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Open Access

A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure

Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, MO 65211, USA.
Christopher S. Bond Life Sciences Center, University of Missouri, MO 65211, USA
Department of Computer Science, City University of Hong Kong, Hong Kong, China.
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Abstract

Protein structure Quality Assessment (QA) is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA. In this work, we developed a new Hidden Markov Model (HMM) to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities: (1) encoding local structure of each position by jointly considering sequence and structure information, and (2) assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP, and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.

Keywords

protein structure predictionstructure quality assessmentHidden Markov Model (HMM)

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Tsinghua Science and Technology
Volume 19 Issue 6,
December 2014
Pages 559-567
DOI: 10.1109/TST.2014.6961026
Cite this article:
He Z, Ma W, Zhang J, et al. A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure. Tsinghua Science and Technology, 2014, 19(6): 559-567. https://doi.org/10.1109/TST.2014.6961026

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Received: 18 June 2014
Accepted: 25 June 2014
Published: 20 November 2014
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