MUFOLD-WQA: A new selective consensus method for quality assessment in protein structure prediction.

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Protein Structure Prediction

Background:

• Assessing protein tertiary structure prediction quality is crucial.
• Consensus quality assessment (QA) methods, using pairwise similarity to reference models, are effective.
• Reference model selection was a key differentiator in past Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments.

Purpose of the Study:

• To develop a novel consensus QA method, SelCon, for improved protein structure prediction.
• To address limitations in reference model selection within existing consensus QA approaches.

Main Methods:

• Developed SelCon, a consensus QA method with adaptive reference model selection.
• Implemented a weighting scheme for reference models, excluding those too similar or dissimilar to the candidate model.
• Validated SelCon using CASP7 and CASP8 datasets.

Main Results:

• SelCon demonstrated improved QA results compared to existing methods on CASP7 and CASP8 data.
• The MUFOLD-WQA server, utilizing SelCon, achieved top performance in CASP9 for global structure QA and top-model selection.
• Both official CASP9 assessments and in-house evaluations confirmed the server's high performance.

Conclusions:

• SelCon offers a significant advancement in protein structure quality assessment.
• Adaptive reference model selection and differential weighting are key to SelCon's success.
• The MUFOLD-WQA server's performance highlights the practical utility of SelCon in real-world prediction challenges.