Protein-ligand binding site detection as an alternative route to molecular docking and drug repurposing
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Daniele Toti
Abstract
After the onset of the genomic era, the detection of ligand binding sites in proteins has emerged over the last few years as a powerful tool for protein function prediction. Several approaches, both sequence and structure based, have been developed, but the full potential of the corresponding tools has not been exploited yet. Here, we describe the development and classification of a large, almost exhaustive, collection of protein-ligand binding sites to be used, in conjunction with the Ligand Binding Site Recognition Application Web Application developed in our laboratory, as an alternative to virtual screening through molecular docking simulations to identify novel lead compounds for known targets. Ligand binding sites derived from the Protein Data Bank have been clustered according to ligand similarity, and given a known ligand, the binding mode of related ligands to the same target can be predicted. The collection of ligand binding sites contains more than 200,000 sites corresponding to more than 20,000 different ligands. Furthermore, the ligand binding sites of all Food and Drug Administration-approved drugs have been classified as well, allowing to investigate the possible binding of each of them (and related compounds) to a given target for drug repurposing and redesign initiatives. Sample usage cases are also described to demonstrate the effectiveness of this approach.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
[1] Katsila T, Spyroulias GA, Patrinos GP, Matsoukas M-T. Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 2016;14:177–84.10.1016/j.csbj.2016.04.004Search in Google Scholar PubMed PubMed Central
[2] Irwin JJ, Shoichet BK. ZINC – a free database of commercially available compounds for virtual screening. J Chem Inf Model 2005;45:177–82.10.1021/ci049714+Search in Google Scholar PubMed PubMed Central
[3] Nagaraj AB, Wang QQ, Joseph P, Zheng C, Chen Y, Kovalenko O, et al. Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment. Oncogene 2017;37:403–14.10.1038/onc.2017.328Search in Google Scholar PubMed PubMed Central
[4] Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009;30:2785–91.10.1002/jcc.21256Search in Google Scholar PubMed PubMed Central
[5] Gaudreault F, Najmanovich RJ. FlexAID: revisiting docking on non-native-complex structures. J Chem Inf Model 2015;55:1323–36.10.1021/acs.jcim.5b00078Search in Google Scholar PubMed
[6] Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 2016;11:905–19.10.1038/nprot.2016.051Search in Google Scholar PubMed PubMed Central
[7] Gaudreault F, Morency LP, Najmanovich RJ. NRGsuite: a PyMOL plugin to perform docking simulations in real time using FlexAID. Bioinformatics 2015;31:3856–8.10.1093/bioinformatics/btv458Search in Google Scholar PubMed PubMed Central
[8] Maia EH, Campos VA, Dos Reis Santos B, Costa MS, Lima IG, Greco SJ, et al. Octopus: a platform for the virtual high-throughput screening of a pool of compounds against a set of molecular targets. J Mol Model 2017;23:26.10.1007/s00894-016-3184-9Search in Google Scholar PubMed
[9] Di Muzio E, Toti D, Polticelli F. DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina. J Comput Aided Mol Des 2017;31:213–8.10.1007/s10822-016-0006-1Search in Google Scholar PubMed
[10] Wang JC, Lin JH. Scoring functions for prediction of protein-ligand interactions. Curr Pharm Des 2013;19:2174–82.10.2174/1381612811319120005Search in Google Scholar PubMed
[11] Du X, Li Y, Xia YL, Ai SM4, Liang J, Sang P, et al. Insights into protein-ligand interactions: mechanisms, models, and methods. Int J Mol Sci 2016;17:144.10.3390/ijms17020144Search in Google Scholar PubMed PubMed Central
[12] Kroemer RT. Structure-based drug design: docking and scoring. Curr Protein Pept Sci 2007;8:312–28.10.2174/138920307781369382Search in Google Scholar PubMed
[13] Tuffery P, Derreumaux P. Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches. J R Soc Interface 2012;7:20–33.10.1098/rsif.2011.0584Search in Google Scholar PubMed PubMed Central
[14] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res 2000;28:235–42.10.1093/nar/28.1.235Search in Google Scholar PubMed PubMed Central
[15] Roche DB, Tetchner SJ, McGuffin LJ. FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins. BMC Bioinformatics 2011;12:160.10.1186/1471-2105-12-160Search in Google Scholar PubMed PubMed Central
[16] Laskowski RA, Watson JD, Thornton JM. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res 2005;33:2.10.1093/nar/gki414Search in Google Scholar PubMed PubMed Central
[17] Wass MN, Kelley LA, Sternberg MJ. 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res 2010;38:2.10.1093/nar/gkq406Search in Google Scholar PubMed PubMed Central
