ArticleProceedings of the National Academy of Sciences of the United States of America2024
Pairing interacting protein sequences using masked language modeling.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- AI-driven insights into protein misfolding and innate immunity in neurodegenerative diseases.Frontiers in immunology · 2026Review
- Isolation and Transcriptomic Characterization of Genotype 5 Japanese Encephalitis Virus E138K Mutant Strain.Transboundary and emerging diseases · 2026Article
- High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.Nature communications · 2025Article
- Endosomal RFFL ubiquitin ligase regulates mitochondrial morphology by targeting mitofusin 2.Journal of cell science · 2025Article
- Leveraging protein structural information to improve variant effect prediction.Current opinion in structural biology · 2025Review
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.Bioinformatics (Oxford, England) · 2024Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Machine learning meets physics: A two-way street.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Genomic language model predicts protein co-regulation and function.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.
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