Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains. (15th December 2017)

• Record Type: Journal Article
• Authors: Zhang, Jian; Ma, Zhiqiang; Kurgan, Lukasz
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 4(2019)
• Page Start: 1250
• Page End: 1268
• Publication Date: 2017-12-15
• Subjects: protein–RNA interactions -- protein–DNA interactions -- protein–nucleic acid interactions -- protein–protein interactions -- DNA-binding residues -- RNA-binding residues; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbx168
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Proteins interact with a variety of molecules including proteins and nucleic acids. We review a comprehensive collection of over 50 studies that analyze and/or predict these interactions. While majority of these studies address either solely protein–DNA or protein–RNA binding, only a few have a wider scope that covers both protein–protein and protein–nucleic acid binding. Our analysis reveals that binding residues are typically characterized with three hallmarks: relative solvent accessibility (RSA), evolutionary conservation and propensity of amino acids (AAs) for binding. Motivated by drawbacks of the prior studies, we perform a large-scale analysis to quantify and contrast the three hallmarks for residues that bind DNA-, RNA-, protein- and (for the first time) multi-ligand-binding residues that interact with DNA and proteins, and with RNA and proteins. Results generated on a well-annotated data set of over 23 000 proteins show that conservation of binding residues is higher for nucleic acid- than protein-binding residues. Multi-ligand-binding residues are more conserved and have higher RSA than single-ligand-binding residues. We empirically show that each hallmark discriminates between binding and nonbinding residues, even predicted RSA, and that combining them improves discriminatory power for each of the five types of interactions. Linear scoring functions that combine these hallmarks offer good predictive performance of residue-level propensity for binding and provide intuitive interpretation of predictions. Better understanding of these residue-level interactions will facilitate development of methods that accurately predict binding in the exponentially growing databases of protein sequences.