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

Zhiquan He1, Wenji Ma2, Jingfen Zhang3

  • 1Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, MO 65211, USA. zhy78@mizzou.edu.

Tsinghua Science and Technology
|July 30, 2015
PubMed
Summary
This summary is machine-generated.

A new Hidden Markov Model (HMM) assesses protein sequence and structure compatibility. This protein structure quality assessment method, HMM.Z, outperforms existing tools in selecting accurate protein models.

Keywords:
Hidden Markov Model (HMM)protein structure predictionstructure quality assessment

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Protein structure Quality Assessment (QA) is crucial for predicting and analyzing protein structures.
  • The relationship between a protein's amino acid sequence and its 3D structure is fundamental to QA.
  • Existing QA methods often struggle to capture the intricate sequence-structure relationship.

Purpose of the Study:

  • To develop a novel Hidden Markov Model (HMM) for assessing protein sequence-structure compatibility.
  • To enhance the accuracy and reliability of protein structure Quality Assessment (QA).
  • To provide both global and local quality scores for comprehensive structure evaluation and refinement guidance.

Main Methods:

  • Developed a Hidden Markov Model (HMM) incorporating local structures (angular space), secondary structures, and sequence profiles.
  • The HMM encodes local structural information by jointly considering sequence and structure data.
  • The model generates global scores for overall structure quality and local scores for specific regions.

Main Results:

  • The developed HMM.Z model demonstrates superior performance in protein structure selection compared to state-of-the-art methods (OPUSCA, DFIRE, GOAP, RW).
  • HMM.Z achieved better overall selection performance on benchmark datasets.
  • The model effectively captures the complex relationship between protein sequence and structure.

Conclusions:

  • The novel HMM approach provides an effective method for protein structure Quality Assessment (QA).
  • HMM.Z offers improved accuracy in selecting correct protein models.
  • The model's ability to provide local scores aids in targeted structure refinement.