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
9 of 16 cited sources checked · 5 supported · 0 flagged · 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)
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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)
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The cited sources support only part of this claim — read each source’s verdict below.
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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Every source the paper cites for this claim was checked and supports it.
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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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 training also results in the emergence of structure in the models.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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 discover that an atomic resolution structure prediction can be projected from the representations of the ESM-2 language models.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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 are able to complete this characterization in 2 weeks on a heterogeneous cluster of 2,000 GPUs”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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 an improvement in speed of up to 60x on the inference forward pass alone”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“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”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
The cited sources support only part of this claim — read each source’s verdict below.
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)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
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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- Supported
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
“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”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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 prediction that is up to 60x faster than state-ofthe-art while maintaining resolution and accuracy.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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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).”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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 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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“ESMFold achieves competitive accuracy with RosettaFold on CAMEO, which averages a TM-score of 0.82.”
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In the paper
“The average long range P@L was 0.4287 with a standard deviation of 0.0028.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
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
“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).”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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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.”
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In the paper
“ESMFold achieves an average TM-score of 0.83 on CAMEO and 0.68 on CASP14.”
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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.”
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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.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Language models greatly accelerate state-of-the-art high resolution structure prediction.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearBorderline substantial claim. It is unclear why the paper says it holds.
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
“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.”
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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”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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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.”
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In the paper
“For RoseTTAfold, the speed of the SE(3) Transformer dominates, especially at low sequence lengths.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“These steps result in a total of 2978 clusters.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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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.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“Differentiation is greater in the ªmediumº and ªhardº classes, which have lower MSA depth and are better predicted by AlphaFold2.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“We see an over 60x speed advantage for shorter protein sequences, and a reasonable speed advantage for longer protein sequences.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“which we were able to do in 2 weeks time.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Unsupervised contact prediction results show that both RoPE and newer data significantly improve the results.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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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”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Language model perplexity and ESMFold pLDDT are both well correlated with actual structure prediction accuracy on CASP14 and CAMEO.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.
Conflicting PDB identifier for the nuclease example
MinorThe main text identifies the comparison structure as PDB 3HR4 A, but the Figure 4 caption identifies it as PDB 3H4R. These are different identifiers for the same described example.
“PDB 3HR4 A, TM-score 0.67”
“The predicted structure of MGYP000936678158 aligns to an experimental structure from a bacterial nuclease (light brown, PDB: 3H4R)”
Citations
Cites
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012) · Science · doi:10.1126/science.1225829 · Cited by 17,116
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850
Flagged first, then by citations
Research works printed but not linked: 1
- Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , Łukasz Kaiser , and Illia Polosukhin . Attention Is All You Need. In Advances in Neural Information Processing Systems, pages 5998–6008, 2017. URL https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf.
Cited by
Sorted by citations
Items 701–800 of 5,663
Training Compute-Optimal Protein Language ModelsPreprint
Cheng, X., Chen, B., Li, P., Gong, J., Tang, J., & Song, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.06.597716 · Cited by 28
Assessment of Protein Complex Predictions in CASP16 : Are We Making Progress?
