The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index; 9 of the 16 sources its claims cite were checked for support.
Evolutionary-scale prediction of atomic level protein structure with a language model
Open accessAbstract
Artificial intelligence has the potential to open insight into the structure of proteins at the scale of evolution. It has only recently been possible to extend protein structure prediction to two hundred million cataloged proteins. Characterizing the structures of the exponentially growing billions of protein sequences revealed by large scale gene sequencing experiments would necessitate a break-through in the speed of folding. Here we show that direct inference of structure from primary sequence using a large language model enables an order of magnitude speed-up in high resolution structure prediction. Leveraging the insight that language models learn evolutionary patterns across millions of sequences, we train models up to 15B parameters, the largest language model of proteins to date. As the language models are scaled they learn information that enables prediction of the three-dimensional structure of a protein at the resolution of individual atoms. This results in prediction that is up to 60x faster than state-of-the-art while maintaining resolution and accuracy. Building on this, we present the ESM Metage-nomic Atlas. This is the first large-scale structural characterization of metagenomic proteins, with more than 617 million structures. The atlas reveals more than 225 million high confidence predictions, including millions whose structures are novel in comparison with experimentally determined structures, giving an unprecedented view into the vast breadth and diversity of the structures of some of the least understood proteins on earth.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper introduces ESM-2 protein language models up to 15B parameters and ESMFold, an end-to-end single-sequence structure predictor that reaches atomic-resolution accuracy while being up to 60x faster than state-of-the-art pipelines. It uses ESMFold to build the ESM Metagenomic Atlas, folding over 617 million MGnify90 sequences and identifying more than 225 million high-confidence structures, many novel relative to known sequences and structures.
Key findings
- Scaling ESM-2 from 8M to 15B parameters improves language modeling and structural information: validation perplexity falls from 10.45 to 6.37, unsupervised long-range contact precision (LR P@L) rises from 0.16 to 0.54, and structure-module-only LDDT rises from 0.48 to 0.72. The 15B model reaches TM-score 0.71 on CAMEO and 0.54 on CASP14, 0.064 higher than the 150M ESM-2 model on both.
- ESMFold achieves average TM-score 0.83 on CAMEO and 0.68 on CASP14, compared with AlphaFold2 at 0.88 and 0.85 and RosettaFold at 0.82 on CAMEO. On a single NVIDIA V100 GPU, ESMFold predicts a 384-residue protein in 14.2 seconds, 6x faster than a single AlphaFold2 model, with up to ~60x speedup on shorter sequences; MSA search alone can take over 10 minutes.
- The ESM Metagenomic Atlas folds over 617 million MGnify90 sequences in 2 weeks on ~2,000 GPUs, yielding ~365 million good-confidence predictions (mean pLDDT > 0.5 and pTM > 0.5) and ~225 million high-confidence predictions (mean pLDDT > 0.7 and pTM > 0.7). In a 1-million high-confidence sample, 76.8% have <90% sequence identity to UniRef90, 12.6% have no PDB structure with TM-score > 0.5, and 10.4% have both low structural similarity (TM-score ≤ 0.5) and no close sequence homolog (<30% identity).
- Confidence is well calibrated: for high-confidence predictions (pLDDT > 0.7), CAMEO accuracy is comparable to AlphaFold2 (ESMFold LDDT = 0.83, AlphaFold2 LDDT = 0.85); for very high confidence (pLDDT > 0.9), median all-atom RMSD95 is 1.42 Å and backbone RMSD95 is 0.94 Å.
Methods
- ESM-2 is a BERT-style transformer masked language model trained on UniRef50/UniRef90 sequences, scaled from 8M to 15B parameters, with models trained for up to 500K updates (15B for 270K updates) and evaluated on held-out UniRef50 clusters.
- ESMFold adds a 48-block folding head to a frozen 3B-parameter ESM-2, trained on ~25K PDB clusters covering ~325K experimentally determined chains plus ~12M AlphaFold2-predicted structures; evaluation uses temporally held-out CAMEO (194 proteins) and CASP14 (51 targets) test sets.
- Metagenomic analysis folded MGnify90 sequences of length 20–1024, covering 99% of the database, and used Foldseek, MMseqs2, blastp, and UMAP for structural and sequence similarity analyses.
