponding to unphosphorylated and phosphorylated states of an IDP. The relative populations of the obtained conformations were then obtained by fitting SAXS curves as well as Ramachandran probability maps, the original finite mixture approach RamaMix being developed for this second task. The similarity between profiles of local gyration radii provides to a certain extent a converged view of the Sic1 and pSic1 conformational space. Profiles and populations are thus proposed for describing IDP conformations. Different variations of the resulting gyration radius between phosphorylated and unphosphorylated states are observed, depending on the set of enumerated conformations as well as on the methods used for obtaining the populations.
Intrinsically disordered proteins (IDP) are at the center of the attention in the structural biology of proteins. Indeed, disordered residues are expected to constitute 35 to 50% of the human proteome and, depending on the organism type, the overall percentage of amino acids predicted to be disordered ranges from about 12% up to 50%¹. In addition, the conformational plasticity of the disordered regions of proteins allows them to interact with numerous partners in the cell, as for example for the three intrinsically disordered domains of the tumor protein P53². This moonlighting³ behavior explains the strong impact of IDPs in cellular signaling, regulation, and control, and the differences observed in their interactomes with respect to globular proteins⁴.
Intrinsically disordered proteins represent a challenge for structural biology for several reasons. In solution, the nuclear Overhauser effects measuring distance between hydrogens are usually not available. Moreover, crystallization processes are hampered by the conformational disorder, or the variability of conformations in the crystal or in the electron cryogenic maps makes impossible the observation of electronic density for disordered regions. Numerous approaches have been proposed^5,6,7,8 for the calculation of protein conformations, based on molecular dynamics or Monte Carlo simulations for generating molecular conformations.
We propose here to explore a new approach for the exploration of the conformational space of IDPs, based on a systematic enumeration of conformations in the frame of the distance geometry problem. We build on our previous work introducing TAiBP as a new tool to investigate structural ensembles of IDPs in a systematic way, by predicting populations and consequently selecting pools of representative conformations. This approach, initiated as the interval Branch-and-Prune (iBP) algorithm by Mucherino and coworkers⁹, was adapted to the protein molecular modeling as threading-augmented interval Branch-and-Prune (TAiBP)^10,11. Based on distance geometry, TAiBP explores the entire conformational space compatible with NMR chemical shifts, retaining conformations that are most different from one another, and thus yielding a diverse set of conformations to be analyzed further. This is in contrast to Monte Carlo methods which are informed by force fields and explore the part of the configurational space that is thermodynamically relevant in more detail. TAiBP was recently shown¹² to allow the analysis of the conformational space of a tandem domain of protein whirlin, in which a disordered linker induces a large orientation variability of two PDZ domains¹³. The application of TAiBP to the tandem domain was made possible by the analysis of unprocessed output of the neural network TALOS-N¹⁴, the Ramachandran likelihood maps. Indeed, drawing boxes on the most probable regions of these maps allowed the determination of intervals on backbone angles, which serve as inputs for the TAiBP algorithm. It should be noticed that the approach MERA has been developed¹⁵ for the prediction of the (phi), (psi) distributions for IDPs.
In the present work, we apply TAiBP to a well-know example of IDP^16,17. The obtained IDP conformations will be filtered and their relative populations determined by BioEn¹⁸ using SAXS data. In parallel, we propose an original method, RamaMix, to select the main conformations, as well as their populations, according the Ramachandran likelihood maps predicted by TALOS-N¹⁴. The principle of RamaMix is to fit a bivariate, periodic, finite mixture model to the output of TALOS-N. The N terminal fragment of the intrinsically disordered protein Sic1, as well as its phosphorylated form pSic1, each one spanning 90 residues, will be studied.
Sic1 prevents premature S-phase entry in the budding yeast Saccharomyces cerevisiae by inhibiting the complex Cdk1-Clb. At the START point in the yeast cell cycle, Sic1 is phosphorylated on three Threonines (residues 7, 35, and 47) and three Serines (residues 71, 78, and 82) in order to be degraded by the proteosome. Sic1 as well as pSic1 were shown^16,19 to contain significant amount of transient secondary structures.
The comparison of repeated runs of TAiBP on Sic1 and pSic1 reveals a good reproducibility of global conformational shape. Qualitatively similar but quantitatively different populations are obtained either by fitting distinct SAXS curves or Ramachandran maps. The sets of individual conformations selected from the fitting of various data are partially distinct, but better convergence is observed for the profiles of local gyration radius. These profiles could be proposed as a low resolution description of the IDP conformational space. Depending on the way the TAiBP conformations are generated, and on the processing method to obtain the populations, different patterns of variations are observed for the resulting gyration radius of Sic1 and pSic1.
The TALOS-N¹⁴ prediction was obtained using the chemical shifts measured for the nuclei H(alpha), HN, (^{15})N, (^{13})C(alpha), (^{13})C(beta) of Sic1 and pSic1 residues, and was used to determine boxes of (phi) and (psi) values, giving the limits in which the conformations will be enumerated. Indeed, from the NMR chemical shifts and the protein sequence information, the TALOS-N neural network predicts the likelihood that a given residue n has backbone torsion angles that fall in any of the 324 voxels, of 20(^{circ }) (times) 20(^{circ }) each, that make up the Ramachandran map¹⁴. Following the approach proposed in Ref. ¹², we define boxes (Figs. S1–S4) using Ramachandran regions displaying largest likelihood for the TALOS-N prediction, and corresponding supposedly to protein conformations populated in solutions.
In order to probe the reliability of the ((phi),(psi)) boxes obtained from the TALOS-N likelihood maps, these boxes were compared to the predictions performed using the approach MERA¹⁵, which predicts the residue-by-residue Ramachandran map distributions for disordered proteins using short-range NOEs, chemical shifts, J couplings and spectral density derived from the N(^{15}) relaxation measurement. As only chemical shifts were available for Sic1 and pSic1, the MERA prediction was performed putting all other possible inputs to zero. The MERA Ramachandran map distributions are plotted for all successful predicted residues, along with the input boxes derived from the TALOS-N prediction (Figs. S5 and S6), showing a reasonable agreement between the two methods.
Two replicates of boxes were generated for Sic1 and pSic1, using threshold values of 0.01 and 0.011 on the Ramachandran probability maps as described in Section “Extraction of boxes from Ramachandran likelihood” in the Supplementary Material. Using these sets of input boxes, five TAiBP runs were performed, named Sic1(^1), Sic1(^2), pSic1(^1), pSic1(^2) and pSic1(^3). The run pSic1(^3) was using the input boxes of pSic1(^2), but differs from the other runs by the procedure for selecting more extended representative conformations after the SOM clustering, as described in the section “Clustering of generated conformations” in the Supplementary Information.
The two replicates of TAiBP calculations introduced in the previous subsection were based on similar numbers of fragments: 14 and 13 for Sic1(^1) and Sic1(^2), 17 for pSic1(^1) and pSic1(^2) and 18 for pSic1(^3) (Table S1). The larger number of fragments used for pSic1 arises from the regions of residues 5-9, 33-37, 45-49, 69-73, 76-84 for which TALOS-N was unable to give a prediction due to the phosphorylated residues and for which generic boxes (Table S2) were used. These boxes being formed of three components, they increase the combinatorics of the enumeration and shorter fragments have to be used, requiring a larger number of fragments to span the protein sequence.
The boxes used as inputs for the TAiBP runs (Figs. S1–S4) are quite similar. The loop region (positive (phi)) is slightly more populated for runs pSic1(^1) and pSic1(^2). For the iBP and assembly steps forming the TAiBP approach, the runs, marked in colors red, green and blue in Fig. 1, produce parameter values similar in most of the protein sequence.
Parameters of the iBP and assembly steps of the TAiBP procedure. The signs red and green correspond respectively to the duplicated runs in which thresholds of 0.01 and 0.011 have been applied on the probability Ramachandran map. The blue crosses correspond to the run pSic1(^3) producing more extended conformations. The positions of phosphorylated Threonines and Serines are marked with T and S for the runs on pSic1. The parameters are plotted along the number of the residue located at the middle of the fragment (iBP step) or at the middle of the last attached fragment (assembly step).