Zhang, J., Yuan, R., Kryshtafovych, A., Pei, J., Kretsch, R. C., Schaeffer, R. D., Zhou, J., Das, R., Grishin, N. V., & Cong, Q. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70068 · Cited by 31
Gonzales, M. E. M., Ureta, J. C., & Shrestha, A. M. S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btaf016 · Cited by 23
Prediction of protein subcellular localization in single cells
(2025) · doi:10.1038/s41592-025-02696-1 · Cited by 25
Vongsouthi, V., Georgelin, R., Matthews, D. S., Saunders, J. W., Lee, B. M., Ton, J., Damry, A. M., Frkic, R. L., Spence, M. A., & Jackson, C. J. (2025) · Science Advances · doi:10.1126/sciadv.ads8318 · Cited by 22
MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction
Glukhov, E., Kalitin, D., Stepanenko, D., Zhu, Y., Nguyen, T., Jones, G. S., Patsahan, T., Simmerling, C. L., Mitchell, J. C., Vajda, S., et al. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.05.011 · Cited by 23
Evaluating large language models for annotating proteins
Vitale, R., Bugnon, L. A., Fenoy, E., Milone, D. H., & Stegmayer, G. S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae177 · Cited by 22
Zhang, C., Wang, Q., Li, Y., Teng, A., Hu, G., Wuyun, Q., & Zheng, W. H. (2024) · Biomolecules · doi:10.3390/biom14121531 · Cited by 29
Li, Y., Hu, W., Zhang, F., & Li, Y. (2025) · Energy · doi:10.1016/j.energy.2025.135055 · Cited by 22
Wang, M., Patsenker, J., Li, H., Kluger, Y., & Kleinstein, S. H. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012153 · Cited by 22
Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes
Wang, X., Xu, K., Tan, Y., Liu, S., & Zhou, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043827 · Cited by 22
CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions
Ma, W., Bi, X., Jiang, H., Zhang, S., & Wei, Z. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3375621 · Cited by 22
Chinery, L., Hummer, A. M., Mehta, B. B., Akbar, R., Rawat, P., Slabodkin, A., Quý, K. L., Lund‐Johansen, F., Greiff, V., Jeliazkov, J. R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.26.586756 · Cited by 22
Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors
Cieślak, M., Danel, T., Krzysztynska‐Kuleta, O. I., & Kalinowska‐Tłuścik, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-58122-7 · Cited by 23
Li, Y., Gupta, V., Kilic, M. N. T., Choudhary, K. K., Wines, D., Liao, W., Choudhary, A., & Agrawal, A. (2024) · Digital Discovery · doi:10.1039/d4dd00199k · Cited by 29
Large-scale discovery, analysis and design of protein energy landscapes
Ferrari, Á. J. R., Dixit, S. M., Thibeault, J., Garcia, M., Houliston, S., Ludwig, R. W., Notin, P., Phoumyvong, C. M., Martell, C. M., Jung, M. D., et al. (2026) · Nature · doi:10.1038/s41586-026-10465-z · Cited by 22
Systems Human Immunology and AI: Immune Setpoint and Immune Health
Lei, Y., & Tsang, J. S. (2025) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090122-042631 · Cited by 27
Guan, J., Xie, P., Meng, D., Yao, L., Yu, D., Chiang, Y., Lee, T.-Y., & Wang, J. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.039 · Cited by 26
MGM as a Large‐Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts
Zhang, H., Zhang, Y., Kang, Z., Xiong, J., Yang, R., & Ning, K. (2026) · Advanced Science · doi:10.1002/advs.202513333 · Cited by 23
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps
Dai, X., Wu, L., Yoo, S., & Liu, Q. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad405 · Cited by 22
Exploring protein functions from structural flexibility using CABS ‐flex modeling
Nithin, C., Fornari, R. P., Pilla, S. P., Wróblewski, K., Zalewski, M., Madaj, R., Koliński, A., Macnar, J. M., & Kmiecik, S. (2024) · Protein Science · doi:10.1002/pro.5090 · Cited by 21
Cha, J. H., Kim, N., Ma, J., Lee, S.-H., Koh, G., Yang, S., Beck, S., Byeon, I., Lee, B., & Lee, I. (2025) · Cell Host & Microbe · doi:10.1016/j.chom.2025.10.001 · Cited by 23
Target identification and assessment in the era of AI
Pun, F. W., Podolskiy, D. I., Izumchenko, E., Mortlock, A. A., Oprea, T. I., Scheibye‐Knudsen, M., Fortney, K., Morgen, E. K., Ren, F., & Zhavoronkov, A. A. (2026) · Nature Reviews Drug Discovery · doi:10.1038/s41573-026-01412-8 · Cited by 31
Innovative strategies for modeling peptide–protein interactions and rational peptide drug design
Scharbert, L., & Strodel, B. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103083 · Cited by 23
