ProtMamba: a homology-aware but alignment-free protein state space model
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Abstract
Motivation: Protein language models are enabling advances in elucidating the sequence-to-function mapping, and have important applications in protein design. Models based on multiple sequence alignments efficiently capture the evolutionary information in homologous protein sequences, but multiple sequence alignment construction is imperfect. Results: We present ProtMamba, a homology-aware but alignment-free protein language model based on the Mamba architecture. In contrast with attention-based models, ProtMamba efficiently handles very long context, comprising hundreds of protein sequences. It is also computationally efficient. We train ProtMamba on a large dataset of concatenated homologous sequences, using two GPUs. We combine autoregressive modeling and masked language modeling through a fill-in-the-middle training objective. This makes the model adapted to various protein design applications. We demonstrate ProtMamba’s usefulness for sequence generation, motif inpainting, fitness prediction, and modeling intrinsically disordered regions. For homolog-conditioned sequence generation, ProtMamba outperforms state-of-the-art models. ProtMamba’s competitive performance, despite its relatively small size, sheds light on the importance of long-context conditioning. Availability and implementation: A Python implementation of ProtMamba is freely available in our GitHub repository: https://github.com/Bitbol-Lab/ProtMamba-ssm and archived at https://doi.org/10.5281/zenodo.15584634.
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Flagged first, then by citations
- Authors
- Sgarbossa, D., Malbranke, C., & Bitbol, A.-F.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Bitbol, Anne-Florence
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-06
- Volume
- 41
- Issue
- 6
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC12206526
- OpenAlex
- W4411299343
- Cited by
- 27
- References
- 27
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 3
- FWCI
- 7.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Funders
- European Research Council
- European Union’s Horizon 2020 research and innovation programme (851173)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein language models, Mamba, protein design, fitness prediction, intrinsically disordered regions, masked language modeling
- MeSH terms
- Algorithms, Proteins, Software, Sequence Alignment, Computational Biology, Sequence Analysis, Protein
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