Data-driven models for predicting intrinsically disordered protein polymer physics directly from composition or sequence
Open accessPublished version of a preprint
Abstract
A new representation for intrinsically disordered protein sequences is developed for usage in both classical and deep learning machine learning models.
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Cites
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 · see citing works
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
Temperature-dependent solvation modulates the dimensions of disordered proteins
Wuttke, R., Hofmann, H., Nettels, D., Borgia, M. B., Mittal, J., Best, R. B., & Schuler, B. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1313006111 · Cited by 192 · see citing works
Self-citationDescribing sequence–ensemble relationships for intrinsically disordered proteins
Mao, A. H., Lyle, N., & Pappu, R. V. (2013) · Biochemical Journal · doi:10.1042/bj20121346 · Cited by 135 · see citing works
Flagged first, then by citations
- Authors
- Chao, T.-H., Rekhi, S., Mittal, J., & Tabor, D. P.
- Affiliations
- Texas A&M University (US)
- Corresponding author
- Tabor, Daniel P.
- Venue
- Molecular Systems Design & Engineering
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2023
- Volume
- 8
- Issue
- 9
- Pages
- 1146-1155
- ISSN
- 2058-9689 (ISSN-L)
- PMC
- PMC10786636
- OpenAlex
- W4379511796
- Cited by
- 14
- References
- 5
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 3 · 2023: 2
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- rsc.li/journals-terms-of-use (am, 522 d)
- Locations
- Molecular Systems Design & Engineering (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM136917)
- Welch Foundation (A-2049-20200401, A-2113-20220331)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- intrinsically disordered proteins, radius of gyration, scaling exponents, protein sequence representation
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