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Protein-ligand binding site detection as an alternative route to molecular docking and drug repurposing

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
art. no. 20180004
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
  • Department of Sciences, Roma Tre University, 00146 Rome, Italy
autor
  • Department of Sciences, Roma Tre University, 00146 Rome, Italy
  • Department of Sciences, Roma Tre University, 00146 Rome, Italy
  • National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy
Bibliografia
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  • [5] Gaudreault F, Najmanovich RJ. FlexAID: revisiting docking on non-native-complex structures. J Chem Inf Model 2015;55:1323-36.
  • [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.
  • [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.
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  • [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.
  • [12] Kroemer RT. Structure-based drug design: docking and scoring. Curr Protein Pept Sci 2007;8:312-28.
  • [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.
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  • [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.
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  • [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.
  • [19] Viet Hung L, Caprari S, Bizai M, Toti D, Polticelli F. LIBRA: Ligand Binding Site Recognition Application. Bioinformatics 2015;31:4020-2.
  • [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.
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  • [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.
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-04c83150-1c79-4bf2-baf9-7479d42e8fa6
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