Fontove Fernando, Del Rio Gabriel
C3 Consensus, Miguel Hidalgo, CDMX, Mexico City 11510, Mexico.
Department of Biochemistry and Structural Biology, Instituto de Fisiología Celular, UNAM, Mexico City 04510, Mexico.
Entropy (Basel). 2020 Apr 20;22(4):472. doi: 10.3390/e22040472.
Proteins are characterized by their structures and functions, and these two fundamental aspects of proteins are assumed to be related. To model such a relationship, a single representation to model both protein structure and function would be convenient, yet so far, the most effective models for protein structure or function classification do not rely on the same protein representation. Here we provide a computationally efficient implementation for large datasets to calculate residue cluster classes (RCCs) from protein three-dimensional structures and show that such representations enable a random forest algorithm to effectively learn the structural and functional classifications of proteins, according to the CATH and Gene Ontology criteria, respectively. RCCs are derived from residue contact maps built from different distance criteria, and we show that 7 or 8 Å with or without amino acid side-chain atoms rendered the best classification models. The potential use of a unified representation of proteins is discussed and possible future areas for improvement and exploration are presented.
蛋白质以其结构和功能为特征，并且假定蛋白质的这两个基本方面是相关的。为了对这种关系进行建模，用单一表示来同时对蛋白质结构和功能进行建模会很方便，然而到目前为止，用于蛋白质结构或功能分类的最有效模型并不依赖于相同的蛋白质表示。在这里，我们为大型数据集提供了一种计算高效的实现方法，用于从蛋白质三维结构计算残基簇类别（RCC），并表明这种表示能够使随机森林算法分别根据CATH和基因本体标准有效地学习蛋白质的结构和功能分类。RCC是从基于不同距离标准构建的残基接触图中推导出来的，并且我们表明，带有或不带有氨基酸侧链原子的7或8埃给出了最佳分类模型。讨论了蛋白质统一表示的潜在用途，并提出了未来可能的改进和探索领域。
