The Application of Artificial Intelligence Accelerates G Protein-Coupled Receptor Ligand Discovery

Wei Chen, Chi Song, Liang Leng, Sanyin Zhang, Shilin Chen

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Engineering ›› 2024, Vol. 32 ›› Issue (1) : 18-28. DOI: 10.1016/j.eng.2023.09.011
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The Application of Artificial Intelligence Accelerates G Protein-Coupled Receptor Ligand Discovery

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Abstract

G protein-coupled receptors (GPCRs) are crucial players in various physiological processes, making them attractive candidates for drug discovery. However, traditional approaches to GPCR ligand discovery are time-consuming and resource-intensive. The emergence of artificial intelligence (AI) methods has revolutionized the field of GPCR ligand discovery and has provided valuable tools for accelerating the identification and optimization of GPCR ligands. In this study, we provide guidelines for effectively utilizing AI methods for GPCR ligand discovery, including data collation and representation, model selection, and specific applications. First, the online resources that are instrumental in GPCR ligand discovery were summarized, including databases and repositories that contain valuable GPCR-related information and ligand data. Next, GPCR and ligand representation schemes that can convert data into computer-readable formats were introduced. Subsequently, the key applications of AI methods in the different stages of GPCR drug discovery were discussed, ranging from GPCR function prediction to ligand design and agonist identification. Furthermore, an AI-driven multi-omics integration strategy for GPCR ligand discovery that combines information from various omics disciplines was proposed. Finally, the challenges and future directions of the application of AI in GPCR research were deliberated. In conclusion, continued advancements in AI techniques coupled with interdisciplinary collaborations will offer great potential for improving the efficiency of GPCR ligand discovery.

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G protein-coupled receptor / Ligand / Artificial intelligence / Multi-omics / Drug discovery

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Wei Chen, Chi Song, Liang Leng, Sanyin Zhang, Shilin Chen. The Application of Artificial Intelligence Accelerates G Protein-Coupled Receptor Ligand Discovery. Engineering, 2024, 32(1): 18‒28 https://doi.org/10.1016/j.eng.2023.09.011

References

[[1]]
D. Yang, Q. Zhou, V. Labroska, S. Qin, S. Darbalaei, Y. Wu, et al.. G protein-coupled receptors: structure- and function-based drug discovery. Signal Transduction Targeted Ther, 6 (1) ( 2021), p. 7
[[2]]
G.A. Nieto, P.H. McDonald. GPCRs: emerging anti-cancer drug targets. Cell Signaling, 41 ( 2017), pp. 65-74
[[3]]
A.S. Hauser, M.M. Attwood, M. Rask-Andersen, H.B. Schiöth, D.E. Gloriam. Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discovery, 16 (12) ( 2017), pp. 829-842
[[4]]
D. Julius, J. Nathans. Signaling by sensory receptors. Cold Spring Harbor Perspect Biol, 4 (1) ( 2012), Article a005991
[[5]]
H.E. Hamm, S.T. Alford. Physiological roles for neuromodulation via Gi/o GPCRs working through Gβγ-SNARE interaction. Neuropsychopharmacology, 45 (1) ( 2020), p. 221
[[6]]
Z. Feng, R. Sun, Y. Cong, Z. Liu. Critical roles of G protein-coupled receptors in regulating intestinal homeostasis and inflammatory bowel disease. Mucosal Immunol, 15 (5) ( 2022), pp. 819-828
