TY - JOUR
T1 - Biomolecular Modeling and Simulation
T2 - A Prospering Multidisciplinary Field
AU - Schlick, Tamar
AU - Portillo-Ledesma, Stephanie
AU - Myers, Christopher G.
AU - Beljak, Lauren
AU - Chen, Justin
AU - Dakhel, Sami
AU - Darling, Daniel
AU - Ghosh, Sayak
AU - Hall, Joseph
AU - Jan, Mikaeel
AU - Liang, Emily
AU - Saju, Sera
AU - Vohr, Mackenzie
AU - Wu, Chris
AU - Xu, Yifan
AU - Xue, Eva
N1 - Funding Information:
Support from the National Institutes of Health, the National Institute of General Medical Sciences award R35-GM122562, the National Science Foundation RAPID Award (2030377) from the Division of Mathematical Sciences and the Division of Chemistry, and Philip Morris USA Inc. and Philip Morris International to T.S. is gratefully acknowledged. We thank the following questionnaire respondents for their time and opinions: Elena Akhmatskaya, Russ B. Altman, Nir Ben Tal,Giovanni Bussi,Qiang Cui, Mauricio Esguerra, James Gumbart, Jonathan Ipsaro,G. Ali Mansoori, Andy McCammon, Mihaly Mezei, StephenNeidle,Chris Oostenbrink, Ognjen Perisic, Stefano Piana-Agnostinetti, Benoit Roux, Robert Skeel, James Skinner, Paul Whitford, Celerino Abad Zapatero, and Yingkai Zhang. We also thank David Baker and Brian Koepnick for providing information on Foldit participants and solved puzzles, and Rhiju Das and Jonathan Romano for providing information on Eterna participants and solved puzzles. We thank David Case, Ron Elber, Alex MacKerell, and Pengyu Ren for their thoughtful comments on polarizable force fields. We apologize in advance to the many authors of excellent biomolecular papers who we could not cite due to page limits.
Publisher Copyright:
Copyright © 2021 by Annual Reviews. All rights reserved.
PY - 2021/5/6
Y1 - 2021/5/6
N2 - We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.
AB - We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.
KW - DNA folding
KW - RNA folding
KW - artificial intelligence
KW - biomolecular dynamics
KW - biomolecular modeling
KW - biomolecular simulation
KW - citizen science projects
KW - machine learning
KW - multiscale modeling
KW - protein folding
KW - structure prediction
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U2 - 10.1146/annurev-biophys-091720-102019
DO - 10.1146/annurev-biophys-091720-102019
M3 - Review article
C2 - 33606945
AN - SCOPUS:85105574000
SN - 1936-122X
VL - 50
SP - 267
EP - 301
JO - Annual Review of Biophysics
JF - Annual Review of Biophysics
ER -