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Decoding functional proteome information in model organisms using protein language models
Open accessPreprint — published as 10.1093/nargab/lqae078 · Paper page
Abstract
Protein language models have been tested and proved to be reliable when used on curated datasets but have not yet been applied to full proteomes. Accordingly, we tested how two different machine learning based methods performed when decoding functional information from the proteomes of selected model organisms. We found that protein Language Models are more precise and informative than Deep Learning methods for all the species tested and across the three gene ontologies studied, and that they better recover functional information from transcriptomics experiments. The results obtained indicate that these Language Models are likely to be suitable for large scale annotation and downstream analyses, and we recommend a guide for their use.
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Highly accurate protein structure prediction with AlphaFold
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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Metadata
- Authors
- Barrios-Núñez, I., Martínez-Redondo, G. I., Medina-Burgos, P., Cases, I., Fernández, R., & Rojas, A. M.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- Centro Andaluz de Biología del Desarrollo (ES)
- Institut de Biologia Evolutiva (ES)
- Corresponding author
- Cases, Ildefonso
- Fernández, Rosa
- Rojas, Ana M.
- ORCID
- Venue
- NAR Genomics and Bioinformatics
- Publisher
- openRxiv
- Published
- 2024-02-15
- Volume
- 6
- Issue
- 3
- Pages
- lqae078-lqae078
- ISSN
- 2631-9268 (ISSN-L)
- PMC
- PMC11217674
- OpenAlex
- W4400242398
- Cited by
- 13
- References
- 1
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 3
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- NAR Genomics and Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
- Funders
- Human Frontier Science Program (RGY0056/2022)
- Generalitat de Catalunya (10.13039/501100011033, 501100011033, MCIN /AEI/10.13039/501100011033, PID2019)
- Ministerio de Ciencia e Innovación (MCIN/AEI/10, PID2021-127503OB-I00, .13039/501100011033, 10.13039, / AEI/10, MCIN/AEI/10.13039/501100011033,, MDM‐2016‐0687, PID2019, 10.13039/501100011033/, PID2019-108824GA-I00, 501100011033, RYC2017‐22492, MCIN /AEI /10.13039, /AEI/10.13039/ 501100011033)
- Agència de Gestió d'Ajuts Universitaris i de Recerca (MCIN/AEI/10.13039/501100011033/, 10.13039/501100011033, 13039/501100011033, 2021-SGR00420)
- Agencia Estatal de Investigación (AEI//10.13039/501100011033/, PID2021-127503OB-I00, 501100011033, RYC2017-22492, 10.13039/501100011033, MCIN/AEI/10.13039/501 100 011 033, 13039, .13039/501100011033, PID2019‐108824GA‐I00, 10.13039, AEI/10, MDM-2016-0687, PID2019)
- H2020 European Research Council (948281)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein language models, model organisms, Gene Ontology, deep learning model comparison, protein function prediction
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