Balancing speed and precision in protein folding: a comparison of AlphaFold2, ESMFold, and OmegaFold

Abstract

The rapid development of protein structure prediction tools has created a need for systematic performance comparisons to guide method selection for different applications, particularly given the trade-offs between computational speed and prediction accuracy. We benchmarked AlphaFold2, ESMFold, and OmegaFold using 1,337 protein chains deposited in the Protein Data Bank between July 2022 and July 2024, ensuring no overlap with training data, and evaluated predictions using Root Mean Square Deviation (RMSD), Template Modeling score (TM-score), Global Distance Test–Total Score (GDT-TS) and predicted Local Distance Difference Test (pLDDT) metrics. AlphaFold2 achieved the highest median TM-score (0.96), highest median GDT-TS (94%), and lowest median RMSD (1.30 Å), outperforming ESMFold (TM-score 0.95, GDT-TS 90%, RMSD 1.74 Å) and OmegaFold (TM-score 0.93, GDT-TS 89%, RMSD 1.98 Å), with all tools showing reduced accuracy for proteins lacking family annotations, leucine-rich repeats, and NMR-determined structures, while alignment-free methods unexpectedly excelled at de novo designed proteins. The performance differences between methods were negligible for many proteins, suggesting that faster alignment-free predictors (10–30 times faster) can be sufficient for numerous applications; we developed LightGBM classifiers using ProtBert embeddings and confidence scores that accurately predict when AlphaFold2’s computational investment is warranted, providing practitioners with actionable guidance for selecting between speed and precision in structural pipelines.