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Systematic Exploitation of Multiple Receptor Conformations for Virtual Ligand Screening
Author(s) -
Giovanni Bottegoni,
Walter Rocchia,
Manuel Rueda,
Ruben Abagyan,
Andrea Cavalli
Publication year - 2011
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0018845
Subject(s) - virtual screening , computational biology , drug discovery , flexibility (engineering) , g protein coupled receptor , computer science , protocol (science) , set (abstract data type) , ligand (biochemistry) , receptor , relevance (law) , small molecule , functional diversity , data mining , biology , bioinformatics , genetics , medicine , mathematics , ecology , statistics , alternative medicine , pathology , political science , law , programming language
The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario.

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