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1. #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

Author

Dommer, Abigail, Casalino, Lorenzo, Kearns, Fiona, Rosenfeld, Mia, Wauer, Nicholas, Ahn, Surl-Hee, Russo, John, Oliveira, Sofia, Morris, Clare, Bogetti, Anthony, Trifan, Anda, Brace, Alexander, Sztain, Terra, Clyde, Austin, Ma, Heng, Chennubhotla, Chakra, Lee, Hyungro, Turilli, Matteo, Khalid, Syma, Tamayo-Mendoza, Teresa, Welborn, Matthew, Christensen, Anders, Smith, Daniel Ga, Qiao, Zhuoran, Sirumalla, Sai K, O'Connor, Michael, Manby, Frederick, Anandkumar, Anima, Hardy, David, Phillips, James, Stern, Abraham, Romero, Josh, Clark, David, Dorrell, Mitchell, Maiden, Tom, Huang, Lei, McCalpin, John, Woods, Christopher, Gray, Alan, Williams, Matt, Barker, Bryan, Rajapaksha, Harinda, Pitts, Richard, Gibbs, Tom, Stone, John, Zuckerman, Daniel M, Mulholland, Adrian J, Miller, Thomas, Jha, Shantenu, Ramanathan, Arvind, Chong, Lillian, and Amaro, Rommie E

Subjects
AI, COVID-19, Delta, GPU, HPC, SARS-CoV-2, aerosols, computational virology, deep learning, molecular dynamics, multiscale simulation, weighted ensemble, Biodefense, Emerging Infectious Diseases, Pneumonia & Influenza, Infectious Diseases, Prevention, Vaccine Related, Lung, Infection, Good Health and Well Being, Distributed Computing
Abstract

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Published
2023

2. Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations

Author

Brace, Alexander, Yakushin, Igor, Ma, Heng, Trifan, Anda, Munson, Todd, Foster, Ian, Ramanathan, Arvind, Lee, Hyungro, Turilli, Matteo, and Jha, Shantenu

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-of-magnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100-1000x acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadership-class platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.

Published
2021

3. Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

Author

Bhati, Agastya P., Wan, Shunzhou, Alfè, Dario, Clyde, Austin R., Bode, Mathis, Tan, Li, Titov, Mikhail, Merzky, Andre, Turilli, Matteo, Jha, Shantenu, Highfield, Roger R., Rocchia, Walter, Scafuri, Nicola, Succi, Sauro, Kranzlmüller, Dieter, Mathias, Gerald, Wifling, David, Donon, Yann, Di Meglio, Alberto, Vallecorsa, Sofia, Ma, Heng, Trifan, Anda, Ramanathan, Arvind, Brettin, Tom, Partin, Alexander, Xia, Fangfang, Duan, Xiaotan, Stevens, Rick, and Coveney, Peter V.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Physics - Biological Physics, Quantitative Biology - Quantitative Methods
Abstract

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

Published
2021
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4. AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

Author

Casalino, Lorenzo, Dommer, Abigail C, Gaieb, Zied, Barros, Emilia P, Sztain, Terra, Ahn, Surl-Hee, Trifan, Anda, Brace, Alexander, Bogetti, Anthony T, Clyde, Austin, Ma, Heng, Lee, Hyungro, Turilli, Matteo, Khalid, Syma, Chong, Lillian T, Simmerling, Carlos, Hardy, David J, Maia, Julio DC, Phillips, James C, Kurth, Thorsten, Stern, Abraham C, Huang, Lei, McCalpin, John D, Tatineni, Mahidhar, Gibbs, Tom, Stone, John E, Jha, Shantenu, Ramanathan, Arvind, and Amaro, Rommie E

Subjects
Vaccine Related, Biodefense, Infectious Diseases, Prevention, Lung, Emerging Infectious Diseases, Infection, Good Health and Well Being, Molecular dynamics, deep learning, multiscale simulation, weighted ensemble, computational virology, SARS-CoV-2, COVID19, HPC, GPU, AI, Distributed Computing
Abstract

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Published
2021

5. Scalable HPC and AI Infrastructure for COVID-19 Therapeutics

Author

Lee, Hyungro, Merzky, Andre, Tan, Li, Titov, Mikhail, Turilli, Matteo, Alfe, Dario, Bhati, Agastya, Brace, Alex, Clyde, Austin, Coveney, Peter, Ma, Heng, Ramanathan, Arvind, Stevens, Rick, Trifan, Anda, Van Dam, Hubertus, Wan, Shunzhou, Wilkinson, Sean, and Jha, Shantenu

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computational Engineering, Finance, and Science
Abstract

COVID-19 has claimed more 1 million lives and resulted in over 40 million infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. In response, the DOE recently established the Medical Therapeutics project as part of the National Virtual Biotechnology Laboratory, and tasked it with creating the computational infrastructure and methods necessary to advance therapeutics development. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation and characterize their performance, and highlight science advances that these capabilities have enabled.

