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Kuznetsova V, Coogan Á, Botov D, Gromova Y, Ushakova EV, Gun'ko YK. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308912. [PMID: 38241607 PMCID: PMC11167410 DOI: 10.1002/adma.202308912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/10/2024] [Indexed: 01/21/2024]
Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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Affiliation(s)
- Vera Kuznetsova
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Áine Coogan
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Dmitry Botov
- Everypixel Media Innovation Group, 021 Fillmore St., PMB 15, San Francisco, CA, 94115, USA
- Neapolis University Pafos, 2 Danais Avenue, Pafos, 8042, Cyprus
| | - Yulia Gromova
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St., Cambridge, MA, 02138, USA
| | - Elena V Ushakova
- Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP), City University of Hong Kong, Hong Kong SAR, 999077, P. R. China
| | - Yurii K Gun'ko
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
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2
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Mustali J, Yasuda I, Hirano Y, Yasuoka K, Gautieri A, Arai N. Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 M pro. RSC Adv 2023; 13:34249-34261. [PMID: 38019981 PMCID: PMC10663885 DOI: 10.1039/d3ra06375e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/06/2023] [Indexed: 12/01/2023] Open
Abstract
Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.
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Affiliation(s)
- Jessica Mustali
- Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy
| | - Ikki Yasuda
- Department of Mechanical Engineering, Keio University Japan
| | | | - Kenji Yasuoka
- Department of Mechanical Engineering, Keio University Japan
| | - Alfonso Gautieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy
| | - Noriyoshi Arai
- Department of Mechanical Engineering, Keio University Japan
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3
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Saar KL, Qian D, Good LL, Morgunov AS, Collepardo-Guevara R, Best RB, Knowles TPJ. Theoretical and Data-Driven Approaches for Biomolecular Condensates. Chem Rev 2023; 123:8988-9009. [PMID: 37171907 PMCID: PMC10375482 DOI: 10.1021/acs.chemrev.2c00586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Indexed: 05/14/2023]
Abstract
Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.
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Affiliation(s)
- Kadi L. Saar
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Transition
Bio Ltd., Cambridge, United Kingdom
| | - Daoyuan Qian
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Lydia L. Good
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Laboratory
of Chemical Physics, National Institute of Diabetes and Digestive
and Kidney Diseases, National Institutes
of Health, Bethesda, Maryland 20892, United States
| | - Alexey S. Morgunov
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Rosana Collepardo-Guevara
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Department
of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Robert B. Best
- Laboratory
of Chemical Physics, National Institute of Diabetes and Digestive
and Kidney Diseases, National Institutes
of Health, Bethesda, Maryland 20892, United States
| | - Tuomas P. J. Knowles
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, Cambridge CB3 0HE, United Kingdom
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4
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Shmilovich K, Ferguson AL. Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables. J Phys Chem A 2023; 127:3497-3517. [PMID: 37036804 DOI: 10.1021/acs.jpca.3c00505] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Molecular dynamics simulations of microscopic phenomena are limited by the short integration time steps which are required for numerical stability but which limit the practically achievable simulation time scales. Collective variable (CV) enhanced sampling techniques apply biases to predefined collective coordinates to promote barrier crossing, phase space exploration, and sampling of rare events. The efficacy of these techniques is contingent on the selection of good CVs correlated with the molecular motions governing the long-time dynamical evolution of the system. In this work, we introduce Girsanov Reweighting Enhanced Sampling Technique (GREST) as an adaptive sampling scheme that interleaves rounds of data-driven slow CV discovery and enhanced sampling along these coordinates. Since slow CVs are inherently dynamical quantities, a key ingredient in our approach is the use of both thermodynamic and dynamical Girsanov reweighting corrections for rigorous estimation of slow CVs from biased simulation data. We demonstrate our approach on a toy 1D 4-well potential, a simple biomolecular system alanine dipeptide, and the Trp-Leu-Ala-Leu-Leu (WLALL) pentapeptide. In each case GREST learns appropriate slow CVs and drives sampling of all thermally accessible metastable states starting from zero prior knowledge of the system. We make GREST accessible to the community via a publicly available open source Python package.