[[7]]
Y.J. Ge, Q.W. Liao, Y.C. Xu, Q. Zhao, B.L. Wu, R.D. Ye. Anti-inflammatory signaling through G protein-coupled receptors. Acta Pharmacol Sin, 41 (12) ( 2020), pp. 1531-1538
[[8]]
R.T. Dorsam, J.S. Gutkind. G-protein-coupled receptors and cancer. Nat Rev Cancer, 7 (2) ( 2007), pp. 79-94
[[9]]
E.A. Yasi, N.S. Kruyer, P. Peralta-Yahya. Advances in G protein-coupled receptor high-throughput screening. Curr Opin Biotechnol, 64 ( 2020), pp. 210-217
[[10]]
K. Sriram, P.A. Insel. G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs?. Mol Pharmacol, 93 (4) ( 2018), pp. 251-258
[[11]]
D.S. Eiger, U. Pham, J. Gardner, C. Hicks, S. Rajagopal. GPCR systems pharmacology: a different perspective on the development of biased therapeutics. Am J Physiol Cell Physiol, 322 (5) ( 2022), pp. C887-C895
[[12]]
P. Zhao, S.G.B. Furness. The nature of efficacy at G protein-coupled receptors. Biochem Pharmacol, 170 ( 2019), Article 113647
[[13]]
A.P. Campbell, A.V. Smrcka. Targeting G protein-coupled receptor signalling by blocking G proteins. Nat Rev Drug Discovery, 17 (11) ( 2018), pp. 789-803
[[14]]
S. Raschka. Automated discovery of GPCR bioactive ligands. Curr Opin Struct Biol, 55 ( 2019), pp. 17-24
[[15]]
A.S. Powers, V. Pham, W.A.C. Burger, G. Thompson, Y. Laloudakis, N.W. Barnes, et al.. Structural basis of efficacy-driven ligand selectivity at GPCRs. Nat Chem Biol, 19 (7) ( 2023), pp. 805-814
[[16]]
J.N. Frei, R.W. Broadhurst, M.J. Bostock, A. Solt, A.J.Y. Jones, F. Gabriel, et al.. Conformational plasticity of ligand-bound and ternary GPCR complexes studied by 19F NMR of the β1-adrenergic receptor. Nat Commun, 11 (1) ( 2020), p. 669
[[17]]
G. Pándy-Szekeres, J. Caroli, A. Mamyrbekov, A.A. Kermani, G.M. Keserű, A.J. Kooistra, et al.. GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources. Nucleic Acids Res, 51 (D1) ( 2023), pp. D395-D402
[[18]]
T. Hou, Y. Bian, T. McGuire, X.Q. Xie. Integrated multi-class classification and prediction of GPCR allosteric modulators by machine learning intelligence. Biomolecules, 11 (6) ( 2021), p. 870
[[19]]
S. Raschka, B. Kaufman. Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition. Methods, 180 ( 2020), pp. 89-110
[[20]]
K. Rataj, Á.A. Kelemen, J. Brea, M.I. Loza, A.J. Bojarski, G.M. Keserű. Fingerprint-based machine learning approach to identify potent and selective 5-HT2BR ligands. Molecules, 23 (5) ( 2018), p. 1137
[[21]]
P. Yadav, P. Mollaei, Z. Cao, Y. Wang, A.B. Farimani. Prediction of GPCR activity using machine learning. Comput Struct Biotechnol J, 20 ( 2022), pp. 2564-2573
[[22]]
Y. Yin, H. Hu, Z. Yang, F. Jiang, Y. Huang, J. Wu. AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins. Brief Bioinform, 23 (3) ( 2022), Article bbac077
[[23]]
S. Lee, S. Kim, G.R. Lee, S. Kwon, H. Woo, C. Seok, et al.. Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction. Comput Struct Biotechnol J, 21 ( 2022), pp. 158-167
[[24]]
B. Sanchez-Lengeling, A. Aspuru-Guzik. Inverse molecular design using machine learning: generative models for matter engineering. Science, 361 (6400) ( 2018), pp. 360-365
[[25]]
M. Thomas, R.T. Smith, N.M. O’Boyle, C. de Graaf, A. Bender.Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform, 13 (1) ( 2021), p. 39
[[26]]
A. Zhavoronkov, Y.A. Ivanenkov, A. Aliper, M.S. Veselov, V.A. Aladinskiy, A.V. Aladinskaya, et al.. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol, 37 (9) ( 2019), pp. 1038-1040