Limitations
- The paper states that ESMFold was not trained on protein complexes, so complex prediction is out of distribution; it also notes that ablating MSAs for AlphaFold and RosettaFold is an artificial single-sequence setting because those models were not explicitly trained for single sequences.
- The paper states that calibration becomes critical as structure prediction scales, and that current models are far from the limit of scale in parameters, sequence data, and compute.
- Evident from the text: this is a bioRxiv preprint not certified by peer review; ESMFold is less accurate than AlphaFold2 overall on CAMEO (0.83 vs 0.88 TM-score) and CASP14 (0.68 vs 0.85), with a larger gap on hard CAMEO targets (0.45 vs 0.62).
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Claims the paper makes
71 Own result · 22 No basis given · 16 Own interpretation · 8 Own method · 5 Sources not found · 4 Supported · 3 Partly supported · 1 Couldn’t confirm · 1 No full text of the sources · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“the vast majority (76.8%) of high confidence predictions being separate from UniRef90 (32) by at least 90% sequence identity”
What the sources say
- Supportedcites [32] source
The citation is attached to UniRef90, identifying the reference database used for the citing paper’s comparison. The Introduction describes UniRef90 as clustering sequences at the 90% identity level; the 76.8% figure is the citing paper’s own result, not a result attributed to this source. (LLM, conf=0.90)
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Very high confidence predictions in the metagenomic atlas are expected to often be reliable at a resolution sufficient for insight similar to experimentally determined structures, such as into the biochemistry of active sites (61);”
What the sources say
- Partly supportedcites [61] sourceExperimental — accuracy not yet measured
The High-resolution models section says predicted models can meet demanding structural requirements and sometimes support ligand-binding studies; the Medium-resolution models section says predictions can help locate active sites. The paper does not assess predictions in the metagenomic atlas or establish that its very high-confidence predictions are often reliable enough to provide experimentally comparable insight into active-site biochemistry. Page numbers are unavailable in our copy. (LLM, conf=0.85)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“We fold over 617 million sequences from the MGnify90 database (25).”
What the sources say
- Partly supportedcites [25] sourceExperimental — accuracy not yet measured
The 'UPDATES TO THE MGnify PROTEIN DATABASE' section describes MGnify protein sequences clustered at 90% identity, supporting the identification of the database. It reports over 1.1 billion sequences in the full database, but does not give a count of 617 million for the 90%-clustered set. The citing authors' folding of those sequences is their own work, not a finding attributed to this source. (LLM, conf=0.84)
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In the paper
“In a comparison with AlphaFold-Multimer (45) on a dataset of 2,978 recent multimeric complexes deposited in the PDB, ESMFold achieves the same qualitative DockQ (39) categorization for 53.2% of chain pairs, despite not being trained on protein complexes (Fig. S6).”
What the sources say
- Supportedcites [45] Richard Evans , Michael O’Neill , Alexander Pritzel , Natasha Antropova , Andrew Senior , Tim Green , Augustin Žídek , Russ Bates , Sam Blackwell , Jason Yim , Olaf Ronneberger , Sebastian Bodenstein , Michal Zielinski , Alex Bridgland , Anna Potapenko , Andrew Cowie , Kathryn Tunyasuvunakool , Rishub Jain , Ellen Clancy , Pushmeet Kohli , John Jumper , and Demis Hassabis . Protein complex prediction with AlphaFold-Multimer, March 2022. URL https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2. Pages: 2021.10.04.463034 Section: New Results. source
The citation identifies AlphaFold-Multimer as the comparator. The Results (p. 5) describe this paper's AlphaFold-Multimer predictions and benchmarks. The 2,978-complex comparison and 53.2% figure are presented as the citing authors' own results, not findings attributed to this source. (LLM, conf=0.92)
- Supportedcites [39] source
Citation (39) is attached to “DockQ” and identifies the quality measure used for the categorization. The Conclusions describe DockQ as a protein–protein docking model quality score that closely recapitulates the four CAPRI quality classes; the citation is not offered as the source of the ESMFold comparison or its 53.2% result. (LLM, conf=0.93)
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In the paper
“This training also results in the emergence of structure in the models.”
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In the paper
“Here we show that direct inference of structure from primary sequence using a large language model enables an order of magnitude speed-up in high resolution structure prediction.”