For the iBP steps, three parameters were compared (Fig. 1, first and second lines) along the residue number located at the middle of each fragment: the number of individual iBP runs ((N_text {iBPrun})), the number of saved conformations ((N_text {iBPconf})) and the number of obtained conformations after clustering ((N_text {clustiBP})). The three analyzed parameters are located in similar ranges for all calculations. Nevertheless, (N_text {iBPrun}) displays the largest observed values (3888) around the positions of phosphorylated Threonines in agreement with the larger generic boxes used in these protein regions (Table S2). Such an increase is not observed for phosphorylated Serines due to shorter fragments used in the region 50-90 (Table S1). A decrease of (N_text {iBPrun}) is observed for pSic1(^3) around the residue 40, due to the shorter peptide fragments used in this region. For every calculation, (N_text {iBPconf}) is smaller than (10^9), which is the input given for the maximum number of solutions: all individual iBP trees have thus been completely parsed. The (N_text {iBPconf}) profiles display smaller values, mostly in the range 10(^6)-10(^7), for all calculations in the region of residues 60-90. Contrarily to (N_text {iBPconf}), the numbers of clustered conformations ((N_text {clustiBP})) display relatively flat profiles for Sic1, but a decrease in the number of conformations of pSic1 in the region of residues 60-90. This larger reduction of conformations due to the clustering is the sign that the conformations generated by iBP in the region 60-90 are more diverse in Sic1 than in pSic1. In all calculations, the C terminal fragments which are smaller than the others (Table S1) display smaller (N_text {iBP}), (N_text {iBPconf}) and (N_text {clustiBP}). The results obtained for the run pSic1(^3) (blue crosses) are quite similar to those of the run pSic(^2), which is not surprising as the fragment definition are the same, except around residues 40-60 (Table S1).
Three parameters are plotted (Fig. 1, third and fourth lines) along the assembled fragments: the number of conformations rejected due to C(alpha) atoms closer than 1Å ((N_text {clashes})), the number of saved conformations ((N_text {saved})) and the number of clustered conformations ((N_text {clust})). Looking at the relative ranges of values of (N_text {clashes}) and (N_text {saved}), between 10% and 15% of the assembled fragments are rejected due to the steric clashes. A smaller percentage of rejection is observed for pSic1(^3) (blue crosses): the more extended conformations display a tendency to produce less clashes. The profiles of (N_text {clust}) are different for Sic1 and pSic1, as the number of clustered conformations increases up to the last fragment, whereas this number already starts to decrease in the region of residues 60-90 in pSic1. This effect can be put in parallel with the decrease of (N_text {clustiBP}) in the same region during the iBP step. The last fragments of proteins have strong decreasing effects on (N_text {clust}) due to their smaller size (Table S1), which probably induces less variability in the generated conformations. The number of clustered and saved conformations for pSic1(^3) is often larger than in other runs, which may be a consequence of the smaller numbers of clashes.
After the distance geometry calculations, a refinement by molecular dynamics (MD), described in Section “Molecular dynamics refinement in implicit solvent” of Supplementary Material, was applied to the generated conformations. The protein conformations do not vary much during MD trajectories. Indeed, the cumulative sums of differences between initial and final values of backbone angles produce values in the range 4.2 to (4.9^{circ }) for (phi) and 0.04 to (2.4^{circ }) for (psi). Similarly, the average coordinate RMSD between the initial and final frames of the refinement trajectories are 0.6 Å for the four runs Sic1(^1), Sic1(^2), pSic1(^1) and pSic1(^2). The drift is slightly larger for pSic1(^3), with backbone angle values in the ranges (-24) to (6^{circ }) for (phi) and (-40) to (-1^{circ }) for (psi), and an average coordinate RMSD of 0.7 Å. The conformations displaying potential energy smaller than (-50) kcal/mol for the runs Sic1(^1) and Sic1(^2) and smaller than (-600) kcal/mol for the runs pSic1(^1), pSic1(^2) and pSic1(^3), were selected for further analyses. This selection produces sets of 98 (Sic1(^1)), 133 (Sic1(^2)), 161 (pSic1(^1)), 121 (pSic1(^2)) and 148 (pSic1(^3)) conformations.
The distributions of gyration radii R(_g) and maximal diameters D(_text {max}) (Fig. 2 two top rows) are quite similar for the duplicate runs on Sic1 and pSic1. The global envelope of generated conformation is thus reproducible between the replicated TAiBP runs. The distributions of gyration radii R(_g) and maximal diameters D(_text {max}) have been plotted in magenta for the run pSic(^3) to display the larger extension of the obtained conformations.
Four panels on top: Distribution of the gyration radii R(_g) and of maximal diameters D(_text {max}) values in the two sets of TAiBP obtained during the first runs Sic1(^1) and pSic1(^1) (solid line) and the second runs Sic1(^2) and pSic1(^2) (dashed line) runs. The R(_g) and D(_text {max}) distribution obtained for the run pSic1(^3) are plotted in magenta. Four panels on bottom: Distribution of the minimum RMSD values (Å) and of the minimum distances (Å) between profiles for the duplicate runs performed for Sic1(^1) and Sic(^2) and for pSic1(^1) and pSic1(^2). full line: first run with respect to the second one, dashed line: second run with respect to the first one.
The individual conformations generated for the duplicated runs of Sic1 and pSic1 were then compared by calculating the two-by-two coordinate root-mean-square deviation (RMSD, Å). The distributions of the minimum RMSD values (Fig. 2 two bottom left panels) observed for each conformation of one run to the conformations of the other run are quite reproducible whatever is the performed comparison. They display sets of values in the ranges of 8-16 Å for both proteins, with a maximum around 11 Å for Sic1 and around 10 Å for pSic1. This range 8-16 Å means that the individual conformations of a given run are not reproducible in the replicated run. This excludes a high resolution determination of representative conformations, which is not surprising due to the enormous size of the conformational space to explore and the heavy clustering procedure used along the TAiBP approach.
By analogy to the cross-sectional gyration radius, we propose here the profiles of local gyration radii to describe the local variation in the shape of conformations. These profiles (P_q) of local gyration radii are calculated along residue number n for each conformation q in the following way:
where ({textbf {X}}_{i}) represents the vector of atomic coordinates for the backbone atoms of residue i in the range (n-N_text {win}), (n+N_text {win}), and (N_text {win}=5) is the residue window around n on which a local gyration radii is calculated, (N_n) being the number of backbone atoms located in this window. ({textbf {X}}^text {ave}_{n}) is the coordinate vector of the centroid of the atomic coordinates of the backbone atoms of residues in the range (n-N_text {win}), (n+N_text {win}).
The profiles (P_q) of local gyration radii were compared two-by-two between conformations using Euclidean distance. The shapes of distributions for minimal distances between (P_q) (Fig. 2 bottom right panels) are similar to those observed for minimal RMSD values (Fig. 2 two bottom left panels), but are drifted toward ranges of 4-11 Å. The comparison between local gyration profiles shows that one half of the obtained conformations displays a distance between profiles located between 1/6 and 1/3 of the average gyration radius. The profile distance smaller than the average gyration radius is the sign of a reduced variation of the profiles (P_q) with respect to the coordinate RMSD. The (P_q) profiles, inspired by the cross-sectional gyration radius, seems thus to capture a better convergence between the duplicate runs than the coordinate RMSD. In the following, the conformations selected by the fitting of SAXS curves and Ramachandran maps will be compared through their (P_q) profiles.
Quite similar global shape of conformations are populated in the duplicated TAiBP runs. The profiles (P_q) of local gyration radii display also some similarity. But, the comparison of atomic coordinates reveals a large variability of the individual conformations selected by the TAiBP approach, which is not surprising due to the enormous considered conformational space.
Once a set of conformations have been selected using TAiBP, one needs to detect the conformations significantly populated and to evaluate their relative populations. Indeed, the systematic enumeration along all possible combination of the (phi)/(psi) boxes induces the generation of conformations spanning a space possibly larger than the conformations effectively populated. The populations were determined, from one side, using BioEn¹⁸ on SAXS data, and on the other side, using on the Ramachandran maps, a finite mixture model, RamaMix, specially developed for this purpose. We first present in this section a validation of RamaMix on synthetic data. The details of RamaMix computation are presented in the section “Determination of the populations from Ramachandran maps” in Methods and in the Supplementary Material.
