Department of Biotehnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India.
Curr Protein Pept Sci. 2011 Sep;12(6):490-502. doi: 10.2174/138920311796957630.
Machine learning algorithms have wide range of applications in bioinformatics and computational biology such as prediction of protein secondary structures, solvent accessibility, binding site residues in protein complexes, protein folding rates, stability of mutant proteins, and discrimination of proteins based on their structure and function. In this work, we focus on two aspects of predictions: (i) protein folding rates and (ii) stability of proteins upon mutations. We briefly introduce the concepts of protein folding rates and stability along with available databases, features for prediction methods and measures for prediction performance. Subsequently, the development of structure based parameters and their relationship with protein folding rates will be outlined. The structure based parameters are helpful to understand the physical basis for protein folding and stability. Further, basic principles of major machine learning techniques will be mentioned and their applications for predicting protein folding rates and stability of mutant proteins will be illustrated. The machine learning techniques could achieve the highest accuracy of predicting protein folding rates and stability. In essence, statistical methods and machine learning algorithms are complimenting each other for understanding and predicting protein folding rates and the stability of protein mutants. The available online resources on protein folding rates and stability will be listed.
机器学习算法在生物信息学和计算生物学中有广泛的应用，例如预测蛋白质的二级结构、溶剂可及性、蛋白质复合物中的结合位点残基、蛋白质折叠速率、突变蛋白的稳定性以及基于结构和功能的蛋白质分类。在这项工作中，我们重点关注两个预测方面：（i）蛋白质折叠速率和（ii）蛋白质突变后的稳定性。我们简要介绍了蛋白质折叠速率和稳定性的概念，以及可用的数据库、预测方法的特征和预测性能的度量。随后，将概述基于结构的参数的发展及其与蛋白质折叠速率的关系。基于结构的参数有助于理解蛋白质折叠和稳定性的物理基础。此外，还将提到主要机器学习技术的基本原理，并说明它们在预测蛋白质折叠速率和突变蛋白稳定性方面的应用。机器学习技术可以实现预测蛋白质折叠速率和稳定性的最高精度。从本质上讲，统计方法和机器学习算法是相辅相成的，有助于理解和预测蛋白质折叠速率和蛋白质突变体的稳定性。还将列出有关蛋白质折叠速率和稳定性的在线资源。
