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Methods for estimation of model accuracy in CASP12.

Arne Elofsson1, Keehyoung Joo2, Chen Keasar3

  • 1Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Box 1031, Solna, 171 21, Sweden.

Proteins
|October 5, 2017
PubMed
Summary
This summary is machine-generated.

New methods for estimating protein model accuracy show significant progress. Pure single model methods now outperform older approaches in selection, especially with contact-based quality measures.

Keywords:
CASPconsensus predictionsestimates of model accuracymachine learningprotein structure predictionquality assessment

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Reliable estimation of 3D protein model quality is crucial for the adoption of protein structure prediction.
  • Advancements in computational methods are continuously improving the accuracy of these predictions.

Purpose of the Study:

  • To present and evaluate the latest methods for estimating model accuracy (EMA) from leading groups in CASP12.
  • To compare the performance of pure single model EMA methods against quasi-single and consensus methods.

Main Methods:

  • Analysis of methods submitted by top-performing groups in the CASP12 experiment.
  • Evaluation of pure single model accuracy estimation techniques (e.g., MESHI, ProQ3, SVMQA).
  • Comparison with quasi-single (e.g., ModFOLD6) and consensus methods (e.g., Pcons, Pcomb-domain).

Main Results:

  • Pure single model EMA methods show clear progress since CASP11, with MESHI, ProQ3, and SVMQA outperforming the previous top method, ProQ2.
  • While superior in model selection, pure single model methods lag behind consensus methods in absolute quality estimation and local quality prediction.
  • Single model methods perform relatively better when employing contact-based quality measures like CAD and lDDT.

Conclusions:

  • Significant progress has been made in pure single model accuracy estimation for protein structures.
  • The choice of method depends on the specific task, with single model methods excelling in selection and consensus methods in absolute quality assessment.
  • Contact-based quality measures enhance the performance of single model accuracy estimation techniques.