Integrating Deep Learning and Molecular Dynamics to Identify GPR17 Ligands for Glioblastoma Therapy

Integrating Deep Learning and Molecular Dynamics to Identify GPR17 Ligands for Glioblastoma Therapy Satyanarayana Ramalakshmi Selvaraj Sathyapriya Renganathan Senthil Thirunavukarasou Anand Konda Mani Saravanan https://orcid.org/0000-0002-5541-234X Background:

Guanine Protein-coupled Receptor 17 (GPR17) plays pivotal roles in various physiological processes and diseases. However, the discovery of ligands binding to GPR17 remains an active area of research.
Methods:

In this study, we utilized our recently published GPCR-specific deep learning approach, molecular docking, and molecular dynamics simulations. Specifically, the DeepGPCR model, employing graph convolutional networks, was used to screen the extensive ZINC database for potential ligands.
Results:

This computational pipeline identified three highly promising lead compounds, ZINC000044404209, ZINC000229938097, and ZINC000005158963. Molecular dynamics simulations confirmed the stability of the protein-ligand complexes while binding free energy calculations highlighted the crucial molecular forces stabilizing these interactions. Notably, ZINC000229938097 exhibited particularly favorable binding energy values among the compounds assessed.
Conclusion:

Our study underscores the efficacy of computational methodologies in identifying potential drug candidates targeting GPR17. Understanding the molecular mechanisms underlying GPR17 activation holds significant promise for developing tailored therapies for Glioblastoma Multiforme.
conclusion:

The current study highlights the utility of computational tools in drug development and provides information on GPR17 as a target for GBM treatment and the directions for further experimental testing and utilization.
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Brain Sci 2023,13(11),1536 Int J Mol Sci Sadowski K. 25 5774 2024 10.3390/ijms25115774 Sadowski K.; Jażdżewska A.; Kozłowski J.; Zacny A.; Lorenc T.; Olejarz W.; Revolutionizing glioblastoma treatment: A comprehensive overview of modern therapeutic approaches. Int J Mol Sci 2024,25(11),5774 Brain Qi D. 146 1281 2023 10.1093/brain/awac450 Qi D.; Li J.; Quarles C.C.; Fonkem E.; Wu E.; Assessment and prediction of glioblastoma therapy response: Challenges and opportunities. Brain 2023,146(4),1281-1298 Front Pharmacol Guo C. 14 1093263 2023 10.3389/fphar.2023.1093263 Guo C.; Yu C.; Gao W.; Ren D.; Zhang Y.; Zheng P.; A novel classifier combining G protein-coupled receptors and the tumor microenvironment is associated with survival status in glioblastoma. Front Pharmacol 2023,14,1093263 Annu Rev Biophys Biomol Struct White S.H. 28 319 1999 10.1146/annurev.biophys.28.1.319 White S.H.; Wimley W.C.; Membrane protein folding and stability: Physical principles. Annu Rev Biophys Biomol Struct 1999,28(1),319-365 Humana Press de Brevern A.G. 387 2010 de Brevern A.G.; Humana Press 2010,387-401 Int J Oncol Khabibov M. 60 69 2022 10.3892/ijo.2022.5359 Khabibov M.; Garifullin A.; Boumber Y.; Khaddour K.; Fernandez M.; Khamitov F.; Khalikova L.; Kuznetsova N.; Kit