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In the paper
“We discover that an atomic resolution structure prediction can be projected from the representations of the ESM-2 language models.”
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In the paper
“We are able to complete this characterization in 2 weeks on a heterogeneous cluster of 2,000 GPUs”
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“This results in an improvement in speed of up to 60x on the inference forward pass alone”
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In the paper
“The atlas reveals more than 225 million high confidence predictions”
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
“High confidence predictions are made for over 225M structures”
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- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
- It may state two separate claims as one. 32
- Probably not a substantial claim. 27
- The quote only partly states this claim. 1
- Borderline: one of the tests for a substantial claim is unclear. 1
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“This is the first large-scale structural characterization of metagenomic proteins, with more than 617 million structures.”
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
“We present the first evolutionary scale structural characterization of a metagenomic resource, folding practically all sequences in MGnify90 (25), over 617M proteins.”
What the sources say
- Could not verifycites [25] sourceExperimental — accuracy not yet measured
The ‘UPDATES TO THE MGnify PROTEIN DATABASE’ section describes more than 1.1 billion protein sequences and their clustering, not predicted structures. Searches for the ESM Metagenomic Atlas, 617 million structures, and protein folding found no passage addressing the claimed structural characterization. (LLM, conf=0.76)
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- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
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In the paper
“Our second test set consists of 51 targets from the CASP14 competition (37).”
What the sources say
- Partly supportedcites [37] sourceExperimental — accuracy not yet measured
The Introduction describes CASP14 modeling targets, supporting their use as a source for a test set. It reports 68 tertiary-structure modeling targets, but does not identify the citing authors’ 51-target selection or verify its size. (LLM, conf=0.85)
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“MGnify sequence MGYP000936678158 has no significant sequence matches to any entry in UniRef90, nor any significant matches via a jackhmmer (47) reference proteome search, but has a predicted structure conserved across many nucleases (PDB 5YET B, TM-score 0.68; PDB 3HR4 A, TM-score 0.67)”
What the sources say
- Supportedcites [47] source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“During method development (e.g. hyperparameter selection), we used a temporally held out validation set obtained from the Continuous Automated Model EvaluatiOn (CAMEO) server (36) by filtering from August 2021 to January 2022.”
What the sources say
- Supportedcites [36] source
The citation identifies the CAMEO platform from which the citing authors obtained data. The Introduction describes CAMEO’s automated evaluations and says its benchmarking results are publicly available for methods development. The August 2021–January 2022 filtering is the citing authors’ own procedure, not a claim attributed to this 2018 source. (LLM, conf=0.92)
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In the paper
“The resulting ESM-2 model family significantly outperforms previously state-of-the-art ESM-1b (a ∼ 650 million parameter model) at a comparable number of parameters, and on structure prediction benchmarks it also outperforms other recent protein language models (Table S3).”
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In the paper
“At 270K training steps the 8M parameter model has a perplexity of 10.45, and the 15B model reaches a perplexity of 6.37”
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In the paper
“This results in prediction that is up to 60x faster than state-ofthe-art while maintaining resolution and accuracy.”
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“This removes costly aspects of current state-of-the-art structure prediction pipelines, eliminating the need for a multiple sequence alignment, while at the same time greatly simplifying the neural architecture used for inference.”
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In the paper
“Attention patterns develop in ESM-2 that correspond to tertiary structure (Fig. 1A), and scaling leads to large improvements in the understanding of structure (Fig. 1B).”
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In the paper
“and ∼ 225 million predictions with high confidence (mean pLDDT > 0 . 7 and pTM > 0 . 7 ) corresponding to ∼ 36% of total structures folded (Fig. 3).”
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In the paper
“The first test is from CAMEO, consisting of all 194 test proteins from April 01, 2022 through June 25, 2022.”
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In the paper
“This results in a speed improvement for structure prediction of more than an order of magnitude while maintaining high resolution accuracy.”
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In the paper
“We were able to complete the predictions in 2 weeks on a cluster of approximately 2,000 GPUs (Appendix A.4.1).”
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In the paper
“On the CAMEO test set, language model perplexity has a Pearson correlation of -0.55 with the TM-score between the predicted and experimental structures; on CASP14, the correlation is -0.67 (Fig. 2B).”
