Computational advances in discovering cryptic pockets for drug discovery

被引：0

作者：

Bemelmans, Martijn P. ^{[1
,4
]}

Cournia, Zoe ^{[2
]}

Damm-Ganamet, Kelly L. ^{[3
]}

Gervasio, Francesco L. ^{[4
]}

Pande, Vineet ^{[1
]}

机构：

[1] Johnson & Johnson Innovat Med, Comp Aided Drug Design, Sil Discovery, Therapeut Discovery, Turnhoutseweg 30, B-2340 Beerse, Belgium

[2] Acad Athens, Biomed Res Fdn, 4 Soranou Ephesiou, Athens 11527, Greece

[3] Johnson & Johnson Innovat Med, Comp Aided Drug Design, Sil Discovery, Therapeut Discovery, 3210 Merryfield Row, San Diego, CA 92121 USA

[4] Univ Geneva, Sch Pharmaceut Sci, Rue Michel Servet 1, CH-1206 Geneva, Switzerland

来源：

CURRENT OPINION IN STRUCTURAL BIOLOGY | 2025年 / 90卷

基金：

瑞士国家科学基金会;

关键词：

cryptic pockets; allostery; mixed-solvent molecular dynamics; enhanced; sampling; ALLOSTERIC SITES; BINDING-SITES;

D O I：

10.1016/j.sbi.2024.102975

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：

A number of promising therapeutic target proteins have been considered "undruggable" due to the lack of well-defined ligandable pockets. Substantial research in protein dynamics has elucidated the existence of "cryptic" pockets that only exist transiently and become favorable for binding in the presence of a ligand. These pockets provide an avenue to target challenging proteins, inspiring the development of multiple computational methods. This review highlights established cryptic pocket modeling approaches like mixed solvent molecular dynamics and recent applications of enhanced sampling and AI-based methods in therapeutically relevant proteins.

引用

页数：10

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