Abstract
The serotonergic systems are the most important therapeutic targets for neurological disorders. Many serotonergic drugs have been used to treat neurological disorders, which are well known for their adverse side effects because of the off-target interactions. Development of selective ligands for a specific target is the suitable approach to minimize the off-target interactions and side effects. To identify selective ligands for serotonin 1B receptor (5-HT1BR), the structural analogs of inverse agonist methiothepin (MT) and natural products were screened against 5-HT1BR and other 5-HTR subtypes (5-HT2AR, 5-HT2BR, and 5-HT2CR). In the present study, five compounds were selected out of 9963 screened compounds having higher binding affinity with 5-HT1BR over other 5-HTRs. Amongst them, ZINC31166967 and ZINC31162553 exhibited relatively higher binding affinity towards 5-HT1BR with the binding energy of − 10.1 and − 9.1 kcal/mol, respectively. The pharmacokinetic assessments considered them safe and non-toxic. Molecular dynamics (MD) simulation revealed the stability of these compounds within the active site of the receptor. The overall analysis suggested that ZINC31166967 and ZINC31162553 may be considered as the selective ligands for 5-HT1BR. However, detailed experimental investigations will be required to substantiate the findings.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and materials
Not applicable.
Code availability
Not applicable.
References
Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25. https://doi.org/10.1016/j.softx.2015.06.001
Ballesteros JA, Weinstein H (1995) Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Methods Neurosci 25:366–428. https://doi.org/10.1016/S1043-9471(05)80049-7
Berger M, Gray JA, Roth BL (2009) The expanded biology of serotonin. Annu Rev Med 60:355–366. https://doi.org/10.1146/annurev.med.60.042307.110802
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242. https://doi.org/10.1093/nar/28.1.235
Bhowmik D, Sharma RD, Prakash A, Kumar D (2021) Identification of Nafamostat and VR23 as COVID-19 drug candidates by targeting 3CLpro and PLpro. J Mol Struct 1233:130094. https://doi.org/10.1016/j.molstruc.2021.130094
Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:014101. https://doi.org/10.1063/1.2408420
Cao J, LaRocque E, Li D (2013) Associations of the 5-hydroxytryptamine (serotonin) Receptor 1B gene (HTR1B) with alcohol, cocaine, and heroin abuse. Am J Med Genet B 162:169–176. https://doi.org/10.1002/ajmg.b.32128
Cheng F, Yu Y, Shen J, Yang L, Li W, Liu G, Lee PW, Tang Y (2011) Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J Chem Inf Model 51:996–1011. https://doi.org/10.1021/ci200028n
Crivori P, Cruciani G, Carrupt P-A, Testa B (2000) Predicting blood−brain barrier permeation from three-dimensional molecular structure. J Med Chem 43:2204–2216. https://doi.org/10.1021/jm990968+
Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. In: Hempel J, Williams C, Hong C (eds) Chemical biology in: chemical biology, methods in molecular biology. Humana Press, New York. https://doi.org/10.1007/978-1-4939-2269-7_19
Deserno M, Holm C (1998) How to mesh up Ewald sums. II. An accurate error estimate for the particle–particle–particle-mesh algorithm. J Chem Phys 109:7694–7701. https://doi.org/10.1063/1.477415
Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8593. https://doi.org/10.1063/1.470117
Fernandes J, Gattass CR (2009) Topological polar surface area defines substrate transport by multidrug resistance associated protein 1 (MRP1/ABCC1). J Med Chem 52:1214–1218. https://doi.org/10.1021/jm801389m
Fredriksson R, Lagerström MC, Lundin L-G, Schiöth HB (2003) The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Mol Pharmacol 63:1256–1272. https://doi.org/10.1124/mol.63.6.1256
Guan L, Yang H, Cai Y, Sun L, Di P, Li W, Liu G, Tang Y (2019) ADMET-score—a comprehensive scoring function for evaluation of chemical drug-likeness. Med Chem Commun 10:148–157. https://doi.org/10.1039/C8MD00472B
