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Predicting Protein-Protein Interfaces that Bind Intrinsically Disordered Protein Regions.

Abstract：

:A long-standing goal in biology is the complete annotation of function and structure on all protein-protein interactions, a large fraction of which is mediated by intrinsically disordered protein regions (IDRs). However, knowledge derived from experimental structures of such protein complexes is disproportionately small due, in part, to challenges in studying interactions of IDRs. Here, we introduce IDRBind, a computational method that by combining gradient boosted trees and conditional random field models predicts binding sites of IDRs with performance approaching state-of-the-art globular interface predictions, making it suitable for proteome-wide applications. Although designed and trained with a focus on molecular recognition features, which are long interaction-mediating-elements in IDRs, IDRBind also predicts the binding sites of short peptides more accurately than existing specialized predictors. Consistent with IDRBind's specificity, a comparison of protein interface categories uncovered uniform trends in multiple physicochemical properties, positioning molecular recognition feature interfaces between peptide and globular interfaces.

journal_name

J Mol Biol

journal_title

Journal of molecular biology

authors

Wong ETC,Gsponer J

doi

10.1016/j.jmb.2019.06.010

subject

Has Abstract

pub_date

2019-08-09 00:00:00

pages

3157-3178

issue

17

eissn

0022-2836

issn

1089-8638

pii

S0022-2836(19)30372-9

journal_volume

431

pub_type

杂志文章

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