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Cited by 33Computational Drug Discovery MethodsProtein Structure and DynamicsReceptor Mechanisms and Signaling

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

Open access
Gu, Xinyu, Aranganathan, Akashnathan, Tiwary, Pratyush·2024·eLife
DOI 10.7554/elife.99702.3PMC PMC11379456

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Abstract

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2’s strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

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Research works printed but not linked: 46

  1. Al-Masri, 2023, Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures, Bioinformatics Advances, 3, 10.1093/bioadv/vbad129 · DOI
  2. Amaro, 2019, Will the real cryptic pocket please stand out?, Biophysical Journal, 116, 753, 10.1016/j.bpj.2019.01.018 · DOI
  3. Beuming, 2022, Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures, Journal of Chemical Information and Modeling, 62, 4351, 10.1021/acs.jcim.2c00796 · DOI · Paper page
  4. Case, 2005, The Amber biomolecular simulation programs, Journal of Computational Chemistry, 26, 1668, 10.1002/jcc.20290 · DOI · Paper page
  5. Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. 2022. Diffdock: Diffusion Steps, Twists, and Turns for Molecular Docking. arXiv. https://arxiv.org/abs/2210.01776. · arXiv
  6. Coskun, 2024, Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation, Journal of Chemical Theory and Computation, 20, 477, 10.1021/acs.jctc.3c00839 · DOI
  7. Darden, 1993, Particle mesh ewald: An n⋅ log (n) method for ewald sums in large systems, The Journal of Chemical Physics, 98, 10089, 10.1063/1.464397 · DOI
  8. Díaz-Rovira, 2023, Are deep learning structural models sufficiently accurate for virtual screening? Application of docking algorithms to alphafold2 predicted structures, Journal of Chemical Information and Modeling, 63, 1668, 10.1021/acs.jcim.2c01270 · DOI
  9. Friesner, 2004, Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy, Journal of Medicinal Chemistry, 47, 1739, 10.1021/jm0306430 · DOI · Paper page
  10. Friesner, 2006, Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes, Journal of Medicinal Chemistry, 49, 6177, 10.1021/jm051256o · DOI
  11. Gizzio, 2022, Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases, eLife, 11, 10.7554/eLife.83368 · DOI
  12. Gizzio, 2024, Evolutionary Sequence and Structural Basis for the Distinct Conformational Landscapes of Tyr and Ser/Thr Kinases, 10.1101/2024.03.08.584161 · DOI
  13. Guterres, 2021, Ligand-binding-site refinement to generate reliable holo protein structure conformations from apo structures, Journal of Chemical Information and Modeling, 61, 535, 10.1021/acs.jcim.0c01354 · DOI
  14. Halgren, 2004, Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening, Journal of Medicinal Chemistry, 47, 1750, 10.1021/jm030644s · DOI
  15. Hanson, 2019, What makes a kinase promiscuous for inhibitors?, Cell Chemical Biology, 26, 390, 10.1016/j.chembiol.2018.11.005 · DOI
  16. Hauser, 2018, Pharmacogenomics of GPCR Drug Targets, Cell, 172, 41, 10.1016/j.cell.2017.11.033 · DOI
  17. Herron L, Mondal K, Schneekloth JS, Tiwary P. 2023. Inferring Phase Transitions and Critical Exponents from Limited Observations with Thermodynamic Maps. arXiv. https://arxiv.org/abs/2308.14885. · arXiv
  18. Hess, 1997, LINCS: A linear constraint solver for molecular simulations, Journal of Computational Chemistry, 18, 1463, 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.3.CO;2-L · DOI
  19. Holcomb, 2023, Evaluation of AlphaFold2 structures as docking targets, Protein Science, 32, 10.1002/pro.4530 · DOI
  20. Jing B, Berger B, Jaakkola T. 2024. Alphafold Meets Flow Matching for Generating Protein Ensembles. arXiv. https://arxiv.org/abs/2402.04845. · arXiv
  21. Jorgensen, 1983, Comparison of simple potential functions for simulating liquid water, The Journal of Chemical Physics, 79, 926, 10.1063/1.445869 · DOI
  22. Lindorff-Larsen, 2010, Improved side-chain torsion potentials for the Amber ff99SB protein force field, Proteins, 78, 1950, 10.1002/prot.22711 · DOI
  23. Lu, 2024, DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model, Nature Communications, 15, 10.1038/s41467-024-45461-2 · DOI
  24. Lyu, 2024, AlphaFold2 structures guide prospective ligand discovery, Science, 384, 10.1126/science.adn6354 · DOI
  25. Maestro, 2023, Schrödinger Release 2023-3: Glide; Induced Fit Docking Protocol; Prime
