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A novel protocol for three-dimensional structure prediction of RNA-protein complexes.

ABSTRACT: Three-dimensional structures of RNA-protein complexes are crucial for understanding their diverse functions. However, the number of the RNA-protein complex structures solved by experiments is still limited at present. To solve this problem, some computational protocols have been proposed to predict three-dimensional RNA-protein complex structures. But the prediction accuracies of these protocols are lower. The reason may be that these protocols don't fully incorporate the features of RNA-protein interfaces. Here we propose a novel computational protocol for three-dimensional RNA-protein complex structure prediction, 3dRPC, which applies new schemes to the discreteness of molecule and charge in docking algorithm and the construction of the reference state in scoring function in order to take account of the features of RNA-protein interfaces. This protocol achieves a high accuracy comparable to the well-developed algorithms for three-dimensional structure prediction of protein-protein complexes when tested on a RNA-protein docking benchmark.

SUBMITTER: Huang Y

PROVIDER: S-EPMC3664894 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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A novel protocol for three-dimensional structure prediction of RNA-protein complexes.

Huang Yangyu Y Liu Shiyong S Guo Dachuan D Li Lin L Xiao Yi Y

Scientific reports 20130101

Three-dimensional structures of RNA-protein complexes are crucial for understanding their diverse functions. However, the number of the RNA-protein complex structures solved by experiments is still limited at present. To solve this problem, some computational protocols have been proposed to predict three-dimensional RNA-protein complex structures. But the prediction accuracies of these protocols are lower. The reason may be that these protocols don't fully incorporate the features of RNA-protein ...[more]

PMID: 23712416

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Project description:Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available.

Dataset Information

A novel protocol for three-dimensional structure prediction of RNA-protein complexes.

Publications

A novel protocol for three-dimensional structure prediction of RNA-protein complexes.

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