Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
16 Sep 2024
16 Sep 2024
Historique:
received:
07
04
2024
accepted:
03
09
2024
medline:
17
9
2024
pubmed:
17
9
2024
entrez:
16
9
2024
Statut:
epublish
Résumé
Allosteric drugs offer a new avenue for modern drug design. However, the identification of cryptic allosteric sites presents a formidable challenge. Following the allostery nature of residue-driven conformation transition, we propose a state-of-the-art computational pipeline by developing a residue-intuitive hybrid machine learning (RHML) model coupled with molecular dynamics (MD) simulation, through which we can efficiently identify the allosteric site and allosteric modulator as well as reveal their regulation mechanism. For the clinical target β2-adrenoceptor (β2AR), we discover an additional allosteric site located around residues D79
Identifiants
pubmed: 39285201
doi: 10.1038/s41467-024-52399-y
pii: 10.1038/s41467-024-52399-y
doi:
Substances chimiques
Receptors, Adrenergic, beta-2
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8130Informations de copyright
© 2024. The Author(s).
Références
Cheng, X. & Jiang, H. Allostery in Drug Development. Adv. Exp. Med. Biol. 1163, 1–23 (2019).
pubmed: 31707697
doi: 10.1007/978-981-13-8719-7_1
Wootten, D., Christopoulos, A. & Sexton, P. M. Emerging paradigms in GPCR allostery: implications for drug discovery. Nat. Rev. Drug Discov. 12, 630–644 (2013).
pubmed: 23903222
doi: 10.1038/nrd4052
Möhler, H., Fritschy, J. M. & Rudolph, U. A new benzodiazepine pharmacology. J. Pharm. Exp. Ther. 300, 2–8 (2002).
doi: 10.1124/jpet.300.1.2
Guarnera, E. & Berezovsky, I. N. Allosteric drugs and mutations: chances, challenges, and necessity. Curr. Opin. Struct. Biol. 62, 149–157 (2020).
pubmed: 32062398
doi: 10.1016/j.sbi.2020.01.010
Chatzigoulas, A. & Cournia, Z. Rational design of allosteric modulators: Challenges and successes. WIREs Comput. Mol. Sci. 11, e1529 (2021).
doi: 10.1002/wcms.1529
Kuzmanic, A., Bowman, G. R., Juarez-Jimenez, J., Michel, J. & Gervasio, F. L. Investigating cryptic binding sites by molecular dynamics simulations. Acc. Chem. Res. 53, 654–661 (2020).
pubmed: 32134250
pmcid: 7263906
doi: 10.1021/acs.accounts.9b00613
Hollingsworth, S. A. et al. Cryptic pocket formation underlies allosteric modulator selectivity at muscarinic GPCRs. Nat. Commun. 10, 3289 (2019).
pubmed: 31337749
pmcid: 6650467
doi: 10.1038/s41467-019-11062-7
Shah, S. D. et al. In silico identification of a β2-adrenoceptor allosteric site that selectively augments canonical β2AR-Gs signaling and function. Proc. Natl. Acad. Sci. USA 119, e2214024119 (2022).
pubmed: 36449547
pmcid: 9894167
doi: 10.1073/pnas.2214024119
Zhang, Q. et al. Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells. Acta Pharm. Sin. B 12, 876–889 (2022).
pubmed: 35256952
doi: 10.1016/j.apsb.2021.06.015
Beglov, D. et al. Exploring the structural origins of cryptic sites on proteins. Proc. Natl. Acad. Sci. USA 115, E3416–E3425 (2018).
pubmed: 29581267
pmcid: 5899430
doi: 10.1073/pnas.1711490115
Jiang, Y. et al. Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials. Nat. Commun. 12, 1–14 (2021).
doi: 10.1038/s41467-021-26226-7
Wallach, I. et al. AI is a viable alternative to high throughput screening: a 318-target study. Sci. Rep. 14, 7526 (2024).
doi: 10.1038/s41598-024-54655-z
Noé, F., Tkatchenko, A., Müller, K.-R. & Clementi, C. Machine learning for molecular simulation. Annu Rev. Phys. Chem. 71, 361–390 (2020).