Deep Learning in Antimicrobial Peptide Prediction
Lin, C., Xiong, S., Cui, F., Zhang, Z., Shi, H. W., & Wei, L. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00530 · Cited by 22
ADCNet: a unified framework for predicting the activity of antibody-drug conjugates
Chen, L., Li, B., Chen, Y., Lin, M., Zhang, S., Li, C., Pang, Y., & Wang, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf228 · Cited by 23
Protein functional site annotation using local structure embeddings
Derry, A., Tartici, A., & Altman, R. B. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2513219122 · Cited by 21
Sugar assimilation underlying dietary evolution of Neotropical bats
Camacho, J. J., Rivera, A. B., Peña, V., Morales‐Sosa, P., Robb, S. M. C., Russell, J., Yi, K., Wang, Y., Tsuchiya, D., Murillo‐García, O. E., et al. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02485-7 · Cited by 23
Evaluating the World Model Implicit in a Generative Model
Vafa, K., Chen, J., Rambachan, A., Kleinberg, J., & Mullainathan, S. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-0846 · Cited by 22
Siwek, J. C., Omelchenko, A. A., Chhibbar, P., Arshad, S., Rosengart, A., Nazarali, I., Patel, A., Nazarali, K., Rahimikollu, J., Tilstra, J. S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02723-1 · Cited by 27
Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data
Stock, M., Criekinge, W. V., Boeckaerts, D., Taelman, S., Haeverbeke, M. V., Dewulf, P., & Bernard De Baets, C. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012426 · Cited by 25
AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding
Clifford, J. N., Richardson, E., Peters, B., & Nielsen, M. (2025) · Science Advances · doi:10.1126/sciadv.adu1823 · Cited by 22
Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models
Tan, Y., Li, M., Zhou, B., Zhong, B., Zheng, L., Tan, P., Zhou, Z., Yu, H., Fan, G., & Hong, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00689 · Cited by 23
Protein structure and dynamics in the era of integrative structural biology
Grandori, R. (2023) · Frontiers in Biophysics · doi:10.3389/frbis.2023.1219843 · Cited by 22
Beyond Biosecurity by Taxonomic Lists: Lessons, Challenges, and Opportunities
Millett, P. D., Alexanian, T., Brink, K. R., Carter, S. R., Diggans, J., Palmer, M. J., Ritterson, R. S., Sandbrink, J. B., & Wheeler, N. E. (2023) · Health Security · doi:10.1089/hs.2022.0109 · Cited by 23
Emerging applications of artificial intelligence in pathogen genomics
Suster, C. J. E., Pham, D., Kok, J., & Sintchenko, V. (2024) · Frontiers in Bacteriology · doi:10.3389/fbrio.2024.1326958 · Cited by 26
Foundation models for bioinformatics
Chen, Z., Wei, L., & Gao, G. (2024) · Quantitative Biology · doi:10.1002/qub2.69 · Cited by 27
Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction
Genc, A. G., & McGuffin, L. J. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4196-5_7 · Cited by 22
Leveraging large language models for peptide antibiotic design
Guan, C., Fernandes, F. C., Franco, O. L., & de la Fuente‐Núñez, C. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102359 · Cited by 23
Recent advances in deep learning and language models for studying the microbiome
Yan, B., Nam, Y., Li, L., Deek, R. A., Li, H., & Ma, S. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2024.1494474 · Cited by 23
Deep learning for RNA structure prediction
Wang, J., Fan, Y., Hong, L., Hu, Z., & Li, Y. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102991 · Cited by 26
Tariq, A., Shoaib, M., Qu, L., Shoukat, S., Nan, X., & Song, J. (2025) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2025.177608 · Cited by 22
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Hou, X., Alagoz, Y., Welsch, R., Mortimer, M. D., Pogson, B. J., & Cazzonelli, C. I. (2023) · Journal of Experimental Botany · doi:10.1093/jxb/erad443 · Cited by 22
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Sun, H., Wang, J., Wu, H., Lin, S. L., Chen, J., Wei, J., Lv, S., Xiong, Y., & Wei, D.-Q. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01527 · Cited by 22
Insights into the inner workings of transformer models for protein function prediction
Wenzel, M. A., Grüner, E., & Strodthoff, N. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae031 · Cited by 24
High-throughput prediction of enzyme promiscuity based on substrate–product pairs
