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Alpha&ESMhFolds: An Updated Web Server for the Comparison, Evaluation, and Annotation of Human AlphaFold2 and ESMFold
Manfredi Matteo1, Vazzana Gabriele1, Castrense Savojardo1
1Biocomputing Group, University of Bologna, Italy; Dept. of Pharmacy and Biotechnology, University of Bologna, Italy.
Journal of Molecular Biology
|January 25, 2026
Summary
We updated the Alpha&ESMhFolds database with new UniProt data, functionally characterizing protein models. Both AlphaFold2 and ESMFold show high confidence in Pfam-annotated regions, aiding downstream analysis.
Area of Science:
- Proteomics
- Structural Bioinformatics
- Computational Biology
Background:
- Human reference proteome modeling relies on predictive tools like AlphaFold2 and ESMFold.
- These methods differ and performance varies with available experimental data.
- A public database previously stored paired predicted models for comparative insights.
Purpose of the Study:
- Update the Alpha&ESMhFolds database with the latest UniProt release (2025_04).
- Functionally characterize protein models by mapping Pfam entries to 3D structures.
- Introduce external quality assessment metrics for model evaluation and comparison.
Main Methods:
- Database update incorporating UniProt (2025_04) data.
- Functional characterization via Pfam domain mapping onto predicted protein structures.
- Implementation of external quality assessment metrics for model evaluation.
Main Results:
- Models were updated and functionally characterized.
- Both AlphaFold2 and ESMFold consistently achieve high pLDDT values in Pfam-annotated regions.
- Model quality and similarity did not affect convergence in Pfam-covered areas.
Conclusions:
- The updated Alpha&ESMhFolds database provides a valuable resource for selecting optimal protein models.
- High confidence predictions from AlphaFold2 and ESMFold are observed in functionally annotated regions.
- The resource aids researchers in downstream analysis of predicted protein structures.
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