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Annual Review of Biophysics

Volume 50, 2021

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

Abstract

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.

Keyword(s): artificial intelligence, biomolecular dynamics, biomolecular modeling, biomolecular simulation, citizen science projects, DNA folding, machine learning, multiscale modeling, protein folding, RNA folding, structure prediction

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Erratum: Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field
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/content/journals/10.1146/annurev-biophys-091720-102019
2021-05-06
2024-04-23
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Literature Cited

  1. 1. 
    Abriata LA, Tamo GE, Monastyrskyy B, Kryshtafovych A, Dal Peraro M 2018. Assessment of hard target modeling in CASP12 reveals an emerging role of alignment-based contact prediction methods. Proteins 86:Suppl. 197–112
     [Google Scholar]
  2. 2. 
    Adamczak B, Kogut M, Czub J. 2018. Effect of osmolytes on the thermal stability of proteins: replica exchange simulations of Trp-cage in urea and betaine solutions. Phys. Chem. Chem. Phys. 20:1611174–82
     [Google Scholar]
  3. 3. 
    Alder BJ, Wainwright TE. 1959. Studies in molecular dynamics. I. General method. J. Chem. Phys. 31:2459–66
     [Google Scholar]
  4. 4. 
    Allinger NL 1976. Calculation of molecular structure and energy by force-field methods. Advances in Physical Organic Chemistry 13 V Gold, D Bethell 1–82 Cambridge, MA: Academic
     [Google Scholar]
  5. 5. 
    Allinger NL, Miller MA, Van Catledge FA, Hirsch JA. 1967. Conformational analysis. LVII. The calculation of the conformational structures of hydrocarbons by the Westheimer-Hendrickson-Wiberg method. J. Am. Chem. Soc. 89:174345–57
     [Google Scholar]
  6. 6. 
    Amezcua M, Mobley D. 2020. SAMPL7 challenge overview: assessing the reliability of polarizable and non-polarizable methods for host-guest binding free energy calculations. ChemRxiv 12768353. https://doi.org/10.26434/chemrxiv.12768353.v1
    [Crossref] [Google Scholar]
  7. 7. 
    Andersen ES. 2010. Prediction and design of DNA and RNA structures. New Biotechnol 27:3184–93
     [Google Scholar]
  8. 8. 
    Anderson-Lee J, Fisker E, Kosaraju V, Wu M, Kong J et al. 2016. Principles for predicting RNA secondary structure design difficulty. J. Mol. Biol. 428:5A748–57
     [Google Scholar]
  9. 9. 
    Anfinsen CB. 1973. Principles that govern the folding of protein chains. Science 181:4096223–30
     [Google Scholar]
  10. 10. 
    Baker CM. 2015. Polarizable force fields for molecular dynamics simulations of biomolecules. WIREs Comput. Mol. Sci. 5:2241–54
     [Google Scholar]
  11. 11. 
    Baker EG, Bartlett GJ, Porter Goff KL, Woolfson DN 2017. Miniprotein design: past, present, and prospects. Acc. Chem. Res. 50:92085–92
     [Google Scholar]
  12. 12. 
    Baker EG, Williams C, Hudson KL, Bartlett GJ, Heal JW et al. 2017. Engineering protein stability with atomic precision in a monomeric miniprotein. Nat. Chem. Biol. 13:7764–70
     [Google Scholar]
  13. 13. 
    Banáš P, Sklenovský P, Wedekind JE, Šponer J, Otyepka M. 2012. Molecular mechanism of preQ1 riboswitch action: a molecular dynamics study. J. Phys. Chem. B 116:4212721–34
     [Google Scholar]
  14. 14. 
    Bauer G, Hoefler T, Kramer W, Fiedler R. 2012. Analyses and modeling of applications used to demonstrate sustained petascale performance on blue waters Paper presented at CUG 2012 Stuttgart, Germany:
  15. 15. 
    Beberg AL, Ensign DL, Jayachandran G, Khaliq S, Pande VS. 2009. Folding@home: lessons from eight years of volunteer distributed computing. 2009 IEEE International Symposium on Parallel Distributed Processing1–8 Piscataway, NJ: IEEE
     [Google Scholar]
  16. 16. 
    Berendsen HJC, van der Spoel D, van Drunen R. 1995. GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91:143–56
     [Google Scholar]
  17. 17. 
    Bezdek JC. 1993. Fuzzy models—what are they, and why? [Editorial].. IEEE Trans. Fuzzy Syst. 1:11–6
     [Google Scholar]
  18. 18. 
    Bixon M, Lifson S. 1967. Potential functions and conformations in cycloalkanes. Tetrahedron 23:2769–84
     [Google Scholar]
  19. 19. 
    Boomsma W, Tian P, Frellsen J, Ferkinghoff-Borg J, Hamelryck T et al. 2014. Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts. PNAS 111:3813852–57
     [Google Scholar]
  20. 20. 
    Boukharta L, Gutiérrez-de Terán H, Åqvist J. 2014. Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. PLOS Comput. Biol. 10:4e1003585
     [Google Scholar]
  21. 21. 
    Boulanger E, Thiel W. 2014. Toward QM/MM simulation of enzymatic reactions with the Drude oscillator polarizable force field. J. Chem. Theory Comput. 10:41795–809
     [Google Scholar]
  22. 22. 
    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP et al. 2006. Scalable algorithms for molecular dynamics simulations on commodity clusters. SC'06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing43–43 New York: ACM
     [Google Scholar]
  23. 23. 
    Bramsen JB, Kjems J. 2012. Development of therapeutic-grade small interfering RNAs by chemical engineering. Front. Genet. 3:154
     [Google Scholar]
  24. 23a. 
    Brini E, Simmerling C, Dill K 2020. Protein storytelling through physics. Science 370:6520eaaz3041
     [Google Scholar]
  25. 24. 
    Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M. 1983. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4:2187–217
     [Google Scholar]
  26. 25. 