  26. Mehdi, 2024, Enhanced sampling with machine learning, Annual Review of Physical Chemistry, 75, 347, 10.1146/annurev-physchem-083122-125941 · DOI
  27. Meller, 2023, Accelerating cryptic pocket discovery using alphafold, Journal of Chemical Theory and Computation, 19, 4355, 10.1021/acs.jctc.2c01189 · DOI
  28. Modi, 2022, Kincore: a web resource for structural classification of protein kinases and their inhibitors, Nucleic Acids Research, 50, D654, 10.1093/nar/gkab920 · DOI · Paper page
  29. Müller, 2015, The ins and outs of selective kinase inhibitor development, Nature Chemical Biology, 11, 818, 10.1038/nchembio.1938 · DOI
  30. Porter, 2023, Colabfold Predicts Alternative Protein Structures from Single Sequences, Coevolution Unnecessary for Af-Cluster, 10.1101/2023.11.21.567977 · DOI
  31. Ren, 2023, AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor, Chemical Science, 14, 1443, 10.1039/d2sc05709c · DOI · Paper page
  32. Roney, 2022, State-of-the-art estimation of protein model accuracy using alphafold, Physical Review Letters, 129, 10.1103/PhysRevLett.129.238101 · DOI · Paper page
  33. Sala, 2023, Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties, Frontiers in Molecular Biosciences, 10, 10.3389/fmolb.2023.1121962 · DOI · Paper page
  34. Scardino, 2023, How good are AlphaFold models for docking-based virtual screening?, iScience, 26, 10.1016/j.isci.2022.105920 · DOI · Paper page
  35. Sherman, 2006, Use of an induced fit receptor structure in virtual screening, Chemical Biology & Drug Design, 67, 83, 10.1111/j.1747-0285.2005.00327.x · DOI
  36. Sherman, 2006, Novel procedure for modeling ligand/receptor induced fit effects, Journal of Medicinal Chemistry, 49, 534, 10.1021/jm050540c · DOI
  37. Smith, 2024, Graph attention site prediction (GrASP): Identifying druggable binding sites using graph neural networks with attention, Journal of Chemical Information and Modeling, 64, 2637, 10.1021/acs.jcim.3c01698 · DOI · Paper page
  38. Thakur, 2024, Potts hamiltonian models and molecular dynamics free energy simulations for predicting the impact of mutations on protein kinase stability, The Journal of Physical Chemistry. B, 128, 1656, 10.1021/acs.jpcb.3c08097 · DOI
  39. Vani, 2023, AlphaFold2-RAVE: From Sequence to Boltzmann Ranking, Journal of Chemical Theory and Computation, 19, 4351, 10.1021/acs.jctc.3c00290 · DOI
  40. Vani, 2024, Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE, Journal of Chemical Information and Modeling, 64, 2789, 10.1021/acs.jcim.3c01436 · DOI
  41. Wang, 2019, Past-future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics, Nature Communications, 10, 10.1038/s41467-019-11405-4 · DOI
  42. Wang, 2021, State predictive information bottleneck, The Journal of Chemical Physics, 154, 10.1063/5.0038198 · DOI
  43. Wang, 2022, From data to noise to data for mixing physics across temperatures with generative artificial intelligence, PNAS, 119, 10.1073/pnas.2203656119 · DOI
  44. Zhang, 2019, Unified efficient thermostat scheme for the canonical ensemble with holonomic or isokinetic constraints via molecular dynamics, The Journal of Physical Chemistry. A, 123, 6056, 10.1021/acs.jpca.9b02771 · DOI
  45. Zhang, 2023, Benchmarking refined and unrefined alphafold2 structures for hit discovery, Journal of Chemical Information and Modeling, 63, 1656, 10.1021/acs.jcim.2c01219 · DOI
  46. Zheng, 2024, Predicting equilibrium distributions for molecular systems with deep learning, Nature Machine Intelligence, 6, 558, 10.1038/s42256-024-00837-3 · DOI

Metadata

Authors
Gu, X., Aranganathan, A., & Tiwary, P.
Affiliations
  • University of Maryland, Baltimore (US)
  • Institute for Physical Science and Technology, University of Maryland
Corresponding author
Tiwary, Pratyush
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2024-09-06
Volume
13
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.99702.3
PMC
PMC11379456
OpenAlex
W4394737072
Cited by
33
References
10
Citations by year
2026: 18 · 2025: 10 · 2024: 5
FWCI
6.98
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • eLife (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Funders
National Institute of General Medical Sciences (R35GM142719)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
Keywords
AlphaFold2, protein conformational sampling, protein kinase inhibitors, type II kinase inhibitors, induced fit docking, enhanced sampling molecular dynamics, virtual screening, protein-ligand binding, small-molecule drug design

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