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HaloClass: Salt-Tolerant Protein Classification with Protein Language Models
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Abstract
Salt-tolerant proteins, also known as halophilic proteins, have unique adaptations to function in high-salinity environments. These proteins have naturally evolved in extremophilic organisms, and more recently, are being increasingly applied as enzymes in industrial processes. Due to an abundance of salt-tolerant sequences and a simultaneous lack of experimental structures, most computational methods to predict stability are sequence-based only. These approaches, however, are hindered by a lack of structural understanding of these proteins. Here, we present HaloClass, an SVM classifier that leverages ESM-2 protein language model embeddings to accurately identify salt-tolerant proteins. On a newer and larger test dataset, HaloClass outperforms existing approaches when predicting the stability of never-before-seen proteins that are distal to its training set. Finally, on a mutation study that evaluated changes in salt tolerance based on single- and multiple-point mutants, HaloClass outperforms existing approaches, suggesting applications in the guided design of salt-tolerant enzymes.
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Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990
Accurate prediction of protein structures and interactions using a 3-track network
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Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483 · cited 1×
“The recent development of protein language models (pLMs) has been an important advancement in computational and structural biology (9). pLMs, such as ESM-2, learn to predict the identity of masked amino acids using the context provided by the rest of the protein sequence (10).”
BackgroundLemke, T., Edte, M., Gebauer, D., & Peter, C. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c04467 · Cited by 34
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Metadata
- Authors
- Narang, K., Nath, A., Hemstrom, W., & Chu, S. K. S.
- Affiliations
- University of California, Davis (US)
- Pt. Jawahar Lal Nehru Memorial Medical College (IN)
- Purdue University West Lafayette (US)
- Corresponding author
- Narang, Kush
- Venue
- The Protein Journal
- Published
- 2024-10-21
- Volume
- 43
- Issue
- 6
- Pages
- 1035-1044
- ISSN
- 1572-3887 (ISSN-L)
- 1875-8355
- PMC
- PMC11543744
- OpenAlex
- W4403605087
- Cited by
- 6
- References
- 6
- Citations by year
- 2026: 4 · 2025: 2
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The Protein Journal (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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 › Enzyme Catalysis and Immobilization
- Keywords
- halophilic proteins, protein stability prediction, protein language models, ESM-2 embeddings, protein classification, support vector classifier, protein mutations, enzyme design, extremophiles
- MeSH terms
- Models, Molecular, Proteins, Computational Biology, Databases, Protein, Salt Tolerance, Support Vector Machine
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