A pseudo Ramachandran map has been generated by randomly choosing up 15 couples of (phi), (psi) values located in most populated regions of the Ramachandran map (Fig. S7). Several sets of more or less scattered values, represented by different colors, have been generated, to investigate the effect of conformational superimposition on the population determination. Corresponding populations were also chosen randomly (see caption of Fig. S7). Noise levels of 0.2, 1, 2, 3, 5 and 10 were added to the histogram obtained from the pseudo Ramachandran map, the maximum value of the histogram being around 15. The starting points for each RamaMix run was the (phi _0), (psi _0) values from the synthetic Ramachandran plot, and random population values. During each RamaMix run, several upper limits were imposed to the drift of the backbone angles during the optimization, with values of: 1(^{circ }), 10(^{circ }), 20(^{circ }), 30(^{circ }), 40(^{circ }) and 50(^{circ }). For each Ramachandran synthetic map, each noise level and each drifting limit value, one hundred runs are performed producing sets of backbone angles ((phi _0) and (psi _0)) (Eq. 7), von Mises parameters (Eq. 8) ((kappa)1, (kappa)2 and (rho)) and populations (gamma _{q}) (Eq. 2). Over the 12600 individual RamaMix runs, only 275 runs were terminated without convergence of the optimization. Averages and standard deviations were calculated from the sets of obtained parameters. The differences between the averaged and the input values, as well as the standard deviations (Fig. 3) are used to evaluate RamaMix.
Efficiency of RamaMix for determining the (phi _0), (psi _0) positions (A–D: Eq. 7), the von Mises shape parameters (kappa)1, (kappa)2 and (rho) (G–L: Eq. 8), and the populations (gamma _{q}) (E–F: Eq. 2) using synthetic data and various noise levels described in Fig. S7. The results obtained for large, medium and narrow scattered synthetic Ramachandran maps are drawn as bullets, triangles and squares.
The differences between average and initial populations (Fig. 3E) as well as the standard deviations of populations are mostly smaller than 30%. Thus, the determination of populations is not much influenced by the level of noise, but the population values are rather qualitative. Interestingly, the standard deviation is of the order of value of the difference.
The efficiency of the determination of backbone angles (Fig. 3A–D) for noise levels of 0.2, 1, 2, 3 and 5, is not much influenced by the scattering of synthetic Ramachandran maps, but rather by the drifting limit imposed on the (phi), (psi) values. Increasing the allowed drift induces larger differences and standard deviations: this would support not allowing large drift for the calculations. Interestingly, for the large scattered Ramachandran map (bullets in Fig. 3), the effect of a large drift is more pronounced than for other synthetic Ramachandran maps. For most of the cases, the standard deviations display larger values than the difference: allowing a drift induces more error on the precision of the calculation than on the average value of angles.
The parameters describing the von Mises distribution (Fig. 3G–L) display contrasted results: the differences are larger for (rho) than for (kappa)1 and (kappa)2. For (kappa)1 and (kappa)2, the standard deviations are much larger than the differences whereas they are similar for (rho). The differences between (rho) and (kappa)1 and (kappa)2, arise from the definition of these parameters (Eq. 8) in which (rho) occupies a different place than (kappa)1 and (kappa)2.
The TAiBP conformations were fitted to the SAXS curves and Ramachandran probability maps using BioEn¹⁸ and RamaMix. The following sets of conformations were processed: the conformations obtained from runs Sic1(^1), Sic1(^2), pSic1(^1), pSic1(^2) and pSic1(^3), as well as two mixed sets of conformations obtained by pooling the conformations from pSic1(^1) and pSic1(^3) and the conformations from pSic1(^2) and pSic1(^3). These mixed sets of conformations will be denoted pSic1(^{13}) and pSic1(^{23}) and encompass respectively 309 and 269 conformations.
BioEn calculations were performed using each of the three SAXS curves available (Tables 1, 2 and S5). The populations larger than 1% found for a given TAiBP run and the fitting of a given SAXS curve, reveal that the same conformations are repeatedly selected: the conformation numbers selected more than once have been written in bold in the Tables. Most of the conformations selected only once, display populations smaller than 15%. But the populations vary significantly from one analysis to another as for example for the conformation 109 from the run Sic1(^2) (Table 1B) which display populations of 26.6, 40.9 and 43.8% for the three SAXS curve processing. Normalized (chi ^2) values smaller than one are found for each calculation along with null final S(_{KL}) values, in agreement with the definition of S(_{KL}) as the Kullback-Leibler divergence^18,20.
Tables 3 and S6 present the populations obtained by RamaMix from the fitting of the Ramachandran probability maps on the same sets of conformations. The variations of backbone angles (phi) and (psi) during the RamaMix optimization are smaller than 0.25(^{circ }) for (phi) and 0.1(^{circ }) for (psi) during all considered calculations. These variations are smaller for pSic1(^3) with 0.12(^{circ }) and 0.03(^{circ }) for (phi) et (psi), and even smaller for the mixed pools of conformations with 0.06(^{circ }) and 0.02(^{circ }). Among six of the seven sets of TAiBP conformations (marked in bold), already repeatedly selected by BioEn, were also selected by RamaMix (Tables 1, 2, 3 and S5, S6).
Similarly to the populations obtained by BioEn between the different SAXS data, the populations found using RamaMix are quite different than the ones determined by BioEn. Another difference between BioEn and RamaMix processing is the smaller number of conformations selected by RamaMix, it can arise from the essential difference between the data, as the SAXS curves describe a global picture of the conformations whereas the Ramachandran maps give a local information. A smaller number of conformations are selected from the sets where more extended conformations were included: this may be due to the important conformational drift induced by the systematic choice of extended conformations during the clustering step (see Supplementary Material section “Clustering of generated conformations”).
In order to compare the conformations selected by BioEn on the three SAXS curves, several curves superimpositions have been realized. The superimposition of SAXS curves reconstructed from the conformations selected from Tables 1 and 2 to the corresponding fitted SAXS curves (Fig. S8) displays a reasonable agreement with (chi ^2) in the range 0.6-2.06. These values are larger than the ones given in the Tables 1 and 2, due to the fact that conformations displaying populations smaller than 1% have been removed. Besides, a comparison of all sets of BioEn conformations with all SAXS curves (Table S3) reveals that the conformations and populations determined from the fit of one SAXS curve display (chi ^2) values with another SAXS curve going up to 4.42. The variability between the three SAXS curves induces thus a drift between conformations and populations selected from the fit of each curve.
A similar comparison has been performed between the SAXS curves and the conformations and populations determined with RamaMix (Fig. S9). In this comparison, the (chi ^2) values are in the range 0.98-4.24 which is similar to what is observed for BioEn selected conformations in Table S3. The variability between the fits to Ramachandran maps and SAXS curves is thus similar to the variability of fit between different SAXS curves.
In order to investigate the possible convergence between the different conformations and populations detected using BioEn and RamaMix, systematic comparison of Euclidean distances between profiles of local gyration (P_q) (Eq. 1) was performed (Figs. 4, S10 and S11) The Euclidean distances within each set of conformations selected by BioEn reveal (Fig. 4, three left columns) that, for several cases, distances smaller than 8 Å are observed between different conformations. In many cases, such small distances are observed between conformations (labeled with asterisk in Fig. 4) for which populations smaller than 10% are observed. The comparison of profiles (P_q) (Eq. 1) between conformations selected by RamaMix (Fig. 4, right column) reveals two features. When few conformations have been selected (as for Sic1(^2) and pSic1(^2)), the distances between their profiles (P_q) are larger than 8 Å. When more conformations are selected (as for Sic1(^1) and pSic1(^1)), profile distances smaller than 8 Å are observed. The small (P_q) distances reveal a certain convergence of the profiles (P_q).
Distances between the profiles (P_q) (Eq. 1) of local gyration radii between the conformations selected from the fit of SAXS curves (BioEn1,BioEn2, BioEn3) or Ramachandran maps (RamaMix). The conformations for which populations smaller than 10% were calculated, are labeled with an asterisk. The diagonals correspond to the comparison of the same conformations and are thus not annotated with distance value. The limit of 8 Å used to display superimposed plots of profiles (P_q) (Fig. 5) is drawn in red on the scale of distance.
