Abstract
Protein–ligand blind docking is a widely used method for studying the binding sites and poses of ligands and receptors in pharmaceutical and biological research. Recently, our new blind docking server named CB-Dock2 has been released and is currently being utilized by researchers worldwide. CB-Dock2 outperforms state-of-the-art methods due to its accuracy in binding site identification and binding pose prediction, which are enabled by its knowledge-based docking engine. This highly automated server offers interactive and intuitive input and output web interfaces, making it an efficient and user-friendly tool for the bioinformatics and cheminformatics communities. This chapter provides a brief overview of the methods, followed by a detailed guide on using the CB-Dock2 server. Additionally, we present a case study that evaluates the performance of protein–ligand blind docking using this tool.
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References
Salentin S, Schreiber S, Haupt VJ et al (2015) PLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res 43:W443–W447
Jacob L, Vert JP (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24:2149–2156
Hassan NM, Alhossary AA, Mu Y et al (2017) Protein-ligand blind docking using QuickVina-W with inter-process Spatio-temporal integration. Sci Rep 7:15451
Hetényi C, van der Spoel D (2009) Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci 11:1729–1737
Hetényi C, van der Spoel D (2006) Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett 580(5):1447–1450
Sánchez-Linares I, Pérez-Sánchez H, Cecilia JM et al (2012) High-throughput parallel blind virtual screening using BINDSURF. BMC Bioinform 13(Suppl 14):S13
Lee HS, Zhang Y (2012) BSP-SLIM: a blind low-resolution ligand-protein docking approach using predicted protein structures. Proteins 80(1):93–110
Tunyasuvunakool K, Adler J, Wu Z et al (2021) Highly accurate protein structure prediction for the human proteome. Nature 596:590–596
Baek M, DiMaio F, Anishchenko I et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373:871–876
Rask-Andersen M, Almén MS, Schiöth HB (2011) Trends in the exploitation of novel drug targets. Nat Rev Drug Discov 10:579–590
Singh V, Mizrahi V (2017) Identification and validation of novel drug targets in mycobacterium tuberculosis. Drug Discov Today 22:503–509
Grosdidier A, Zoete V, Michielin O (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39:W270–W277
Qi W, Peng Z, Yang Zhang JY (2018) COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic Acids Res 46:313–338
Zhang W, Bell EW, Yin M et al (2020) EDock: blind protein-ligand docking by replica-exchange Monte Carlo simulation. J Cheminform 12:37
Labbé CM, Rey J, Lagorce D et al (2015) MTiOpenScreen: a web server for structure-based virtual screening. Nucleic Acids Res 43:W448–W454
Liu Y, Yang X, Gan J et al (2022) CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res 50:W159–W164
Liu Y, Grimm M, Dai WT, Hou MC, Xiao ZX, Cao Y (2020) CB-Dock: a web server for cavity detection-guided protein–ligand blind docking. Acta Pharmacol Sin 41:138–144
Cao Y, Dai W, Miao Z (2018) Evaluation of protein–ligand docking by Cyscore. Methods Mol Biol 1762:233–243
Cao Y, Li L (2014) Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model. Bioinformatics 30:1674–1680
Trott O, Olson A (2010) Autodock vina: improving the speed and accuracy of docking. J Comput Chem 31(2):455–461
Huey R, Morris GM, Forli S (2012) Using AutoDock 4 and AutoDock Vina with AutoDockTools: a tutorial. The Scripps Research Institute Molecular 32
Trott O, Olson AJ (2010) Software news and update AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461
Yang X, Liu Y, Gan J et al (2022) FitDock: protein-ligand docking by template fitting. Brief Bioinform 23(3):bbac087
Hartshorn MJ, Verdonk ML, Chessari G et al (2007) Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem 50(4):726–741
Bienfait B, Ertl P (2013) JSME: a free molecule editor in JavaScript. J Cheminform 5:24
Liu JL, Miao ZC, Li L et al (2016) DRSP: a structural database for single residue substitutions in PDB. Prog Biochem Biophys 43:810–816
Dolinsky TJ, Czodrowski P, Li H et al (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:W522–W525
Cao Y, Song L, Miao Z et al (2011) Improved side-chain modeling by coupling clash-detection guided iterative search with rotamer relaxation. Bioinformatics 27:785–790
Perola E, Herman L, Weiss J (2012) Development of a rule-based method for the assessment of protein Druggability. J Chem Inf Model 52:1027–1038
Liu N, Xu Z (2019) Using LeDock as a docking tool for computational drug design. IOP Conf Ser Earth Environ Sci 218:012143
Burley SK, Bhikadiya C, Bi C et al (2021) RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res 49:D437–D451
O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33
Krucinska J, Lombardo MN, Erlandsen H et al (2022) Structure-guided functional studies of plasmid-encoded dihydrofolate reductases reveal a common mechanism of trimethoprim resistance in Gram-negative pathogens. Commun Biol 5:459
Klon AE, Héroux A, Ross LJ et al (2002) Atomic structures of human Dihydrofolate reductase complexed with NADPH and two lipophilic Antifolates at 1.09Å and 1.05Å resolution. J Mol Biol 320:677–693
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant [81973243].
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Liu, Y., Cao, Y. (2024). Protein–Ligand Blind Docking Using CB-Dock2. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_6
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DOI: https://doi.org/10.1007/978-1-0716-3441-7_6
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