pubmed: 32092281
doi: 10.1146/annurev-physchem-042018-052331
Zhu, J., Wang, J., Han, W. & Xu, D. Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations. Nat. Commun. 13, 1661 (2022).
pubmed: 35351887
pmcid: 8964751
doi: 10.1038/s41467-022-29331-3
Zhou, H., Dong, Z. & Tao, P. Recognition of protein allosteric states and residues: Machine learning approaches. J. Comput. Chem. 39, 1481–1490 (2018).
pubmed: 29604117
doi: 10.1002/jcc.25218
Hayatshahi, H. S., Ahuactzin, E., Tao, P., Wang, S. & Liu, J. Probing protein allostery as a residue-specific concept via residue response maps. J. Chem. Inf. Model. 59, 4691–4705 (2019).
pubmed: 31589429
doi: 10.1021/acs.jcim.9b00447
Wold, E. A., Chen, J., Cunningham, K. A. & Zhou, J. Allosteric modulation of class A GPCRs: Targets, agents, and emerging concepts. J. Med. Chem. 62, 88–127 (2019).
pubmed: 30106578
doi: 10.1021/acs.jmedchem.8b00875
Baker, J. G. The selectivity of beta-adrenoceptor antagonists at the human beta1, beta2 and beta3 adrenoceptors. Br. J. Pharm. 144, 317–322 (2005).
doi: 10.1038/sj.bjp.0706048
Karoli, N. A. & Rebrov, A. P. [Possibilities and limitations of the use of beta-blockers in patients with cardiovascular disease and chronic obstructive pulmonary disease]. Kardiologiia 61, 89–98 (2021).
pubmed: 34763643
doi: 10.18087/cardio.2021.10.n1119
Liu, X. et al. An allosteric modulator binds to a conformational hub in the β2 adrenergic receptor. Nat. Chem. Biol. 16, 749–755 (2020).
pubmed: 32483378
pmcid: 7816728
doi: 10.1038/s41589-020-0549-2
Liu, X. et al. Mechanism of β2AR regulation by an intracellular positive allosteric modulator. Science 364, 1283–1287 (2019).
pubmed: 31249059
pmcid: 6705129
doi: 10.1126/science.aaw8981
Liu, X. et al. Mechanism of intracellular allosteric β2AR antagonist revealed by X-ray crystal structure. Nature 548, 480–484 (2017).
pubmed: 28813418
pmcid: 5818265
doi: 10.1038/nature23652
Masureel, M. et al. Structural insights into binding specificity, efficacy and bias of a β2AR partial agonist. Nat. Chem. Biol. 14, 1059–1066 (2018).
pubmed: 30327561
pmcid: 6197491
doi: 10.1038/s41589-018-0145-x
Swaminath, G., Lee, T. W. & Kobilka, B. Identification of an allosteric binding site for Zn2+ on the beta2 adrenergic receptor. J. Biol. Chem. 278, 352–356 (2003).
pubmed: 12409304
doi: 10.1074/jbc.M206424200
Shi, C. et al. Auto-dialabel: labeling dialogue data with unsupervised learning. 2018 Conference on Empirical Methods in Natural Language Processing (Emnlp 2018), 684–689 (2018).
Dhamija, A., Pandoi, D., Singh, K. & Malhotra, S. An improved K-means clustering with convolutional neural network for financial crisis prediction. In Advances in Computational Intelligence and Communication Technology (Springer Singapore, Singapore, 2022).