Published
2020

6. IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

Author

Saadi, Aymen Al, Alfe, Dario, Babuji, Yadu, Bhati, Agastya, Blaiszik, Ben, Brettin, Thomas, Chard, Kyle, Chard, Ryan, Coveney, Peter, Trifan, Anda, Brace, Alex, Clyde, Austin, Foster, Ian, Gibbs, Tom, Jha, Shantenu, Keipert, Kristopher, Kurth, Thorsten, Kranzlmüller, Dieter, Lee, Hyungro, Li, Zhuozhao, Ma, Heng, Merzky, Andre, Mathias, Gerald, Partin, Alexander, Yin, Junqi, Ramanathan, Arvind, Shah, Ashka, Stern, Abraham, Stevens, Rick, Tan, Li, Titov, Mikhail, Tsaris, Aristeidis, Turilli, Matteo, Van Dam, Huub, Wan, Shunzhou, and Wifling, David

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Quantitative Methods
Abstract

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine.

Published
2020

8. #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

Author

Dommer, Abigail, primary, Casalino, Lorenzo, additional, Kearns, Fiona, additional, Rosenfeld, Mia, additional, Wauer, Nicholas, additional, Ahn, Surl-Hee, additional, Russo, John, additional, Oliveira, Sofia, additional, Morris, Clare, additional, Bogetti, Anthony, additional, Trifan, Anda, additional, Brace, Alexander, additional, Sztain, Terra, additional, Clyde, Austin, additional, Ma, Heng, additional, Chennubhotla, Chakra, additional, Lee, Hyungro, additional, Turilli, Matteo, additional, Khalid, Syma, additional, Tamayo-Mendoza, Teresa, additional, Welborn, Matthew, additional, Christensen, Anders, additional, Smith, Daniel GA, additional, Qiao, Zhuoran, additional, Sirumalla, Sai K, additional, O’Connor, Michael, additional, Manby, Frederick, additional, Anandkumar, Anima, additional, Hardy, David, additional, Phillips, James, additional, Stern, Abraham, additional, Romero, Josh, additional, Clark, David, additional, Dorrell, Mitchell, additional, Maiden, Tom, additional, Huang, Lei, additional, McCalpin, John, additional, Woods, Christopher, additional, Gray, Alan, additional, Williams, Matt, additional, Barker, Bryan, additional, Rajapaksha, Harinda, additional, Pitts, Richard, additional, Gibbs, Tom, additional, Stone, John, additional, Zuckerman, Daniel M., additional, Mulholland, Adrian J., additional, Miller, Thomas, additional, Jha, Shantenu, additional, Ramanathan, Arvind, additional, Chong, Lillian, additional, and Amaro, Rommie E, additional

Published
2022
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9. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

Author

Trifan, Anda, primary, Gorgun, Defne, additional, Salim, Michael, additional, Li, Zongyi, additional, Brace, Alexander, additional, Zvyagin, Maxim, additional, Ma, Heng, additional, Clyde, Austin, additional, Clark, David, additional, Hardy, David J, additional, Burnley, Tom, additional, Huang, Lei, additional, McCalpin, John, additional, Emani, Murali, additional, Yoo, Hyenseung, additional, Yin, Junqi, additional, Tsaris, Aristeidis, additional, Subbiah, Vishal, additional, Raza, Tanveer, additional, Liu, Jessica, additional, Trebesch, Noah, additional, Wells, Geoffrey, additional, Mysore, Venkatesh, additional, Gibbs, Thomas, additional, Phillips, James, additional, Chennubhotla, S Chakra, additional, Foster, Ian, additional, Stevens, Rick, additional, Anandkumar, Anima, additional, Vishwanath, Venkatram, additional, Stone, John E, additional, Tajkhorshid, Emad, additional, Harris, Sarah A, additional, and Ramanathan, Arvind, additional

