PNImodeler: web server for inferring protein-binding nucleotides from sequence data.

Jinyong Im,Wook Lee,De-Shuang Huang,Narankhuu Tuvshinjargal,Kyungsook Han,Byungkyu Park

doi:10.1186/1471-2164-16-s3-s6

Abstract

BackgroundInteractions between DNA and proteins are essential to many biological processes such as transcriptional regulation and DNA replication. With the increased availability of structures of protein-DNA complexes, several computational studies have been conducted to predict DNA binding sites in proteins. However, little attempt has been made to predict protein binding sites in DNA.ResultsFrom an extensive analysis of protein-DNA complexes, we identified powerful features of DNA and protein sequences which can be used in predicting protein binding sites in DNA sequences. We developed two support vector machine (SVM) models that predict protein binding nucleotides from DNA and/or protein sequences. One SVM model that used DNA sequence data alone achieved a sensitivity of 73.4%, a specificity of 64.8%, an accuracy of 68.9% and a correlation coefficient of 0.382 with a test dataset that was not used in training. Another SVM model that used both DNA and protein sequences achieved a sensitivity of 67.6%, a specificity of 74.3%, an accuracy of 71.4% and a correlation coefficient of 0.418.ConclusionsPredicting binding sites in double-stranded DNAs is a more difficult task than predicting binding sites in single-stranded molecules. Our study showed that protein binding sites in double-stranded DNA molecules can be predicted with a comparable accuracy as those in single-stranded molecules. Our study also demonstrated that using both DNA and protein sequences resulted in a better prediction performance than using DNA sequence data alone. The SVM models and datasets constructed in this study are available at http://bclab.inha.ac.kr/pnimodeler.

Highlights

Interactions between DNA and proteins are essential to many biological processes such as transcriptional regulation and DNA replication
Dataset We collected protein-DNA complexes which are determined by X-ray crystallography with a resolution of 3.0 Å or better from the Protein Data Bank (PDB) [12]
The performance of the prediction models was evaluated with respect to six measures: sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient

Summary

Introduction

Interactions between DNA and proteins are essential to many biological processes such as transcriptional regulation and DNA replication. With the increased availability of structures of protein-DNA complexes, several computational studies have been conducted to predict DNA binding sites in proteins. As many structures of protein-DNA complexes have been determined, theoretical and experimental studies have been conducted in recent years to study protein-DNA interactions, but Several computational methods have been developed to predict DNA- or RNA-binding residues in protein sequences using machine learning methods such as support vector machines (SVM) as classifiers. BindN [1] uses SVM to predict RNA- or DNA-binding residues in proteins based on the biochemical features of amino acids. DP-Bind [3] predicts DNA-binding residues in proteins and uses SVM with a position specific scoring matrix (PSSM) and amino acid properties.

Methods

Results

Conclusion