Glielmo, A. et al. Unsupervised learning methods for molecular simulation data. Chem. Rev. 121, 9722–9758 (2021).
pubmed: 33945269
pmcid: 8391792
doi: 10.1021/acs.chemrev.0c01195
Nainggolan, R., Perangin-angin, R., Simarmata, E. & Tarigan, A. F. Improved the performance of the K-means cluster using the sum of squared error (SSE) optimized by using the Elbow Method. J. Phys. Conf. Ser. 1361, 012015 (2019).
doi: 10.1088/1742-6596/1361/1/012015
Akhanli, S. E. & Hennig, C. Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes. Stat. Comput. 30, 1523–1544 (2020).
doi: 10.1007/s11222-020-09958-2
Wakefield, A. E., Mason, J. S., Vajda, S. & Keserű, G. M. Analysis of tractable allosteric sites in G protein-coupled receptors. Sci. Rep. 9, 6180 (2019).
pubmed: 30992500
pmcid: 6467999
doi: 10.1038/s41598-019-42618-8
Srivastava, A. et al. High-resolution structure of the human GPR40 receptor bound to allosteric agonist TAK-875. Nature 513, 124–127 (2014).
pubmed: 25043059
doi: 10.1038/nature13494
Oswald, C. et al. Intracellular allosteric antagonism of the CCR9 receptor. Nature 540, 462–465 (2016).
pubmed: 27926729
doi: 10.1038/nature20606
Jaeger, K. et al. Structural basis for allosteric ligand recognition in the human CC chemokine receptor 7. Cell 178, 1222–1230.e10 (2019).
pubmed: 31442409
pmcid: 6709783
doi: 10.1016/j.cell.2019.07.028
Shao, Z. et al. Structure of an allosteric modulator bound to the CB1 cannabinoid receptor. Nat. Chem. Biol. 15, 1199–1205 (2019).
pubmed: 31659318
doi: 10.1038/s41589-019-0387-2
Shen, S. et al. Allosteric modulation of G protein-coupled receptor signaling. Front. Endocrinol. 14, https://doi.org/10.3389/fendo.2023.1137604 (2023).
Yuan, J. et al. In silico prediction and validation of CB2 allosteric binding sites to aid the design of allosteric modulators. Molecules 27, 453 (2022).
pubmed: 35056767
pmcid: 8781014
doi: 10.3390/molecules27020453
Sadybekov, A. V. & Katritch, V. Computational approaches streamlining drug discovery. Nature 616, 673–685 (2023).
pubmed: 37100941
doi: 10.1038/s41586-023-05905-z
De Vivo, M., Masetti, M., Bottegoni, G. & Cavalli, A. Role of molecular dynamics and related methods in drug discovery. J. Med. Chem. 59, 4035–4061 (2016).
pubmed: 26807648
doi: 10.1021/acs.jmedchem.5b01684
Labbé, C. M. et al. MTiOpenScreen: a web server for structure-based virtual screening. Nucleic Acids Res. 43, W448–W454 (2015).
pubmed: 25855812
pmcid: 4489289
doi: 10.1093/nar/gkv306
Maffucci, I., Hu, X., Fumagalli, V. & Contini, A. An efficient implementation of the Nwat-MMGBSA method to rescore docking results in medium-throughput virtual screenings. Front. Chem. 6, 43 (2018).
pubmed: 29556494
pmcid: 5844977
doi: 10.3389/fchem.2018.00043
Xu, X. et al. Binding pathway determines norepinephrine selectivity for the human β1AR over β2AR. Cell Res. 31, 569–579 (2021).
pubmed: 33093660
doi: 10.1038/s41422-020-00424-2
Zhou, Q. et al. Common activation mechanism of class A GPCRs. ELife 8, e50279 (2019).
pubmed: 31855179
pmcid: 6954041
doi: 10.7554/eLife.50279
Latorraca, N. R., Venkatakrishnan, A. J. & Dror, R. O. GPCR Dynamics: Structures in motion. Chem. Rev. 117, 139–155 (2017).
pubmed: 27622975
doi: 10.1021/acs.chemrev.6b00177
VanWart, A. T., Eargle, J., Luthey-Schulten, Z. & Amaro, R. E. Exploring residue component contributions to dynamical network models of allostery. J. Chem. Theory Comput. 8, 2949–2961 (2012).
pubmed: 23139645
pmcid: 3489502
doi: 10.1021/ct300377a
Filipek, S. Molecular switches in GPCRs. Curr. Opin. Struct. Biol. 55, 114–120 (2019).
