Balancing Speed and Precision in Protein Folding: A Comparison of AlphaFold2, ESMFold, and OmegaFold

Petr Simecek

Keywords: Protein structure prediction, AlphaFold2, ESMFold, OmegaFold, structural bioinformatics, foundation models

TL;DR: We trained LightGBM models using ProtBert BFD embeddings and pLDDT scores to predict when AlphaFold2’s superior accuracy is critical versus when ESMFold or OmegaFold suffice with negligible performance differences for protein structure prediction.

Abstract: We present a systematic benchmark of AlphaFold2, ESMFold, and OmegaFold on 1,336 protein chains deposited in the PDB between July 2022 and July 2024, ensuring no overlap with the training data of any tool. As expected, AlphaFold2 achieves the highest median TM-score (0.96) and lowest median RMSD (1.30 Å), outperforming ESMFold (TM-score 0.95, RMSD 1.74 Å) and OmegaFold (TM-score 0.93, RMSD 1.98 Å). Crucially, however, many cases exist in which the performance gap among these methods is negligible, suggesting that the faster, alignment-free predictors (10-30 times faster) can be sufficient. We identify the sequence length, structural family, and experimental context features that drive substantial discrepancies in accuracy, and—leveraging ProtBert embeddings and per-residue confidence scores—train LightGBM classifiers that accurately predict when AlphaFold2’s added investment is warranted. Our framework thus provides actionable guidance for practitioners deciding between speed and precision in large-scale structural pipelines.

Submission Number: 167