    Canessa Fortuna A, Zerbetto De Palma G, Aliperti Car L, Armentia L, Vitali V et al. 2019. Gating in plant plasma membrane aquaporins: the involvement of leucine in the formation of a pore constriction in the closed state. FEBS J 286:173473–87
     [Google Scholar]
  27. 26. 
    Carr JK, Zabuga AV, Roy S, Rizzo TR, Skinner JL. 2014. Assessment of amide I spectroscopic maps for a gas-phase peptide using IR-UV double-resonance spectroscopy and density functional theory calculations. J. Chem. Phys. 140:22224111
     [Google Scholar]
  28. 27. 
    Carter Childers M, Daggett V 2017. Insights from molecular dynamics simulations for computational protein design. Mol. Syst. Des. Eng. 2:19–33
     [Google Scholar]
  29. 28. 
    Chen P-C, Hub JS. 2014. Validating solution ensembles from molecular dynamics simulation by wide-angle X-ray scattering data. Biophys. J. 107:2435–47
     [Google Scholar]
  30. 29. 
    Chevalier A, Silva D-A, Rocklin GJ, Hicks DR, Vergara R et al. 2017. Massively parallel de novo protein design for targeted therapeutics. Nature 550:767474–79
     [Google Scholar]
  31. 30. 
    Chou F-C, Lipfert J, Das R. 2014. Blind predictions of DNA and RNA tweezers experiments with force and torque. PLOS Comput. Biol. 10:8e1003756
     [Google Scholar]
  32. 31. 
    Chowdhary J, Harder E, Lopes PEM, Huang L, MacKerell AD, Roux B. 2013. A polarizable force field of dipalmitoylphosphatidylcholine based on the classical Drude model for molecular dynamics simulations of lipids. J. Phys. Chem. B 117:319142–60
     [Google Scholar]
  33. 32. 
    Cino EA, Choy W-Y, Karttunen M. 2012. Comparison of secondary structure formation using 10 different force fields in microsecond molecular dynamics simulations. J. Chem. Theory Comput. 8:82725–40
     [Google Scholar]
  34. 33. 
    Cooper S, Khatib F, Treuille A, Barbero J, Lee J et al. 2010. Predicting protein structures with a multiplayer online game. Nature 466:756–60
     [Google Scholar]
  35. 34. 
    Craven TW, Cho M-K, Traaseth NJ, Bonneau R, Kirshenbaum K. 2016. A miniature protein stabilized by a cation-π interaction network core. J. Am. Chem. Soc. 138:51543–50
     [Google Scholar]
  36. 35. 
    Cruz JA, Blanchet M-F, Boniecki M, Bujnicki JM, Chen S-J et al. 2012. RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. RNA 18:4610–25
     [Google Scholar]
  37. 36. 
    Daura X, Jaun B, Seebach D, van Gunsteren WF, Mark AE. 1998. Reversible peptide folding in solution by molecular dynamics simulation. J. Mol. Biol. 280:5925–32
     [Google Scholar]
  38. 37. 
    de Brevern AG, Bornot A, Craveur P, Etchebest C, Gelly J-C. 2012. PredyFlexy: flexibility and local structure prediction from sequence. Nucleic Acids Res 40:W317–22
     [Google Scholar]
  39. 38. 
    Di Palma F, Bottaro S, Bussi G. 2015. Kissing loop interaction in adenine riboswitch: insights from umbrella sampling simulations. BMC Bioinformat 16:Suppl. 9S6
     [Google Scholar]
  40. 38a. 
    Di Pierro M, Cheng RR, Aiden EL, Wolynes PG, Onuchic JN 2017. De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture. PNAS 114:4612126–31
     [Google Scholar]
  41. 39. 
    Dirks RM, Lin M, Winfree E, Pierce NA 2004. Paradigms for computational nucleic acid design. Nucleic Acids Res 32:41392–403
     [Google Scholar]
  42. 40. 
    Dror RO, Green HF, Valant C, Borhani DW, Valcourt JR et al. 2013. Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs. Nature 503:7475295–99
     [Google Scholar]
  43. 41. 
    Dror RO, Mildorf TJ, Hilger D, Manglik A, Borhani DW et al. 2015. Structural basis for nucleotide exchange in heterotrimeric G proteins. Science 348:62411361–65
     [Google Scholar]
  44. 42. 
    Duan L, Guo X, Cong Y, Feng G, Li Y, Zhang JZH. 2019. Accelerated molecular dynamics simulation for helical proteins folding in explicit water. Front. Chem. 7:540
     [Google Scholar]
  45. 43. 
    Duan Y, Kollman PA. 1998. Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 282:5389740–44
     [Google Scholar]
  46. 44. 
    Durrant JD, Kochanek SE, Casalino L, Ieong PU, Dommer AC, Amaro RE. 2020. Mesoscale all-atom influenza virus simulations suggest new substrate binding mechanism. ACS Central Sci 6:2189–96
     [Google Scholar]
  47. 45. 
    El Hage K, Hédin F, Gupta PK, Meuwly M, Karplus M. 2018. Valid molecular dynamics simulations of human hemoglobin require a surprisingly large box size. eLife 7:e35560
     [Google Scholar]
  48. 46. 
    El Hage K, Hédin F, Gupta PK, Meuwly M, Karplus M. 2019. Response to comment on “Valid molecular dynamics simulations of human hemoglobin require a surprisingly large box size”. eLife 8:e45318
     [Google Scholar]
  49. 47. 
    Deleted in proof
  50. 48. 
    Esguerra M, Siretskiy A, Bello X, Sallander J, Gutiérrez-de Terán H. 2016. GPCR-ModSim: a comprehensive web based solution for modeling G-protein coupled receptors. Nucleic Acids Res 44:W1W455–62
     [Google Scholar]
  51. 49. 
    Freddolino PL, Liu F, Gruebele M, Schulten K. 2008. Ten-microsecond molecular dynamics simulation of a fast-folding WW domain. Biophys. J. 94:10L75–77
     [Google Scholar]
  52. 50. 
