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A Protocol for Computer-Based Protein Structure and Function Prediction
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Selecting Near-Native Protein Structures from Predicted Decoy Sets Using Ordered Graphlet Degree Similarity.

Xu Han1, Li Li2, Yonggang Lu3

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China. hanxu16@lzu.edu.cn.

Genes
|February 14, 2019
PubMed
Summary

Selecting accurate protein structures from computational predictions is challenging. This study introduces a new method using contact map overlap and graphlets to identify near-native protein structures more effectively than existing approaches.

Keywords:
GR_scoredynamic programminggap penaltynear-native proteinprotein structure prediction

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Area of Science:

  • Computational structural biology
  • Bioinformatics
  • Protein structure prediction

Background:

  • Predicting protein tertiary structure from amino acid sequence is a fundamental challenge.
  • Ab initio methods generate numerous candidate structures (decoy sets), but selecting the most accurate near-native structure is difficult.

Purpose of the Study:

  • To develop a novel computational method for improved selection of near-native protein structures from decoy sets.
  • To enhance the accuracy of protein structure prediction using sequence-only information.

Main Methods:

  • A new method combining contact map overlap (CMO) and generalized graphlets to calculate a GR_score for assessing similarity between 3D decoy structures.
  • Dynamic programming with a gap penalty was employed for optimal alignment.
  • Ensemble clustering was used to group similar decoy structures based on GR_scores.

Main Results:

  • The proposed GR_score method was tested on CASP10 and CASP11 datasets.
  • The most frequent centroid structure from clusters was selected as the representative near-native structure.
  • The method demonstrated improved performance in selecting near-native structures compared to the SPICKER method.

Conclusions:

  • The novel method based on CMO and graphlets offers a more effective approach for identifying near-native protein structures from predicted decoy sets.
  • This advancement contributes to more accurate protein structure prediction in computational structural biology.