Prediction of DNA-binding protein based on statistical and geometric features and support vector machines

Weiqiang Zhou,Hong Yan

doi:10.1186/1477-5956-9-s1-s1

Weiqiang Zhou, Hong Yan

Open Access

https://doi.org/10.1186/1477-5956-9-s1-s1

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Abstract

BackgroundPrevious studies on protein-DNA interaction mostly focused on the bound structure of DNA-binding proteins but few paid enough attention to the unbound structures. As more new proteins are discovered, it is useful and imperative to develop algorithms for the functional prediction of unbound proteins. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and extract useful statistical and geometric features, and use structural alignment and support vector machines for the prediction of unbound DNA-binding proteins.ResultsThe performance of our method is evaluated by discriminating a set of 104 DNA-binding proteins from 401 non-DNA-binding proteins. In the same test, the proposed method outperforms the other method using conditional probability. The results achieved by our proposed method for; precision, 83.33%; accuracy, 86.53%; and MCC, 0.5368 demonstrate its good performance.ConclusionsIn this study we develop an effective method for the prediction of protein-DNA interactions based on statistical and geometric features and support vector machines. Our results show that interface surface features play an important role in protein-DNA interaction. Our technique is able to predict unbound DNA-binding protein and discriminatory DNA-binding proteins from proteins that bind with other molecules.

Highlights

Previous studies on protein-DNA interaction mostly focused on the bound structure of DNA-binding proteins but few paid enough attention to the unbound structures
In order to evaluate the performance of the proposed method, we perform two experiments to discriminate the unbound DNA-binding proteins from the unbound non-DNA-binding proteins in Testset using the proposed method and the discriminatory function developed by Zhou and Yan [13] respectively
Performance of Support vector machine (SVM) classifier Structural alignment is carried out using TemLib as the template library for the Trainset and Testset

Summary

Introduction

Previous studies on protein-DNA interaction mostly focused on the bound structure of DNA-binding proteins but few paid enough attention to the unbound structures. We apply an alpha shape model to represent the surface structure of the protein-DNA complex and extract useful statistical and geometric features, and use structural alignment and support vector machines for the prediction of unbound DNA-binding proteins. Few of the previous studies paid enough attention to the 3D interface surface characteristics of the protein-DNA complex. Interface surface characteristics such as atom type, residue type, surface curvature, accessible surface area, etc. Liang et al first proposed to use alpha shape modeling to compute the molecular area, volume and to detect the inaccessible cavities in proteins [8,9]. Zhou and Yan applied alpha shape modeling and conditional probability in the study of proteinDNA interface properties [13,14]

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Discussion

Conclusion