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In the paper
“This results in 25,450 clusters covering a total of 325,498 chains.”
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In the paper
“ESMFold achieves competitive accuracy with RosettaFold on CAMEO, which averages a TM-score of 0.82.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The average long range P@L was 0.4287 with a standard deviation of 0.0028.”
plainn = 20- Asserted: passes — The authors put it forward as true.
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In the paper
“MGnify sequence MGYP004000959047 has no significant UniRef90 or jackhmmer reference proteome matches but its predicted structure has high similarity to experimental structures of lipid binding domains (PDB 6BYM A, TMscore 0.80; PDB 5YQP B, TM-score 0.78)”
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In the paper
“Using the search protocols released with AlphaFold2, including MSAs and templates, AlphaFold2 achieves 0.88 and 0.85 on CAMEO and CASP14 respectively.”
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In the paper
“For Hsu et al. (62) we generated a set of 13,477,259 structure predictions with AlphaFold2 using MSAs generated via the process in Rao et al. (40).”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The 15 billion parameter model reaches a TM-score of 0.71 on the CAMEO test set and 0.54 on the CASP14 test set, 0.064 points higher than the 150 million parameter ESM-2 model on both (Fig. 1E).”
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In the paper
“This characterization reveals the structures of hundreds of millions proteins that have been previously unknown, millions of which are novel in comparison to experimentally determined structures.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“ESMFold achieves an average TM-score of 0.83 on CAMEO and 0.68 on CASP14.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“No filtering is performed on these test sets, as ESMFold is able to make predictions on all sequences, including the length-2166 target T1044.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Because of the way the dataset is constructed, only 1.5% of the dataset is removed with this filter.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Language models greatly accelerate state-of-the-art high resolution structure prediction.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“On a single NVIDIA V100 GPU, ESMFold makes a prediction on a protein with 384 residues in 14.2 seconds, 6x faster than a single AlphaFold2 model.”
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Across all models (Table S3) there is a correlation of -0.99 between validation perplexity and CASP14 TM-score, and -1.00 between validation perplexity and CAMEO TM-score”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“On the CAMEO test set, ESMFold predictions have a median all-atom RMSD 95 (root-mean-squared deviation at 95% residue coverage) of 1.91 ˚ A and backbone RMSD 95 of 1.33 ˚ A.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For RoseTTAfold, the speed of the SE(3) Transformer dominates, especially at low sequence lengths.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These steps result in a total of 2978 clusters.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At low sequence lengths, ESMFold is dominated by language model performance, while the O ( N 3 ) computation of pairwise representations takes over at high sequence lengths.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Differentiation is greater in the ªmediumº and ªhardº classes, which have lower MSA depth and are better predicted by AlphaFold2.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We see an over 60x speed advantage for shorter protein sequences, and a reasonable speed advantage for longer protein sequences.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which we were able to do in 2 weeks time.”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unsupervised contact prediction results show that both RoPE and newer data significantly improve the results.”
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We estimate that on a homogeneous network GPU cluster of V100s, the entire 620 million sequences would take approximately 28,000 GPU days to fold”
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Language model perplexity and ESMFold pLDDT are both well correlated with actual structure prediction accuracy on CASP14 and CAMEO.”
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After 270K updates the 8M parameter model has a perplexity of 10.45, and the 15B model reaches a perplexity of 6.37.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After filtering, there are 104 sequences with MSA depth ≤ 100 , 70 sequences with MSA depth ≤ 10 , and 22 sequences with MSA depth = 1.”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“analyzed the high-confidence subset with mean pLDDT greater than 0.9, corresponding to ∼ 59K structures.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in a total of 3505 unique chain pairs.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We do observe a slight regression when sampling from UniRef90 clusters”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These capabilities develop as a function of scale, with greater capabilities emerging as computation, data, and number of parameters increase.”
What the sources say
- Not matchedcites 19±22
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Within a protein family, structure and function can be inferred from the patterns in sequences (4, 5).”
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that improvement on sequences with high evolutionary depth saturates at lower model scales, and improvement on sequences with low evolutionary depth continues as models increase in size.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“language models of text, despite the simplicity of their training objectives, such as filling in missing words or predicting the next word, are shown to develop emergent capabilities that are connected to the underlying meaning of the text.”
