Abstract
The major histocompatibility complex (MHC) class I-related molecule, MR1, is a key component of the immune system, presenting antigens to T-cell receptors (TCRs) and modulating the immune response against various antigens. MR1 possesses a compact ligand-binding pocket despite its ability to interact with ligands that can have either agonistic or antagonistic effects on the immune system. Agonistic ligands can stimulate the immune response, while antagonistic ligands do not elicit an immune response. In most cases, ligand binding to MR1 is mediated through a covalent bond with Lys43. However, recent studies have suggested that a variety of small molecules can interact with the MR1-binding site. In this study, we have used several approaches to improve the binding pose prediction of covalent ligands to MR1, including docking in mutated receptors, and imposing simple pharmacophore constraints and structural water molecules. The careful assignment of pharmacophore constraints and inclusion of structural water molecules in the challenging docking process of covalent docking improved the binding pose prediction and virtual screening performance. In a retrospective virtual screening, the proposed approach exhibited EF1% and EF2% values of 7.4 and 5.5, respectively. Conversely, when using the mutated receptor, both EF1% and EF2% were recorded as 0 for the conventional docking method. The performance of the pharmacophore constraints was also evaluated on other covalent docking cases, and compared to previously reported results for common covalent docking methods. The proposed approach achieved an average RMSD of 2.55, while AutoDock4, CovDock, FITTED, GOLD, ICM-Pro, and MOE exhibited average RMSD values of 3.0, 2.93, 3.04, 4.93, 2.44, and 3.36, respectively. Our results demonstrate that the inclusion of simple pharmacophore constraints and structural waters can improve the prediction of binding poses of covalent ligands to MR1, which can aid in the discovery of novel immunotherapeutic agents.
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
The detailed results of the docking ligands reacted through nucleophilic addition to nitrile are presented as a Microsoft Excel file.
Code availability
Not applicable.
References
Amaro RE, Baudry J, Chodera J, Demir Ö, McCammon JA, Miao Y, Smith JC (2018) Ensemble docking in drug discovery. Biophys J 114(10):2271–2278. https://doi.org/10.1016/j.bpj.2018.02.038
Bianco G, Forli S, Goodsell DS, Olson AJ (2016) Covalent docking using autodock: two-point attractor and flexible side chain methods. Protein Sci 25(1):295–301. https://doi.org/10.1002/pro.2733
Birkinshaw RW, Kjer-Nielsen L, Eckle SB, McCluskey J, Rossjohn J (2014) MAITs, MR1 and vitamin B metabolites. Curr Opin Immunol 26:7–13. https://doi.org/10.1016/j.coi.2013.09.007
Corbeil CR, Englebienne P, Moitessier N (2007) Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J Chem Inf Model 47(2):435–449. https://doi.org/10.1021/ci6002637
Corbeil CR, Englebienne P, Yannopoulos CG, Chan L, Das SK, Bilimoria D, Lheureux L, Moitessier N (2008) Docking ligands into flexible and solvated macromolecules. 2. Development and application of fitted 1.5 to the virtual screening of potential HCV polymerase inhibitors. J Chem Inf Model 48(4):902–909. https://doi.org/10.1021/ci700398h
de Ruyck J, Brysbaert G, Blossey R, Lensink MF (2016) Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem AABC 9:1–11. https://doi.org/10.2147/AABC.S105289
Eckle SB, Corbett AJ, Keller AN, Chen Z, Godfrey DI, Liu L, Mak JY, Fairlie DP, Rossjohn J, McCluskey J (2015) Recognition of vitamin B precursors and byproducts by mucosal associated invariant T cells. J Biol Chem 290(51):30204–30211. https://doi.org/10.1074/jbc.R115.685990
Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ (2016) Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11(5):905–919. https://doi.org/10.1038/nprot.2016.051
Ha EJ, Lwin CT, Durrant JD (2020) LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates. J Cheminform 12(1):69. https://doi.org/10.1186/s13321-020-00471-2
Hawkins PC, Warren GL, Skillman AG, Nicholls A (2008) How to do an evaluation: pitfalls and traps. J Comput Aided Mol Des 22(3–4):179–190. https://doi.org/10.1007/s10822-007-9166-3
Keller AN, Corbett AJ, Wubben JM, McCluskey J, Rossjohn J (2017a) MAIT cells and MR1-antigen recognition. Curr Opin Immunol 46:66–74. https://doi.org/10.1016/j.coi.2017.04.002
Keller AN, Eckle SB, Xu W, Liu L, Hughes VA, Mak JY, Meehan BS, Pediongco T, Birkinshaw RW, Chen Z, Wang H, D’Souza C, Kjer-Nielsen L, Gherardin NA, Godfrey DI, Kostenko L, Corbett AJ, Purcell AW, Fairlie DP, McCluskey J, Rossjohn J (2017b) Drugs and drug-like molecules can modulate the function of mucosal-associated invariant T cells. Nat Immunol 18(4):402–411. https://doi.org/10.1038/ni.3679
Lopez-Sagaseta J, Dulberger CL, McFedries A, Cushman M, Saghatelian A, Adams EJ (2013) MAIT recognition of a stimulatory bacterial antigen bound to MR1. J Immunol 191(10):5268–5277. https://doi.org/10.4049/jimmunol.1301958
