Estimating the Similarity between Protein Pockets

被引:13
作者
Eguida, Merveille [1 ]
Rognan, Didier [1 ]
机构
[1] Univ Strasbourg, Lab Innovat Therapeut, UMR7200, CNRS, F-67400 Illkirch Graffenstaden, France
关键词
binding site; alignment; off-target; library design; virtual screening; machine learning; artificial intelligence drug design; LIGAND-BINDING-SITES; BIOLOGICAL MACROMOLECULES; CRYSTALLIZATION ADDITIVES; COMPUTATIONAL ANALYSIS; EFFICIENT DETECTION; WEB SERVER; G-LOSA; PREDICTION; DRUG; IDENTIFICATION;
D O I
10.3390/ijms232012462
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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页数:24
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