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PreprintCited by 41Protein Structure and DynamicsComputational Drug Discovery MethodsRNA and protein synthesis mechanisms

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Approximating conformational Boltzmann distributions with AlphaFold2 predictions

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Brown, Benjamin P., Stein, Richard A., Meiler, Jens, Mchaourab, Hassane·2023·Journal of Chemical Theory and Computation
DOI 10.1101/2023.08.06.552168PMC PMC10867840

Preprint — published as 10.1021/acs.jctc.3c01081 · Paper page

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Abstract

Protein dynamics are intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria are evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, the computational and experimental methods capable of quantifying them are limited to systems of modest size. Here, we demonstrate that AlphaFold2 contact distance distributions can approximate conformational Boltzmann distributions, which we evaluate through examination of the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Further, we show that contact distance probability distributions generated by AlphaFold2 are sensitive to points mutations thus AF2 can predict the structural effects of mutations in some systems. We anticipate that our approach will be a valuable tool to model the thermodynamics of conformational changes in large biomolecular systems.

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Research works printed but not linked: 61

  1. 10.1126/science.181.4096.223 · DOI · Paper page
  2. 10.1038/225563a0 · DOI
  3. Brown, B. P. et al. Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer. Proceedings of the National Academy of Sciences 119, e2206588119 (2022). https://doi.org:10.1073/pnas.2206588119 · DOI
  4. Du, Z. et al. Structure–function analysis of oncogenic EGFR Kinase Domain Duplication reveals insights into activation and a potential approach for therapeutic targeting. Nature Communications 12, 1382 (2021). https://doi.org:10.1038/s41467-021-21613-6 · DOI
  5. 2019, On-target Resistance to the Mutant-Selective EGFR Inhibitor Osimertinib Can Develop in an Allele-Specific Manner Dependent on the Original EGFR-Activating Mutation, Clinical cancer research : an official journal of the American Association for Cancer Research, 25, 3341, 10.1158/1078-0432.CCR-18-3829 · DOI
  6. Needham, S. R. et al. EGFR oligomerization organizes kinase-active dimers into competent signalling platforms. Nature Communications 7, 13307 (2016). https://doi.org:10.1038/ncomms13307 https://www.nature.com/articles/ncomms13307#supplementary-information · DOI
  7. Needham, S. R. et al. EGFR oligomerization organizes kinase-active dimers into competent signalling platforms. Nat Commun 7, 13307 (2016). https://doi.org:10.1038/ncomms13307 · DOI
  8. Shan, Y. , Arkhipov, A. , Kim, E. T. , Pan, A. C. & Shaw, D. E . Transitions to catalytically inactive conformations in EGFR kinase. Proceedings of the National Academy of Sciences 110, 7270 (2013). https://doi.org:10.1073/pnas.1220843110 · DOI
  9. 10.1016/j.cell.2012.02.063 · DOI
  10. Galdadas, I. et al. Structural basis of the effect of activating mutations on the EGF receptor. eLife 10, e65824 (2021). https://doi.org:10.7554/eLife.65824 · DOI
  11. 10.1073/pnas.1803152115 · DOI
  12. 10.1126/science.aaa1823 · DOI
  13. 10.1038/nsmb.2891 · DOI
  14. 10.1016/j.cell.2012.12.030 · DOI
  15. 10.1016/j.cell.2012.12.032 · DOI
  16. Shaw, D. E. et al. in SC ’14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 41–53.
  17. Meller, A. , Bhakat, S. , Solieva, S. & Bowman, G. R . Accelerating Cryptic Pocket Discovery Using AlphaFold. J. Chem. Theory Comput. (2023). https://doi.org:10.1021/acs.jctc.2c01189 · DOI
  18. Vani, B. P. , Aranganathan, A. , Wang, D. & Tiwary, P . AlphaFold2-RAVE: From Sequence to Boltzmann Ranking. J. Chem. Theory Comput. (2023). https://doi.org:10.1021/acs.jctc.3c00290 · DOI
  19. Ray, D. , Trizio, E. & Parrinello, M . Deep learning collective variables from transition path ensemble. The Journal of Chemical Physics 158, 204102 (2023). https://doi.org:10.1063/5.0148872 · DOI
  20. Bonati, L. , Piccini, G. & Parrinello, M . Deep learning the slow modes for rare events sampling. Proceedings of the National Academy of Sciences 118, e2113533118 (2021). https://doi.org:10.1073/pnas.2113533118 · DOI
  21. 2022, Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events, J. Chem. Theory Comput, 18, 2341, 10.1021/acs.jctc.1c00143 · DOI
  22. 2022, Collective variable discovery in the age of machine learning: reality, hype and everything in between, RSC Advances, 12, 25010, 10.1039/D2RA03660F · DOI
  23. 2022, Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers, J. Chem. Theory Comput, 18, 1929, 10.1021/acs.jctc.1c00916 · DOI
  24. 10.1073/pnas.1506788112 · DOI
  25. 2020, Machine learning approach for accurate backmapping of coarse-grained models to all-atom models, Chem. Commun, 56, 9312, 10.1039/D0CC02651D · DOI
  26. 2019, Machine Learning of Coarse-Grained Molecular Dynamics Force Fields, ACS Central Science, 5, 755, 10.1021/acscentsci.8b00913 · DOI
  27. Guo, H.-B. et al. AlphaFold2 models indicate that protein sequence determines both structure and dynamics. Sci. Rep. 12, 10696 (2022). https://doi.org:10.1038/s41598-022-14382-9 · DOI · Paper page
  28. 2023, Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models, J. Chem. Theory Comput, 19, 1965, 10.1021/acs.jctc.2c01027 · DOI · Paper page
  29. 2023, Predicting protein flexibility with AlphaFold, Proteins: Structure, Function, and Bioinformatics, 91, 847, 10.1002/prot.26471 · DOI
  30. Roney, J. P. & Ovchinnikov, S . State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold. Phys. Rev. Lett. 129, 238101 (2022). https://doi.org:10.1103/PhysRevLett.129.238101 · DOI · Paper page
  31. Stein, R. A. & McHaourab, H. S . SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2. PLOS Computational Biology 18, e1010483 (2022). https://doi.org:10.1371/journal.pcbi.1010483 · DOI · Paper page
  32. 10.1016/j.str.2007.11.015 · DOI
  33. 10.1021/ja020040y · DOI
  34. 10.1016/j.cell.2006.05.013 · DOI · Paper page
  35. 10.1021/acs.jctc.5b00743 · DOI
  36. Red Brewer, M. , et al. Mechanism for activation of mutated epidermal growth factor receptors in lung cancer. Proceedings of the National Academy of Sciences 110, E3595 (2013). https://doi.org:10.1073/pnas.1220050110 · DOI
  37. 10.1681/ASN.2018111160 · DOI
  38. Borza, C. M. et al. Discoidin domain receptor 1 kinase activity is required for regulating collagen IV synthesis. Matrix Biol. 57-58, 258-271 (2017). https://doi.org:10.1016/j.matbio.2016.11.009 · DOI
  39. 2019, What Makes a Kinase Promiscuous for Inhibitors?, Cell Chem Biol, 26, 390, 10.1016/j.chembiol.2018.11.005 · DOI
  40. 10.1016/S1097-2765(00)80004-0 · DOI
  41. 10.1016/S1097-2765(00)80003-9 · DOI · Paper page
  42. Voelz, V. A. , Pande, V. S. & Bowman, G. R . Folding@home: Achievements from over 20 years of citizen science herald the exascale era. Biophys. J. (2023). https://doi.org:10.1016/j.bpj.2023.03.028 · DOI
  43. Pak, M. A. et al. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS One 18, e0282689 (2023). https://doi.org:10.1371/journal.pone.0282689 · DOI · Paper page
  44. 10.1038/s41594-021-00714-2 · DOI · Paper page
  45. 10.1021/bi035391j · DOI
  46. 10.1073/pnas.1634989100 · DOI
  47. John, M. M. et al. AlphaFold2 can predict single-mutation effects on structure and phenotype. bioRxiv, 2022.2004.2014.488301 (2023). https://doi.org:10.1101/2022.04.14.488301 · DOI · Paper page
  48. 10.1038/s41586-018-0219-7 · DOI
  49. 10.1146/annurev-biochem-060614-033910 · DOI · Paper page
  50. 2019, Universal Activation Index for Class A GPCRs, J. Chem. Inf. Model, 59, 3938, 10.1021/acs.jcim.9b00604 · DOI
  51. Stahl, K. , Graziadei, A. , Dau, T. , Brock, O. & Rappsilber, J . Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning. Nat. Biotechnol. (2023). https://doi.org:10.1038/s41587-023-01704-z · DOI
  52. Gustaf, A. et al. OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. bioRxiv, 2022.2011.2020.517210 (2022). https://doi.org:10.1101/2022.11.20.517210 · DOI
  53. 10.1002/pro.3588 · DOI · Paper page
  54. 10.1038/s41592-020-0848-2 · DOI · Paper page
  55. 10.1016/0022-2836(87)90636-X · DOI
  56. 2020, ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution, J Chem Theory Comput, 16, 528, 10.1021/acs.jctc.9b00591 · DOI
  57. 10.1021/jp8001614 · DOI
  58. 10.1021/jz501780a · DOI
  59. 10.1021/ct5010406 · DOI
  60. 10.1016/0021-9991(77)90098-5 · DOI
  61. 10.1021/ct400341p · DOI