Xing, H., Cai, P., Liu, D., Han, M., Liu, J., Le, Y., Zhang, D., & Hu, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae089 · Cited by 20
Predicting adaptive immune receptor specificities by machine learning is a data generation problem
Mason, D. M., & Reddy, S. T. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.008 · Cited by 19
Baltoumas, F. A., Karatzas, E., Páez-Espino, D., Venetsianou, N. K., Aplakidou, E., Oulas, A., Finn, R. D., Ovchinnikov, S., Pafilis, E., Kyrpides, N. C., et al. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1157956 · Cited by 21
The Evolving T Cell Receptor Recognition Code: The Rules Are More Like Guidelines
Gray, G. I., Chukwuma, P. C., Eldaly, B., Perera, W. W. J. G., Brambley, C. A., Rosales, T. J., & Baker, B. M. (2025) · Immunological Reviews · doi:10.1111/imr.13439 · Cited by 22
pLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides
Kumar, N., Du, Z., & Li, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01338 · Cited by 22
Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models
Zhu, W., Ding, X., Shen, H., & Pan, X. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169010 · Cited by 21
Large language models for bioinformatics
Ruan, W., Lyu, Y., Zhang, J., Cai, J., Shu, P., Yang, G., Lu, Y., Gao, S., Wang, Y., Wang, P., et al. (2025) · Quantitative Biology · doi:10.1002/qub2.70014 · Cited by 37
Protein Design by Directed Evolution Guided by Large Language Models
Tran, T. P., & Hy, T. S. (2024) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2024.3439690 · Cited by 26
An integrative approach to protein sequence design through multiobjective optimization
Hong, L., & Kortemme, T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011953 · Cited by 25
Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises
Kosmopoulos, J. C., & Anantharaman, K. (2025) · Biochemistry · doi:10.1021/acs.biochem.5c00349 · Cited by 23
Evers, A., Guarnera, E., Pekar, L., & Zielonka, S. (2025) · mAbs · doi:10.1080/19420862.2025.2583210 · Cited by 23
Generalizable and scalable protein stability prediction with rewired protein generative models
Li, Z., & Luo, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-67609-4 · Cited by 24
Protein and genomic language models uncover the unexplored diversity of bacterial immunity
Mordret, E., Hervé, A., Tesson, F., Vaysset, H., Clabby, T., Loubat, A., Shomar, H., Planel, R., Lavenir, R., Cury, J., et al. (2026) · Science · doi:10.1126/science.adv8275 · Cited by 36
ESMFold Hallucinates Native-Like Protein SequencesPreprint
Jeliazkov, J. R., del Alamo, D., & Karpiak, J. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.23.541774 · Cited by 24
Leveraging ancestral sequence reconstruction for protein representation learning
Matthews, D. S., Spence, M. A., Mater, A. C., Nichols, J., Pulsford, S. B., Sandhu, M., Kaczmarski, J. A., Miton, C. M., Tokuriki, N., & Jackson, C. J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00935-2 · Cited by 22
DenAdel, A., Hughes, M., Thoutam, A., Gupta, A., Navia, A. W., Fusi, N., Raghavan, S., Winter, P. S., Amini, A. P., & Crawford, L. A. (2026) · Nature Methods · doi:10.1038/s41592-026-03120-y · Cited by 29
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Wang, H., Yang, Y., Zhou, X., Tian, J., Duan, X., Li, A., Lu, T. J., Li, X., Pei, D., & Xu, F. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101545 · Cited by 25
Wong, D. R., Hill, A. S., & Moccia, R. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf317 · Cited by 27
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Zou, S., Tao, T., Mahbub, S., Ellington, C. N., Algayres, R., Li, D., Zhuang, Y., Wang, H., Song, L., & Xing, E. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.28.625345 · Cited by 27
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Williams, D. O., & Fadda, E. (2023) · Glycobiology · doi:10.1093/glycob/cwad064 · Cited by 20
Rao, B., Yu, X., Bai, J., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01298 · Cited by 20
Deng, J., Gu, M., Zhang, P., Dong, M., Liu, T., Zhang, Y., & Liu, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00940-5 · Cited by 23
Wang, T., Wang, H., Lian, Q., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2310542120 · Cited by 20
Raisinghani, N., Alshahrani, M., Gupta, G., Tian, H., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00222 · Cited by 17
