Generic placeholder image

Current Pharmaceutical Design

Author(s): Amit Michaeli*, Immanuel Lerner, Maria Zatsepin, Shaul Mezan and Alexandra Vardi Kilshtain

DOI: 10.2174/1381612826666201113104150

DownloadDownload PDF Flyer Cite As
Discovery of Novel GABAAR Allosteric Modulators Through Reinforcement Learning

Page: [5713 - 5719] Pages: 7

  • * (Excluding Mailing and Handling)

Explore Articles
About Journal
For Authors
For Editors
For Reviewers

Abstract

Background: As not all target proteins can be easily screened in vitro, advanced virtual screening is becoming critical.

Objective: In this study, we demonstrate the application of reinforcement learning guided virtual screening for γ-aminobutyric acid A receptor (GABAAR) modulating peptides.

Methods: Structure-based virtual screening was performed on a receptor homology model. Screened molecules deemed to be novel were synthesized and analyzed using patch-clamp analysis.

Results: 13 molecules were synthesized and 11 showed positive allosteric modulation, with two showing 50% activation at the low micromolar range.

Conclusion: Reinforcement learning guided virtual screening is a viable method for the discovery of novel molecules that modulate a difficult to screen transmembrane receptor.

Keywords: Virtual Screening, structure-based drug design, peptides, chlorine channel, allosteric, in silico, reinforcement learning.

[1]
Macarron R, Banks MN, Bojanic D, et al. Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 2011; 10(3): 188-95.
[http://dx.doi.org/10.1038/nrd3368] [PMID: 21358738]
[2]
Smith GP. Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. Science 1985; 228(4705): 1315-7.
[http://dx.doi.org/10.1126/science.4001944] [PMID: 4001944]
[3]
Smith GP, Petrenko VA. Phage display. Chem Rev 1997; 97(2): 391-410.
[http://dx.doi.org/10.1021/cr960065d] [PMID: 11848876]
[4]
Hirose H, Tsiamantas C, Katoh T, Suga H. In vitro expression of genetically encoded non-standard peptides consisting of exotic amino acid building blocks. Curr Opin Biotechnol 2019; 58: 28-36.
[http://dx.doi.org/10.1016/j.copbio.2018.10.012] [PMID: 30453154]
[5]
Passioura T, Suga H. A RaPID way to discover nonstandard macrocyclic peptide modulators of drug targets. Chem Commun (Camb) 2017; 53(12): 1931-40.
[http://dx.doi.org/10.1039/C6CC06951G] [PMID: 28091672]
[6]
Pinzi L, Rastelli G. Molecular docking: Shifting paradigms in drug discovery. Int J Mol Sci 2019; 20(18): 4331.
[http://dx.doi.org/10.3390/ijms20184331] [PMID: 31487867]
[7]
Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res 2000; 28(1): 235-42.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[8]
Gou ZH, Wang X, Wang W. Evolution of neurotransmitter gamma-aminobutyric acid, glutamate and their receptors Dongwuxue Yanjiu 2012; 33(E5-6): E75-81.
[PMID: 23266985]
[9]
Jembrek MJ, Vlainic J. GABA receptors: Pharmacological potential and pitfalls. Curr Pharm Des 2015; 21(34): 4943-59.
[http://dx.doi.org/10.2174/1381612821666150914121624] [PMID: 26365137]
[10]
Kuenzel K, Friedrich O, Gilbert DF. A recombinant human pluripotent stem cell line stably expressing halide-sensitive YFP-I152L for GABAAR and GlyR-targeted high-throughput drug screening and toxicity testing. Front Mol Neurosci 2016; 9: 51.
[http://dx.doi.org/10.3389/fnmol.2016.00051] [PMID: 27445687]
[11]
Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: A survey. J Artif Intell Res 1996; 4: 237-85.
[http://dx.doi.org/10.1613/jair.301]
[12]
Froese DS, Michaeli A, McCorvie TJ, et al. Structural basis of glycogen branching enzyme deficiency and pharmacologic rescue by rational peptide design. Hum Mol Genet 2015; 24(20): 5667-76.
[http://dx.doi.org/10.1093/hmg/ddv280] [PMID: 26199317]
[13]
Michaeli A, Mezan S, Kühbacher A, et al. Computationally designed bispecific MD2/CD14 binding peptides show TLR4 agonist activity. J Immunol 2018; 201(11): 3383-91.
[http://dx.doi.org/10.4049/jimmunol.1800380] [PMID: 30348734]
[14]
Miller PS, Aricescu AR. Crystal structure of a human GABAA receptor. Nature 2014; 512(7514): 270-5.
[http://dx.doi.org/10.1038/nature13293] [PMID: 24909990]
[15]
Laverty D, Desai R, Uchański T, et al. Cryo-EM structure of the human α1β3γ2 GABAA receptor in a lipid bilayer. Nature 2019; 565(7740): 516-20.
[http://dx.doi.org/10.1038/s41586-018-0833-4] [PMID: 30602789]
[16]
Lerner I, Goldblum A, Rayan A, Vardi A, Michaeli A. From finance to molecular modeling algorithms: The risk and return heuristic. Curr Top Pept Protein Res 2017; 18: 117-31.
[17]
Phulera S, Zhu H, Yu J, Claxton DP, Yoder N, Yoshioka C, et al. Cryo-EM structure of the benzodiazepine-sensitive α1β1γ2S triheteromeric GABAA receptor in complex with GABA. Elife 2018. Available from: https://elifesciences.org/articles/39383
[18]
Squires RF, Brastrup C. Benzodiazepine receptors in rat brain. Nature 1977; 266(5604): 732-4.
[http://dx.doi.org/10.1038/266732a0] [PMID: 876354]
[19]
Witkin JM, Cerne R, Wakulchik M, et al. Further evaluation of the potential anxiolytic activity of imidazo[1,5-a][1,4]diazepin agents selective for α2/3-containing GABAA receptors. Pharmacol Biochem Behav 2017; 157: 35-40.
[http://dx.doi.org/10.1016/j.pbb.2017.04.009] [PMID: 28442369]
[20]
Masiulis S, Desai R, Uchański T, et al. GABAA receptor signalling mechanisms revealed by structural pharmacology. Nature 2019; 565(7740): 454-9.
[http://dx.doi.org/10.1038/s41586-018-0832-5] [PMID: 30602790 ]