    Gamini R, Han W, Stone JE, Schulten K. 2014. Assembly of Nsp1 nucleoporins provides insight into nuclear pore complex gating. PLOS Comput. Biol. 10:3e1003488
     [Google Scholar]
  53. 51. 
    Ganguly A, Boulanger E, Thiel W. 2017. Importance of MM polarization in QM/MM studies of enzymatic reactions: assessment of the QM/MM Drude oscillator model. J. Chem. Theory Comput. 13:62954–61
     [Google Scholar]
  54. 52. 
    Gapsys V, de Groot BL. 2019. Comment on “Valid molecular dynamics simulations of human hemoglobin require a surprisingly large box size. ”. eLife 8:e44718
     [Google Scholar]
  55. 53. 
    Gapsys V, de Groot BL. 2020. On the importance of statistics in molecular simulations for thermodynamics, kinetics and simulation box size. eLife 9:e57589
     [Google Scholar]
  56. 54. 
    Genheden S, Ryde U. 2012. Will molecular dynamics simulations of proteins ever reach equilibrium?. Phys. Chem. Chem. Phys. 14:248662–77
     [Google Scholar]
  57. 55. 
    Gniewek P, Kolinski A, Jernigan RL, Kloczkowski A. 2012. How noise in force fields can affect the structural refinement of protein models?. Proteins 80:2335–41
     [Google Scholar]
  58. 56. 
    Grigoryev SA, Bascom G, Buckwalter JM, Schubert MB, Woodcock CL, Schlick T 2016. Hierarchical looping of zigzag nucleosome chains in metaphase chromosomes. PNAS 113:51238–43
     [Google Scholar]
  59. 57. 
    Gunsteren WFV. 1996. Biomolecular Simulation: The GROMOS96 Manual and User Guide Zürich: Biomos
  60. 58. 
    Guvench O, MacKerell AD Jr. 2009. Computational fragment-based binding site identification by ligand competitive saturation. PLOS Comput. Biol. 5:7e1000435
     [Google Scholar]
  61. 59. 
    Haghighatlari M, Hachmann J. 2019. Advances of machine learning in molecular modeling and simulation. Curr. Opin. Chem. Eng. 23:51–57
     [Google Scholar]
  62. 60. 
    Halgren TA. 1996. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 17:5–6490–519
     [Google Scholar]
  63. 61. 
    Hallberg ZF, Su Y, Kitto RZ, Hammond MC. 2017. Engineering and in vivo applications of riboswitches. Annu. Rev. Biochem. 86:515–39
     [Google Scholar]
  64. 62. 
    Hamp T, Rost B. 2015. More challenges for machine-learning protein interactions. Bioinformatics 31:101521–25
     [Google Scholar]
  65. 63. 
    He X, Lopes PEM, MacKerell AD. 2013. Polarizable empirical force field for acyclic polyalcohols based on the classical Drude oscillator. Biopolymers 99:10724–38
     [Google Scholar]
  66. 64. 
    Hsieh T-HS, Cattoglio C, Slobodyanyuk E, Hansen AS, Rando OJ et al. 2020. Resolving the 3D landscape of transcription-linked mammalian chromatin folding. Mol. Cell 78:3539–53.e8
     [Google Scholar]
  67. 65. 
    Hu H, Liu H. 2013. Pitfall in quantum mechanical/molecular mechanical molecular dynamics simulation of small solutes in solution. J. Phys. Chem. B 117:216505–11
     [Google Scholar]
  68. 66. 
    Huang P-S, Boyken SE, Baker D. 2016. The coming of age of de novo protein design. Nature 537:7620320–27
     [Google Scholar]
  69. 67. 
    Huang X, Pearce R, Zhang Y 2020. De novo design of protein peptides to block association of the SARS-CoV-2 spike protein with human ACE2. Aging 12:1211263–76
     [Google Scholar]
  70. 68. 
    Inakollu VSS, Geerke DP, Rowley CN, Yu H. 2020. Polarisable force fields: What do they add in biomolecular simulations?. Curr. Opin. Struct. Biol. 61:182–90
     [Google Scholar]
  71. 69. 
    Irobalieva RN, Fogg JM, Catanese DJ Jr., Sutthibutpong T, Chen M et al. 2015. Structural diversity of supercoiled DNA. Nat. Commun. 6:8440
     [Google Scholar]
  72. 70. 
    Izrailev S, Crofts AR, Berry EA, Schulten K. 1999. Steered molecular dynamics simulation of the Rieske subunit motion in the cytochrome bc1 complex. Biophys. J. 77:41753–68
     [Google Scholar]
  73. 71. 
    Jack A, Levitt M. 1978. Refinement of large structures by simultaneous minimization of energy and R factor. Acta Crystallogr. A 34:6931–35
     [Google Scholar]
  74. 72. 
    Jackson NE, Bowen AS, Antony LW, Webb MA, Vishwanath V, de Pablo JJ. 2019. Electronic structure at coarse-grained resolutions from supervised machine learning. Sci. Adv. 5:3eaav1190
     [Google Scholar]
  75. 73. 
    Jain S, Laederach A, Ramos SBV, Schlick T. 2018. A pipeline for computational design of novel RNA-like topologies. Nucleic Acids Res 46:147040–51
     [Google Scholar]
  76. 74. 
    Jain S, Schlick T. 2017. F-rag: generating atomic coordinates from RNA graphs by fragment assembly. J. Mol. Biol. 429:233587–605
     [Google Scholar]
  77. 75. 
    Jain S, Zhu Q, Paz AS, Schlick T. 2020. Identification of novel RNA design candidates by clustering the extended RNA-As-Graphs library. Biochim. Biophys. Acta Gen. Subj. 1864:6129534
     [Google Scholar]
  78. 76. 