O.; Kharin L.; Signaling pathways and therapeutic approaches in glioblastoma multiforme (Review). Int J Oncol 2022,60(6),69 Cell Death Dis Liu H. 12 610 2021 10.1038/s41419-021-03897-0 Liu H.; Xing R.; Ou Z.; Zhao J.; Hong G.; Zhao T.J.; Han Y.; Chen Y.; G-protein-coupled receptor GPR17 inhibits glioma development by increasing polycomb repressive complex 1-mediated ROS production. Cell Death Dis 2021,12(6),610 Int J Mol Sci Dziedzic A. 21 1852 2020 10.3390/ijms21051852 Dziedzic A.; Miller E.; Bijak S.J.; Bijak M.; The GPR17 receptor—A promising goal for therapy and a potential marker of the neurodegenerative process in multiple sclerosis. Int J Mol Sci 2020,21(5),1852 Front Pharmacol Lecca D. 11 629618 2021 10.3389/fphar.2020.629618 Lecca D.; Abbracchio M.P.; Fumagalli M.; Purinergic receptors on oligodendrocyte progenitors: Promising targets for myelin repair in multiple sclerosis? Front Pharmacol 2021,11,629618 J Cell Biochem Mani K.S. 124 533 2023 10.1002/jcb.30388 Mani K.S.; Thiyagarajan R.; Harja Y.O.; Kandhavelu M.; Murugesan A.; Structural analysis of human G‐protein‐coupled receptor 17 ligand binding sites. J Cell Biochem 2023,124(4),533-544 Nat Neurosci Chen Y. 12 1398 2009 10.1038/nn.2410 Chen Y.; Wu H.; Wang S.; Koito H.; Li J.; Ye F.; Hoang J.; Escobar S.S.; Gow A.; Arnett H.A.; Trapp B.D.; Karandikar N.J.; Hsieh J.; Lu Q.R.; The oligodendrocyte-specific G protein–coupled receptor GPR17 is a cell-intrinsic timer of myelination. Nat Neurosci 2009,12(11),1398-1406 Biomed Pharmacother Alavi M.S. 98 222 2018 10.1016/j.biopha.2017.12.056 Alavi M.S.; Shamsizadeh A.; Zarmehri A.H.; Roohbakhsh A.; Orphan G protein-coupled receptors: The role in CNS disorders. Biomed Pharmacother 2018,98,222-232 Mol Pharmacol Simon K 91 518 2017 10.1124/mol.116.107904 Simon K; Merten N; Schröder R; The orphan receptor GPR17 is unresponsive to uracil nucleotides and cysteinyl leukotrienes. Mol Pharmacol 2017,91(5),518-532 J Med Chem Nguyen P. 64 10908 2021 10.1021/acs.jmedchem.1c00277 Nguyen P.; Doan P.; Rimpilainen T.; Mani K.S.; Murugesan A.; Harja Y.O.; Candeias N.R.; Kandhavelu M.; Synthesis and preclinical validation of novel indole derivatives as a GPR17 agonist for glioblastoma treatment. J Med Chem 2021,64(15),10908-10918 Int J Biol Macromol Saravanan K.M. 106 901 2018 10.1016/j.ijbiomac.2017.08.088 Saravanan K.M.; Palanivel S.; Harja Y.O.; Kandhavelu M.; Identification of novel GPR17-agonists by structural bioinformatics and signaling activation. Int J Biol Macromol 2018,106,901-907 BMC Struct Biol Parravicini C. 10 8 2010 10.1186/1472-6807-10-8 Parravicini C.; Abbracchio M.P.; Fantucci P.; Ranghino G.; Forced unbinding of GPR17 ligands from wild type and R255I mutant receptor models through a computational approach. BMC Struct Biol 2010,10(1),8 BMC Bioinformatics Parravicini C. 9 263 2008 10.1186/1471-2105-9-263 Parravicini C.; Ranghino G.; Abbracchio M.P.; Fantucci P.; GPR17: Molecular modeling and dynamics studies of the 3-D structure and purinergic ligand binding features in comparison with P2Y receptors. BMC Bioinformatics 2008,9(1),263 PeerJ Zhang H. 7 e7362 2019 10.7717/peerj.7362 Zhang H.; Liao