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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

An iterative self-refining and self-evaluating approach for protein model quality estimation.

Zheng Wang1, Jianlin Cheng

  • 1Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.

Protein Science : a Publication of the Protein Society
|November 8, 2011
PubMed
Summary

This study introduces an iterative method to enhance protein model quality assessment programs (MQAPs). The approach refines quality scores, improving accuracy and reducing errors in predicting protein structures without native templates.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Accurate prediction of protein tertiary structures is crucial for understanding protein function.
  • Evaluating protein model quality without native structures is a significant challenge in structural bioinformatics.
  • Current protein Model Quality Assurance Programs (MQAPs) have limitations in performance.

Purpose of the Study:

  • To develop and evaluate an iterative method for improving the performance of protein Model Quality Assurance Programs (MQAPs).
  • To assess the effectiveness of the proposed method in refining protein model quality scores and rankings.
  • To enable performance evaluation of MQAPs without relying on native structure information.

Main Methods:

  • An iterative score refinement approach was developed, combining original MQAP methods with pair-wise comparison clustering.
  • The method was applied to model quality assessment data from 30 MQAPs in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8).
  • Experiments were also conducted on CASP9 MQAP data to validate the method's effectiveness.

Main Results:

  • The iterative method significantly improved the average correlation between predicted and real quality scores for 25 out of 30 MQAPs.
  • Average loss was reduced for 28 out of 30 MQAPs, particularly for those with initially low correlations.
  • The hybrid method achieved high accuracy comparable to full pair-wise clustering but with reduced computational cost.

Conclusions:

  • The iterative refining method effectively enhances the performance of protein Model Quality Assurance Programs.
  • This approach offers a computationally efficient way to improve protein model quality assessment.
  • The method provides a means to evaluate MQAP performance without requiring knowledge of native protein structures.