The comparison of conformations selected by BioEn and RamaMix as well as the comparison between conformations selected from the fit of the various SAXS curves is displayed in Figs. S10 and S11. A close inspection of these distance matrices for BioEn conformations (Fig. S10) shows that, if one excludes the conformations populated less than 10%, there are only three conformations displaying profile distances larger than 8 Å and selected in two distinct BioEn runs: (i) for Sic1(^2), the conformation 106 selected on the SAXS curve BioEn1 compared to the conformations selected on the two other SAXS curves; (ii) for pSic1(^2), the conformation 74, selected in the runs BioEn1 and BioEn2, and compared to the conformations selected from the run BioEn3; (iii) for pSic1(^2), the conformation 139 selected from the run BioEn3, and compared to the conformations from the run BioEn1. Overall, most of the conformations populated more than 10% from the fitting of different SAXS curves display profile distances smaller than 8 Å, supporting a convergence of the profiles in the different fits.
On the other hand, the comparison between BioEn and RamaMix fitting (Fig. S11) displays contrasted behaviors between the duplicated TAiBP runs. For Sic1(^2) and pSic1(^2), all RamaMix conformations display profiles closer than 8 Å to the profiles of BioEn conformations. For pSic1(^1), this is also the case for three RamaMix conformations (16, 98, 101) over five. For Sic1(^1), only the conformation 79 displays profile distances smaller than 8 Å for the three comparisons.
Examples of profiles (P_q) superimposition have been chosen accordingly to the values of their distances (Fig. 5) and give an estimation of the connection between the information related to atomic coordinates and the distance between the profiles. These examples represent distances in the 4.05–7.88 Å range. The examination of Fig. 5 reveals that the profile peaks are mostly located at similar places in the protein sequence. This gives a qualitative description of the conformations separated in extended regions (profile maxima) and in aggregated regions (profile minima).
Superposition of profiles (P_q) (Eq. 1) displaying distances smaller than 8 Å extracted from Figs. 4 and S8, S9. The name of the run (Sic1(^1), Sic1(^2), pSic1(^1), pSic1(^2)) is given, along with the name of the considered fits (BioEn1, BioEn2, BioEn3, RamaMix, RamaMix/BioEn) and the conformations numbers. The labels RamaMix/BioEn correspond to the comparison of conformations selected by BioEn on one side and RamaMix on the other side. The labels BioEn1/2 and BioEn2/3 correspond to the comparison of conformations selected by BioEn from two different SAXS curves.
The description of IDP conformations by (P_q) profiles permits to detect some convergence between the various Bioen fits and also between RamaMix and BioEn fit. This is extremely encouraging due to the enormous conformational size and to the heterogeneity of the measurements (SAXS, NMR) used for fitting the populations. Nevertheless, this comparison remains extremely qualitative, and far from any high resolution description. It could represent a starting point for deeper investigation of IDP conformations.
The sets of Sic1 and pSic1 conformations selected from the fitting of SAXS curves and of Ramachandran probability maps, were compared to the sets of protein conformations deposited in the Protein Ensemble Database protein ensemble.org²¹.
The values of the resulting gyration radii were calculated (Table 4) from the populations determined by BioEn and RamaMix, and using the individual gyration radii of selected conformations. Globally, the resulting gyration radii display orders of values agreeing with the measurements reported in Fig. 2E of Ref. ¹⁷. For the conformations extracted from the data-sets Sic1(^1) and Sic1(^2), the resulting gyration radii agree with the measurement of 3.0 ± 4.1 Å given in Fig. 2E of Ref. ¹⁷. But, for pSic1(^1) and pSic1(^2), the resulting gyration radii are smaller than the measurements of Ref. ¹⁷: this is particularly true for the BioEn processing, while the RamaMix processing displays values closer to those of Gomes et al¹⁷. On the more extended conformations (pSic1(^3)), all resulting gyration radii are significantly closer to Gomes et al¹⁷ measurements, for BioEn and RamaMix processing (Table 4). Pooling pSic1(^3) with the conformations of pSic1(^1) or pSic1(^2) (sets pSic1(^{13}) and pSic1(^{23})) produces different effects for BioEn and RamaMix processing. For these mixed data-sets, the resulting gyration radii obtained from the BioEn processing (range 27.2-28.1 Å) decrease to reach a level just slightly larger than the one obtained for pSic1(^1) and pSic1(^2) (range 26.1-27.9 Å). On the contrary, the gyration radii obtained by RamaMix processing are the same than the ones obtained for pSic1(^3). Overall, the BioEn processing is more sensitive than RamaMix to the presence of conformations with lower gyration radii. The discrepancy of the results obtained here on pSic1 with those shown in Ref. ¹⁷ arises in part from a tendency to obtain smaller gyration radii by processing of the whole SAXS curve with respect to the larger gyration radii obtained by using the Guinier approximation within the low-q region of the SAXS data.
The selected TAiBP conformations were also compared to the PED conformations by realizing a principal component analysis (PCA) of the atomic coordinates. The coordinates projected on the first and second or on the second and third component (Fig. 6) reveal that most of the TAiBP conformations are located in similar space regions than the PED conformations.
Projections of the Sic1 and pSic1 conformations along the three largest components of their principal component analysis (PCA). On these projections, the TAiBP conformations selected by BioEn or RamaMix are colored in magenta and the conformations stored in PED²¹ are colored in black.
The presence of phosphorylated residues decreases obviously the global charge of pSic1 with respect to Sic1. It was pointed out that the induced variation in long-range electrostatic interactions plays a role in the electrostatic interaction of pSic1 with its target Cdc14²². However, the variations of charges also gives various opportunities for the formation of hydrogen bonds, which were analyzed for the whole set of conformations from the TAiBP runs as well as for the PED sets of conformations. All PED conformations were submitted to the same refinement than the one used on TAiBP conformations described in the Section “Molecular dynamics refinement in implicit solvent” of the Supplementary Material, using positional restraints on protein backbone atoms with a constant force of 50 kcal/mol. The cumulative variations of (psi) and (phi) angles during the refinement were in the range 0.8-1.2(^{circ }) for (phi) and in the range 0.3-0.8(^{circ }) for (psi), and the coordinate RMSD around 0.1 Å. All hydrogen bonds were detected on the refined PED conformations as well as on the TAiBP conformations. Cumulative contact maps (Fig. S12) display these hydrogen bonds, according to the involved residues, the hydrogen bonds involving phosphorylated residues being colored in magenta. The inspection of these contact maps reveals that the PED and TAiBP conformations display distinct tendencies. Long range hydrogen bonds involving phosphorylated residues are more present in the less extended set of TAiBP conformations pSic1(^1) and pSic1(^2) than in pSic1(^3). On the other hand, the conformation set PED161 of pSic1 displays the largest number of long-range hydrogen bonds involving the sidechains of phosphorylated residues. Thus, the presence of phosphorylated residues can induce the appearance of long-range hydrogen bonds whatever is the variation of resulting gyration radius.
The TAiBP approach enumerating the protein conformations in the frame of the distance geometry problem has been used for describing the conformational space of two IDPs, Sic1 and pSic1, corresponding to the unphosphorylated and phosphorylated states of a disordered region involved in the control of S phase in the cellular cycle. The present study represents a test for a new approach able to systematically enumerate protein conformations in the frame of a distance geometry approach. Indeed, up to now, most of the approaches for calculating IDP conformations are based on Monte Carlo approaches^8,23,24 which do not guarantee an exhaustive exploration of the conformational space.
One should notice that TAiBP overcome the exponential complexity of the branch-and-prune algorithm, due to the parallel calculations on fragments, to the rejection of too close solutions, and to the systematic use of clustering. A major advantage of this approach is the availability of a systematic procedure. Nevertheless, the obtained conformations are only representative conformations, and the represented conformational space has still to be defined.
