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Molecules 2017, 22(12), 2079; doi:10.3390/molecules22122079

Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information

1,2
,
1,2
,
1,2,4,* , 3
and
1,2,*
1
School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
2
Tianjin University Institute of Computational Biology, Tianjin University, Tianjin 300350, China
3
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
4
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
*
Authors to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 22 November 2017 / Accepted: 24 November 2017 / Published: 28 November 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
View Full-Text   |   Download PDF [1548 KB, uploaded 28 November 2017]   |  

Abstract

DNA–protein interactions appear as pivotal roles in diverse biological procedures and are paramount for cell metabolism, while identifying them with computational means is a kind of prudent scenario in depleting in vitro and in vivo experimental charging. A variety of state-of-the-art investigations have been elucidated to improve the accuracy of the DNA–protein binding sites prediction. Nevertheless, structure-based approaches are limited under the condition without 3D information, and the predictive validity is still refinable. In this essay, we address a kind of competitive method called Multi-scale Local Average Blocks (MLAB) algorithm to solve this issue. Different from structure-based routes, MLAB exploits a strategy that not only extracts local evolutionary information from primary sequences, but also using predicts solvent accessibility. Moreover, the construction about predictors of DNA–protein binding sites wields an ensemble weighted sparse representation model with random under-sampling. To evaluate the performance of MLAB, we conduct comprehensive experiments of DNA–protein binding sites prediction. MLAB gives M C C of 0.392 , 0.315 , 0.439 and 0.245 on PDNA-543, PDNA-41, PDNA-316 and PDNA-52 datasets, respectively. It shows that MLAB gains advantages by comparing with other outstanding methods. M C C for our method is increased by at least 0.053 , 0.015 and 0.064 on PDNA-543, PDNA-41 and PDNA-316 datasets, respectively. View Full-Text
Keywords: DNA–protein binding sites; ensemble classifier; feature extraction; random sub-sampling; sparse representation model DNA–protein binding sites; ensemble classifier; feature extraction; random sub-sampling; sparse representation model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shen, C.; Ding, Y.; Tang, J.; Song, J.; Guo, F. Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information. Molecules 2017, 22, 2079.

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