Abstract
Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.
Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2. We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.
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References
Walters WP, Stahl MT, Murcko MA (1998) Virtual screening—an overview. Drug Discov Today 3(4):160–178
Schwartz J, Awale M, Reymond JL (2013) SMIfp (SMILES fingerprint) chemical space for virtual screening and visualization of large databases of organic molecules. J Chem Info Model 53(8):1979–1989
Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42(6):1273–1280
Raymond JW, Gardiner EJ, Willett P (2002) RASCAL: calculation of graph similarity using maximum common edge subgraphs. Comput J 45(6):631–644
Bender A, Mussa HY, Glen RC (2004) Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance. J Chem Inf Comput Sci 44(5):1708–1718
Ballester PJ, Richards WG (2007) Ultrafast shape recognition to search compound databases for similar molecular shapes. J Comput Chem 28(10):1711–1723
Hawkins PCD, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82
Jain AN (2007) Surflex-dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J Comput-Aided Mol Des 21(5):281–306
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(2):455–461
Allen WJ, Balius TE, Mukherjee S, Brozell SR, Moustakus DT, Lang PT, Case DA, Kuntz ID, Rizzo RC (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36(15):1132–1156
Leach AR, Gillet VJ, Lewis RA, Taylor R (2010) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53(2):539–558
Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Info Model 45(1):160–169
Shin WH, Christoffer CW, Wang J, Kihara D (2016) PL-PatchSurfer2: improved local surface matching-based virtual screening method that is tolerant to target and ligand structure variation. J Chem Info Model 56(9):1676–1691
Novotni M, Klein R (2003) 3D Zernike descriptors for content based shape retrieval. In: Proceedings of eighth ACM symposium on solid modeling and applications, Washington, pp 216–225
Shin WH, Zhu X, Bures MG, Kihara D (2015) Three-dimensional compound comparison methods and their application in drug discovery. Molecules 20(7):12841–12962
Zhu X, Xiong Y, Kihara D (2015) Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0. Bioinformatics 31(5):707–713
Esquivel-Rodriguez J, Xiong Y, Han X, Gang S, Christoffer CW, Kihara D (2015) Navigating 3D electron microscopy maps with EM-SURFER. BMC Bioinf 16:181
Venkatraman V, Yang YD, Sael L, Kihara D (2009) Protein-protein docking using region-based 3D Zernike descriptors. BMC Bioinf 10:407
Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci U S A 98(18):10037–10041
Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and Cambridge Structural Database. J Chem Info Model 50(4):572–584
Sael L, Kihara D (2012) Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison. Proteins 80(4):1177–1185
Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R (2007) Computation of octanol−water partition coefficients by guiding an additive model with knowledge. J Chem Info Model 47(6):2140–2148
Heiden W, Moeckel G, Brickmann J (1993) A new approach to analysis and display of local lipophilicity/hydrophilicity mapped on molecular surfaces. J Comput-Aided Mol Des 7(5):503–514
O’Boyle NM, Banck M, James CA, Morley C, Vandermeersh T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminf 3:33
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(13):1605–1612
Li B, Turuvekere S, Agrawal M, La D, Ramani K, Kihara D (2008) Characterization of local geometry of protein surfaces with the visibility criterion. Proteins 71(2):670–683
Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman E (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768
Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Krüger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42(D1):D1083–D1090
Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH (2016) PubChem substance and compound databases. Nucleic Acids Res 44(D1):D1202–D1213
Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44(1):235–249
Volgt JH, Blenfalt B, Wang S, Nicklaus MC (2001) Comparison of the NCI open database with seven large chemical structural databases. J Chem Inf Comput Sci 41(3):702–712
Wheeler DL, Iida M, Dunn EF (2009) The role of Src in solid tumors. Oncologist 14(7):667–678
Dehm SM, Bonham K (2004) SRC gene expression in human cancer: the role of transcriptional activation. Biochem Cell Biol 82(2):263–274
Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801
Acknowledgment
We acknowledge Dan K. Ntala for proofreading the manuscript. This work was partly supported by grants from the National Science Foundation (IIS1319551). D.K. also acknowledges supports from National Institutes of Health (R01GM097528, R01GM123055) and the National Science Foundation (IOS1127027, DMS1614777).
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Shin, WH., Kihara, D. (2018). Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein–Ligand Docking Method. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_7
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DOI: https://doi.org/10.1007/978-1-4939-7756-7_7
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