Hauser AS, Chavali S, Masuho I, Jahn LJ, Martemyanov KA, Gloriam DE, Babu MM (2018) Pharmacogenomics of GPCR drug targets. Cell 172:41–54. https://doi.org/10.1016/j.cell.2017.11.033
Hess B, Bekker H, Berendsen HJ, Fraaije JG (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12%3c1463::AID-JCC4%3e3.0.CO;2-H
Hou T, Zhang W, Xia K, Qiao X, Xu X (2004) ADME evaluation in drug discovery. 5. Correlation of Caco-2 permeation with simple molecular properties. J Chem Inf Comput Sci 44:1585–1600. https://doi.org/10.1021/ci049884m
Hummer G (1995) The numerical accuracy of truncated Ewald sums for periodic systems with long-range Coulomb interactions. Chem Phys Lett 235:297–302. https://doi.org/10.1016/0009-2614(95)00117-M
Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768. https://doi.org/10.1021/ci3001277
Jaillet L, Artemova S, Redon S (2017) IM-UFF: extending the universal force field for interactive molecular modeling. J Mol Graph Model 77:350–362. https://doi.org/10.1016/j.jmgm.2017.08.023
Kim K, Che T, Panova O, DiBerto JF, Lyu J, Krumm BE, Wacker D, Robertson MJ, Seven AB, Nichols DE (2020) Structure of a hallucinogen-activated Gq-coupled 5-HT2A serotonin receptor. Cell 182:1574–1588. https://doi.org/10.1016/j.cell.2020.08.024
Kimura KT, Asada H, Inoue A, Kadji FMN, Im D, Mori C, Arakawa T, Hirata K, Nomura Y, Nomura N (2019) Structures of the 5-HT 2A receptor in complex with the antipsychotics risperidone and zotepine. Nat Struct Mol Biol 26:121–128. https://doi.org/10.1038/s41594-018-0180-z
Lanfumey L, Hamon M (2004) 5-HT1 receptors. Curr Drug Targets CNS Neurol Disord 3:1–10. https://doi.org/10.2174/1568007043482570
Laskowski RA, Swindells MB (2011) LigPlot+: multiple ligand–protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786. https://doi.org/10.1021/ci200227u
Lipinski CA (2004) Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
Lipinski CA (2008) Compound properties and drug quality. The practice of medicinal chemistry, Elsevier, pp 481–490. https://doi.org/10.1016/B978-0-12-374194-3.00022-6
McCorvy JD, Roth BL (2015) Structure and function of serotonin G protein-coupled receptors. Pharmacol Ther 150:129–142. https://doi.org/10.1016/j.pharmthera.2015.01.009
McCorvy JD, Wacker D, Wang S, Agegnehu B, Liu J, Lansu K, Tribo AR, Olsen RH, Che T, Jin J (2018) Structural determinants of 5-HT 2B receptor activation and biased agonism. Nat Struct Mol Biol 25:787–796. https://doi.org/10.1038/s41594-018-0116-7
Oostenbrink C, Villa A, Mark AE, Van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25:1656–1676. https://doi.org/10.1002/jcc.20090
Oostenbrink C, Soares TA, Van der Vegt NF, Van Gunsteren WF (2005) Validation of the 53A6 GROMOS force field. Eur Biophys J 34:273–284. https://doi.org/10.1007/s00249-004-0448-6
Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182–7190. https://doi.org/10.1063/1.328693
Peng Y, McCorvy JD, Harpsøe K, Lansu K, Yuan S, Popov P, Qu L, Pu M, Che T, Nikolajsen LF (2018) 5-HT2C receptor structures reveal the structural basis of GPCR polypharmacology. Cell 172:719–730. https://doi.org/10.1016/j.cell.2018.01.001
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612. https://doi.org/10.1002/jcc.20084
Pham The H, González-Álvarez I, Bermejo M, Mangas Sanjuan V, Centelles I, Garrigues TM, Cabrera-Pérez MÁ (2011) In silico prediction of Caco-2 cell permeability by a classification QSAR approach. Mol Inform 30:376–385. https://doi.org/10.1002/minf.201000118
Rappé AK, Casewit CJ, Colwell K, Goddard WA III, Skiff WM (1992) UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114:10024–10035. https://doi.org/10.1021/ja00051a040
Sander T, Freyss J, von Korff M, Rufener C (2015) DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 55:460–473. https://doi.org/10.1021/ci500588j
Sari Y (2004) Serotonin1B receptors: from protein to physiological function and behavior. Neurosci Biobehav Rev 28:565–582. https://doi.org/10.1016/j.neubiorev.2004.08.008
Schüttelkopf AW, Van Aalten DM (2004) PRODRG: a tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr D 60:1355–1363. https://doi.org/10.1107/S0907444904011679
