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Refined template selection and combination algorithm significantly improves template-based modeling accuracy.

Ashish Runthala1, Shibasish Chowdhury1

  • 11 Department of Biological Sciences, Birla Institute of Technology and Science, Pilani-333031, India.

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|May 7, 2019
PubMed
Summary

A new template-ranking algorithm improves protein structure prediction by selecting reliable templates. This method enhances modeling accuracy, aiding in the development of near-native protein models.

Keywords:
CASPTBMprotein modelingtemplate rankingtemplate selection

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Comparative modeling is a popular protein structure prediction method.
  • Selecting optimal templates remains a significant challenge in comparative modeling.

Purpose of the Study:

  • To develop an effective template-ranking algorithm for improved protein structure prediction.
  • To enhance the reliability and accuracy of comparative modeling.

Main Methods:

  • Developed a template-ranking algorithm utilizing pairwise and multiple sequence alignments.
  • Incorporated sequence and structural information from template hits into scoring.
  • Evaluated the algorithm on CASP8, CASP9, and CASP10 targets.

Main Results:

  • The template-ranking algorithm significantly improved modeling accuracy.
  • Average improvements observed in GDT-TS (3.531), GDT-HA (4.814), and TM_Score (0.022).
  • Inclusion of diverse, structurally similar templates is crucial for accurate modeling.

Conclusions:

  • The developed algorithm effectively ranks and selects reliable templates for protein modeling.
  • Optimal template selection and model sampling are key for predicting near-native protein structures.
  • This approach advances the field of protein structure prediction.