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Affiliation(s)
- Kirill Shmilovich
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
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5
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Janson G, Valdes-Garcia G, Heo L, Feig M. Direct generation of protein conformational ensembles via machine learning. Nat Commun 2023; 14:774. [PMID: 36774359 PMCID: PMC9922302 DOI: 10.1038/s41467-023-36443-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 02/01/2023] [Indexed: 02/13/2023] Open
Abstract
Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied. Here, we demonstrate that machine learning can be trained with simulation data to directly generate physically realistic conformational ensembles of proteins without the need for any sampling and at negligible computational cost. As a proof-of-principle we train a generative adversarial network based on a transformer architecture with self-attention on coarse-grained simulations of intrinsically disordered peptides. The resulting model, idpGAN, can predict sequence-dependent coarse-grained ensembles for sequences that are not present in the training set demonstrating that transferability can be achieved beyond the limited training data. We also retrain idpGAN on atomistic simulation data to show that the approach can be extended in principle to higher-resolution conformational ensemble generation.
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Affiliation(s)
- Giacomo Janson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Gilberto Valdes-Garcia
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.
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6
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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 DG, 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 DM, Mulholland AJ, Miller T, Jha S, Ramanathan A, Chong L, Amaro RE. #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2023; 37:28-44. [PMID: 36647365 PMCID: PMC9527558 DOI: 10.1177/10943420221128233] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
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.
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Affiliation(s)
| | | | | | | | | | | | - John Russo
- Oregon Health & Science University, Portland, OR, USA
| | | | | | | | - Anda Trifan
- Argonne National Laboratory, Lemont, IL, USA
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alexander Brace
- Argonne National Laboratory, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Terra Sztain
- UC San Diego, La Jolla, CA, USA
- Freie Universitat Berlin
| | - Austin Clyde
- Argonne National Laboratory, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Heng Ma
- Argonne National Laboratory, Lemont, IL, USA
| | | | - Hyungro Lee
- Brookhaven National Lab and Rutgers University
| | | | | | | | | | | | | | - Zhuoran Qiao
- California Institute of Technology, Pasadena, CA, USA
| | | | | | | | - Anima Anandkumar
- California Institute of Technology, Pasadena, CA, USA
- NVIDIA Corp, Santa Clara, CA, USA
| | - David Hardy
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - James Phillips
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | | | | | - Tom Maiden
- Pittsburgh Supercomputing Center, Pittsburgh, PA, USA
| | - Lei Huang
- Texas Advanced Computing Center, Austin, TX, USA
| | | | | | | | | | | | | | | | | | - John Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
- NVIDIA Corp, Santa Clara, CA, USA
| | | | | | - Thomas Miller
- Entos, Inc., San Diego, CA, USA
- California Institute of Technology, Pasadena, CA, USA
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7
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Trifan A, Gorgun D, Salim M, Li Z, Brace A, Zvyagin M, Ma H, Clyde A, Clark D, Hardy DJ, Burnley T, Huang L, McCalpin J, Emani M, Yoo H, Yin J, Tsaris A, Subbiah V, Raza T, Liu J, Trebesch N, Wells G, Mysore V, Gibbs T, Phillips J, Chennubhotla SC, Foster I, Stevens R, Anandkumar A, Vishwanath V, Stone JE, Tajkhorshid E, A. Harris S, Ramanathan A. 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. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2022; 36:603-623. [PMID: 38464362 PMCID: PMC10923581 DOI: 10.1177/10943420221113513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
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.
