水准点(测量)
计算机科学
人工智能
序列(生物学)
蛋白质测序
机器学习
计算生物学
数据挖掘
肽序列
生物
遗传学
地理
大地测量学
基因
作者
Vít Škrhák,Marián Novotný,Christos P. Feidakis,Radoslav Krivák,David Hoksza
标识
DOI:10.1093/bioinformatics/btae745
摘要
Structure-based methods for detecting protein-ligand binding sites play a crucial role in various domains, from fundamental research to biomedical applications. However, current prediction methodologies often rely on holo (ligand-bound) protein conformations for training and evaluation, overlooking the significance of the apo (ligand-free) states. This oversight is particularly problematic in the case of cryptic binding sites (CBSs) where holo-based assessment yields unrealistic performance expectations.
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