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ModFOLD8: accurate global and local quality estimates for 3D protein models
Liam J McGuffin1, Fahd M F Aldowsari1, Shuaa M A Alharbi1
1School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK.
Nucleic Acids Research
|May 8, 2021
Summary
ModFOLD8 enhances 3D protein model quality estimation using advanced deep learning and new scoring methods. This leading server provides accurate global and local quality predictions for the bioscience community.
Area of Science:
- Structural bioinformatics
- Computational biology
- Protein structure prediction
Background:
- Accurate estimation of 3D protein model quality is crucial for the biosciences.
- The ModFOLD server has been a leading resource for protein model quality assessment for over a decade.
- Previous versions have been validated through rigorous community-wide experiments.
Purpose of the Study:
- To introduce major updates to the ModFOLD server, specifically ModFOLD8.
- To enhance the prediction accuracy of global and local quality for 3D protein models.
- To maintain ModFOLD's position as a premier tool for protein structure quality estimation.
Main Methods:
- Integration of newly developed scoring methods.
- Incorporation of advanced deep learning-based residue contact predictions.
- Combination of multiple pure-single and quasi-single model approaches.
Main Results:
- ModFOLD8 demonstrates successive increases in prediction accuracy.
- ModFOLD variants (ModFOLD7 and ModFOLD8) ranked among the top 10 quality estimation methods in CASP13 and CASP14.
- ModFOLD8 was utilized for SARS-CoV-2 protein model evaluation in CASP Commons 2020.
Conclusions:
- ModFOLD8 represents a significant advancement in predicting the quality of 3D protein models.
- The server's continuous improvement and validation ensure its utility for the bioscience community.
- ModFOLD8 is freely available, promoting wider accessibility and application in structural biology research.