    Jindal G, Slanska K, Kolev V, Damborsky J, Prokop Z, Warshel A 2019. Exploring the challenges of computational enzyme design by rebuilding the active site of a dehalogenase. PNAS 116:2389–94
     [Google Scholar]
  79. 77. 
    Jing Z, Liu C, Cheng SY, Qi R, Walker BD et al. 2019. Polarizable force fields for biomolecular simulations: recent advances and applications. Annu. Rev. Biophys. 48:371–94
     [Google Scholar]
  80. 78. 
    Jing Z, Liu C, Qi R, Ren P 2018. Many-body effect determines the selectivity for Ca2+ and Mg2+ in proteins. PNAS 115:32E7495–501
     [Google Scholar]
  81. 79. 
    Jing Z, Qi R, Liu C, Ren P. 2017. Study of interactions between metal ions and protein model compounds by energy decomposition analyses and the AMOEBA force field. J. Chem. Phys. 147:16161733
     [Google Scholar]
  82. 80. 
    Johnson GT, Goodsell DS, Autin L, Forli S, Sanner MF, Olson AJ. 2014. 3D molecular models of whole HIV-1 virions generated with cellPACK. Faraday Discuss 169:23–44
     [Google Scholar]
  83. 81. 
    Jorgensen WL, Madura JD, Swenson CJ. 1984. Optimized intermolecular potential functions for liquid hydrocarbons. J. Am. Chem. Soc. 106:226638–46
     [Google Scholar]
  84. 82. 
    Jumper J, Tunyasuvunakool K, Kohlim P, Hassabis D, Team A. 2020. Computational predictions of protein structures associated with COVID-19 Rep., DeepMind London: https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19
  85. 83. 
    Jung J, Nishima W, Daniels M, Bascom G, Kobayashi C et al. 2019. Scaling molecular dynamics beyond 100,000 processor cores for large-scale biophysical simulations. J. Comput. Chem. 40:211919–30
     [Google Scholar]
  86. 84. 
    Jungmann R, Avendaño MS, Woehrstein JB, Dai M, Shih WM, Yin P. 2014. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods 11:313–18
     [Google Scholar]
  87. 85. 
    Karplus M, Lavery R. 2014. Significance of molecular dynamics simulations for life sciences. Isr. J. Chem. 54:8-91042–51
     [Google Scholar]
  88. 86. 
    Khabiri M, Freddolino PL. 2017. Deficiencies in molecular dynamics simulation-based prediction of protein–DNA binding free energy landscapes. J. Phys. Chem. B 121:205151–61
     [Google Scholar]
  89. 87. 
    Khafizov K, Perez C, Koshy C, Quick M, Fendler K et al. 2012. Investigation of the sodium-binding sites in the sodium-coupled betaine transporter BetP. PNAS 109:44E3035–44
     [Google Scholar]
  90. 88. 
    Kilic S, Felekyan S, Doroshenko O, Boichenko I, Dimura M et al. 2018. Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α. Nat. Commun. 9:1235
     [Google Scholar]
  91. 89. 
    Kinana AD, Vargiu AV, Nikaido H. 2016. Effect of site-directed mutations in multidrug efflux pump AcrB examined by quantitative efflux assays. Biochem. Biophys. Res. Commun. 480:4552–57
     [Google Scholar]
  92. 90. 
    Koepnick B, Flatten J, Husain T, Ford A, Silva D-A et al. 2019. De novo protein design by citizen scientists. Nature 570:7761390–94
     [Google Scholar]
  93. 91. 
    Krepl M, Havrila M, Stadlbauer P, Banas P, Otyepka M et al. 2015. Can we execute stable microsecond-scale atomistic simulations of protein–RNA complexes?. J. Chem. Theory Comput. 11:31220–43
     [Google Scholar]
  94. 92. 
    Krietenstein N, Abraham S, Venev SV, Abdennur N, Gibcus J et al. 2020. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78:3554–65.e7
     [Google Scholar]
  95. 93. 
    Kryshtafovych A, Monastyrskyy B, Fidelis K, Moult J, Schwede T, Tramontano A. 2018. Evaluation of the template-based modeling in CASP12. Proteins 86:Suppl. 1321–34
     [Google Scholar]
  96. 94. 
    Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. 2019. Critical assessment of methods of protein structure prediction (CASP)—round XIII. Proteins 87:121011–20
     [Google Scholar]
  97. 95. 
    Kührová P, Best RB, Bottaro S, Bussi G, Šponer J et al. 2016. Computer folding of RNA tetraloops: identification of key force field deficiencies. J. Chem. Theory Comput. 12:94534–48
     [Google Scholar]
  98. 96. 
    Lankaš F, Lavery R, Maddocks JH. 2006. Kinking occurs during molecular dynamics simulations of small DNA minicircles. Structure 14:101527–34
     [Google Scholar]
  99. 97. 
    Latorraca NR, Venkatakrishnan AJ, Dror RO. 2017. GPCR dynamics: structures in motion. Chem. Rev. 117:1139–55
     [Google Scholar]
  100. 98. 
    Lee J, Kladwang W, Lee M, Cantu D, Azizyan M et al. 2014. RNA design rules from a massive open laboratory. PNAS 111:62122–27
     [Google Scholar]
  101. 99. 
    Leferink NGH, Ranaghan KE, Karuppiah V, Currin A, van der Kamp MW et al. 2018. Experiment and simulation reveal how mutations in functional plasticity regions guide plant monoterpene synthase product outcome. ACS Catal 8:53780–91
     [Google Scholar]
  102. 100. 
    Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N et al. 2020. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods 17:7665–80
     [Google Scholar]
  103. 101. 
    Lemkul JA, MacKerell AD. 2018. Polarizable force field for RNA based on the classical Drude oscillator. J. Comput. Chem. 39:322624–46
     [Google Scholar]
  104. 102. 
    Lemkul JA, Savelyev A, MacKerell AD Jr. 2014. Induced polarization influences the fundamental forces in DNA base flipping. J. Phys. Chem. Lett. 5:122077–83
     [Google Scholar]
  105. 103. 
