Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), Madrid, Spain.
Adv Protein Chem Struct Biol. 2022;130:39-57. doi: 10.1016/bs.apcsb.2021.12.001. Epub 2022 Jan 17.
There are many computational approaches for predicting protein functional sites based on different sequence and structural features. These methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. They complement the more expensive and time-consuming experimental approaches by pointing them to possible candidate positions. In many cases they are jointly used to characterize the functional sites in proteins of biotechnological and biomedical interest and eventually modify them for different purposes. There is a clear trend towards approaches based on machine learning and those using structural information, due to the recent developments in these areas. Nevertheless, "classic" methods based on sequence and evolutionary features are still playing an important role as these features are strongly related to functionality. In this review, the main approaches for predicting general functional sites in a protein are discussed, with a focus on sequence-based approaches.
基于不同的序列和结构特征,有许多用于预测蛋白质功能位点的计算方法。这些方法对于应对填充着未表征蛋白质序列的数据库的序列泛滥至关重要。它们通过指向可能的候选位置来补充更昂贵和耗时的实验方法。在许多情况下,它们被联合用于表征具有生物技术和生物医学意义的蛋白质中的功能位点,并最终根据不同的目的对其进行修饰。由于这些领域的最新发展,基于机器学习和使用结构信息的方法具有明显的趋势。然而,基于序列和进化特征的“经典”方法仍然起着重要的作用,因为这些特征与功能密切相关。在这篇综述中,主要讨论了预测蛋白质中一般功能位点的方法,重点是基于序列的方法。