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.
Keywords: Bioinformatics, Molecular descriptor, Prediction, Protein-RNA interaction, Protein-RNA recognition.
Current Topics in Medicinal Chemistry
Title:Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors
Volume: 16 Issue: 6
Author(s): Zhi-Ping Liu and Luonan Chen
Affiliation:
Keywords: Bioinformatics, Molecular descriptor, Prediction, Protein-RNA interaction, Protein-RNA recognition.
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.
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Cite this article as:
Liu Zhi-Ping and Chen Luonan, Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors, Current Topics in Medicinal Chemistry 2016; 16 (6) . https://dx.doi.org/10.2174/1568026615666150819110703
DOI https://dx.doi.org/10.2174/1568026615666150819110703 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
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