    Lensink MF, Velankar S, Wodak SJ. 2017. Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition. Proteins 85:3359–77
     [Google Scholar]
  106. 104. 
    Leonard AN, Wang E, Monje-Galvan V, Klauda JB. 2019. Developing and testing of lipid force fields with applications to modeling cellular membranes. Chem. Rev. 119:96227–69
     [Google Scholar]
  107. 105. 
    Liang H, Chen H, Fan K, Wei P, Guo X et al. 2009. De novo design of a beta alpha beta motif. Angew. Chem. 48:183301–3
     [Google Scholar]
  108. 106. 
    Lifson S. 1986. Theoretical foundation for the empirical force field method. Gazz. Chim. Ital. 116:12687–92
     [Google Scholar]
  109. 107. 
    Lin F-Y, Huang J, Pandey P, Rupakheti C, Li J et al. 2020. Further optimization and validation of the classical Drude polarizable protein force field. J. Chem. Theory Comput. 16:53221–39
     [Google Scholar]
  110. 108. 
    Lin F-Y, MacKerell ADJ. 2019. Force fields for small molecules. Methods Mol. Biol. 2022:21–54
     [Google Scholar]
  111. 109. 
    Lin X, Schafer NP, Lu W, Jin S, Chen X et al. 2019. Forging tools for refining predicted protein structures. PNAS 116:199400–9
     [Google Scholar]
  112. 110. 
    Lindorff-Larsen K, Piana S, Dror RO, Shaw DE. 2011. How fast-folding proteins fold. Science 334:6055517–20
     [Google Scholar]
  113. 111. 
    Liu C, Perilla JR, Ning J, Lu M, Hou G et al. 2016. Cyclophilin A stabilizes the HIV-1 capsid through a novel non-canonical binding site. Nat. Commun. 7:10714
     [Google Scholar]
  114. 112. 
    Liu S, Liu C, Deng L. 2018. Machine learning approaches for protein-protein interaction hot spot prediction: progress and comparative assessment. Molecules 23:102535
     [Google Scholar]
  115. 113. 
    Loco D, Lagardère L, Cisneros GA, Scalmani G, Frisch M et al. 2019. Towards large scale hybrid QM/MM dynamics of complex systems with advanced point dipole polarizable embeddings. Chem. Sci. 10:307200–11
     [Google Scholar]
  116. 114. 
    Mayr A, Klambauer G, Unterthiner T, Hochreiter S. 2016. Deeptox: toxicity prediction using deep learning. Front. Environ. Sci. 3:80
     [Google Scholar]
  117. 115. 
    McCammon JA, Gelin BR, Karplus M. 1977. Dynamics of folded proteins. Nature 267:5612585–90
     [Google Scholar]
  118. 116. 
    Melcr J, Piquemal J-P. 2019. Accurate biomolecular simulations account for electronic polarization. Front. Mol. Biosci. 6:143
     [Google Scholar]
  119. 117. 
    Meng G, Tariq M, Jain S, Elmetwaly S, Schlick T. 2019. RAG-Web: RNA structure prediction/design using RNA-As-Graphs. Bioinformatics 36:2647–48
     [Google Scholar]
  120. 118. 
    Mezei M. 2017. Rescore protein–protein docked ensembles with an interface contact statistics. Proteins 85:2235–41
     [Google Scholar]
  121. 119. 
    Miao Z, Adamiak RW, Antczak M, Batey RT, Becka AJ et al. 2017. RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme. RNA 23:5655–72
     [Google Scholar]
  122. 120. 
    Miao Z, Adamiak RW, Blanchet M-F, Boniecki M, Bujnicki JM et al. 2015. RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. RNA 21:61066–84
     [Google Scholar]
  123. 121. 
    Miao Y, Goldfeld DA, Moo EV, Sexton PM, Christopoulos A et al. 2016. Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor. PNAS 113:38E5675–84
     [Google Scholar]
  124. 122. 
    Miao Y, Huang Y-M, Walker RC, McCammon JA, Chang C-EA. 2018. Ligand binding pathways and conformational transitions of the HIV protease. Biochemistry 57:91533–41
     [Google Scholar]
  125. 123. 
    Mitchell JS, Laughton CA, Harris SA. 2011. Atomistic simulations reveal bubbles, kinks and wrinkles in supercoiled DNA. Nucleic Acids Res 39:93928–38
     [Google Scholar]
  126. 124. 
    Mlýnský V, Banás P, Hollas D, Réblová K, Walter NG et al. 2010. Extensive molecular dynamics simulations showing that canonical G8 and protonated A38H+ forms are most consistent with crystal structures of hairpin ribozyme. J. Phys. Chem. B 114:196642–52
     [Google Scholar]
  127. 125. 
    Moult J, Pedersen JT, Judson R, Fidelis K 1995. A large-scale experiment to assess protein structure prediction methods. Proteins 23:32–4
     [Google Scholar]
  128. 126. 
    Munos B. 2009. Lessons from 60 years of pharmaceutical innovation. Nat. Rev. Drug Discov. 8:12959–68
     [Google Scholar]
  129. 127. 
    Neale C, Pomès R. 2016. Sampling errors in free energy simulations of small molecules in lipid bilayers. Biochim. Biophys. Acta Biomembranes 1858:102539–48
     [Google Scholar]
  130. 128. 
    Neidigh JW, Fesinmeyer RM, Andersen NH. 2002. Designing a 20-residue protein. Nat. Struct. Mol. Biol. 9:6425–30
     [Google Scholar]
  131. 129. 
    Némethy G, Scheraga HA. 1965. Theoretical determination of sterically allowed conformations of a polypeptide chain by a computer method. Biopolymers 3:2155–84
     [Google Scholar]
  132. 130. 
    Ngo VA, Fanning JK, Noskov SY. 2019. Comparative analysis of protein hydration from MD simulations with additive and polarizable force fields. Adv. Theory Simul. 2:21800106
     [Google Scholar]
  133. 131. 