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Affiliation(s)
- Anda Trifan
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | - Defne Gorgun
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | | | | | | | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ian Foster
- Argonne National Laboratory
- University of Chicago
| | - Rick Stevens
- Argonne National Laboratory
- University of Chicago
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8
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Floyd JE, Lukes JR. A neural network-assisted open boundary molecular dynamics simulation method. J Chem Phys 2022; 156:184114. [PMID: 35568556 DOI: 10.1063/5.0083198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
A neural network-assisted molecular dynamics method is developed to reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicitly modeled Lennard-Jones atoms in order to represent the effects of the unmodeled surrounding fluid. Canonical ensemble simulations with periodic boundaries are used to train the neural network and to sample boundary fluxes. The method, as implemented in the LAMMPS, yields temperature, kinetic energy, potential energy, and pressure values within 2.5% of those calculated using periodic molecular dynamics and runs two orders of magnitude faster than a comparable grand canonical molecular dynamics system.
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Affiliation(s)
- J E Floyd
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - J R Lukes
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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9
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Challenges and frontiers of computational modelling of biomolecular recognition. QRB DISCOVERY 2022. [DOI: 10.1017/qrd.2022.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Abstract
Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.
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10
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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, Jha S, Ramanathan A, Chong L, Amaro R. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.11.12.468428. [PMID: 34816263 PMCID: PMC8609898 DOI: 10.1101/2021.11.12.468428] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Anda Trifan
- Argonne National Laboratory
- University of Illinois at Urbana-Champaign
| | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | - Hyungro Lee
- Brookhaven National Lab & Rutgers University
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - John Stone
- University of Illinois at Urbana-Champaign
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11
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Casalino L, Dommer AC, Gaieb Z, Barros EP, Sztain T, Ahn SH, Trifan A, Brace A, Bogetti AT, Clyde A, Ma H, Lee H, Turilli M, Khalid S, Chong LT, Simmerling C, Hardy DJ, Maia JD, Phillips JC, Kurth T, Stern AC, Huang L, McCalpin JD, Tatineni M, Gibbs T, Stone JE, Jha S, Ramanathan A, Amaro RE. AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2021; 35:432-451. [PMID: 38603008 PMCID: PMC8064023 DOI: 10.1177/10943420211006452] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
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.
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Affiliation(s)
- Lorenzo Casalino
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | - Abigail C Dommer
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | - Zied Gaieb
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | | | - Terra Sztain
- University of California San Diego, La Jolla, CA, USA
| | - Surl-Hee Ahn
- University of California San Diego, La Jolla, CA, USA
| | - Anda Trifan
- Argonne National Lab, Lemont, IL, USA
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | - Austin Clyde
- Argonne National Lab, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Heng Ma
- Argonne National Lab, Lemont, IL, USA
| | | | | | | | | | | | - David J Hardy
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Julio Dc Maia
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | | | - Lei Huang
- Texas Advanced Computing Center, Austin, TX, USA
| | | | | | - Tom Gibbs
- NVIDIA Corporation, Santa Clara, CA, USA
| | - John E Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Shantenu Jha
- Rutgers University, Piscataway, NJ, USA
- Brookhaven National Lab, Upton, NY, USA
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12
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Husic BE, Charron NE, Lemm D, Wang J, Pérez A, Majewski M, Krämer A, Chen Y, Olsson S, de Fabritiis G, Noé F, Clementi C. Coarse graining molecular dynamics with graph neural networks. J Chem Phys 2020; 153:194101. [PMID: 33218238 PMCID: PMC7671749 DOI: 10.1063/5.0026133] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/27/2020] [Indexed: 11/14/2022] Open
Abstract
Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems.
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Affiliation(s)
| | | | - Dominik Lemm
- Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Dr. Aiguader 88, Barcelona, Spain
| | | | - Adrià Pérez
- Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Dr. Aiguader 88, Barcelona, Spain
| | - Maciej Majewski
- Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Dr. Aiguader 88, Barcelona, Spain
| | - Andreas Krämer
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
| | | | - Simon Olsson
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
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13
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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, Amaro RE. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.11.19.390187. [PMID: 33236007 PMCID: PMC7685317 DOI: 10.1101/2020.11.19.390187] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
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.
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Affiliation(s)
| | | | | | | | | | | | - Anda Trifan
- Argonne National Lab
- University of Illinois at Urbana-Champaign
| | | | | | | | - Hyungro Lee
- Rutgers University & Brookhaven National Lab
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