L.; Saravanan K.M.; Yin P.; Wei Y.; DeepBindRG: A deep learning based method for estimating effective protein–ligand affinity. PeerJ 2019,7,e7362 Curr Bioinform Sreeraman S. 18 208 2023 10.2174/1574893618666230227105703 Sreeraman S.; Kannan M.P.; Kushwah S.R.B.; Sundaram V.; Veluchamy A.; Thirunavukarasou A.; Saravanan K.M.; Drug design and disease diagnosis: The potential of deep learning models in biology. Curr Bioinform 2023,18(3),208-220 Curr Bioinform Zhang H. 19 891 2024 10.2174/0115748936285690240101041704 Zhang H.; Saravanan K.M.; Advances in deep learning assisted drug discovery methods: A self-review. Curr Bioinform 2024,19(10),891-907 Molecules Zhang H. 28 4691 2023 10.3390/molecules28124691 Zhang H.; Saravanan K.M.; Zhang J.Z.H.; DeepBindGCN: Integrating molecular vector representation with graph convolutional neural networks for protein–ligand interaction prediction. Molecules 2023,28(12),4691 Brief Bioinform Zhang H. 25 bbae281 2024 10.1093/bib/bbae281 Zhang H.; Fan H.; Wang J.; Hou T.; Saravanan K.M.; Xia W.; Kan H.W.; Li J.; Zhang J.Z.H.; Liang X.; Chen Y.; Revolutionizing GPCR–ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery. Brief Bioinform 2024,25(4),bbae281 Struct Chem Feng Y. 33 1503 2022 10.1007/s11224-022-01960-w Feng Y.; Cheng X.; Wu S.; Saravanan M.K.; Liu W.; Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease. Struct Chem 2022,33(5),1503-1515 MedComm Ye F. 3 e159 2022 10.1002/mco2.159 Ye F.; Wong T.S.; Chen G.; Zhang Z.; Zhang B.; Gan S.; Gao W.; Li J.; Wu Z.; Pan X.; Du Y.; Cryo‐EM structure of G‐protein‐coupled receptor GPR17 in complex with inhibitory G protein. MedComm 2022,3(4),e159 EMBO J Ciana P. 25 4615 2006 10.1038/sj.emboj.7601341 Ciana P.; Fumagalli M.; Trincavelli M.L.; Verderio C.; Rosa P.; Lecca D.; Ferrario S.; Parravicini C.; Capra V.; Gelosa P.; Guerrini U.; Belcredito S.; Cimino M.; Sironi L.; Tremoli E.; Rovati G.E.; Martini C.; Abbracchio M.P.; The orphan receptor GPR17 identified as a new dual uracil nucleotides/cysteinyl-leukotrienes receptor. EMBO J 2006,25(19),4615-4627 PLoS One Lecca D. 3 e3579 2008 10.1371/journal.pone.0003579 Lecca D.; Trincavelli M.L.; Gelosa P.; Sironi L.; Ciana P.; Fumagalli M.; Villa G.; Verderio C.; Grumelli C.; Guerrini U.; Tremoli E.; Rosa P.; Cuboni S.; Martini C.; Buffo A.; Cimino M.; Abbracchio M.P.; The recently identified P2Y-like receptor GPR17 is a sensor of brain damage and a new target for brain repair. PLoS One 2008,3(10),e3579 Aging Cell Rivera A.D. 20 e13335 2021 10.1111/acel.13335 Rivera A.D.; Pieropan F.; Rocha C.D.L.I.; Lecca D.; Abbracchio M.P.; Azim K.; Butt A.M.; Functional genomic analyses highlight a shift in Gpr17 ‐regulated cellular processes in oligodendrocyte progenitor cells and underlying myelin dysregulation in the aged mouse cerebrum. Aging Cell 2021,20(4),e13335 J Biomol Struct Dyn Mutharasu G. 40 2586 2022 10.1080/07391102.2020.1841029 Mutharasu G.; Murugesan A.; Mani K.S.; Harja Y.O.; Kandhavelu M.; Transcriptomic analysis of glioblastoma multiforme providing new insights into GPR17 signaling communication. J Biomol Struct Dyn 2022,40(6),2586-2599 J Chem Inf Model Irwin J.J. 