ArticleFrontiers in genetics2025
Balancing speed and precision in protein folding: a comparison of AlphaFold2, ESMFold, and OmegaFold.
Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Foldify: Web Application for Protein Structure Prediction.Journal of chemical information and modeling · 2026Article
- Artificial Intelligence-Assisted Design of Plant-Protein Meat Analogues: Integrating Nutrition, Functionality, and Fibrillation-Based Texturization.Comprehensive reviews in food science and food safety · 2026Review
- Protein folding stability estimation with explicit consideration of unfolded states.Nature communications · 2026Article
- Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.Computational and structural biotechnology journal · 2026Article
- Analysing open-source protein folding models for nanobody binding prediction.Frontiers in bioinformatics · 2026Article
- Artificial intelligence in vaccine development: applications, implementation, and future directions.Frontiers in cellular and infection microbiology · 2026Review
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- BAGEL: Protein engineering via exploration of an energy landscape.PLoS computational biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.