ArticleeLife2023
Generative power of a protein language model trained on multiple sequence alignments.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Mind the gap: An embedding guide to safely travel in sequence space.PLoS computational biology · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Much ado about nothing: modeling amino acid replacement with predicted protein structures.Bioinformatics (Oxford, England) · 2026Article
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Large language models in bioinformatics: a comprehensive survey.Frontiers in genetics · 2026Review
- Evaluating Pretrained Protein Language Model Embeddings as Proxies for Functional Similarity.Journal of molecular evolution · 2025Article
- Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models.Physical review research · 2025Article
- ALLM-Ab: Active Learning-Driven Antibody Optimization Using Fine-Tuned Protein Language Models.Journal of chemical information and modeling · 2025Article
- TheProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- ProtMamba: a homology-aware but alignment-free protein state space model.Bioinformatics (Oxford, England) · 2025Article
- Computational scoring and experimental evaluation of enzymes generated by neural networks.Nature biotechnology · 2025Article
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
- Article
- G-Computational and structural biotechnology journal · 2024Article
- Context-aware geometric deep learning for protein sequence design.Nature communications · 2024Article
- Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation.bioRxiv : the preprint server for biology · 2024Article
- Article
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3 authors.
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Abstract
Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating novel sequences belonging to protein families. Protein language models trained on multiple sequence alignments, such as MSA Transformer, are highly attractive candidates to this end. We propose and test an iterative method that directly employs the masked language modeling objective to generate sequences using MSA Transformer. We demonstrate that the resulting sequences score as well as natural sequences, for homology, coevolution, and structure-based measures. For large protein families, our synthetic sequences have similar or better properties compared to sequences generated by Potts models, including experimentally validated ones. Moreover, for small protein families, our generation method based on MSA Transformer outperforms Potts models. Our method also more accurately reproduces the higher-order statistics and the distribution of sequences in sequence space of natural data than Potts models. MSA Transformer is thus a strong candidate for protein sequence generation and protein design.
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