[[27]]
W. Chen, X. Liu, S. Zhang, S. Chen. Artificial intelligence for drug discovery: resources, methods, and applications. Mol Ther Nucleic Acids, 31 ( 2023), pp. 691-702
[[28]]
S.P.H. Alexander, A. Christopoulos, A.P. Davenport, E. Kelly, A. Mathie, J.A. Peters, et al.. The concise guide to pharmacology 2021/22: G protein-coupled receptors. Br J Pharmacol, 178 (Suppl 1) ( 2021), pp. S27-S156
[[29]]
W.K.B. Chan, H. Zhang, J. Yang, J.R. Brender, J. Hur, A. Özgür, et al.. GLASS: a comprehensive database for experimentally validated GPCR-ligand associations. Bioinformatics, 31 (18) ( 2015), pp. 3035-3042
[[30]]
J. Zhang, J. Yang, R. Jang, Y. Zhang. GPCR-I-TASSER: a hybrid approach to G protein-coupled receptor structure modeling and the application to the human genome. Structure, 23 (8) ( 2015), pp. 1538-1549
[[31]]
J. Zhang, Y. Zhang. GPCRRD: G protein-coupled receptor spatial restraint database for 3D structure modeling and function annotation. Bioinformatics, 26 (23) ( 2010), pp. 3004-3005
[[32]]
W.K.B. Chan, Y. Zhang. Virtual screening of human class-A GPCRs using ligand profiles built on multiple ligand-receptor interactions. J Mol Biol, 432 (17) ( 2020), pp. 4872-4890
[[33]]
M.C. Theodoropoulou, P.G. Bagos, I.C. Spyropoulos, S.J. Hamodrakas. gpDB: a database of GPCRs, G-proteins, effectors and their interactions. Bioinformatics, 24 (12) ( 2008), pp. 1471-1472
[[34]]
M. Esguerra, A. Siretskiy, X. Bello, J. Sallander, H. Gutiérrez-de-Terán. GPCR-ModSim: a comprehensive web based solution for modeling G-protein coupled receptors. Nucleic Acids Res, 44 (W1) ( 2016), pp. W455-W462
[[35]]
M. Sandal, T.P. Duy, M. Cona, H. Zung, P. Carloni, F. Musiani, et al.. GOMoDo: a GPCRs online modeling and docking webserver. PLoS One, 8 (9) ( 2013), p. e74092
[[36]]
S.K. Burley, C. Bhikadiya, C. Bi, S. Bittrich, H. Chao, L. Chen, et al.. RCSB protein data bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res, 51 (D1) ( 2023), pp. D488-D508
[[37]]
A. Bateman, M.J. Martin, S. Orchard, M. Magrane, S. Ahmad, E. Alpi, et al.. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res, 51 (D1) ( 2023), pp. D523-D531
[[38]]
S.H. White. Biophysical dissection of membrane proteins. Nature, 459 (7245) ( 2009), pp. 344-346
[[39]]
T.D. Newport, M.S.P. Sansom, P.J. Stansfeld.The MemProtMD database: a resource for membrane-embedded protein structures and their lipid interactions. Nucleic Acids Res, 47 (D1) ( 2019), pp. D390-D397
[[40]]
S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, et al.. PubChem 2023 update. Nucleic Acids Res, 51 (D1) ( 2023), pp. D1373-D1380
[[41]]
D. Mendez, A. Gaulton, A.P. Bento, J. Chambers, M. de Veij, E. Félix, et al.. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res, 47 (D1) ( 2019), pp. D930-D940
[[42]]
J.J. Irwin, K.G. Tang, J. Young, C. Dandarchuluun, B.R. Wong, M. Khurelbaatar, et al..ZINC20—a free ultralarge-scale chemical database for ligand discovery. J Chem Inf Model, 60 (12) ( 2020), pp. 6065-6073
[[43]]
D.S. Wishart, Y.D. Feunang, A.C. Guo, E.J. Lo, A. Marcu, J.R. Grant, et al..DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res, 46 (D1) ( 2018), pp. D1074-D1082
[[44]]
T. Liu, Y. Lin, X. Wen, R.N. Jorissen, M.K. Gilson.BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res, 35 (Database issue) ( 2007), pp. D198-D201
[[45]]
M.M. Mysinger, M. Carchia, J.J. Irwin, B.K. Shoichet. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem, 55 (14) ( 2012), pp. 6582-6594