Published
2022
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11. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

Author

Trifan, Anda, Gorgun, Defne, Salim, Michael, Li, Zongyi, Brace, Alexander, Zvyagin, Maxim, Ma, Heng, Clyde, Austin, Clark, David, Hardy, David J., Burnley, Tom, Huang, Lei, McCalpin, John, Emani, Murali, Yoo, Hyenseung, Yin, Junqi, Tsaris, Aristeidis, Subbiah, Vishal, Raza, Tanveer, Liu, Jessica, Trebesch, Noah, Wells, Geoffrey, Mysore, Venkatesh, Gibbs, Thomas, Phillips, James, Chennubhotla, S. Chakra, Foster, Ian, Stevens, Rick, Anandkumar, Anima, Vishwanath, Venkatram, Stone, John E., Tajkhorshid, Emad, Harris, Sarah A., Ramanathan, Arvind, Trifan, Anda, Gorgun, Defne, Salim, Michael, Li, Zongyi, Brace, Alexander, Zvyagin, Maxim, Ma, Heng, Clyde, Austin, Clark, David, Hardy, David J., Burnley, Tom, Huang, Lei, McCalpin, John, Emani, Murali, Yoo, Hyenseung, Yin, Junqi, Tsaris, Aristeidis, Subbiah, Vishal, Raza, Tanveer, Liu, Jessica, Trebesch, Noah, Wells, Geoffrey, Mysore, Venkatesh, Gibbs, Thomas, Phillips, James, Chennubhotla, S. Chakra, Foster, Ian, Stevens, Rick, Anandkumar, Anima, Vishwanath, Venkatram, Stone, John E., Tajkhorshid, Emad, Harris, Sarah A., and Ramanathan, Arvind

Abstract

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Published
2022

12. High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

Author

Clyde, Austin, primary, Galanie, Stephanie, additional, Kneller, Daniel W., additional, Ma, Heng, additional, Babuji, Yadu, additional, Blaiszik, Ben, additional, Brace, Alexander, additional, Brettin, Thomas, additional, Chard, Kyle, additional, Chard, Ryan, additional, Coates, Leighton, additional, Foster, Ian, additional, Hauner, Darin, additional, Kertesz, Vilmos, additional, Kumar, Neeraj, additional, Lee, Hyungro, additional, Li, Zhuozhao, additional, Merzky, Andre, additional, Schmidt, Jurgen G., additional, Tan, Li, additional, Titov, Mikhail, additional, Trifan, Anda, additional, Turilli, Matteo, additional, Van Dam, Hubertus, additional, Chennubhotla, Srinivas C., additional, Jha, Shantenu, additional, Kovalevsky, Andrey, additional, Ramanathan, Arvind, additional, Head, Martha S., additional, and Stevens, Rick, additional

Published
2021
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13. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

Author

Dommer, Abigail, primary, Casalino, Lorenzo, additional, Kearns, Fiona, additional, Rosenfeld, Mia, additional, Wauer, Nicholas, additional, Ahn, Surl-Hee, additional, Russo, John, additional, Oliveira, Sofia, additional, Morris, Clare, additional, Bogetti, Anthony, additional, Trifan, Anda, additional, Brace, Alexander, additional, Sztain, Terra, additional, Clyde, Austin, additional, Ma, Heng, additional, Chennubhotla, Chakra, additional, Lee, Hyungro, additional, Turilli, Matteo, additional, Khalid, Syma, additional, Tamayo-Mendoza, Teresa, additional, Welborn, Matthew, additional, Christensen, Anders, additional, Smith, Daniel G. A., additional, Qiao, Zhuoran, additional, Sirumalla, Sai Krishna, additional, O’Connor, Michael, additional, Manby, Frederick, additional, Anandkumar, Anima, additional, Hardy, David, additional, Phillips, James, additional, Stern, Abraham, additional, Romero, Josh, additional, Clark, David, additional, Dorrell, Mitchell, additional, Maiden, Tom, additional, Huang, Lei, additional, McCalpin, John, additional, Woods, Christopher, additional, Gray, Alan, additional, Williams, Matt, additional, Barker, Bryan, additional, Rajapaksha, Harinda, additional, Pitts, Richard, additional, Gibbs, Tom, additional, Stone, John, additional, Zuckerman, Daniel, additional, Mulholland, Adrian, additional, Miller, Thomas, additional, Jha, Shantenu, additional, Ramanathan, Arvind, additional, Chong, Lillian, additional, and Amaro, Rommie, additional

