Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

Abstract:

:Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

journal_name

Curr Top Med Chem

authors

Liu ZP,Chen L

doi

10.2174/1568026615666150819110703

subject

Has Abstract

pub_date

2016-01-01 00:00:00

pages

604-15

issue

6

eissn

1568-0266

issn

1873-4294

pii

CTMC-EPUB-69654

journal_volume

16

pub_type

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