[[46]]
P. Feng, W. Liu, C. Huang, Z. Tang. Classifying the superfamily of small heat shock proteins by using G-gap dipeptide compositions. Int J Biol Macromol, 167 ( 2021), pp. 1575-1578
[[47]]
N.Q. Khanh Le, Q.H. Nguyen, X. Chen, S. Rahardja, B.P. Nguyen.Classification of adaptor proteins using recurrent neural networks and PSSM profiles. BMC Genomics, 20 (Suppl 9) ( 2019), p. 966
[[48]]
G. Zhang, Q. Tang, P. Feng, W. Chen. IPs-GRUAtt: an attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection. Mol Ther Nucleic Acids, 32 ( 2023), pp. 28-35
[[49]]
D.W.A. Buchan, D.T. Jones. Learning a functional grammar of protein domains using natural language word embedding techniques. Proteins, 88 (4) ( 2020), pp. 616-624
[[50]]
D. Ofer, N. Brandes, M. Linial. The language of proteins: NLP, machine learning & protein sequences. Comput Struct Biotechnol J, 19 ( 2021), pp. 1750-1758
[[51]]
A. Elnaggar, M. Heinzinger, C. Dallago, G. Rehawi, Y. Wang, L. Jones, et al.. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans Pattern Anal Mach Intell, 44 (10) ( 2022), pp. 7112-7127
[[52]]
R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, X. Chen, J. Canny, et al.. Evaluating protein transfer learning with TAPE. Adv Neural Inf Process Syst, 32 ( 2019), pp. 9689-9701
[[53]]
J. Wu, Q. Yin, C. Zhang, J. Geng, H. Wu, H. Hu, et al.. Function prediction for G protein-coupled receptors through text mining and induction matrix completion. ACS Omega, 4 (2) ( 2019), pp. 3045-3054
[[54]]
J.A. Ballesteros, H. Weinstein. Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Methods Neurosci, 25 ( 1995), pp. 366-428
[[55]]
D. Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci, 28 (1) ( 1988), pp. 31-36
[[56]]
D. Rogers, M. Hahn. Extended-connectivity fingerprints. J Chem Inf Model, 50 (5) ( 2010), pp. 742-754
[[57]]
J.L. Durant, B.A. Leland, D.R. Henry, J.G. Nourse. Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci, 42 (6) ( 2002), pp. 1273-1280
[[58]]
B. Zagidullin, Z. Wang, Y. Guan, E. Pitkänen, J. Tang. Comparative analysis of molecular fingerprints in prediction of drug combination effects. Brief Bioinform, 22(6) ( 2021), Article bbab291
[[59]]
Z. Wu, J. Wang, H. Du, D. Jiang, Y. Kang, D. Li, et al.. Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking. Nat Commun, 14 (1) ( 2023), p. 2585
[[60]]
A.T.N. Nguyen, D.T.N. Nguyen, H.Y. Koh, J. Toskov, W. MacLean, A. Xu, et al.. The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery. Br J Pharmacol ( 2023 May:), Article bph.16140
[[61]]
S.A. Aleksander, J. Balhoff, S. Carbon, J.M. Cherry, H.J. Drabkin, D. Ebert, et al.. The Gene Ontology knowledgebase in 2023. Genetics, 224 (1) ( 2023), Article iyad031
[[62]]
X.S. Wei, J. Wu, Z.H. Zhou. Scalable algorithms for multi-instance learning. IEEE Trans Neural Netw Learn Syst, 28 (4) ( 2017), pp. 975-987
[[63]]
S. Seo, J. Choi, S.K. Ahn, K.W. Kim, J. Kim, J. Choi, et al.. Prediction of GPCR-ligand binding using machine learning algorithms. Comput Math Methods Med, 2018 ( 2018), p. 6565241
[[64]]
Y. Cao, L. Li. Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model. Bioinformatics, 30 (12) ( 2014), pp. 1674-1680
[[65]]
L. Di Rienzo, L. de Flaviis, G. Ruocco, V. Folli, E. Milanetti. Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors. J Comput Aided Mol Des, 36 (1) ( 2022), pp. 11-24
[[66]]
J. Wu, Q. Zhang, W. Wu, T. Pang, H. Hu, W.K.B. Chan, et al.. WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest. Bioinformatics, 34 (13) ( 2018), pp. 2271-2282