Metadata

Authors
Brown, B. P., Stein, R. A., Meiler, J., & Mchaourab, H.
Affiliations
Vanderbilt University (US)
Corresponding author
  • Brown, Benjamin P.
  • Meiler, Jens
  • Mchaourab, Hassane
ORCID
Venue
Journal of Chemical Theory and Computation
Publisher
openRxiv
Published
2023-08-07
Volume
20
Issue
3
Pages
1434-1447
ISSN
1549-9618 (ISSN-L)
DOI
10.1101/2023.08.06.552168
PMC
PMC10867840
OpenAlex
W4390816949
Cited by
41
References
2
Citations by year
2026: 10 · 2025: 14 · 2024: 16 · 2023: 1
FWCI
5.42
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of Chemical Theory and Computation (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc)
Funders
  • Alexander von Humboldt-Stiftung
  • Vanderbilt University
  • Deutsche Forschungsgemeinschaft (421152132, SFB 1423)
  • Bundesministerium für Bildung und Forschung (57616814)
  • National Institutes of Health (1s10od020154-01, 1s10od032234-01, 1s10od016216-01)
  • National Institute on Aging (RF1AG06862)
  • National Institute on Drug Abuse (R01DA046138, DP1DA058349)
  • National Cancer Institute (R01CA227833)
  • National Institute of General Medical Sciences (R01GM077659, R01GM128087, R01GM080403, R35GM152382)
  • U.S. National Library of Medicine (R01LM013434)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
AlphaFold2, protein conformational ensembles, protein thermodynamics, conformational free energy, collective variables, point mutations, band broadening, protein conformational change

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