The use of TAiBP approach permits to avoid the question of the convergence of solutions for protein conformations. The introduction of the profiles of local gyration radii (P_q) along with their relative populations allows the reintroduction of a convergence criterion into the problem, and this is essential for validation purposes. In the present work, the validity of this convergence criterion has been assessed by the comparison of the profiles (P_q) obtained from independent fits. In that frame, the profile of local gyration radii could be proposed for describing the IDP conformational space: the knowledge, even qualitative, of the profiles should provide geometrical restraints allowing a more precise exploration of the conformational space. The profiles are closer between the conformations selected by the various fits of SAXS curves, than between the conformations selected by BioEn and RamaMix. This is expected as the various fits of SAXS curves use homogeneous information. More surprisingly, similar profiles are observed between conformations selected by RamaMix and BioEn, for the runs Sic1(^2) and pSic1(^2), and for many conformations of the runs Sic1(^1) and pSic1(^1).
One specific advantage of the mixture method RamaMix for determining populations of conformations from the likelihood Ramachandran maps is that it has a larger domain of applicability that the BioEn method based on the SAXS curves. Indeed, polydispersity in protein solutions can make difficult to extract conformational information from the SAXS curve. In addition, the chemical shifts from which the likelihood Ramachandran maps are extracted, can be measured in solution as well as in in-cell NMR or for a sequence inserted in a larger protein²⁵.
The comparison of the resulting gyration radii obtained from the BioEn and RamaMix processing with the values measured in Ref. ¹⁷ showed (Table 4) that various ranges of gyration radii are obtained, depending on the clustering procedure in TAiBP, as well as on the method for SAXS processing. In particular, the processing of the whole SAXS curve with BioEn displays a tendency to underestimate the gyration value with respect to the processing of the Guinier curve. The determination of populations from the Ramachandran probability maps, using RamaMix, seems to be less prone to the underestimation of the gyration radius.
The discrepancy between resulting gyration radii obtained by processing the whole SAXS curve (BioEn) or restricting the analysis to the low-q region (Guinier approximation¹⁷) agrees with independent calculations performed using coarse-grained protein model²⁶, in which various distribution of gyration values produce very similar SAXS spectra (Fig. 10a of Ref. ²⁶) or different disordered ensembles produce similar Kratky plots (Fig. 13 of Ref. ²⁶).
Three sets of conformations for Sic1 and pSic1 were available from the Protein Ensemble Database (PED) proteinensemble.org²¹: PED159 and PED160 for pSic1 and PED161 for Sic1¹⁷. The residue numbering used here is the one proposed in the PED. The NMR chemical shifts were downloaded from the Biological Magnetic Resonance Data Bank (BMRB)²⁷ as entries: 16657 for Sic1¹⁷ and 16659 for pSic1¹⁹. The SAXS data-sets recorded as triplicate sets in the conditions described in Ref. ¹⁷ were provided by Tanja Mittag.
The protein conformations have been enumerated using the recently proposed TAiBP approach^10,11,12, which generalizes the interval branch-and-prune (iBP) algorithm^{9,28,29,30,31} so as to overcome the combinatorial barrier arising from the enormous space of IDP conformations³². TAiBP is composed of two steps: (i) the enumeration of conformations for peptide fragments (Table S1) spanning the studied protein using individual iBP calculations; (ii) the enumeration of Sic1 and pSic1 conformations by systematic assembly of fragment conformations in a way similar to what is used in the field of protein prediction³³.
The boxes of backbone angles (phi) and (psi) used as inputs for the iBP step were determined from the Ramachandran likelihood maps predicted by TALOS-N¹⁴ (see section “Extraction of boxes from Ramachandran likelihood maps” and Figs. S1–S4 in Supplementary Material). The (phi)/(psi) boxes were systematically combined by permutation to prepare individual iBP calculations as in Ref¹¹. The enumeration of conformations is realized by the building of a tree, each node of the tree corresponding to an atomic position. The tree building allows the enumeration of the various possibilities for atom positions (branching step) whereas additional geometric information is used to accept or reject a newly built branch (pruning step). As the angles (phi) and (psi) are straightforwardly related to distances between atoms C and N of residues successive in the sequence^11,12, the discretization of intervals of these angles is used in the branching step. In the iBP step, the pruning was applied by preventing atoms to be closer than the sum of their van der Waals radii and by checking that the improper angle values are correct. In addition, each solution displaying a coordinate root-mean-square deviation (RMSD) smaller than 2 Å with the previously stored solution, is rejected. The details of the iBP step calculation are described in the section “Enumeration of conformations” of the Supplementary Material.
The assembly step is also performed with a branch-and-prune approach using as elementary blocks, not the atoms, but the fragment conformations previously determined during the iBP step. Two peptide fragments are assembled by superimposing the three last and initial residues of the fragments successive in the protein sequence. The fragments are then merged in the following way: the atom at which the smallest distance was observed between corresponding atoms in the two peptides was used to decide where to stop with the first peptide and to continue with the second one. The assembled conformations in which C(alpha) atoms closer than 1 Å are observed, were pruned from the calculation. The fragment assembly was implemented using python scripting based on the MDAnalysis^34,35 and numpy 1.7.1³⁶ packages.
To scale down the combinatorial explosion of the calculation, a clustering approach based on Self-Organizing Maps (SOM)^37,38,39,40 was systematically applied to the generated sets of conformations larger than 100 during the iBP and assembly steps. The details of this approach are described in the section “Clustering of generated conformations” of the Supplementary Material.
After the assembly step, the sidechains have been added to the conformation backbones, and the conformations were refined by molecular dynamics simulations as described in the section ”Molecular dynamics refinement in implicit solvent” in the Supplementary Material.
The approach RamaMix, based on a finite mixture model, was designed to determine the populations of conformations by fitting on the Ramachandran probability maps. The setting-up of this approach is based on the hypothesis that the likelihood maps describing the likelihood of the TALOS-N prediction¹⁴ can be transformed by normalization into the probability density of the presence of (phi) and (psi) values in the set of conformations populated in solution.
Consequently, for each residue n, the Ramachandran probability map is denoted as a 2D probability density (p^n(phi ,psi )), modeled as a mixture of probability densities (p_q^n(phi ,psi )) determined on each conformation q:
where (gamma _q ge 0) is the population of conformation q in solution.
RamaMix intends to decompose the probability map (p^n(phi ,psi )) according to Eq. (2) along the following lines: (i) the total number Q of conformations is taken from output of TAiBP; (ii) for each conformation q and each residue n, (p_{q}^n(phi ,psi )) is a periodized Gaussian density characterized by averaged values of backbone angles ((phi ^n_q, psi ^n_q)) and by a (2times 2) covariance matrix (C^n_q); (iii) the populations (gamma _q) have to be adjusted in order to maximize the fit between the Ramachandran probability maps and the mixture model (Eq. 2).
The Ramachandran probability maps (p^n(phi ,psi )) are jointly fitted to the finite mixture model (Eq. 2) using a discrepancy measure between both probability maps given by the Kullback-Leibler divergence:
Calculations detailed in the Supplementary material (Sections “Determination of the populations from the Ramachandran maps” and “Maximum likelihood estimation for bivariate sine mixtures”) show that using the Kullback-Leibler divergence is equivalent to the maximization of the log-likelihood of the data⁴¹:
For the sake of clarity, let us first introduce a standard, non-periodized Gaussian density (p_{q}^n(phi ,psi )) for the residue n in conformation q:
where (V^n_q(phi ,psi )) represents the free energy surface for the basin around the conformation q. The free energy surface is described in the frame of an elastic network model on the backbone dihedral angles^42,43,44,45:
where: (theta _q = (phi -phi ^n_q, psi -psi ^n_q)^{t}), (phi ^n_q) and (psi ^n_q) are the values of dihedral angles of the residue n in the conformation q and (C^n_q) is the corresponding covariance. The software IMOD⁴² was used for determining the full Hessian (N, N) (N is the total number of residues in the protein) matrix (H_q) along the backbone dihedral angles. The Hessian matrix is then inverted to produce: (C_q = H_q^{-1}). The covariance matrix (C^n_q) of the angles (phi) and (psi) of the considered residue n is the (2,2) sub-matrix of (C_q), centered on the two (phi _q^n) and (psi _q^n) angles. The inverse of this matrix ([C^n_q]^{-1}) is used in Eq. (6).
