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1,035 claims from 648 papers are on the record. 39 have been checked so far; the other 996 have no check with a result yet.

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Keyword: protein language models ×Clear all

17 claims from 10 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

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   1. Unchecked“Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences.”
   2. Unchecked“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-ce…
   3. Unchecked“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 › 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

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   1. Unchecked“We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model.”
   2. Unchecked“We find that the attention output correlates well with the position of sorting signals.”

4. 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

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   1. Unchecked“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.”

5. 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

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   1. Unchecked“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 t…
   2. Unchecked“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.”

6. 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

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   1. Unchecked“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. Unchecked“Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections.”

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

   MSA Transformer

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

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   1. Unchecked“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.”

8. 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

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   1. Unchecked“In this paper we demonstrate that Transformer attention maps learn contacts from the unsupervised language modeling objective.”
   2. Unchecked“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.”

9. 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

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   1. Unchecked“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 pro…

10. 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

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    1. Unchecked“Our new method, EMBER2, which never requires any MSAs, performed similarly to other methods that fully rely on co-evolution.”