Published
2021
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14. Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

Author

Bhati, Agastya P., primary, Wan, Shunzhou, additional, Alfè, Dario, additional, Clyde, Austin R., additional, Bode, Mathis, additional, Tan, Li, additional, Titov, Mikhail, additional, Merzky, Andre, additional, Turilli, Matteo, additional, Jha, Shantenu, additional, Highfield, Roger R., additional, Rocchia, Walter, additional, Scafuri, Nicola, additional, Succi, Sauro, additional, Kranzlmüller, Dieter, additional, Mathias, Gerald, additional, Wifling, David, additional, Donon, Yann, additional, Di Meglio, Alberto, additional, Vallecorsa, Sofia, additional, Ma, Heng, additional, Trifan, Anda, additional, Ramanathan, Arvind, additional, Brettin, Tom, additional, Partin, Alexander, additional, Xia, Fangfang, additional, Duan, Xiaotan, additional, Stevens, Rick, additional, and Coveney, Peter V., additional

Published
2021
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15. Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

Author

Trifan, Anda, primary, Gorgun, Defne, additional, Li, Zongyi, additional, Brace, Alexander, additional, Zvyagin, Maxim, additional, Ma, Heng, additional, Clyde, Austin, additional, Clark, David, additional, Salim, Michael, additional, Hardy, David J., additional, Burnley, Tom, additional, Huang, Lei, additional, McCalpin, John, additional, Emani, Murali, additional, Yoo, Hyenseung, additional, Yin, Junqi, additional, Tsaris, Aristeidis, additional, Subbiah, Vishal, additional, Raza, Tanveer, additional, Liu, Jessica, additional, Trebesch, Noah, additional, Wells, Geoffrey, additional, Mysore, Venkatesh, additional, Gibbs, Thomas, additional, Phillips, James, additional, Chennubhotla, S. Chakra, additional, Foster, Ian, additional, Stevens, Rick, additional, Anandkumar, Anima, additional, Vishwanath, Venkatram, additional, Stone, John E., additional, Tajkhorshid, Emad, additional, Harris, Sarah A., additional, and Ramanathan, Arvind, additional

Published
2021
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16. IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

Author

Saadi, Aymen Al, primary, Alfe, Dario, additional, Babuji, Yadu, additional, Bhati, Agastya, additional, Blaiszik, Ben, additional, Brace, Alexander, additional, Brettin, Thomas, additional, Chard, Kyle, additional, Chard, Ryan, additional, Clyde, Austin, additional, Coveney, Peter, additional, Foster, Ian, additional, Gibbs, Tom, additional, Jha, Shantenu, additional, Keipert, Kristopher, additional, Kranzlmüller, Dieter, additional, Kurth, Thorsten, additional, Lee, Hyungro, additional, Li, Zhuozhao, additional, Ma, Heng, additional, Mathias, Gerald, additional, Merzky, Andre, additional, Partin, Alexander, additional, Ramanathan, Arvind, additional, Shah, Ashka, additional, Stern, Abraham, additional, Stevens, Rick, additional, Tan, Li, additional, Titov, Mikhail, additional, Trifan, Anda, additional, Tsaris, Aristeidis, additional, Turilli, Matteo, additional, Van Dam, Huub, additional, Wan, Shunzhou, additional, Wifling, David, additional, and Yin, Junqi, additional

Published
2021
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17. Scalable HPC & AI infrastructure for COVID-19 therapeutics

Author

Lee, Hyungro, primary, Merzky, Andre, additional, Tan, Li, additional, Titov, Mikhail, additional, Turilli, Matteo, additional, Alfe, Dario, additional, Bhati, Agastya, additional, Brace, Alex, additional, Clyde, Austin, additional, Coveney, Peter, additional, Ma, Heng, additional, Ramanathan, Arvind, additional, Stevens, Rick, additional, Trifan, Anda, additional, Van Dam, Hubertus, additional, Wan, Shunzhou, additional, Wilkinson, Sean, additional, and Jha, Shantenu, additional

Published
2021
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18. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