What the sources say
- Not matchedcites 19±22
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the language models are scaled they learn information that enables prediction of the three-dimensional structure of a protein at the resolution of individual atoms.”
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
“We discover that atomic resolution structure prediction emerges and continues to improve in language models over the four orders of magnitude in parameter scale.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No filtering is performed on these test sets, as ESMFold is able to make predictions on all sequences, including the length-2166 target T1044.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All train sequences which match a validation sequence with 50% sequence identity under this search are removed from the train set.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We report results by testing 3D structure prediction models on two test sets, both chosen to be temporally held out from our supervised training set.”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All compared sequences are temporally held out from the training set.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“however we believe this difference is small and the UniRef90 cluster sampling is likely to help for the larger models.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The number of SE(3) max-iterations are artificially limited to 20 (default 200) and no MSAs are used as input for these measurements.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, this only measures the network forward time, and does not include the time taken to compute sidechains with PyRosetta or search for MSAs.”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Since BERT style models have been shown to be amenable to very large batch sizes (67), we increased our effective batch size to 2M tokens.”
What the sources say
- Not matchedcites [67] Yang You , Jing Li , Sashank Reddi , Jonathan Hseu , Sanjiv Kumar , Srinadh Bhojanapalli , Xiaodan Song , James Demmel , Kurt Keutzer , and Cho-Jui Hsieh. Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes. page 38, 2020.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“even though ESMFold is not trained on protein complexes.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
AlphaFold2 predictions provide a valid reference for assessing the accuracy of ESMFold predictions on metagenomic proteins.
“We also assess calibration against AlphaFold predictions on metagenomic proteins.”
“On a random subset of ∼ 4K metagenomic sequences, there is a high correlation (Pearson r = 0.79) between ESMFold pLDDT and the LDDT to AlphaFold2 predictions (Fig. 3A).”
The paper relies on this without support.
The MGnify90 database is representative of metagenomic protein diversity.
“We fold over 617 million sequences from the MGnify90 database (25).”
The paper relies on this without support.
The PDB database provides a comprehensive reference for experimentally determined protein structures.
“no structure is found with TM-score over 0.5 in the PDB database”
“no experimentally determined structure with a similar fold could be identified”
The paper relies on this without support.
Absolute positional encoding methods don't extrapolate well beyond the context window they are trained on.
“However, absolute positional encoding methods don't extrapolate well beyond the context window they are trained on.”
The paper relies on this without support.
Orphan proteins are very challenging for current structure prediction models due to a lack of evolutionary information.
“Due to a lack of evolutionary information, these sequences can be very challenging for current structure prediction models.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Evo also reaches competitive performance with leading protein-specific language models (41, 46–48) (Fig. 2B).”
From Sequence modeling and design from molecular to genome scale with Evo (2024) · cites this paper as [47]
The cited source says what the claim attributes to it.
In the citing paper
“Such modeling paradigms have been used successfully in the biomolecular realm, for example, to predict three-dimensional (3D) molecular structures from sequences32–34 and interactions between different molecular components.35–38”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [34]
In the citing paper
“are the best and most accurate answer to the structure prediction problem to date”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [19]
In the citing paper
“The unprecedented accuracy of AlphaFold2,127 RosettaFold,128 and others129,130 offers, at least in principle, the opportunity to virtually screen any protein of interest with the structure-based approach, irrespective of its a priori ligandability (after AlphaFold2, the structural coverage of the human proteome increased from 48% to 76% of the total residues).131”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [129]
In the citing paper
“This difficulty is amplified by the fact that the repository of publicly available structures, consisting of the combined databases of AFDB and the ESMatlas38, is approaching a billion entries.”
From Clustering predicted structures at the scale of the known protein universe (2023) · cites this paper as [38]
In the citing paper
“the ESM Atlas contains over 617 million metagenomic structures predicted by ESMFold4.”
From Fast and accurate protein structure search with Foldseek (2024) · cites this paper as [4]
In the citing paper
“Our current proof-of-concept model is considerably smaller—by 200 times—than the largest published protein language model (49).”