Shukla R, Shukla H, Kalita P, Tripathi T (2018) Structural insights into natural compounds as inhibitors of Fasciola gigantica thioredoxin glutathione reductase. J Cell Biochem 119:3067–3080. https://doi.org/10.1002/jcb.26444
Shukla R, Shukla H, Tripathi T (2019) Structural and energetic understanding of novel natural inhibitors of Mycobacterium tuberculosis malate synthase. J Cell Biochem 120:2469–2482. https://doi.org/10.1002/jcb.27538
Shukla R, Shukla H, Tripathi T (2020) Structure-based discovery of phenyl-diketo acids derivatives as Mycobacterium tuberculosis malate synthase inhibitors. J Biomol Struct Dyn 39:2945–2958. https://doi.org/10.1080/07391102.2020.1758787
Slassi A (2002) Recent advances in 5-HT1B/1D receptor antagonists and agonists and their potential therapeutic applications. Curr Top Med Chem 2:559–574. https://doi.org/10.2174/1568026023393903
Sterling T, Irwin JJ (2015) ZINC 15–ligand discovery for everyone. J Chem Inf Model 55:2324–2337. https://doi.org/10.1021/acs.jcim.5b00559
Tiger M, Varnäs K, Okubo Y, Lundberg J (2018) The 5-HT 1B receptor-a potential target for antidepressant treatment. Psychopharmacol 235:1317–1334. https://doi.org/10.1007/s00213-018-4872-1
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334
Wacker D, Wang C, Katritch V, Han GW, Huang XP, Vardy E, McCorvy JD, Jiang Y, Chu M, Siu FY, Liu W (2013) Structural features for functional selectivity at serotonin receptors. Science 340:615–619. https://doi.org/10.1126/science.1232808
Wacker D, Wang S, McCorvy JD, Betz RM, Venkatakrishnan A, Levit A, Lansu K, Schools ZL, Che T, Nichols DE (2017) Crystal structure of an LSD-bound human serotonin receptor. Cell 168:377–389. https://doi.org/10.1016/j.cell.2016.12.033
Walum E (1998) Acute Oral Toxicity. Environ Health Persp 106:497–503. https://doi.org/10.1289/ehp.98106497
Wang C, Jiang Y, Ma J, Wu H, Wacker D, Katritch V, Han GW, Liu W, Huang X-P, Vardy E (2013) Structural basis for molecular recognition at serotonin receptors. Science 340:610–614. https://doi.org/10.1126/science.1232807
Webb B, Sali A (2016) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinform 54:5.6.1-5.6.37. https://doi.org/10.1002/cpbi.3
Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, Li W, Liu G, Tang Y (2019) admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 35:1067–1069. https://doi.org/10.1093/bioinformatics/bty707
Yin W, Zhou XE, Yang D, de Waal PW, Wang M, Dai A, Cai X, Huang CY, Liu P, Wang X, Yin Y (2018) Crystal structure of the human 5-HT 1B serotonin receptor bound to an inverse agonist. Cell Discov 4:12. https://doi.org/10.1038/s41421-018-0009-2
Zhang T, Li Y, Gao P, Shao Q, Shao M, Zhang M, Zhang J, Duan X, Liu Z, Gan L (2019) SW_GROMACS: accelerate GROMACS on Sunway TaihuLight. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp 1–14. https://doi.org/10.1145/3295500.3356190
Zhao YH, Abraham MH, Le J, Hersey A, Luscombe CN, Beck G, Sherborne B, Cooper I (2002) Rate-limited steps of human oral absorption and QSAR studies. Pharm Res 19:1446–1457. https://doi.org/10.1023/A:1020444330011
Acknowledgements
The authors would like to thank the Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio), IIT Delhi and Department of Biotechnology and Bioinformatics, Sambalpur University for providing computational facilities.
Funding
None.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethical approval
This review does not contain any studies with human participants or animals performed by the author.
Consent to participate
Not applicable.
Consent for publication
Manuscript has been read and approved by all named authors.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Bag, B.P., Prusty, S.S. & Patel, A.K. Identification of effective and specific serotonin1B receptor ligands by structure-based virtual screening and molecular dynamics. J Proteins Proteom 12, 213–226 (2021). https://doi.org/10.1007/s42485-021-00069-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42485-021-00069-8