Liang Yuchao, Yang Siqi, Zheng Lei, Wang Hao, Zhou Jian, Huang Shenghui, Yang Lei, Zuo Yongchun
State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Comput Struct Biotechnol J. 2022 Jul 4;20:3503-3510. doi: 10.1016/j.csbj.2022.07.001. eCollection 2022.
Proteins are the executors of cellular physiological activities, and accurate structural and function elucidation are crucial for the refined mapping of proteins. As a feature engineering method, the reduction of amino acid composition is not only an important method for protein structure and function analysis, but also opens a broad horizon for the complex field of machine learning. Representing sequences with fewer amino acid types greatly reduces the complexity and noise of traditional feature engineering in dimension, and provides more interpretable predictive models for machine learning to capture key features. In this paper, we systematically reviewed the strategy and method studies of the reduced amino acid (RAA) alphabets, and summarized its main research in protein sequence alignment, functional classification, and prediction of structural properties, respectively. In the end, we gave a comprehensive analysis of 672 RAA alphabets from 74 reduction methods.
蛋白质是细胞生理活动的执行者，准确阐明其结构和功能对于蛋白质的精细图谱绘制至关重要。作为一种特征工程方法，氨基酸组成约简不仅是蛋白质结构和功能分析的重要方法，也为复杂的机器学习领域开辟了广阔前景。用更少的氨基酸类型来表示序列，极大地降低了传统特征工程在维度上的复杂性和噪声，并为机器学习提供了更具可解释性的预测模型，以捕捉关键特征。本文系统综述了约简氨基酸（RAA）字母表的策略和方法研究，并分别总结了其在蛋白质序列比对、功能分类和结构性质预测方面的主要研究。最后，我们对来自74种约简方法的672个RAA字母表进行了全面分析。