    Nguyen K, Whitford PC. 2016. Steric interactions lead to collective tilting motion in the ribosome during mRNA–tRNA translocation. Nat. Commun. 7:10586
     [Google Scholar]
  134. 132. 
    Noé F, Tkatchenko A, Müller K-R, Clementi C. 2020. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71:361–90
     [Google Scholar]
  135. 133. 
    Noinaj N, Kuszak AJ, Balusek C, Gumbart JC, Buchanan SK. 2014. Lateral opening and exit pore formation are required for BamA function. Structure 22:71055–62
     [Google Scholar]
  136. 134. 
    Noinaj N, Kuszak AJ, Gumbart JC, Lukacik P, Chang H et al. 2013. Structural insight into the biogenesis of β-barrel membrane proteins. Nature 501:7467385–90
     [Google Scholar]
  137. 135. 
    Oliveira ASF, Edsall CJ, Woods CJ, Bates P, Nunez GV et al. 2019. A general mechanism for signal propagation in the nicotinic acetylcholine receptor family. J. Am. Chem. Soc. 141:5119953–58
     [Google Scholar]
  138. 136. 
    Olson WK, Colasanti AV, Czapla L, Zheng G. 2008. Insights into the sequence-dependent macromolecular properties of DNA from base-pair level modeling: coarse-graining of condensed phase and biomolecular systems. Coarse-Graining of Condensed Phase and Biomolecular Systems GA Voth 205–23 Boca Raton, FL: CRC Press
     [Google Scholar]
  139. 137. 
    Ostmeyer J, Chakrapani S, Pan AC, Perozo E, Roux B. 2013. Recovery from slow inactivation in K+ channels is controlled by water molecules. Nature 501:7465121–24
     [Google Scholar]
  140. 138. 
    Pandey P, Mallajosyula SS. 2016. Influence of polarization on carbohydrate hydration: a comparative study using additive and polarizable force fields. J. Phys. Chem. B 120:276621–33
     [Google Scholar]
  141. 139. 
    Patel DS, He X, MacKerell AD. 2015. Polarizable empirical force field for hexopyranose monosaccharides based on the classical Drude oscillator. J. Phys. Chem. B 119:3637–52
     [Google Scholar]
  142. 140. 
    Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham TE et al. 1995. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 91:11–41
     [Google Scholar]
  143. 141. 
    Pérez A, Luque FJ, Orozco M. 2007. Dynamics of B-DNA on the microsecond time scale. J. Am. Chem. Soc. 129:4714739–45
     [Google Scholar]
  144. 142. 
    Perez C, Faust B, Mehdipour AR, Francesconi KA, Forrest LR, Ziegler C. 2014. Substrate-bound outward-open state of the betaine transporter BetP provides insights into Na+ coupling. Nat. Commun. 5:14231
     [Google Scholar]
  145. 143. 
    Perilla JR, Schulten K. 2017. Physical properties of the HIV-1 capsid from all-atom molecular dynamics simulations. Nat. Commun. 8:15959
     [Google Scholar]
  146. 144. 
    Perthold JW, Oostenbrink C. 2017. Simulation of reversible protein–protein binding and calculation of binding free energies using perturbed distance restraints. J. Chem. Theory Comput. 13:115697–708
     [Google Scholar]
  147. 145. 
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E et al. 2005. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26:161781–802
     [Google Scholar]
  148. 146. 
    Piana S, Donchev AG, Robustelli P, Shaw DE. 2015. Water dispersion interactions strongly influence simulated structural properties of disordered protein states. J. Phys. Chem. B 119:165113–23
     [Google Scholar]
  149. 147. 
    Piana S, Klepeis JL, Shaw DE. 2014. Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Curr. Opin. Struct. Biol. 24:98–105
     [Google Scholar]
  150. 148. 
    Piana S, Shaw DE. 2018. Atomic-level description of protein folding inside the GroEL cavity. J. Phys. Chem. B 122:4911440–49
     [Google Scholar]
  151. 149. 
    Deleted in proof
  152. 150. 
    Poma AB, Guzman HV, Li MS, Theodorakis PE. 2019. Mechanical and thermodynamic properties of αβ42, αβ40, and α-synuclein fibrils: a coarse-grained method to complement experimental studies. Beilstein J. Nanotechnol. 10:500–13
     [Google Scholar]
  153. 151. 
    Portillo-Ledesma S, Schlick T. 2020. Bridging chromatin structure and function over a range of experimental and spatial temporal scales by molecular modeling. WIREs Comput. Mol. Sci. 10:e1434
     [Google Scholar]
  154. 152. 
    Prigozhin MB, Zhang Y, Schulten K, Gruebele M, Pogorelov TV 2019. Fast pressure-jump all-atom simulations and experiments reveal site-specific protein dehydration-folding dynamics. PNAS 116:125356–61
     [Google Scholar]
  155. 153. 
    Pyle AM, Schlick T. 2016. Challenges in RNA structural modeling and design. J. Mol. Biol. 428:5A733–35
     [Google Scholar]
  156. 154. 
    Rahman A, Stillinger FH. 1971. Molecular dynamics study of liquid water. J. Chem. Phys. 55:73336–59
     [Google Scholar]
  157. 155. 
    Ramis R, Ortega-Castro J, Casasnovas R, Mariño L, Vilanova B et al. 2019. A coarse-grained molecular dynamics approach to the study of the intrinsically disordered protein α-synuclein. J. Chem. Inf. Model. 59:41458–71
     [Google Scholar]
  158. 156. 
    Rao SSP, Huang S-C, Glenn St. Hilaire B, Engreitz JM, Perez EM et al. 2017. Cohesin loss eliminates all loop domains. Cell 171:2305–20
     [Google Scholar]
  159. 157. 
    Rapp AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM 1992. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 114:2510024–35
     [Google Scholar]
  160. 158. 