As the protein conformations are described by couples of angles, we must consider that the support of the probability densities (p_{q}^n(phi ,psi )) is a torus, i.e., that they are doubly circular. Following^46,47,48, we replaced Eq. (6) by a bivariate extension of the von Mises distribution, as being more easily tractable than a Gaussian density wrapped on the torus. More precisely, we adopt a bivariate periodic sine model⁴⁶:
with
(kappa _1,kappa _2ge 0) and (lambda ^2<kappa _1kappa _2). According to Ref. ⁴⁶, the integration constant T is expressed as an infinite series, depending on parameters ((kappa _1,kappa _2,lambda )):
where (I_m) denotes the modified Bessel functions of the first kind of order m⁴⁹.
In Ref. ⁴⁶, expressions of ((kappa _1, kappa _2, lambda )) are given as functions of the parameters ((sigma _1^2,sigma _2^2,rho )) of a bivariate Gaussian where (rho in (-1,1)) denotes the normalized correlation coefficient between the two components of the bivariate Gaussian:
These expressions are valid only in the case where (sigma _1^2) and (sigma _2^2) are small. They are easily inverted as
Using (11), we can replace a Gaussian mode (p_{q}^n) by a periodized version, with approximately the same location and the same spread. In the following, we will describe basin shapes around conformations using the triplets of parameters ((kappa _1, kappa _2, rho )) rather than ((kappa _1, kappa _2, lambda )), since (rho ^2<1) is a simpler constraint than its counterpart on (lambda).
A well-known local optimization scheme to identify finite mixture models by maximum likelihood is the Expectation-Maximization (EM) algorithm^50,51. Unfortunately, the M step of the EM has no analytical expression in the case of mixtures of bivariate Von-Mises densities. Therefore, we have performed local optimization based on L-BFGS-B⁵² instead, given that both the likelihood and its gradient can be evaluated efficiently, and that some parameters are subject to box constraints. The implementation details and equations are given in the sections “Determination of the populations from the Ramachandran maps” and “Maximum likelihood estimation for bivariate sine mixtures” of the Supplementary Material.
By optimization of the log-likelihood, the RamaMix approach will thus produce the Q normalized populations (gamma _q), the (Qtimes N) couples of backbone angles (phi _q^n) and (phi _q^n), as well as the (Qtimes N) triplets ((kappa _1^n, kappa _2, rho _q^n)) describing the von Mises distributions. The calculations were performed starting from the (phi) and (psi) values observed in the set of TAiBP conformations, complemented by von Mises parameters allowing us to approximate the Gaussian distributions determined by IMOD. Moreover, the variation of (phi) and (psi) values was limited by a threshold of 15(^{circ }) during the optimization in order to avoid inappropriate drift.
The RamaMix approach was implemented in Fortran90, and the software is available at github.com/tmalliavin/RamaMix.
The software BioEn 0.1.1¹⁸ was used in order to determine the populations from SAXS data. On each considered conformation, theoretical SAXS curves were calculated using CRYSOL⁵³ available in the package ATSAS 3.0.3⁵⁴ with 847 points, a maximum scattering vector of 0.503 nm(^{-1}) and a maximum order of harmonics of 18. A 1D cubic interpolation⁵⁵ was used to obtained the theoretical SAXS values at the same sets of scattering vectors q than the ones at which the experimental SAXS curve was recorded.
The processing with BioEn was performed in the following way. For each TAiBP run and each SAXS curve, the optimization was run for 1000 steps using the GSL library⁵⁶. Ten runs were performed independently on all considered conformations, and the subset of conformations for which the sum of observed populations is larger than 0.01, was selected. Ten additional BioEn runs were performed on the subset of conformations, and from the results of these ten repetitions, average values and standard deviations were computed for the populations.
The datasets used for producing the Figures of the main text as well as the generated IDP conformations are available at: 10.5281/zenodo.7198645. Other datasets used or/analyzed during the current study are available from the corresponding author on reasonable request.
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The project ANR-19-CE45-0019 (multiBioStruct) is acknowledged for funding, as well as Institut Pasteur, CNRS, Ecole Polytechnique and University of Rennes. Tanja Mittag is acknowledged for providing SAXS data recorded as triplicate sets in the conditions described in Ref. ¹⁷. Cyprien Bertran is acknowledged for fruitful discussions.
UMR7374 Interfaces, Confinement, Matériaux et Nanostructures, Université d’Orléans, Orléans, France
Daniel Förster
UMR6004 Laboratoire des Sciences du Numérique de Nantes, Nantes, France
Jérôme Idier
LIX UMR 7161 CNRS École Polytechnique, Institut Polytechnique de Paris, 91128, Palaiseau, France
Leo Liberti
IRISA, University of Rennes 1, Rennes, France
Antonio Mucherino
Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan
Jung-Hsin Lin
Institut Pasteur, Université Paris Cité, CNRS UMR3528, Unité de Bioinformatique Structurale, F-75015, Paris, France
Thérèse E. Malliavin
Université de Lorraine, CNRS UMR7019, LPCT, F-54000, Nancy, France
Thérèse E. Malliavin
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J.I. designed the approach RamaMix and D.F. implemented it. T.M. ran the enumeration of conformations of proteins, and the BioEn and RamaMix analyses. T.M., J.I. and L.L. wrote the manuscript. All authors reviewed the manuscript.
Correspondence to Jérôme Idier.
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‘STAND’, at The Watermill Center, New York, is the most comprehensive survey to date at the institution that jumpstarted the artist’s 50-year career
‘This is Chris. Is this Chris. Chris this is. This is Chris. Is this Chris his. Chris his is.’ A tape recording of the artist’s speech resounds throughout the space, filling it with language (This Is Chris, early 1970s). This is ‘Christopher Knowles / STAND’, a landmark exhibition curated by Noah Khoshbin at The Watermill Center. Knowles’s largest show to date, it is a comprehensive survey of his prolific, five-decade, multidisciplinary practice, featuring more than 200 drawings, typings, audio recordings, paintings and sculptures, many of which are presented here for the first time.

As the recording unravels, sounds merge – ‘this’, ‘his’, ‘is’, ‘Chris’ – texturing the room with a fractured rhythm that seems to mimic the apparently unpredictable typings on the walls. ‘Falling C typings’ (1977), a series of 52 untitled works on paper produced with a typewriter, depict constellations of abstract patterns, some loosely structured, others tightly ordered. Most are comprised solely of a lowercase ‘c’, but occasionally spell out words or other combinations of letters: ‘rage’, ‘rrrrgggg’, ‘RED’, ‘GREEN’. Eclipsing its semantic function, language acquires an otherness that disorients and destabilizes our expectations. Knowles’s words shift away from meaning, and instead gravitate toward the purity of sound and form, the performative act of creation. This is the beauty of Knowles’s practice: he constructs new worlds with familiar building-blocks of language, pattern, pop culture, politics and systemized organizations of time and space (clocks, maps, flags). His work encourages us to move beyond habitual, normative modes of thinking, observing, reasoning and judging: our obsessive search for meaning, our compulsion to fill voids of incomprehension and uncertainty.

Indeed, what may at first seem whimsical or naïve in Knowles’s work ultimately reveals itself as methodical and calculated, governed by specific sets of rules and structures. A flower, a lamp, ladders and staircases: these are some of the images that occupy the pages of another series of 12 black and red typings constituted by repeated letter c’s, playful and pixelated, produced between 1985–86. The images are not mapped out in advance. Knowles is able to visualize the underlying geometric structure of space, the mechanical grid inherent to the typewriter apparatus, to generate precisely spaced, perfectly centred arrangements, which he can re-create from memory. Bird typing (1982), for instance, is an abstracted image of a flying bird, identically reproduced at least five  times. His capacious memory also manifests in his list typings, such as New York City’s Top 40 of 1964 (2011), in which he meticulously tabulates the top hits of a given year in neatly ordered rows. Meridians of both personal and collective memory, these works memorialize specific historical moments by invoking music as a powerful and intimate mnemonic device.

Knowles contracted a rare blood disease in utero and was born with a neurological condition that resembles autism. His creativity and unique talents were nourished by his family, who often feature lovingly in his art. One of his best-known works is the early 1970s audio recording Emily Likes the TV, in which the artist’s voice repeats variations on the phrase: “Emily likes the TV, because she watches the TV, because she likes it.” This tape made its way into the hands of renowned theatre director Robert Wilson, founder of The Watermill Center, catalysing their ongoing collaboration and jumpstarting Knowles’s independent artistic career. He still performs the piece live – from memory, of course. It’s challenging, funny and charming to watch – or listen to, in the case of this show. At the end of some performances, he say: ‘And that’s how it goes.’