pubmed: 31082695
doi: 10.1016/j.sbi.2019.03.017
Hauser, A. S. et al. GPCR activation mechanisms across classes and macro/microscales. Nat. Struct. Mol. Biol. 28, 879–888 (2021).
pubmed: 34759375
pmcid: 8580822
doi: 10.1038/s41594-021-00674-7
Slosky, L. M., Caron, M. G. & Barak, L. S. Biased allosteric modulators: New frontiers in GPCR drug discovery. Trends Pharmacol. Sci. 42, 283–299 (2021).
pubmed: 33581873
pmcid: 9797227
doi: 10.1016/j.tips.2020.12.005
Ahn, S. et al. Allosteric “beta-blocker” isolated from a DNA-encoded small molecule library. Proc. Natl. Acad. Sci. USA 114, 1708 (2017).
pubmed: 28130548
pmcid: 5321036
doi: 10.1073/pnas.1620645114
Ippolito, M. et al. Identification of a β-arrestin-biased negative allosteric modulator for the β2-adrenergic receptor. Proc. Natl. Acad. Sci. USA 120, e2302668120 (2023).
pubmed: 37490535
pmcid: 10401000
doi: 10.1073/pnas.2302668120
Whitty, A. & Kumaravel, G. Between a rock and a hard place? Nat. Chem. Biol. 2, 112–118 (2006).
pubmed: 16484997
doi: 10.1038/nchembio0306-112
Wisler, J. W. et al. A unique mechanism of beta-blocker action: carvedilol stimulates beta-arrestin signaling. Proc. Natl. Acad. Sci. USA 104, 16657–16662 (2007).
pubmed: 17925438
pmcid: 2034221
doi: 10.1073/pnas.0707936104
Villalona-Calero, M. A. et al. A phase I and pharmacological study of protracted infusions of crisnatol mesylate in patients with solid malignancies. Clin. Cancer Res. 5, 3369–3378 (1999).
pubmed: 10589747
Cherezov, V. et al. High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor. Science 318, 1258–1265 (2007).
pubmed: 17962520
pmcid: 2583103
doi: 10.1126/science.1150577
Ring, A. M. et al. Adrenaline-activated structure of β2-adrenoceptor stabilized by an engineered nanobody. Nature 502, 575–579 (2013).
pubmed: 24056936
pmcid: 3822040
doi: 10.1038/nature12572
Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinforma. 54, 5.6.1–5.6.37 (2016).
doi: 10.1002/cpbi.3
Kim, S. et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 49, D1388–D1395 (2021).
pubmed: 33151290
doi: 10.1093/nar/gkaa971
Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E. & Fox, D. J. Gaussian 09. Revision A.01. (Gaussian Inc, Wallingford, 2009).
Morris, G. M. et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).
pubmed: 19399780
pmcid: 2760638
doi: 10.1002/jcc.21256
Anandakrishnan, R., Aguilar, B. & Onufriev, A. V. H. ++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res. 40, W537–W541 (2012).
pubmed: 22570416
pmcid: 3394296
doi: 10.1093/nar/gks375
Huang, J. & MacKerell, A. D. Jr CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013).
pubmed: 23832629
pmcid: 3800559
doi: 10.1002/jcc.23354
Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010).
pubmed: 19575467
pmcid: 2888302
doi: 10.1002/jcc.21367
Thakur, N. et al. Anionic phospholipids control mechanisms of GPCR-G protein recognition. Nat. Commun. 14, 794 (2023).
pubmed: 36781870
pmcid: 9925817
doi: 10.1038/s41467-023-36425-z
Chan, H. C. S. et al. Exploring a new ligand binding site of G protein-coupled receptors. Chem. Sci. 9, 6480–6489 (2018).
pubmed: 30310578
pmcid: 6115637
doi: 10.1039/C8SC01680A
Lee, J. et al. CHARMM-GUI Input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405–413 (2016).
pubmed: 26631602
doi: 10.1021/acs.jctc.5b00935
Miao, Y., Feher, V. A. & McCammon, J. A. Gaussian accelerated molecular dynamics: Unconstrained enhanced sampling and free energy calculation. J. Chem. Theory Comput. 11, 3584–3595 (2015).
pubmed: 26300708
pmcid: 4535365
doi: 10.1021/acs.jctc.5b00436
D. A. Case, R. M. Betz, D. S. Cerutti, T. Cheatham, P. A. Kollman, Amber 16. (University of California, San Francisco, 2016).
