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Status: Unchecked ×Keyword: protein language models ×Clear all

31 claims from 20 papers

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · Proceedings of the National Academy of Sciences · 2021

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   1. Unchecked“We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity.”
   2. Unchecked“Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

   Elnaggar, Heinzinger, Dallago et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021

   The authors trained six language models on huge protein sequence sets and showed their embeddings, used alone, could predict protein structure and location, with the best beating methods that need sequence alignments.

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   1. UncheckedSimplifying protein language model embeddings from unlabelled sequences showed they captured some biophysical features of proteins.“Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences.”
   2. UncheckedProtein language model embeddings alone, used as input, predicted secondary structure, cell location and membrane status with the stated accuracies.“We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=…”
   3. UncheckedThe best ProtTrans embeddings (ProtT5) predicted protein secondary structure better than the prior best method without alignments or evolutionary information.“For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches.”

3. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   ProtGPT2 is a deep unsupervised language model for protein design

   Ferruz, Schmidt and Höcker · Nature Communications · 2022

   The authors describe ProtGPT2, a language model trained on protein sequences that generates new protein sequences resembling natural ones, yet distantly related to them and covering unexplored regions of protein space.

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   1. UncheckedProteins generated by the ProtGPT2 language model show natural amino acid propensities, and disorder predictions suggest 88% are globular, like natural sequences.“The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences.”
   2. UncheckedDatabase searches suggest ProtGPT2's generated protein sequences are only distantly related to natural ones and sample unexplored regions of protein space.“Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space.”

4. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

   Thumuluri, Armenteros, Johansen, Nielsen and Winther · Nucleic Acids Research · 2022

   The authors update the DeepLoc tool to predict multiple subcellular locations per protein, using a protein language model, with better performance and interpretability, and release it as a webserver.

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   1. UncheckedDeepLoc 2.0, which uses a pre-trained protein language model, reports state-of-the-art performance in predicting where proteins sit in cells.“We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model.”
   2. UncheckedIn DeepLoc 2.0, the model's attention output along a protein sequence lines up well with where sorting signals are located.“We find that the attention output correlates well with the position of sorting signals.”

5. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Language models enable zero-shot prediction of the effects of mutations on protein function

   Meier, Rao, Verkuil, Liu, Sercu and Rives · bioRxiv (Cold Spring Harbor Laboratory) · 2021

   The paper shows that protein language models can predict how sequence changes affect function without task-specific training, rather than fitting a new model to each family of related sequences.

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   1. UncheckedProtein language models, used zero-shot with no experimental data or extra training, predict the functional effects of sequence variation at state-of-the-art level.“We show that using only zero-shot inference, without any supervision from experimental data or additional training, protein language models capture the functional effects of sequence variation, performing at state-of-the-art.”

6. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Single-sequence protein structure prediction using a language model and deep learning

   Chowdhury, Bouatta, Biswas et al. · Nature Biotechnology · 2022

   The authors built RGN2, a deep-learning system with a protein language model that predicts structure from a single sequence, aimed at cases where alignment-based tools such as AlphaFold2 struggle.

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   1. UncheckedOn average, RGN2 predicted structures of orphan proteins and some designed proteins better than AlphaFold2 and RoseTTAFold, using up to a millionfold less compute time.“On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 10 6 -fold reduction in compute time.”

7. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   High-resolution de novo structure prediction from primary sequence

   Wu, Ding, Wang et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2022

   The paper introduces OmegaFold, a method that predicts high-resolution protein structures from a single amino acid sequence, without needing multiple sequence alignments.

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   1. UncheckedOmegaFold, which predicts protein structure from a single sequence, is reported to beat RoseTTAFold and match AlphaFold2 on recently released structures.“Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structu…”
   2. UncheckedOmegaFold is reported to predict structures accurately for orphan proteins and for antibodies, whose sequence alignments tend to be noisy.“OmegaFold enables accurate predictions on orphan proteins that do not belong to any functionally characterized protein family and antibodies that tend to have noisy MSAs due to fast evolution.”

8. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2019

   A deep language model trained without labels on 250 million protein sequences learns representations that encode biological properties, structure and function, and support state-of-the-art predictions.

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   1. UncheckedA protein language model's internal representations are organised across scales, from amino acid chemistry up to distant evolutionary relationships between proteins.“The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins.”
   2. UncheckedA protein language model trained only on sequences is reported to encode secondary and tertiary structure, readable by simple linear projections.“Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections.”

9. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   MSA Transformer

   Rao, Liu, Verkuil et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2021

   The authors introduce a protein language model that takes a multiple sequence alignment as input, combining single-sequence language models with family-based methods, and report strong structure-learning performance.

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   1. UncheckedA protein language model fed aligned sets of related sequences beats leading unsupervised structure-learning methods by a wide margin, using far fewer parameters.“The performance of the model surpasses current state-of-the-art unsupervised structure learning methods by a wide margin, with far greater parameter efficiency than prior state-of-the-art protein language models.”

10. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

    Transformer protein language models are unsupervised structure learners

    Rao, Meier, Sercu, Ovchinnikov and Rives · bioRxiv (Cold Spring Harbor Laboratory) · 2020

    The paper reports that the attention maps of large Transformer protein language models capture residue contacts, and that the largest models outperform a state-of-the-art unsupervised contact prediction pipeline.

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    1. UncheckedThe paper reports that attention maps in Transformer protein language models learn which amino acids touch in 3D, despite training only on unlabelled sequences.“In this paper we demonstrate that Transformer attention maps learn contacts from the unsupervised language modeling objective.”
    2. UncheckedThe largest protein language models trained so far already beat a state-of-the-art unsupervised contact prediction pipeline, which they may be able to replace.“We find the highest capacity models that have been trained to date already outperform a state-of-the-art unsupervised contact prediction pipeline, suggesting these pipelines can be replaced with a single forward pass of an end-to-end model.”

11. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

    Bilingual language model for protein sequence and structure

    Heinzinger, Weißenow, Sánchez et al. · NAR Genomics and Bioinformatics · 2024

    The authors fine-tuned the protein language model ProtT5 to translate between amino acid sequences and Foldseek's 3Di structure alphabet, creating ProstT5, a single model covering both sequence and structure.

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    1. UncheckedA protein language model, ProstT5, improved structure-related predictions and derived 3Di structure tokens about a thousand times faster than before.“As a proof-of-concept for our novel approach, dubbed Protein ‘structure-sequence’ T5 (ProstT5), we showed improved performance for subsequent, structure-related prediction tasks, leading to three orders of magnitude speedup for deriving 3Di.”

12. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

    BERTology Meets Biology: Interpreting Attention in Protein Language Models

    Vig, Madani, Varshney, Xiong, Socher and Rajani · bioRxiv (Cold Spring Harbor Laboratory) · 2020

    The authors present methods for interpreting protein Transformer models through attention, and report that attention reflects protein structure and function across three architectures and two datasets.

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    1. UncheckedIn protein Transformer models, attention links amino acids that sit close together in 3D, targets binding sites, and tracks more complex properties in deeper layers.“We show that attention: (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively m…”

13. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

    SaProt: Protein Language Modeling with Structure-aware Vocabulary

    Su, Han, Zhou, Shan, Zhou and Yuan · bioRxiv (Cold Spring Harbor Laboratory) · 2023

    The authors build SaProt, a protein language model that combines residue tokens with structure tokens from Foldseek, trained on about 40 million protein sequences and structures.

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    1. UncheckedThe SaProt protein model is reported to beat well-established baseline models across 10 downstream tasks, which the authors take as showing broad applicability.“Through extensive evaluation, our SaProt model surpasses well-established and renowned baselines across 10 significant downstream tasks, demonstrating its exceptional capacity and broad applicability.”

14. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

    Rapid in silico directed evolution by a protein language model with EVOLVEpro

    Jiang, Yan, Di Bernardo et al. · Science · 2024

    The paper presents EVOLVEpro, a few-shot active learning framework combining protein language models and regression models to rapidly improve protein activity, with up to 100-fold gains in desired properties.

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    1. UncheckedThe authors report that EVOLVEpro, their protein-improvement method, worked across six proteins used in RNA production, genome editing and antibody binding.“We demonstrate its effectiveness across six proteins in RNA production, genome editing, and antibody binding applications.”
    2. UncheckedThe paper says that few-shot active learning with little experimental data has advantages over zero-shot predictions in improving protein activity.“These results highlight the advantages of few-shot active learning with minimal experimental data over zero-shot predictions.”

15. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

    Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction

    Weißenow, Heinzinger and Rost · Structure · 2022

    The authors predict inter-residue distances from single protein sequences using ProtT5 language-model embeddings and a small convolutional network, aiming for speed without alignments.

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    1. UncheckedEMBER2, which needs no multiple sequence alignments, performed similarly to other methods that fully rely on co-evolution, though not matching AlphaFold2.“Our new method, EMBER2, which never requires any MSAs, performed similarly to other methods that fully rely on co-evolution.”

16. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

    Biophysics-based protein language models for protein engineering

    Gelman, Johnson, Freschlin et al. · Nature Methods · 2025

    The authors built METL, a protein language model pretrained on biophysical simulation data then fine-tuned on experimental data, and tested it on predicting and designing protein properties.

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    1. UncheckedA model called METL, pretrained on biophysical simulations, designed functional green fluorescent protein variants after training on only 64 examples.“We demonstrate METL’s ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.”

17. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

    ProGen2: Exploring the Boundaries of Protein Language Models

    Nijkamp, Ruffolo, Weinstein, Naik and Ali · arXiv (Cornell University) · 2022

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    1. Unchecked“ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional finetuning.”

18. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

    Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

    Li, Amini, Yue, Yang and Lu · bioRxiv (Cold Spring Harbor Laboratory) · 2024

    The authors ran 370 transfer-learning experiments with protein language models and found that, for most downstream tasks, more pretraining does not help, which suggests current pretraining methods fit applications poorly.

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    1. UncheckedAlmost all protein tasks gain from pretrained language models, but most do not improve with more pretraining, relying on low-level features learned early.“We observe that while almost all down-stream tasks do benefit from pretrained models compared to naive sequence representations, for the majority of tasks performance does not scale with pretraining, and instead relies on low-level features learned early in pretraining.”

19. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

    Evolutionary-scale enzymology enables exploration of a rugged catalytic landscape

    Muir, Asper, Notin et al. · Science · 2025

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    1. Unchecked“These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not universally slower than their mesophilic counterparts.”
    2. Unchecked“Semisupervised models that combine our data with the rich sequence representations from large protein language models predict orthologous ADK-sequence catalytic parameters better than existing approaches.”
    3. Unchecked“We dissected this sequence-catalysis landscape’s topology, navigability, and mechanistic underpinnings, revealing catalytically heterogeneous neighborhoods organized by domain architecture.”

20. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

    EvoPool: Evolution-Guided Pooling of Protein Language Model Embeddings

    NaderiAlizadeh and Singh · bioRxiv (Cold Spring Harbor Laboratory) · 2026

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    1. Unchecked“Experiments across multiple state-of-the-art PLM families on the ProteinGym benchmark show that EvoPool consistently outperforms standard pooling baselines for variant effect prediction, demonstrating that explicit evolutionary guidance substantially enhance…