‘Christopher Knowles / STAND’ is on view at The Watermill Center, New York, through December. 
^{Main image: ‘Christopher Knowles / STAND’, installation view. Courtesy: the artist and The Watermill Center, New York; photograph: Maria Baranova}
Rebecca Rose Cuomo is an independent curator and writer based in Brooklyn, USA.
At Tanya Bonakdar Gallery, New York, the artist reimagines our celestial and natural worlds in a suite of new paintings 
At Galería Curro, Guadalajara, the artist presents an immersive installation featuring neon sculptures and photographs visualizing the malleability of time
At Lehmann Maupin, New York, the artist envisions new ways of understanding the complex and violent history of the region through weather phenomena
At Galerie Lelong & Co., the late artist’s paintings fuse real and imagined histories of his life in Eritrea
Specially commissioned recipes by nine artists, including Otobong Nkanga, Heather Phillipson and Rirkrit Tiravanija, ranging from abstract to delectable 
The Black female gaze shines resiliently through the stormy surfaces of the artist’s paintings on view at Hauser & Wirth
At Monique Meloche, Chicago, the artist subverts the worst of American racial stereotyping in briskly emotive figurative paintings
In two interrelated solo exhibitions at Silverlens, New York, Atienza refuses a touristic gaze of the Philippines’s landscape while Yee urges grassroots governance
 
From Rose B. Simpson’s figurative clay sculptures at the ICA/Boston to an homage of Lucy Lippard’s show of women artists at the Aldrich Contemporary Art Museum, here are the shows currently on our radar
At Museu de Arte de São Paulo, the artist memorializes the Black leaders and notable individuals overlooked in the country’s visual past
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Pokémon Journeys: The Series removes Hidekazu Tanaka’s music arrangement credit from recent episodes after he was charged with forcible indecency.
Pokémon Journeys: The Series has removed composer Hidekazu Tanaka's music arrangement credit.
As reported by Anime News Network, the anime has omitted the 35-year-old composer's name from recent episodes due to this arrest on the charge of attempted forcible indecency. Forcible indecency is the Japanese legal term, which can also include sexual assault. Tanaka composed the second and fourth versions of the opening theme "1・2・3" for Pokémon Journeys. He also received credit for arranging music for The Idolmaster Cinderella Girls and the poorly rated anime Is It Wrong to Try to Pick Up Girls in a Dungeon? On the Side: Sword Oratoria.
RELATED: Pokémon: The Consequences of Every Outcome for Ash vs. Leon
On Oct. 24, Japanese police arrested Tanaka after a teenage girl accused him of allegedly speaking inappropriately and forcibly pulling her hand near a train station's bicycle parking area in Tokyo's Meguro ward in August. Police opened an investigation of the incident and discovered security camera footage of Tanaka following the girl. As of writing, the Metropolitan Police Department is still continuing its investigation. Although some users flooded Tanaka's recent Instagram posts with comments like, "Why you wanna have s*x with underaged girls man."
During his 10-year tenure at the music production company MONACA, Tanaka composed pieces for various anime and video games, including Nyaruko: Crawling with Love! and Servant × Service. The 35-year-old composer also wrote the ending theme "Calendar Girl" for the Aikatsu! anime. "In July 2021, Tanaka left the music production company after his contract expired. Therefore, the 35-year-old composer started his freelance career. Despite his departure, MONACA still wished him the best in his future endeavors.
RELATED: Pokémon: Leon Did Ash a Favor in the Masters Eight Finals – But Will It Matter?
In addition to Pokémon Journeys, Uzaki-chan Wants to Hang Out! has also responded to Tanaka's recent controversy. The virtual YouTuber Kano's official website announced they were canceling the release date for "Ichigo Ichie Celebration (Once-in-a-Lifetime Celebration)," the opening theme for the anime's second season. Tanaka's song featured Kano and Naomi Ozora, the voice actor behind Uzaki-chan. Kano's website claimed they halted the song's sales for "various reasons."
Originally supposed to release alongside the opening theme, the second season's ending theme song, "Happy Life" will now make its debut on its own on Nov. 30. Additionally, voice actor and singer Momo Asakura has removed her song "Day and night dream" from her "Piacere!" tour's setlist, considering Tanaka composed and arranged the track.
Pokémon Journeys: The Series is available to stream on Netflix. Uzaki-Chan Wants to Hang Out! is available on Funimation.
Source: Anime News Network
Cassidy Stephenson is a reader, writer, and video game enthusiast. She now works as a Senior Writer for Comic Book Resources. In her free time, she loves watching movies, listening to music, and pulling 5-stars on Genshin Impact. Follow her on Twitter for more shenanigans @Cassidy_Lee18 You can also contact her at [email protected]

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Nath Papadacis left the firm at the end of October.
No direct replacement has been made, a spokesperson confirmed, as the company has two joint CEOs in place – Sophie Kennedy and Toby Walker.  
This is the second CEO to depart the firm this year. Mark Howlett stepped down from the joint CEO role and position as head of advice and wealth management in January after four years. 
Papadacis first joined EQ as a technical architect in May 2015, before becoming head of technology and operations in September 2016, a position he held for four years until he was appointed joint CEO in March 2021.
EQ Investors appoints head of sales for DFM business
In a statement, a spokesperson said: “Nath Papadacis left EQ at the end of November to seek a new challenge with a start-up and on the best of terms.
“We are grateful for Nath’s seven and a half years of exceptional service to EQ and its clients and wish him all the best for the future.”
Kennedy, who has been joint CEO since March 2021 when founder John Spiers stepped back, is responsible for the investment and DFM service side of the business. She first joined EQ in 2015 as an investment analyst and spent nearly three years as head of investments before being promoted. 
Meet the ESG Investment Influencers: Jeannie Boyle of EQ Investors
Walker was appointed joint CEO in March 2022 and has oversight of the financial planning arm of the business. He joined the company in 2015 as a client director and was promoted to head of planning in October 2021. 
The spokesperson added: “Under the joint leadership of both joint CEOs, EQ continues to perform strongly, demonstrating profitable growth over the past years.”
COP27: US debuts carbon markets plan as financial climate alliances face fresh criticism
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Hoffman & Associates has secured approximately $198.9 million in financing for the second phase of its $500 million Seaboard Station mixed-use development in Raleigh, N.C., and has broken ground on the project that will include two new apartment buildings with 279 units.
The second phase of the downtown Raleigh project is expected to deliver by summer 2024. It will also include about 55,000 square feet of retail space and a three-star, extended-stay, 149-key hotel that is expected to be announced later this year. At full build-out, the development will offer a walkable core with more than 1 million square feet of mixed-use space.
Construction is underway on the first phase, which will be delivered in early 2023, and features The Signal, a 298-unit apartment building, as well as restaurants and shops along a double-sided corridor with more than 18,000 square feet of outdoor seating and café terraces connecting to the neighborhood’s underground parking.
Located at 721 Halifax St., The Signal is now leasing, with move-ins expected early next year. The building will have studio, one-, two- and three-bedroom layouts and will be managed by Greystar. The units will feature open layouts with kitchen islands, quartz countertops, stainless steel appliances, wood-style flooring, tiled backsplashes, in-unit washers and dryers and smart home technology with keyless entry and balconies.
Community amenities include a fitness center, a yoga studio and private training room with Echelon Smart Mirror; an entertaining kitchen; a gaming and TV lounge; a coworking lounge; a wine lounge and a pet spa. Outdoor amenities will feature a resort-style saltwater pool and sun shelf; poolside courtyard with fire pits, dining area and grills and lounge seating with outdoor TV.
Hoffman & Associates, a Washington, D.C.,-based leader in mixed-use and residential development in the Mid-Atlantic and Southeast regions, has secured total project funding from United Bank. United Bank led the financing transactions for both the first and second phases. Financing for the first phase totaled about $300 million. In addition to United Bank, Burke & Herbert Bank, Sandy Spring Bank and Dogwood State Bank, served as syndicate partners for the second phase. Together they provided the $198.85 million of construction and development financing needed to complete the project.
