4.7 Article

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co -evolutionary information

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BRIEFINGS IN BIOINFORMATICS
卷 24, 期 1, 页码 -

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OXFORD UNIV PRESS
DOI: 10.1093/bibibbac502

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intrinsic disorder; protein-protein interactions; protein-nucleic acids interactions; linear interacting peptides; protein function; molecular recognition features

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One of the key features of IDRs is to facilitate interactions between proteins and nucleic acids. There are different types of disordered binding regions, including MoRFs, SLiMs, and longer binding domains. Recent research has introduced a new class of disordered binding regions called LIPs, which undergo disorderto-order transition upon binding. However, there are currently no dedicated sequence-based predictors for LIPs.
One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein -binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorderto -order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein -binding domains. Although current predictors of MoRFs and disordered protein -binding regions could be used to identify some LIPs, there are no dedicated sequence -based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co -evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co -evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein -binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.cid.sdu.edu.cntdownload/CLIPt.

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