    Reddy T, Shorthouse D, Parton DL, Jefferys E, Fowler PW et al. 2015. Nothing to sneeze at: a dynamic and integrative computational model of an influenza A virion. Structure 23:3584–97
     [Google Scholar]
  161. 159. 
    Robustelli P, Piana S, Shaw DE 2018. Developing a molecular dynamics force field for both folded and disordered protein states. PNAS 115:21E4758–66
     [Google Scholar]
  162. 160. 
    Ruggerone P, Murakami S, Pos KM, Vargiu AV. 2013. RND efflux pumps: structural information translated into function and inhibition mechanisms. Curr. Top. Med. Chem. 13:243079–100
     [Google Scholar]
  163. 161. 
    Rothemund PWK. 2006. Folding DNA to create nanoscale shapes and patterns. Nature 440:7082297–302
     [Google Scholar]
  164. 162. 
    Savelyev A, MacKerell AD. 2015. Competition among Li+, Na+, K+, and Rb+ monovalent ions for DNA in molecular dynamics simulations using the additive CHARMM36 and Drude polarizable force fields. J. Phys. Chem. B 119:124428–40
     [Google Scholar]
  165. 163. 
    Schalch T, Duda S, Sargent DF, Richmond TJ. 2005. X-ray structure of a tetranucleosome and its implications for the chromatin fibre. Nature 436:7047138–41
     [Google Scholar]
  166. 164. 
    Scheraga HA. 2011. Respice, Adspice, and Prospice. Annu. Rev. Biophys. 40:1–39
     [Google Scholar]
  167. 165. 
    Schlick T. 2009. Molecular dynamics-based approaches for enhanced sampling of long-time, large-scale conformational changes in biomolecules. F1000 Biol. Rep. 1:51
     [Google Scholar]
  168. 166. 
    Schlick T. 2009. Monte Carlo, harmonic approximation, and coarse-graining approaches for enhanced sampling of biomolecular structure. F1000 Biol. Rep. 1:48
     [Google Scholar]
  169. 167. 
    Schlick T. 2010. Molecular Modeling and Simulation: An Interdisciplinary Guide Berlin: Springer, 2nd ed..
  170. 168. 
    Schlick T. 2013. The 2013 Nobel Prize in Chemistry celebrates computations in chemistry and biology. SIAM News 46:1–4
     [Google Scholar]
  171. 169. 
    Schlick T. 2018. Adventures with RNA graphs. Methods 143:16–33
     [Google Scholar]
  172. 170. 
    Schlick T. 2020. Eight suggestions for future leaders of science and technology. Biophysicist 1:11–5
     [Google Scholar]
  173. 171. 
    Schlick T, Collepardo-Guevara R, Halvorsen LA, Jung S, Xiao X 2011. Biomolecular modeling and simulation: a field coming of age. Q. Rev. Biophys. 44:2191–228
     [Google Scholar]
  174. 172. 
    Schlick T, Portillo-Ledesma S. 2020. Biomolecular modeling thrives in the age of technology. Nat. Comput. Sci. In press
    [Google Scholar]
  175. 173. 
    Schlick T, Pyle AM. 2017. Opportunities and challenges in RNA structural modeling and design. Biophys. J. 113:2225–34
     [Google Scholar]
  176. 174. 
    Schlick T, Zhu Q, Jain S, Yan S 2021. Structure-altering mutations of the SARS-CoV-2 frameshifting RNA element. Biophys. J. 119:1–14
     [Google Scholar]
  177. 175. 
    Seeman NC. 1982. Nucleic acid junctions and lattices. J. Theor. Biol. 99:2237–47
     [Google Scholar]
  178. 176. 
    Seeman NC, Sleiman HF. 2017. DNA nanotechnology. Nat. Rev. Mater 3:17068
     [Google Scholar]
  179. 177. 
    Sengupta D, Chattopadhyay A. 2015. Molecular dynamics simulations of GPCR–cholesterol interaction: an emerging paradigm. Biochim. Biophys. Acta Biomembr. 1848:91775–82
     [Google Scholar]
  180. 178. 
    Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L et al. 2019. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins 87:121141–48
     [Google Scholar]
  181. 179. 
    Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L et al. 2020. Improved protein structure prediction using potentials from deep learning. Nature 577:7792706–10
     [Google Scholar]
  182. 180. 
    Shaw DE, Chao JC, Eastwood MP, Gagliardo J, Grossman JP et al. 2008. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51:791–97
     [Google Scholar]
  183. 181. 
    Shaw DE, Grossman JP, Bank JA, Batson B, Butts JA et al. 2014. Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. SC14: International Conference for High Performance Computing, Networking, Storage and Analysis41–53 Piscataway, NJ: IEEE
     [Google Scholar]
  184. 182. 
    Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO et al. 2010. Atomic-level characterization of the structural dynamics of proteins. Science 330:6002341–46
     [Google Scholar]
  185. 183. 
    Sirur A, De Sancho D, Best RB. 2016. Markov state models of protein misfolding. J. Chem. Phys. 144:7075101
     [Google Scholar]
  186. 184. 
    Smith MD, Smith JC. 2020. Repurposing therapeutics for COVID-19: supercomputer-based docking to the SARS-CoV-2 viral spike protein and viral spike protein-human ACE2 interface. ChemRxiv 11871402. https://doi.org/10.26434.chemrxiv.11871402.v4
    [Crossref]
  187. 185. 
    Song D, Wang W, Ye W, Ji D, Luo R, Chen H-F 2017. ff14IDPs force field improving the conformation sampling of intrinsically disordered proteins. Chem. Biol. Drug Des. 89:15–15
     [Google Scholar]
  188. 186. 
    Song F, Chen P, Sun D, Wang M, Dong L et al. 2014. Cryo-EM study of the chromatin fiber reveals a double helix twisted by tetranucleosomal units. Science 344:6182376–80
     [Google Scholar]
  189. 187. 