45 177 2005 10.1021/ci049714+ Irwin J.J.; Shoichet B.K.; ZINC--A free database of commercially available compounds for virtual screening. J Chem Inf Model 2005,45(1),177-182 Bioinformatics Chan W.K.B. 31 3035 2015 10.1093/bioinformatics/btv302 Chan W.K.B.; Zhang H.; Yang J.; Brender J.R.; Hur J.; Özgür A.; Zhang Y.; GLASS: A comprehensive database for experimentally validated GPCR-ligand associations. Bioinformatics 2015,31(18),3035-3042 J Chem Inf Model Jaeger S. 58 27 2018 10.1021/acs.jcim.7b00616 Jaeger S.; Fulle S.; Turk S.; Mol2vec: Unsupervised machine learning approach with chemical intuition. J Chem Inf Model 2018,58(1),27-35 PeerJ Zhang H. 8 e8864 2020 10.7717/peerj.8864 Zhang H.; Saravanan K.M.; Lin J.; Liao L.; Ng J.T.Y.; Zhou J.; Wei Y.; DeepBindPoc: A deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ 2020,8,e8864 J Cheminform Bento A.P. 12 51 2020 10.1186/s13321-020-00456-1 Bento A.P.; Hersey A.; Félix E.; Landrum G.; Gaulton A.; Atkinson F.; Bellis L.J.; De Veij M.; Leach A.R.; An open source chemical structure curation pipeline using RDKit. J Cheminform 2020,12(1),51 Curr Opin Struct Biol Borkakoti N. 78 102526 2023 10.1016/j.sbi.2022.102526 Borkakoti N.; Thornton J.M.; AlphaFold2 protein structure prediction: Implications for drug discovery. Curr Opin Struct Biol 2023,78,102526 Nucleic Acids Res Roy A. 40 W471 2012 10.1093/nar/gks372 Roy A.; Yang J.; Zhang Y.; COFACTOR: An accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res 2012,40(W1),W471-W477 Protein Sci Goodsell D.S. 30 31 2021 10.1002/pro.3934 Goodsell D.S.; Sanner M.F.; Olson A.J.; Forli S.; The suite at 30. Protein Sci 2021,30(1),31-43 J Phys Chem Kaminski G. 100 18010 1996 10.1021/jp9624257 Kaminski G.; Jorgensen W.L.; Performance of the AMBER94, MMFF94, and OPLS-AA force fields for modeling organic liquids. J Phys Chem 1996,100(46),18010-18013 J Comput Chem Trott O. 31 455 2010 10.1002/jcc.21334 Trott O.; Olson A.J.; Autodock vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010,31(2),455-461 Biophys J Jo S. 97 50 2009 10.1016/j.bpj.2009.04.013 Jo S.; Lim J.B.; Klauda J.B.; Im W.; CHARMM-GUI membrane builder for mixed bilayers and its application to yeast membranes. Biophys J 2009,97(1),50-58 Nucleic Acids Res Lomize M.A. 40 D370 2012 10.1093/nar/gkr703 Lomize M.A.; Pogozheva I.D.; Joo H.; Mosberg H.I.; Lomize A.L.; OPM database and PPM web server: Resources for positioning of proteins in membranes. Nucleic Acids Res 2012,40(D1),D370-D376 Sci Rep Bruzzese A. 8 4456 2018 10.1038/s41598-018-22735-6 Bruzzese A.; Gil C.; Dalton J.A.R.; Giraldo J.; Structural insights into positive and negative allosteric regulation of a G protein-coupled receptor through protein-lipid interactions. Sci Rep 2018,8(1),4456 J Mol Biol Im W. 319 1177 2002 10.1016/S0022-2836(02)00380-7 Im W.; Roux B.; Ions and counterions in a biological channel: A molecular dynamics simulation of OmpF porin from Escherichia coli in an explicit membrane with 1 M KCl aqueous salt solution. J Mol Biol 2002,319(5),1177-1197 J Chem Theory Comput Hess B. 4 435 2008 10.1021/ct700301q Hess B.; Kutzner C.; Spoel V.D.D.; Lindahl