Author

Dommer, Abigail, Casalino, Lorenzo, Kearns, Fiona, Rosenfeld, Mia, Wauer, Nicholas, Ahn, Surl-Hee, Russo, John, Oliveira, Sofia, Morris, Clare, Bogetti, Anthony, Trifan, Anda, Brace, Alexander, Sztain, Terra, Clyde, Austin, Ma, Heng, Chennubhotla, Chakra, Lee, Hyungro, Turilli, Matteo, Khalid, Syma, Tamayo-Mendoza, Teresa, Welborn, Matthew, Christensen, Anders, Smith, Daniel G. A., Qiao, Zhuoran, Sirumalla, Sai Krishna, O'Connor, Michael, Manby, Frederick, Anandkumar, Anima, Hardy, David, Phillips, James, Stern, Abraham, Romero, Josh, Clark, David, Dorrell, Mitchell, Maiden, Tom, Huang, Lei, McCalpin, John, Woods, Christopher, Gray, Alan, Williams, Matt, Barker, Bryan, Rajapaksha, Harinda, Pitts, Richard, Gibbs, Tom, Stone, John, Zuckerman, Daniel, Mulholland, Adrian, Miller, Thomas, III, Jha, Shantenu, Ramanathan, Arvind, Chong, Lillian, Amaro, Rommie, Dommer, Abigail, Casalino, Lorenzo, Kearns, Fiona, Rosenfeld, Mia, Wauer, Nicholas, Ahn, Surl-Hee, Russo, John, Oliveira, Sofia, Morris, Clare, Bogetti, Anthony, Trifan, Anda, Brace, Alexander, Sztain, Terra, Clyde, Austin, Ma, Heng, Chennubhotla, Chakra, Lee, Hyungro, Turilli, Matteo, Khalid, Syma, Tamayo-Mendoza, Teresa, Welborn, Matthew, Christensen, Anders, Smith, Daniel G. A., Qiao, Zhuoran, Sirumalla, Sai Krishna, O'Connor, Michael, Manby, Frederick, Anandkumar, Anima, Hardy, David, Phillips, James, Stern, Abraham, Romero, Josh, Clark, David, Dorrell, Mitchell, Maiden, Tom, Huang, Lei, McCalpin, John, Woods, Christopher, Gray, Alan, Williams, Matt, Barker, Bryan, Rajapaksha, Harinda, Pitts, Richard, Gibbs, Tom, Stone, John, Zuckerman, Daniel, Mulholland, Adrian, Miller, Thomas, III, Jha, Shantenu, Ramanathan, Arvind, Chong, Lillian, and Amaro, Rommie

Abstract

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Published
2021

19. Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

Author

Trifan, Anda, Gorgun, Defne, Li, Zongyi, Brace, Alexander, Zvyagin, Maxim, Ma, Heng, Clyde, Austin, Clark, David, Salim, Michael, Hardy, David J., Burnley, Tom, Huang, Lei, McCalpin, John, Emani, Murali, Yoo, Hyenseung, Yin, Junqi, Tsaris, Aristeidis, Subbiah, Vishal, Raza, Tanveer, Liu, Jessica, Trebesch, Noah, Wells, Geoffrey, Mysore, Venkatesh, Gibbs, Thomas, Phillips, James, Chennubhotla, S. Chakra, Foster, Ian, Stevens, Rick, Anandkumar, Anima, Vishwanath, Venkatram, Stone, John E., Tajkhorshid, Emad, Harris, Sarah A., Ramanathan, Arvind, Trifan, Anda, Gorgun, Defne, Li, Zongyi, Brace, Alexander, Zvyagin, Maxim, Ma, Heng, Clyde, Austin, Clark, David, Salim, Michael, Hardy, David J., Burnley, Tom, Huang, Lei, McCalpin, John, Emani, Murali, Yoo, Hyenseung, Yin, Junqi, Tsaris, Aristeidis, Subbiah, Vishal, Raza, Tanveer, Liu, Jessica, Trebesch, Noah, Wells, Geoffrey, Mysore, Venkatesh, Gibbs, Thomas, Phillips, James, Chennubhotla, S. Chakra, Foster, Ian, Stevens, Rick, Anandkumar, Anima, Vishwanath, Venkatram, Stone, John E., Tajkhorshid, Emad, Harris, Sarah A., and Ramanathan, Arvind