Li, C. et al. An onterpretable convolutional neural network framework for analyzing molecular dynamics trajectories: A case study on functional states for G-protein-coupled receptors. J. Chem. Inf. Model 62, 1399–1410 (2022).
pubmed: 35257580
doi: 10.1021/acs.jcim.2c00085
Ribeiro, M. T., Singh, S. & Guestrin, C. ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (Association for Computing Machinery, New York, NY, USA, 2016). https://doi.org/10.1145/2939672.2939778 .
Huang, L. et al. A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nat. Commun. 15, 2657 (2024).
pubmed: 38531837
pmcid: 10965937
doi: 10.1038/s41467-024-46569-1
Lionta, E., Spyrou, G., Vassilatis, D. K. & Cournia, Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr. Top. Med. Chem. 14, 1923–1938 (2014).
pubmed: 25262799
pmcid: 4443793
doi: 10.2174/1568026614666140929124445
Wichapong, K. et al. Structure-based design of peptidic inhibitors of the interaction between CC chemokine ligand 5 (CCL5) and human neutrophil peptides 1 (HNP1). J. Med. Chem. 59, 4289–4301 (2016).
pubmed: 26871718
doi: 10.1021/acs.jmedchem.5b01952
Lei, T. et al. Exploring the activation mechanism of a metabotropic glutamate receptor homodimer via molecular dynamics simulation. ACS Chem. Neurosci. 11, 133–145 (2020).
pubmed: 31815422
doi: 10.1021/acschemneuro.9b00425
Hou, T., Wang, J., Li, Y. & Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model 51, 69–82 (2011).
pubmed: 21117705
doi: 10.1021/ci100275a
Zhang, F. et al. Molecular insights into the allosteric coupling mechanism between an agonist and two different transducers for μ-opioid receptors. Phys. Chem. Chem. Phys. 24, 5282–5293 (2022).
pubmed: 35170592
doi: 10.1039/D1CP05736G
Seeber, M., Cecchini, M., Rao, F., Settanni, G. & Caflisch, A. Wordom: a program for efficient analysis of molecular dynamics simulations. Bioinformatics 23, 2625–2627 (2007).
pubmed: 17717034
doi: 10.1093/bioinformatics/btm378
Feng, Y. et al. Mechanism of activation and biased signaling in complement receptor C5aR1. Cell Res. 33, 312–324 (2023).
pubmed: 36806352
pmcid: 9937529
doi: 10.1038/s41422-023-00779-2
Zhao, J. et al. Prospect of acromegaly therapy: molecular mechanism of clinical drugs octreotide and paltusotine. Nat. Commun. 14, 962 (2023).
pubmed: 36810324
pmcid: 9944328
doi: 10.1038/s41467-023-36673-z
Leach, K., Sexton, P. M. & Christopoulos, A. Allosteric GPCR modulators: taking advantage of permissive receptor pharmacology. Trends Pharm. Sci. 28, 382–389 (2007).
pubmed: 17629965
doi: 10.1016/j.tips.2007.06.004
Zhao, C. et al. Biased allosteric activation of ketone body receptor HCAR2 suppresses inflammation. Mol. Cell 83, 3171–3187 (2023).
pubmed: 37597514
doi: 10.1016/j.molcel.2023.07.030
Chen, X. et al. Integrative Residue-intuitive Machine Learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR. figshare, https://doi.org/10.6084/m9.figshare.26129632 (2024).
Chen, X. et al. Integrative Residue-intuitive Machine Learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR. Zenodo, https://doi.org/10.5281/zenodo.13325067 (2024).