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ProQ3: Improved model quality assessments using Rosetta energy terms.

Karolis Uziela1, Nanjiang Shu1,2, Björn Wallner3

  • 1Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, 171 21 Solna, Sweden.

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|October 5, 2016
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We developed ProQRosFA and ProQRosCen, novel protein model quality assessment tools based on Rosetta energies. These methods, along with the combined ProQ3 predictor, outperform existing programs for protein structure prediction.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Structure Prediction

Background:

  • Protein model quality assessment is crucial for structure prediction.
  • Existing methods like ProQ2 rely on contact-based features.
  • Novel approaches are needed to improve accuracy and efficiency.

Purpose of the Study:

  • Introduce two new protein model quality assessment methods, ProQRosFA and ProQRosCen.
  • Evaluate their performance against state-of-the-art methods.
  • Develop a combined predictor, ProQ3, for enhanced accuracy.

Main Methods:

  • Developed ProQRosFA using Rosetta full-atom energy.
  • Developed ProQRosCen using Rosetta coarse-grained centroid energy.
  • Incorporated residue conservation and predicted secondary structure/surface area agreement.

Main Results:

  • ProQRosFA and ProQRosCen performance is comparable to ProQ2.
  • The new predictors significantly outperform other existing model quality assessment programs.
  • The combined predictor ProQ3 achieves superior performance over individual methods.

Conclusions:

  • ProQRosFA, ProQRosCen, and ProQ3 represent advancements in protein model quality assessment.
  • These tools offer improved accuracy for protein structure prediction.
  • Available as webserver and stand-alone programs at http://proq3.bioinfo.se/.