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This study enhanced protein structure prediction by developing new automated methods for model selection and refinement. These advancements improved accuracy self-estimate (ASE) scores and overall prediction quality in the CASP12 assessment.

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3D modelingASEEMAQAaccuracy self estimateestimates of model accuracymodel quality assessmentprotein structure predictionrefinement

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Modeling

Background:

  • Template-Based Modeling (TBM) is crucial for protein structure prediction.
  • Improving model selection, accuracy self-estimate (ASE) scores, and refinement are key challenges.
  • The CASP12 experiment provided a benchmark for evaluating protein structure prediction methods.

Purpose of the Study:

  • To enhance Template-Based Modeling (TBM) methods for protein structure prediction.
  • To develop and integrate new automated methods for model scoring, ranking, and refinement.
  • To improve the accuracy and reliability of protein model predictions.

Main Methods:

  • Developed ModFOLD6_rank for global Quality Assessment (QA), model ranking, and local error detection.
  • Developed ReFOLD for iterative QA-guided refinement to fix errors in predicted models.
  • Integrated ModFOLD6_rank into the IntFOLD4-TS protocol for scoring multiple-template models.

Main Results:

  • The ModFOLD6_rank method improved model selection and ASE scores compared to previous approaches.
  • Error repair using ReFOLD led to an overall gain in prediction performance.
  • The IntFOLD4 server achieved high rankings in the CASP12 assessment (3rd/5th on server targets).

Conclusions:

  • The developed automated methods (ModFOLD6_rank and ReFOLD) represent a significant improvement in TBM.
  • Iterative refinement using ReFOLD is a valuable strategy for enhancing protein model quality.
  • The McGuffin group's manual predictions achieved top rankings, demonstrating the effectiveness of the integrated approach.