Abstract

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Published
2021

20. Calmodulin complexes with brain and muscle creatine kinase peptides

Author

Sprenger, Janina, Trifan, Anda, Patel, Neal, Vanderbeck, Ashley, Bredfelt, Jenny, Tajkhorshid, Emad, Rowlett, Roger, Lo Leggio, Leila, Åkerfeldt, Karin S., Linse, Sara, Sprenger, Janina, Trifan, Anda, Patel, Neal, Vanderbeck, Ashley, Bredfelt, Jenny, Tajkhorshid, Emad, Rowlett, Roger, Lo Leggio, Leila, Åkerfeldt, Karin S., and Linse, Sara

Abstract

Calmodulin (CaM) is a ubiquitous Ca2+ sensing protein that binds to and modulates numerous target proteins and enzymes during cellular signaling processes. A large number of CaM-target complexes have been identified and structurally characterized, revealing a wide diversity of CaM-binding modes. A newly identified target is creatine kinase (CK), a central enzyme in cellular energy homeostasis. This study reports two high-resolution X-ray structures, determined to 1.24 ​Å and 1.43 ​Å resolution, of calmodulin in complex with peptides from human brain and muscle CK, respectively. Both complexes adopt a rare extended binding mode with an observed stoichiometry of 1:2 CaM:peptide, confirmed by isothermal titration calorimetry, suggesting that each CaM domain independently binds one CK peptide in a Ca2+-depended manner. While the overall binding mode is similar between the structures with muscle or brain-type CK peptides, the most significant difference is the opposite binding orientation of the peptides in the N-terminal domain. This may extrapolate into distinct binding modes and regulation of the full-length CK isoforms. The structural insights gained in this study strengthen the link between cellular energy homeostasis and Ca2+-mediated cell signaling and may shed light on ways by which cells can ‘fine tune’ their energy levels to match the spatial and temporal demands.

Published
2021

21. High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor

Author

Clyde, Austin, primary, Galanie, Stephanie, additional, Kneller, Daniel W., additional, Ma, Heng, additional, Babuji, Yadu, additional, Blaiszik, Ben, additional, Brace, Alexander, additional, Brettin, Thomas, additional, Chard, Kyle, additional, Chard, Ryan, additional, Coates, Leighton, additional, Foster, Ian, additional, Hauner, Darin, additional, Kertesz, Vilmos, additional, Kumar, Neeraj, additional, Lee, Hyungro, additional, Li, Zhuozhao, additional, Merzky, Andre, additional, Schmidt, Jurgen G., additional, Tan, Li, additional, Titov, Mikhail, additional, Trifan, Anda, additional, Turilli, Matteo, additional, Van Dam, Hubertus, additional, Chennubhotla, Srinivas C., additional, Jha, Shantenu, additional, Kovalevsky, Andrey, additional, Ramanathan, Arvind, additional, Head, Martha S., additional, and Stevens, Rick, additional

Published
2021
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24. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

Author

Casalino, Lorenzo, primary, Dommer, Abigail, additional, Gaieb, Zied, additional, Barros, Emilia P., additional, Sztain, Terra, additional, Ahn, Surl-Hee, additional, Trifan, Anda, additional, Brace, Alexander, additional, Bogetti, Anthony, additional, Ma, Heng, additional, Lee, Hyungro, additional, Turilli, Matteo, additional, Khalid, Syma, additional, Chong, Lillian, additional, Simmerling, Carlos, additional, Hardy, David J., additional, Maia, Julio D. C., additional, Phillips, James C., additional, Kurth, Thorsten, additional, Stern, Abraham, additional, Huang, Lei, additional, McCalpin, John, additional, Tatineni, Mahidhar, additional, Gibbs, Tom, additional, Stone, John E., additional, Jha, Shantenu, additional, Ramanathan, Arvind, additional, and Amaro, Rommie E., additional

Published
2020
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27. Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

