Computational prediction of RNA-binding proteins and binding sites

Jingna Si, Jing Cui, Jin Cheng, Rongling Wu

Research output: Contribution to journalReview article

26 Citations (Scopus)

Abstract

Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%-8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein-RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein-RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

Original languageEnglish (US)
Pages (from-to)26303-26317
Number of pages15
JournalInternational journal of molecular sciences
Volume16
Issue number11
DOIs
StatePublished - Nov 3 2015

Fingerprint

RNA-Binding Proteins
Binding sites
RNA
Protein Binding
Binding Sites
proteins
Proteins
predictions
Transfer RNA
Gene expression
Learning systems
Carrier Proteins
ribonucleic acids
protein synthesis
Personnel
machine learning
labor
gene expression
Research Personnel
biology

All Science Journal Classification (ASJC) codes

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

Cite this

Si, Jingna ; Cui, Jing ; Cheng, Jin ; Wu, Rongling. / Computational prediction of RNA-binding proteins and binding sites. In: International journal of molecular sciences. 2015 ; Vol. 16, No. 11. pp. 26303-26317.
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Computational prediction of RNA-binding proteins and binding sites. / Si, Jingna; Cui, Jing; Cheng, Jin; Wu, Rongling.

In: International journal of molecular sciences, Vol. 16, No. 11, 03.11.2015, p. 26303-26317.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Computational prediction of RNA-binding proteins and binding sites

AU - Si, Jingna

AU - Cui, Jing

AU - Cheng, Jin

AU - Wu, Rongling

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N2 - Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%-8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein-RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein-RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

AB - Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%-8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein-RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein-RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

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