E.; GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 2008,4(3),435-447 SoftwareX Abraham M.J. 1-2 19 2015 10.1016/j.softx.2015.06.001 Abraham M.J.; Murtola T.; Schulz R.; Páll S.; Smith J.C.; Hess B.; Lindahl E.; GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015,1-2,19-25 J Chem Phys Bussi G. 130 074101 2009 10.1063/1.3073889 Bussi G.; Timan Z.T.; Parrinello M.; Isothermal-isobaric molecular dynamics using stochastic velocity rescaling. J Chem Phys 2009,130(7),074101 Proceedings of the Fourteenth Jerusalem Symposium on Quantum Chemistry and Biochemistry Held in Jerusalem Berendsen H.J.C. 331 10.1007/978-94-015-7658-1_21 Berendsen H.J.C.; Postma J.P.M.; Gunsteren V.W.F.; Hermans J.; Interaction models for water in relation to protein hydration BT - intermolecular forces Proceedings of the Fourteenth Jerusalem Symposium on Quantum Chemistry and Biochemistry Held in Jerusalem ,331-342 J Chem Inf Model Kumari R. 54 1951 2014 10.1021/ci500020m Kumari R.; Kumar R.; Lynn A.; G_MMPBSA--A GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 2014,54(7),1951-1962 Front Mol Biosci Wang C. 4 87 2018 10.3389/fmolb.2017.00087 Wang C.; Greene D.A.; Xiao L.; Qi R.; Luo R.; Recent developments and applications of the MMPBSA method. Front Mol Biosci 2018,4,87 Protein Eng Des Sel Saravanan K.M. 23 911 2010 10.1093/protein/gzq070 Saravanan K.M.; Balasubramanian H.; Nallusamy S.; Samuel S.; Sequence and structural analysis of two designed proteins with 88% identity adopting different folds. Protein Eng Des Sel 2010,23(12),911-918 J Biol Phys Saravanan K.M. 43 265 2017 10.1007/s10867-017-9451-x Saravanan K.M.; Selvaraj S.; Dihedral angle preferences of amino acid residues forming various non-local interactions in proteins. J Biol Phys 2017,43(2),265-278 Int J Mol Sci Obrador E. 25 2529 2024 10.3390/ijms25052529 Obrador E.; Murciano M.P.; Caballo O.M.; Blanch L.R.; Pineda B.; Arroyo G.J.; Loras A.; Bonet G.L.; Cadenas M.C.; Estrela J.; Torrejón M.M.; Glioblastoma therapy: Past, present and future. Int J Mol Sci 2024,25(5),2529 Cancers Doan P. 13 3773 2021 10.3390/cancers13153773 Doan P.; Nguyen P.; Murugesan A.; Subramanian K.; Konda Mani S.; Kalimuthu V.; Abraham B.G.; Stringer B.W.; Balamuthu K.; Yli-Harja O.; Kandhavelu M.; Targeting orphan G protein-coupled receptor 17 with T0 ligand impairs glioblastoma growth. Cancers 2021,13(15),3773 J Biomol Struct Dyn Murugesan A. 40 12908 2022 10.1080/07391102.2021.1977707 Murugesan A.; Nguyen P.; Ramesh T.; Harja Y.O.; Kandhavelu M.; Saravanan K.M.; Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor. J Biomol Struct Dyn 2022,40(23),12908-12916 Cells Doan P. 10 1975 2021 10.3390/cells10081975 Doan P.; Nguyen P.; Murugesan A.; Candeias N.R.; Harja Y.O.; Kandhavelu M.; Alkylaminophenol and GPR17 agonist for glioblastoma therapy: A combinational approach for enhanced cell death activity. Cells 2021,10(8),1975 Brain Ceruti S. 132 2206 2009 10.1093/brain/awp147 Ceruti S.; Villa G.; Genovese T.; Mazzon E.; Longhi R.; Rosa P.; Bramanti P.; Cuzzocrea S.; Abbracchio M.P.; The P2Y-like receptor GPR17 as a sensor of damage and a new potential target in spinal cord injury. Brain 2009,132(8),2206-2218