预测蛋白质结合残基：基于序列的方法在使用结构复合物与无规则蛋白质时的二分法。

Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins.

机构信息

School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.

出版信息

Bioinformatics. 2020 Sep 15;36(18):4729-4738. doi: 10.1093/bioinformatics/btaa573.

DOI:10.1093/bioinformatics/btaa573

PMID:32860044

Abstract

MOTIVATION

There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions).

RESULTS

Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to cross-over, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs.

AVAILABILITY AND IMPLEMENTATION

HybridPBRpred webserver, benchmark dataset and supplementary information are available at http://biomine.cs.vcu.edu/servers/hybridPBRpred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

有超过 30 种基于序列的蛋白质结合残基（PBR）预测器。它们使用结构注释或无序注释的训练数据集，这可能导致一种二分法，即结构/无序特异性模型可能无法交叉准确预测另一种类型。此外，研究表明，结构训练的预测器可以在与非蛋白质伴侣（核酸和小分子配体）相互作用的残基之间大量交叉预测 PBR。我们通过对具有结构和无序衍生注释的 PBR（用于分析交叉）以及蛋白质、核酸和小分子结合蛋白（用于研究交叉预测）的综合基准集，对无序和结构训练的代表性预测器进行了首次比较研究，来解决这些问题。

结果

有三种预测器提供了准确的结果：SCRIBER、ANCHOR 和 disoRDPbind。一些结构训练的方法可以对结构注释的蛋白质进行准确预测。同样，无序训练的预测器可以很好地预测无序注释的蛋白质。然而，考虑到的预测器通常无法交叉，除了 SCRIBER。我们的研究还表明，除了 SCRIBER 对结构注释的蛋白质和 disoRDPbind 对无序注释的蛋白质之外，几乎所有方法都可以大量交叉预测 PBR。我们提出了一种新的混合预测器，hybridPBRpred，它结合了 disoRDPbind 和 SCRIBER 的结果，以准确预测无序和结构注释的 PBR。hybridPBRpred 生成准确的结果，可以交叉结构和无序注释的蛋白质，并产生相对较少的交叉预测，为预测 PBR 提供了一种准确的替代方案。