[[67]]
J. Wu, B. Liu, W.K.B. Chan, W. Wu, T. Pang, H. Hu, et al.. Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors. Bioinformatics, 35 (14) ( 2019), pp. i324-i332
[[68]]
J.P.L. Velloso, D.B. Ascher, D.E.V. Pires. pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures. Bioinform Adv, 1 (1) ( 2021), Article vbab031
[[69]]
A. Manglik, H. Lin, D.K. Aryal, J.D. McCorvy, D. Dengler, G. Corder, et al.. Structure-based discovery of opioid analgesics with reduced side effects. Nature, 537 (7619) ( 2016), pp. 185-190
[[70]]
S. Kampen, D. Rodriguez, M. Jørgensen, M. Kruszyk-Kujawa, X. Huang, M. Collins Jr, et al.. Structure-based discovery of negative allosteric modulators of the metabotropic glutamate receptor 5. ACS Chem Biol, 17 (10) ( 2022), pp. 2744-2752
[[71]]
B.L. Roth, J.J. Irwin, B.K. Shoichet. Discovery of new GPCR ligands to illuminate new biology. Nat Chem Biol, 13 (11) ( 2017), pp. 1143-1151
[[72]]
X. Liu, K. Ye, H.W.T. van Vlijmen, A.P. IJzerman, G.J.P. van Westen.An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor. J Cheminform, 11 (1) ( 2019), p. 35
[[73]]
M. Olivecrona, T. Blaschke, O. Engkvist, H. Chen.Molecular de-novo design through deep reinforcement learning. J Cheminform, 9 (1) ( 2017), p. 48
[[74]]
X. Liu, K. Ye, H.W.T. van Vlijmen, M.T.M. Emmerich, A.P. IJzerman, G.J.P. van Westen.DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology. J Cheminform, 13 (1) ( 2021), p. 85
[[75]]
X. Liu, K. Ye, H.W.T. van Vlijmen, A.P. IJzerman, G.J.P. van Westen.DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning. J Cheminform, 15 (1) ( 2023), p. 24
[[76]]
P.J. Flor, F.C. Acher. Orthosteric versus allosteric GPCR activation: the great challenge of group-III mGluRs. Biochem Pharmacol, 84 (4) ( 2012), pp. 414-424
[[77]]
J.D.A. Tyndall, R. Sandilya. GPCR agonists and antagonists in the clinic. Med Chem, 1 (4) ( 2005), pp. 405-421
[[78]]
C.S. Sum, B.J. Murphy, Z. Li, T. Wang, L. Zhang, M.E. Cvijic. Pharmacological characterization of GPCR agonists, antagonists, allosteric modulators and biased ligands from HTS hits to lead optimization. S. Markossian, A. Grossman, K. Brimacombe, M. Arkin, D. Auld, C. Austin, et al. ( Eds.), Assay guidance manual, Eli Lilly & Company and the National Center for Advancing Translational Sciences, Bethesda ( 2004)
[[79]]
J. Oh, H.T. Ceong, D. Na, C. Park.A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists. BMC Bioinf, 23 (Suppl 9) ( 2022), p. 346
[[80]]
M. Kang, E. Ko, T.B. Mersha. A roadmap for multi-omics data integration using deep learning. Brief Bioinform, 23 (1) ( 2022), Article bbab454
[[81]]
M.C. Lagerström, H.B. Schiöth. Structural diversity of G protein-coupled receptors and significance for drug discovery. Nat Rev Drug Discov, 7 (4) ( 2008), pp. 339-357
[[82]]
Q. Tang, F. Nie, Q. Zhao, W. Chen. A merged molecular representation deep learning method for blood-brain barrier permeability prediction. Brief Bioinform, 23 (5) ( 2022), Article bbac357
[[83]]
C.S. Odoemelam, B. Percival, H. Wallis, M.W. Chang, Z. Ahmad, D. Scholey, et al.. G-protein coupled receptors: structure and function in drug discovery. RSC Adv, 10 (60) ( 2020), pp. 36337-36348
[[84]]
I.A. Guedes, A.M.S. Barreto, D. Marinho, E. Krempser, M.A. Kuenemann, O. Sperandio, et al.. New machine learning and physics-based scoring functions for drug discovery. Sci Rep, 11 (1) ( 2021), p. 3198
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