Protein Function Analysis through Machine Learning.
Protein Function Analysis through Machine Learning.
复制标题
基于机器学习的蛋白质功能分析。
DOI：
10.3390/biom12091246
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发表时间：
2022-09-06
期刊：
影响因子：
5.5
通讯作者：
中科院分区：
文献类型：
--
作者：
几十年来，机器学习一直是计算生物学中用于阐明蛋白质功能的重要武器。随着最近新的最大似然方法和应用的蓬勃发展，新的最大似然方法已经被结合到计算生物学的许多领域中来处理蛋白质功能。我们研究了ML如何被集成到广泛的计算模型中，以提高预测精度并更好地理解蛋白质的功能。讨论的应用包括蛋白质结构预测，通过序列修改实现稳定性和药物特性的蛋白质工程，在蛋白质-配体结合方面的分子对接，包括变构效应，蛋白质-蛋白质相互作用和以蛋白质为中心的药物发现。为了量化蛋白质功能的潜在机制，需要一种考虑结构、灵活性、稳定性和动力学的整体方法，因为这些方面通过它们的相互依赖变得密不可分。蛋白质功能的另一个关键组成部分是构象动力学，它通常表现为蛋白质动力学。本文介绍了利用最大似然法生成具有代表性的构象系综及量化构象系综间差异的计算方法。这些主题中的每一个都强调了未来的机遇。
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
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