From DNA language models are powerful predictors of genome-wide variant effects (2023) · cites this paper as [49]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 4. Evolutionary-scale structural characterization of metagenomics · p. 7
“PDB 3HR4 A, TM-score 0.67”
§ Evolutionary-scale prediction of atomic level protein structure with a language model · p. 9
“The predicted structure of MGYP000936678158 aligns to an experimental structure from a bacterial nuclease (light brown, PDB: 3H4R)”
Citations
Cites
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Cited by
Sorted by citations
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Protein Language Models: Is Scaling Necessary?Preprint
Fournier, Q., Vernon, R. M., van der Sloot, A. M., Schulz, B., Chandar, S., & LANGMEAD, C. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.23.614603 · Cited by 42
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Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature
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A Robust Drug–Target Interaction Prediction Framework with Capsule Network and Transfer Learning
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Cross-prediction-powered inference
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Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges
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ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM2
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Semantic design of functional de novo genes from a genomic language model
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Self-citationDeorphanizing Peptides Using Structure Prediction
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A multimodal conversational agent for DNA, RNA and protein tasks
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Peptide-Aware Chemical Language Model Successfully Predicts Membrane Diffusion of Cyclic Peptides
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Artificial intelligence methods for protein folding and design
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Enhancing Antibody Language Models with Structural InformationPreprint
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Artificial intelligence-driven computational methods for antibody design and optimization
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Contrastive learning of T cell receptor representations
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ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models
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Using protein language models for protein interaction hot spot prediction with limited data
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From beasts to bytes: Revolutionizing zoological research with artificial intelligence
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Large-language models facilitate discovery of the molecular signatures regulating sleep and activity
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Protein Language Model Fitness Is a Matter of PreferencePreprint
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Killer yeasts: expanding frontiers in the age of synthetic biology
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Progress of AI-Driven Drug–Target Interaction Prediction and Lead Optimization
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Improved prediction of MHC-peptide binding using protein language models
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Artificial intelligence drives the digital transformation of pharma
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Protein language models can capture protein quaternary state
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Adapting protein language models for structure-conditioned designPreprint
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De novo-designed transmembrane proteins bind and regulate a cytokine receptor
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AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease
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Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning
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AI.zymes: A Modular Platform for Evolutionary Enzyme Design
Merlicek, L. P., Neumann, J., Lear, A., Degiorgi, V., de Waal, M. M., Cotet, T., Mulholland, A. J., & Bunzel, H. A. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202507031 · Cited by 30
AI-driven innovation in antibody-drug conjugate design
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Machine learning methods for predicting protein structure from single sequences
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Decoding biology with massively parallel reporter assays and machine learningPreprint
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AlphaFold and what is next: bridging functional, systems and structural biology
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A multichaperone condensate enhances protein folding in the endoplasmic reticulum
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Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?
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Artificial intelligence in bioinformatics: a survey
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CryoAtom improves model building for cryo-EM
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Protein–Peptide Docking with ESMFold Language Model
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RNA language models predict mutations that improve RNA function
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From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction
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Random, de novo, and conserved proteins: How structure and disorder predictors perform differently
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Top-Down Machine Learning of Coarse-Grained Protein Force Fields
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Generative AI for the Design of Molecules: Advances and Challenges
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A transcriptomic atlas of astrocyte heterogeneity across space and time in mouse and marmoset
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On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering
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A unified evolution-driven deep learning framework for virus variation driver prediction
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Biology-driven insights into the power of single-cell foundation models
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NetTCR-struc, a structure driven approach for prediction of TCR-pMHC interactions
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Deep generative modeling of temperature-dependent structural ensembles of proteins
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Computational design of superstable proteins through maximized hydrogen bonding
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Metadata
- Authors
- Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., & Rives, A.
- Affiliations
- New York University (US)
- Stanford University (US)
- Corresponding author
- Rives, Alexander
- ORCID
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2022-07-21
- Volume
- 379
- Issue
- 6637
- Pages
- 1123-1130
- ISSN
- 0036-8075 (ISSN-L)
- OpenAlex
- W4327550249
- Cited by
- 5,990
- References
- 71
- Citations by year
- 2026: 2468 · 2025: 1837 · 2024: 1170 · 2023: 485 · 2022: 23
- FWCI
- 460.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein structure prediction, large language models, protein evolution, atomic-scale structure, protein folding, structural diversity
- MeSH terms
- Machine Learning, Amino Acid Sequence, Protein Conformation, Proteins, Evolution, Molecular, Sequence Analysis, Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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