Author

Nicola Scafuri, Andre Merzky, Dieter Kranzlmüller, Shantenu Jha, Mikhail Titov, Thomas Brettin, Heng Ma, Li Tan, Sauro Succi, Xiaotian Duan, D. Wifling, Mathis Bode, Anda Trifan, G. Mathias, Austin Clyde, Y. Donon, Alexander Partin, Fangfang Xia, Matteo Turilli, Peter V. Coveney, Sofia Vallecorsa, Agastya P. Bhati, Shunzhou Wan, Dario Alfè, A Di Meglio, Arvind Ramanathan, Walter Rocchia, Roger Highfield, Rick Stevens, Bhati, Agastya P., Wan, Shunzhou, Alfè, Dario, Clyde, Austin R., Bode, Mathi, Tan, Li, Titov, Mikhail, Merzky, Andre, Turilli, Matteo, Jha, Shantenu, Highfield, Roger R., Rocchia, Walter, Scafuri, Nicola, Succi, Sauro, Kranzlmüller, Dieter, Mathias, Gerald, Wifling, David, Donon, Yann, Di Meglio, Alberto, Vallecorsa, Sofia, Heng, Ma, Trifan, Anda, Ramanathan, Arvind, Brettin, Tom, Partin, Alexander, Xia, Fangfang, Duan, Xiaotan, Stevens, Rick, and Coveney, Peter V.

Subjects
FOS: Computer and information sciences, 2019-20 coronavirus outbreak, Computer Science - Machine Learning, Coronavirus disease 2019 (COVID-19), Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Biomedical Engineering, Biophysics, FOS: Physical sciences, Bioengineering, 01 natural sciences, Biochemistry, Quantitative Biology - Quantitative Methods, Bottleneck, Machine Learning (cs.LG), Biomaterials, Computational Engineering, Finance, and Science (cs.CE), 03 medical and health sciences, novel drug design, 0103 physical sciences, Pandemic, Physics - Biological Physics, Computer Science - Computational Engineering, Finance, and Science, Quantitative Methods (q-bio.QM), Research Articles, 030304 developmental biology, 0303 health sciences, Hybrid machine, 010304 chemical physics, Drug discovery, Articles, Physics based, artificial intelligence, Data science, molecular dynamics, machine learning, Computer Science - Distributed, Parallel, and Cluster Computing, Biological Physics (physics.bio-ph), FOS: Biological sciences, Distributed, Parallel, and Cluster Computing (cs.DC), free energy predictions, Biotechnology
Abstract

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The twoin silicomethods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

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28. High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor.

Author

Clyde A, Galanie S, Kneller DW, Ma H, Babuji Y, Blaiszik B, Brace A, Brettin T, Chard K, Chard R, Coates L, Foster I, Hauner D, Kertesz V, Kumar N, Lee H, Li Z, Merzky A, Schmidt JG, Tan L, Titov M, Trifan A, Turilli M, Van Dam H, Chennubhotla SC, Jha S, Kovalevsky A, Ramanathan A, Head MS, and Stevens R

Subjects
Antiviral Agents, Coronavirus 3C Proteases, Humans, Molecular Docking Simulation, Molecular Dynamics Simulation, Orotic Acid analogs & derivatives, Piperazines, SARS-CoV-2, COVID-19, Protease Inhibitors
Abstract

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (M pro ) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this M pro inhibitor with an inhibition constant ( K i ) of 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of M pro forming stable hydrogen bond and hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by M pro , involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits M pro and offers a springboard for further therapeutic design.

Published
2022
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29. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol.

Author

Dommer A, Casalino L, Kearns F, Rosenfeld M, Wauer N, Ahn SH, Russo J, Oliveira S, Morris C, Bogetti A, Trifan A, Brace A, Sztain T, Clyde A, Ma H, Chennubhotla C, Lee H, Turilli M, Khalid S, Tamayo-Mendoza T, Welborn M, Christensen A, Smith DGA, Qiao Z, Sirumalla SK, O'Connor M, Manby F, Anandkumar A, Hardy D, Phillips J, Stern A, Romero J, Clark D, Dorrell M, Maiden T, Huang L, McCalpin J, Woods C, Gray A, Williams M, Barker B, Rajapaksha H, Pitts R, Gibbs T, Stone J, Zuckerman D, Mulholland A, Miller T 3rd, Jha S, Ramanathan A, Chong L, and Amaro R

Abstract

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized., Acm Reference Format: Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.

Published
2021
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30. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics.

Author

Casalino L, Dommer A, Gaieb Z, Barros EP, Sztain T, Ahn SH, Trifan A, Brace A, Bogetti A, Ma H, Lee H, Turilli M, Khalid S, Chong L, Simmerling C, Hardy DJ, Maia JDC, Phillips JC, Kurth T, Stern A, Huang L, McCalpin J, Tatineni M, Gibbs T, Stone JE, Jha S, Ramanathan A, and Amaro RE

Abstract

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Published
2020
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