McWilliam HE, Birkinshaw RW, Villadangos JA, McCluskey J, Rossjohn J (2015) MR1 presentation of vitamin B-based metabolite ligands. Curr Opin Immunol 34:28–34. https://doi.org/10.1016/j.coi.2014.12.004
Mohan V, Gibbs AC, Cummings MD, Jaeger EP, DesJarlais RL (2005) Docking: successes and challenges. Curr Pharm Des 11(3):323–333
Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594. https://doi.org/10.1021/jm300687e
Neves MA, Totrov M, Abagyan R (2012) Docking and scoring with ICM: the benchmarking results and strategies for improvement. J Comput Aided Mol Des 26(6):675–686. https://doi.org/10.1007/s10822-012-9547-0
Patel O, Kjer-Nielsen L, Le Nours J, Eckle SB, Birkinshaw R, Beddoe T, Corbett AJ, Liu L, Miles JJ, Meehan B, Reantragoon R, Sandoval-Romero ML, Sullivan LC, Brooks AG, Chen Z, Fairlie DP, McCluskey J, Rossjohn J (2013) Recognition of vitamin B metabolites by mucosal-associated invariant T cells. Nat Commun 4:2142. https://doi.org/10.1038/ncomms3142
Ramezani M, Shamsara J (2015) A cross-docking study on matrix metalloproteinase family. Antiinflamm Antiallergy Agents Med Chem 14(3):164–171
Ramezani M, Shamsara J (2018) An integrated structure- and pharmacophore-based MMP-12 virtual screening. Mol Divers 22(2):383–395. https://doi.org/10.1007/s11030-017-9804-1
Rudling A, Orro A, Carlsson J (2018) Prediction of ordered water molecules in protein binding sites from molecular dynamics simulations: the impact of ligand binding on hydration networks. J Chem Inf Model 58(2):350–361. https://doi.org/10.1021/acs.jcim.7b00520
Salmaso V, Sturlese M, Cuzzolin A, Moro S (2018) Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2. J Comput Aided Mol Des 32(1):251–264. https://doi.org/10.1007/s10822-017-0051-4
Scarpino A, Ferenczy GG, Keserű GM (2018) Comparative evaluation of covalent docking tools. J Chem Inf Model 58(7):1441–1458. https://doi.org/10.1021/acs.jcim.8b00228
Scarpino A, Petri L, Knez D, Imre T, Ábrányi-Balogh P, Ferenczy GG, Gobec S, Keserű GM (2021) WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors. J Comput Aided Mol Des 35(2):223–244. https://doi.org/10.1007/s10822-020-00371-5
Shamsara J (2016) CrossDocker: a tool for performing cross-docking using Autodock Vina. Springerplus 5(1):1
Shamsara J, Schüürmann G (2020) A machine learning approach to discriminate MR1 binders: the importance of the phenol and carbonyl fragments. J Mol Struct 1217:128459. https://doi.org/10.1016/j.molstruc.2020.128459
Shamsara E, Shamsara J (2017) Developing target-specific scoring using black-box optimisation. Int J Comput Biol Drug Des 10(1):12–23. https://doi.org/10.1504/ijcbdd.2017.082806
Singh R, Bhardwaj V, Das P, Purohit R (2020a) Natural analogues inhibiting selective cyclin-dependent kinase protein isoforms: a computational perspective. J Biomol Struct Dyn 38(17):5126–5135. https://doi.org/10.1080/07391102.2019.1696709
Singh R, Bhardwaj VK, Sharma J, Purohit R (2020b) Identification of novel and selective agonists for ABA receptor PYL3. Plant Physiol Biochem 154:387–395. https://doi.org/10.1016/j.plaphy.2020.05.005
Singh R, Bhardwaj V, Purohit R (2021) Identification of a novel binding mechanism of Quinoline based molecules with lactate dehydrogenase of Plasmodium falciparum. J Biomol Struct Dyn 39(1):348–356. https://doi.org/10.1080/07391102.2020.1711809
Śledź P, Caflisch A (2018) Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Struct Biol 48:93–102. https://doi.org/10.1016/j.sbi.2017.10.010
Sorensen J, Demir O, Swift RV, Feher VA, Amaro RE (2015) Molecular docking to flexible targets. Methods Mol Biol 1215:445–469. https://doi.org/10.1007/978-1-4939-1465-4_20
Sotriffer C (2018) Docking of covalent ligands: challenges and approaches. Mol Inform 37(9–10):e1800062. https://doi.org/10.1002/minf.201800062
Stierand K, Maass PC, Rarey M (2006) Molecular complexes at a glance: automated generation of two-dimensional complex diagrams. Bioinformatics 22(14):1710–1716. https://doi.org/10.1093/bioinformatics/btl150
Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49(11):3315–3321. https://doi.org/10.1021/jm051197e
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52(4):609–623. https://doi.org/10.1002/prot.10465
Zhang X, Wong SE, Lightstone FC (2014) Toward fully automated high performance computing drug discovery: a massively parallel virtual screening pipeline for docking and molecular mechanics/generalized Born surface area rescoring to improve enrichment. J Chem Inf Model 54(1):324–337. https://doi.org/10.1021/ci4005145
Zhu K, Borrelli KW, Greenwood JR, Day T, Abel R, Farid RS, Harder E (2014) Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J Chem Inf Model 54(7):1932–1940. https://doi.org/10.1021/ci500118s
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest, financial or otherwise.
Human and animal rights
This study does not include any human or animal involvement.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shamsara, J., Schüürmann, G. Improvement of binding pose prediction of the MR1 covalent ligands by inclusion of simple pharmacophore constraints and structural waters in the docking process. 3 Biotech 13, 279 (2023). https://doi.org/10.1007/s13205-023-03694-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13205-023-03694-w