[18] Yang J, Roy A, Zhang Y. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 2013;29:2588–95.10.1093/bioinformatics/btt447Search in Google Scholar PubMed PubMed Central
[19] Viet Hung L, Caprari S, Bizai M, Toti D, Polticelli F. LIBRA: Ligand Binding Site Recognition Application. Bioinformatics 2015;31:4020–2.10.1093/bioinformatics/btv489Search in Google Scholar PubMed
[20] Toti D., Viet Hung L., Tortosa V., Brandi V., Polticelli F.. LIBRA-WA: a web application for ligand binding site detection and protein function recognition. Bioinformatics. 2018;34:878–880.10.1093/bioinformatics/btx715Search in Google Scholar PubMed PubMed Central
[21] Caprari S, Toti D, Viet Hung L, Di Stefano M, Polticelli F. ASSIST: a fast versatile local structural comparison tool. Bioinformatics 2014;30:1022–4.10.1093/bioinformatics/btt664Search in Google Scholar PubMed
[22] Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem 2010;31:455–61.10.1002/jcc.21334Search in Google Scholar PubMed PubMed Central
[23] Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42:D1091–1097 .10.1093/nar/gkt1068Search in Google Scholar PubMed PubMed Central
[24] Shimamura T, Shiroishi M, Weyand S, Tsujimoto H, Winter G, Katritch V, et al. Structure of the human histamine H1 receptor complex with doxepin. Nature 2011;475:65–70.10.1038/nature10236Search in Google Scholar PubMed PubMed Central
[25] Kruse AC, Hu J, Pan AC, Arlow DH, Rosenbaum DM, Rosemond E, et al. Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature 2012;482:552–6.10.1038/nature10867Search in Google Scholar PubMed PubMed Central
[26] Laskowski RA, Swindells MB. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 2011;51:2778–86.10.1021/ci200227uSearch in Google Scholar PubMed
[27] Kim JH, Park SH, Moon YW, Hwang S, Kim D, Jo SH, et al. Histamine H1 receptor induces cytosolic calcium increase and aquaporin translocation in human salivary gland cells. J Pharmacol Exp Ther 2009;330:403–12.10.1124/jpet.109.153023Search in Google Scholar PubMed
[28] Lazar MA. Thyroid hormone receptors: multiple forms, multiple possibilities. Endocr Rev 1993;14:184–93.10.1210/edrv-14-2-184Search in Google Scholar PubMed
[29] Hodin RA, Lazar MA, Chin WW. Differential and tissue-specific regulation of the multiple rat c-erbA messenger RNA species by thyroid hormone. J Clin Invest 1990;85:101–5.10.1172/JCI114398Search in Google Scholar PubMed PubMed Central
[30] Pramfalk C, Pedrelli M, Parini P. Role of thyroid receptor β in lipid metabolism. Biochim Biophys Acta 2011;1812:929–37.10.1016/j.bbadis.2010.12.019Search in Google Scholar PubMed
[31] Koehler K, Gordon S, Brandt P, Carlsson B, Bäcksbro-Saeidi A, Apelqvist T, et al. Thyroid receptor ligands. 6. A high affinity “direct antagonist” selective for the thyroid hormone receptor. J Med Chem 2006;49:6635–7.10.1021/jm060521iSearch in Google Scholar PubMed
[32] Singh N, Jabeen T, Sharma S, Somvanshi RK, Dey S, Srinivasan A, et al. Specific binding of non-steroidal anti-inflammatory drugs (NSAIDs) to phospholipase A2: structure of the complex formed between phospholipase A2 and diclofenac at 2.7 Å resolution. Acta Crystallogr Sect D Biol Crystallogr 2006;62:410–6.10.1107/S0907444906003660Search in Google Scholar PubMed
[33] Sekula B, Bujacz A. Structural insights into the competitive binding of diclofenac and naproxen by equine serum albumin. J Med Chem 2016;59:82–9.10.1021/acs.jmedchem.5b00909Search in Google Scholar PubMed
[34] Gargiulo G, Capodanno D, Longo G, Capranzano P, Tamburino C. Updates on NSAIDs in patients with and without coronary artery disease: pitfalls, interactions and cardiovascular outcomes. Expert Rev Cardiovasc Ther 2014;12:1185–203.10.1586/14779072.2014.964687Search in Google Scholar PubMed
[35] Zloh M, Perez-Diaz N, Tang L, Patel P, Mackenzie LS. Evidence that diclofenac and celecoxib are thyroid hormone receptor beta antagonists. Life Sci 2016;146:66–72.10.1016/j.lfs.2016.01.013Search in Google Scholar PubMed
[36] Barelier S, Sterling T, O’Meara MJ, Shoichet BK. The recognition of identical ligands by unrelated proteins. ACS Chem Biol 2015;10:2772–84.10.1021/acschembio.5b00683Search in Google Scholar PubMed PubMed Central
Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/bams-2018-0004).
©2018 Walter de Gruyter GmbH, Berlin/Boston
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- Review
- Brain stem modeling at a system level – chances and limitations
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- Protein-ligand binding site detection as an alternative route to molecular docking and drug repurposing
- Comparison of the structure of Aβ(1-40) amyloid with the one in complex with polyphenol ε-viniferin glucoside (EVG)
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