Wang, R., Zhao, Y. C., Xue, W., Xia, Y., & Liang, G. (2024) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2024.139284 · Cited by 19
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Erckert, K., & Rost, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-71783-8 · Cited by 21
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Risheng, Z., Ji, Z., Wang, S., & Chen, H. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104700 · Cited by 21
Bernatavicius, A., Šícho, M., Janssen, A. P. A., Hassen, A. K., Preuß, M., & van Westen, G. J. P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00309 · Cited by 25
p-IgGen: a paired antibody generative language model
Turnbull, O. M., Oglić, D., Croasdale-Wood, R., & Deane, C. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae659 · Cited by 27
Deep representation learning of protein-protein interaction networks for enhanced pattern discovery
Yan, R., Islam, M. T., & Xing, L. (2024) · Science Advances · doi:10.1126/sciadv.adq4324 · Cited by 21
Benchmarking protein language models for protein crystallization
Mall, R., Kaushik, R., Martinez, Z. A., Thomson, M. W., & Castiglione, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-86519-5 · Cited by 20
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 23
Li, J., Ding, C., Liu, D., Chen, L., & Jiang, J. (2025) · Digital Discovery · doi:10.1039/d5dd00072f · Cited by 27
Phage therapy against Klebsiella pneumoniae: An evolving perspective
Xing, J., Zhang, H., Zheng, L., Zhao, J., Zhang, Y., Xu, Z., Zhai, Y., Hu, G., Li, J., & Wu, H. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108689 · Cited by 22
Extracellular vesicles for developing targeted hearing loss therapy
Pan, X., Li, Y., Huang, P., Staecker, H., & He, M. (2024) · Journal of Controlled Release · doi:10.1016/j.jconrel.2023.12.050 · Cited by 23
Structure‐based computational design of antibody mimetics: challenges and perspectives
Chaves, E. J. F., Coêlho, D. F., da Cruz, C. H. B., Moreira, E. G., Simões, J., Nascimento‐Filho, M. J., & Lins, R. D. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13855 · Cited by 22
Easy and accurate protein structure prediction using ColabFoldPreprint
Kim, G., Lee, S., Karin, E. L., et al. (2023) · Research Square · doi:10.21203/rs.3.pex-2490/v1 · Cited by 20
GenePert: Leveraging GenePT Embeddings for Gene Perturbation PredictionPreprint
Chen, Y. T., & Zou, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.27.620513 · Cited by 24
SP-DTI: subpocket-informed transformer for drug–target interaction prediction
Liu, S., Liu, Y., Xu, H., Xia, J., & Li, S. Z. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf011 · Cited by 22
Wolf, B., Shehu, P., Brenker, L., von Bachmann, A.-L., Kroell, A.-S. H., Southern, N. T., Holderbach, S., Eigenmann, J., Aschenbrenner, S., Mathony, J., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02741-z · Cited by 24
Nguyen, T.-T., Jiang, Z., Nguyen, V., Le, N. Q. K., & Chua, M. C. H. (2025) · ACS Omega · doi:10.1021/acsomega.5c05484 · Cited by 24
Protein Structural Phylogenetics
Puente-Lelièvre, C., Malik, A. J., & Douglas, J. (2025) · Genome Biology and Evolution · doi:10.1093/gbe/evaf139 · Cited by 27
Learning Binding Affinities via Fine-Tuning of Protein and Ligand Language Models
Gorantla, R., Gema, A. P., Yang, I. X., Serrano‐Morrás, Á., Suutari, B. S., Juárez‐Jiménez, J., & Mey, A. S. J. S. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02063 · Cited by 22
Protein language models trained on biophysical dynamics inform mutation effects
Hou, C., Zhao, H., & Shen, Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2530466123 · Cited by 27
Chen, S., Tang, Z., You, L., & Chen, C. Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112209 · Cited by 20
Sutherland, C. A., Stevens, D. M., Seong, K., Wei, W., & Krasileva, K. V. (2025) · The Plant Cell · doi:10.1093/plcell/koaf109 · Cited by 20
Protein Language Models: Applications and Perspectives
Leclercq, M., & Droit, A. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00506 · Cited by 29
Zhu, J., Li, Z., Tong, H., Lu, Z., Zhang, N.-J., Wei, T., & Chen, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad429 · Cited by 22
Tan, Y., Li, M., Zhou, Z., Tan, P., Yu, H., Fan, G., & Liang, H. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00884-3 · Cited by 22
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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