    Song X, Jensen MØ, Jogini V, Stein RA, Lee C-H et al. 2018. Mechanism of NMDA receptor channel block by MK-801 and memantine. Nature 556:7702515–19
     [Google Scholar]
  190. 188. 
    Stillinger FH, Rahman A. 1974. Improved simulation of liquid water by molecular dynamics. J. Chem. Phys. 60:41545–57
     [Google Scholar]
  191. 189. 
    Stranges PB, Kuhlman B. 2013. A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds. Protein Sci 22:174–82
     [Google Scholar]
  192. 190. 
    Sun D, Forsman J, Woodward CE. 2015. Evaluating force fields for the computational prediction of ionized arginine and lysine side-chains partitioning into lipid bilayers and octanol. J. Chem. Theory Comp. 11:41775–91
     [Google Scholar]
  193. 191. 
    Sun H. 1998. COMPASS: an ab initio force-field optimized for condensed-phase applications—overview with details on alkane and benzene compounds. J. Phys. Chem. B 102:387338–64
     [Google Scholar]
  194. 192. 
    Tautermann CS, Seeliger D, Kriegl JM. 2015. What can we learn from molecular dynamics simulations for GPCR drug design?. Comput. Struct. Biotechnol. J. 13:111–21
     [Google Scholar]
  195. 193. 
    Vargiu AV, Collu F, Schulz R, Pos KM, Zacharias M et al. 2011. Effect of the F610A mutation on substrate extrusion in the AcrB transporter: explanation and rationale by molecular dynamics simulations. J. Am. Chem. Soc. 133:2810704–7
     [Google Scholar]
  196. 194. 
    Vargiu AV, Nikaido H 2012. Multidrug binding properties of the AcrB efflux pump characterized by molecular dynamics simulations. PNAS 109:5020637–42
     [Google Scholar]
  197. 195. 
    Vendruscolo M, Dobson CM. 2011. Protein dynamics: Moore's law in molecular biology. Curr. Biol. 21:2R68–70
     [Google Scholar]
  198. 196. 
    Walker B, Jing Z, Ren P. 2020. Molecular dynamics free energy simulations of ATP:Mg2+ and ADP:Mg2+ using the polarisable force field AMOEBA. Mol. Simul. https://doi.org/10.1080/08927022.2020.1725003
    [Crossref] [Google Scholar]
  199. 197. 
    Wang A, Zhang Z, Li G. 2018. Higher accuracy achieved in the simulations of protein structure refinement, protein folding, and intrinsically disordered proteins using polarizable force fields. J. Phys. Chem. Lett. 9:247110–16
     [Google Scholar]
  200. 198. 
    Wang Q, Irobalieva RN, Chiu W, Schmid MF, Fogg JM et al. 2017. Influence of DNA sequence on the structure of minicircles under torsional stress. Nucleic Acids Res 45:137633–42
     [Google Scholar]
  201. 199. 
    Warshel A, Levitt M. 1976. Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. J. Mol. Biol. 103:2227–49
     [Google Scholar]
  202. 200. 
    Wasserman MR, Alejo JL, Altman RB, Blanchard SC. 2016. Multiperspective smFRET reveals rate-determining late intermediates of ribosomal translocation. Nat. Struct. Mol. Biol. 23:4333–41
     [Google Scholar]
  203. 201. 
    Williford J-M, Santos JL, Shyam R, Mao H-Q. 2015. Shape control in engineering of polymeric nanoparticles for therapeutic delivery. Biomater. Sci. 3:894–907
     [Google Scholar]
  204. 202. 
    Woys AM, Almeida AM, Wang L, Chiu C-C, McGovern M et al. 2012. Parallel β-sheet vibrational couplings revealed by 2D IR spectroscopy of an isotopically labeled macrocycle: quantitative benchmark for the interpretation of amyloid and protein infrared spectra. J. Am. Chem. Soc. 134:4619118–28
     [Google Scholar]
  205. 203. 
    Yaseen A, Nijim M, Williams B, Qian L, Li M et al. 2016. FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information. BMC Bioinformat 17:8281
     [Google Scholar]
  206. 204. 
    Young MA, Beveridge DL. 1998. Molecular dynamics simulations of an oligonucleotide duplex with adenine tracts phased by a full helix turn. J. Mol. Biol. 281:4675–87
     [Google Scholar]
  207. 205. 
    Yu K, Jiang T, Cui Y, Tajkhorshid E, Hartzell HC 2019. A network of phosphatidylinositol 4,5-bisphosphate binding sites regulates gating of the Ca2+-activated Cl- channel ANO1 (TMEM16A). PNAS 116:4019952–62
     [Google Scholar]
  208. 206. 
    Yu W, Lopes PEM, Roux B, MacKerell AD. 2013. Six-site polarizable model of water based on the classical Drude oscillator. J. Chem. Phys. 138:3034508
     [Google Scholar]
  209. 207. 
    Yusufova N, Kloetgen A, Teater M, Osunsade A, Camarillo Jet al 2021. Histone H1 loss drives lymphoma by disrupting 3D chromatin architecture. Nature 589:7841299–305
     [Google Scholar]
  210. 208. 
    Zhang C, Bell D, Harger M, Ren P. 2017. Polarizable multipole-based force field for aromatic molecules and nucleobases. J. Chem. Theory Comput. 13:2666–78
     [Google Scholar]
  211. 209. 
    Zhang C, Lu C, Jing Z, Wu C, Piquemal J-P et al. 2018. AMOEBA polarizable atomic multipole force field for nucleic acids. J. Chem. Theory Comp. 14:42084–108
     [Google Scholar]
  212. 210. 
    Barragan AM, Crofts AR, Schulten K, Solov'yov IA 2015. Identification of ubiquinol binding motifs at the Qo-site of the cytochrome bc1 complex. J. Phys. Chem. B 119:2433–47 [ Erratum]
    [Google Scholar]
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Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field
Annual Review of Biophysics 50, 267 (2021); https://doi.org/10.1146/annurev-biophys-091720-102019
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