John Florian, executive vice president of development for Hoffman & Associates, said in a prepared statement the groundbreaking of the second phase of Seaboard Station is a milestone achievement for the firm, its partners and the downtown Raleigh community.
Hoffman & Associates, which also has a Raleigh office, has developed more than 75 mixed-use, residential, office and retail projects. The company also has large-scale developments throughout the Washington, D.C., area including Falls Church, Va., in a portfolio totaling $6 billion. Hoffman & Associates, in a joint venture with Madison Marquette, is developing The Wharf, a $3.6 billion, 3.3 million-square-foot neighborhood along Washington, D.C.’s waterfront. The partnership, Hoffman-Madison Waterfront, last month celebrated the fifth anniversary of The Wharf’s first phase and completion of the second phase of the mixed-use complex. The development now has a total of 1,490 residential units between condominiums and rentals. Most recently, two new residential properties have opened–a 12-story, 96-unit condominium and a 12-story, 225-unit apartment building.
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October 20, 2022 – Fort Wayne Parks and Recreation will host a public meeting to present the final drawings for the proposed Master Plan for Foster Park Golf Course. Two public meetings earlier in the year and an online survey provided input on upgrades and improvements to the course from golfers and residents who live near or around this legacy park which originally opened in 1928. The public meeting is scheduled on Thursday, November 3, from 5:00-7:00 p.m. in the Foster Park Golf Clubhouse. The park is located at 3900 Old Mill Road.
Fort Wayne Parks and Recreation director Steve McDaniel notes “we are excited to share the outcome of the representation from golfers, residents and the Hills-Forrest-Smith design team and invite the public to see the refined final drawings. The process of celebrating the 100^th anniversary of Foster Park Golf Course and ensuring that it will be prepared for the next 100 years is right on track.” We will start fundraising for the proposed improvements with the goal of completion in the spring of 2028 when the golf course opens for the season.”
The public is welcome to join anytime within those hours.    

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James Tyrrell
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Music charts: data visualizations help firms to understand their sonic branding. Image credit: Amp.
Voice control and hands-free operation have added a sonic dimension to modern electronic devices. We have become used to gadgets capable of generating audio alerts and prompts. And that presents both a challenge and an opportunity for device makers. How should a brand sound? What’s more, if we add social media, podcasts, and other audio-rich channels into the discussion, sonic branding suddenly becomes a talking point for any firm, not just device makers.
For companies that have time and resources aplenty, sonic branding is something that they can take their time over. BMW famously worked with multiple Oscar and Grammy award-winning composer Hans Zimmer to create the sound for its electric vehicles. But not all firms will have this luxury. And they don’t need to. Thanks to the power of artificial intelligence (AI), clients have a number of time- and cost-saving tools at their disposal.
“We don’t create jingles, we create sonic DNA,” Michele Arnese – founder and Global CEO of Amp – told TechHQ. “It’s the sonic expression of a brand.” Arnese studied engineering at the University of Pisa, Italy, and has a background in information technology and AI. He also has a passion for music production and quickly recognized the capabilities that algorithms could bring, particularly in helping to quantify sonic branding.
The firm – which has offices in Germany, the US, Singapore, and China – has fed data gathered on 1000 leading brands into its ‘sonic radar’ tool, which uses AI to classify and cluster key properties based on human research. And this gives clients a headstart in the first step of their sonic branding journey – understanding the music that they currently use.
For example, the AI tool can report on whether the audio deployed is happy, romantic, ethereal, and many other mood types, as well as genres. A company may want its soundtrack to be uplifting, and data analysis can quickly pinpoint whether this is actually the case – relating back to the codified knowledge of music experts. Usefully, the tool also provides competitor breakdowns too, so firms can see how they compare in the audio space.
The next tool in the AI-powered chain is ‘Sonic check’ – software that can validate whether sonic branding assets are well-matched to KPI’s such as ‘customer recall’. The process also considers emotional engagement and how likable the content is perceived to be. “It adds objectivity,” explains Arnese. “And clients can use the data to back up their decisions.”
Having numbers to hand can dramatically speed things up for customers – as anyone who’s been involved in a branding exercise will testify. Also, the data provides transparency even when members of the original client team have moved on or switched roles.
When users are happy that their audio has the required features, or Sonic DNA, they are ready for the third element dubbed ‘Sonic space’. “It’s a repository where customers can easily find their music,” said Bjorn Thorleifsson – Head of Strategy & Research at Amp, and an expert in data. “And when they use it, they can be confident that the assets will be on brand.”
With key characteristics tagged, algorithms make it straightforward to switch from one genre to another – for example from Hip Hop to EDM – while preserving the sonic branding, such as those ‘uplifting’ qualities mentioned earlier. “AI is terrible when it comes to creating, but it is fantastic when it comes to editing,” adds Arnese.
Clients can provide the software with a few simple instructions such as the length and cue points – for example, whether the track needs a ‘drop’ or a ‘grand finale’ – and an edited version will be generated to suit. It gives companies a huge amount of flexibility, whether they are compiling a 10-second video for TikTok, a 30-second Instagram Reel or minutes long content for YouTube – all clips will share the same sonic branding qualities.
There’s also the capability to integrate synthetic voices. And Amp has collaborated with Aflorithmic – a tech firm with expertise in converting text into engaging audio clips – so that clients can easily add voice-overs to video content. Together, the AI toolchain puts a lot of production expertise into the hands of users, while keeping the process straightforward for customers.
The team has a fourth AI-based component in the works and will be saying more about those features at an upcoming event in London, UK. But our conversation didn’t stop there. Arnese and Thorleifsson had a few more tips for companies, especially on the topic of product sounds. “You can create functional sounds out of the branded asset,” Thorleifsson points out.
Amp has worked with neuroscientists to understand which audio characteristics appeal to users. Interestingly, more complex tones were associated with value, with simple sinusoidal sounds being perceived as cheap in the research findings. There’s no shortage of insights for device makers to tap into. And with voice-based features and audio alerts on the rise, sonic branding is a term not just for marketeers, but also for product developers to remember.
James Tyrrell
@more__hybrid
[email protected]
10 November 2022
10 November 2022
9 November 2022

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From left: Britton Derkac, Ralph Gilbertsen, and Saira Mohan (LinkedIn, Getty)
Recent Chicago real estate career changes point to movement across the commercial sector, as the city’s office vacancy rates make for stiffer competition among leading professionals.
➤Britton Derkac’s 14 years at EQ Office have paid off with a promotion. The company, which is owned by Blackstone and manages an office portfolio of 20 million square feet, announced Monday that Derkac is its new head of leasing.
Derkac will oversee leasing activity within the EQ portfolio — it includes Chicago’s tallest building, Willis Tower, the tallest tower in suburban Chicago, Oakbrook Terrace Tower, which is being marketed for sale, and properties around the country. He will also manage client and broker relationships.
The career change comes just after Willis Tower unveiled a $500 million renovation that the Blackstone affiliate kicked off before the pandemic and became even more necessary as a result of the health crisis, which has diminished demand in buildings lacking updated amenities. Similar overhauls, albeit on smaller scales, have been required to increase interest in a number of downtown Chicago office buildings amid the health crisis.
Derkac said his career move is coming at “a pivotal moment in the industry,” and promised that EQ will shape the workplace’s evolution by designing space “that not only fits, but exceeds future needs.”
➤Saira Mohan is moving to JLL’s retail agency team representing 105 property listings across the Chicago area with her departure from Colliers.
With Colliers, she developed relationships with occupier clients alongside brokers, and provided real estate services to various lifestyle, hospitality and digitally native retail brands such as Shopify Partners. She has specialized in helping digital brands transition from e-commerce to brick-and-mortar stores.
➤Longtime CBRE executive Ralph Gilbertsen has moved on from the world’s largest commercial brokerage, joining suburban Deer Park-based Government Investment Partners.
The move comes after more than three decades with CBRE, where he was last in its Chicago office managing a team of seven who served multi-market needs of corporations including McDonald’s and IBM.
In his new role, Gilbertsen will oversee Government Investment Partners’ property management business. The firm specializes in acquiring, developing or re-purposing properties mostly leased to federal, state and other government agencies. His duties will also include managing overall corporate operations and investor relations and fundraising strategies.
— Sam Lounsberry
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