Domain size distributions can predict domain boundaries
Open accessAbstract
Motivation: The sizes of protein domains observed in the 3D-structure database follow a surprisingly narrow distribution. Structural domains are furthermore formed from a single-chain continuous segment in over 80% of instances. These observations imply that some choices of domain boundaries on an otherwise uncharacterized sequence are more likely than others, based solely on the size and segment number of predicted domains. This property might be used to guess the locations of protein domain boundaries. Results: To test this possibility we enumerate putative domain boundaries and calculate their relative likelihood under a probability model that considers only the size and segment number of predicted domains. We ask, in a cross-validated test using sequences with known 3D structure, whether the most likely guesses agree with the observed domain structure. We find that domain boundary predictions are surprisingly successful for sequences up to 400 residues long and that guessing domain boundaries in this way can improve the sensitivity of threading analysis. Availability: The DGS algorithm, for ‘Domain Guess by Size’, is available as a web service at http://www.ncbi.nlm.nih.gov/dgs. This site also provides the DGS source code. Contact: bryant@ncbi.nlm.nih.gov * To whom correspondence should be addressed.
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The distribution of median confidence scores of a fragment versus fragment length shows an enrichment for high-confidence predictions with a length of 100-500 residues, consistent with the size of a typical protein domain.
“The distribution of median confidence scores of a fragment versus fragment length shows an enrichment for high-confidence predictions with a length of 100–500 residues (Fig. 1c and Supplementary Fig. 3), consistent with the size of a typical protein domain21.”
From A structural biology community assessment of AlphaFold2 applications (2022) · cites this paper as [21]
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Trifonov, E. N., & Berezovsky, I. N. (2002) · FEBS Letters · doi:10.1016/s0014-5793(02)03165-4 · Cited by 19 · see citing works
Prediction of Protein Function
Eisenhaber, F. (2007) · Springer eBooks · doi:10.1007/0-387-36747-0_4 · Cited by 19 · see citing works
Predicting protein conformation by statistical methods
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Dynamics of domain coverage of the protein sequence universe
Rekapalli, B., Wuichet, K., Peterson, G. D., & Zhulin, I. B. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-634 · Cited by 18 · see citing works
Folding by Numbers: Primary Sequence Statistics and Their Use in Studying Protein Folding
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Identifying Structural Domains in Proteins
Wernisch, L., & Wodak, S. J. (2003) · Methods of biochemical analysis · doi:10.1002/0471721204.ch18 · Cited by 18 · see citing works
Towards functional repertoire of the earliest proteins
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OPUS-Dom: Applying the Folding-Based Method VECFOLD to Determine Protein Domain Boundaries
Wu, Y., Dousis, A. D., Chen, M., Li, J., & Ma, J. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.10.093 · Cited by 17 · see citing works
- Authors
- Wheelan, S. J., Marchler-Bauer, A., & Bryant, S. H.
- Affiliations
- National Institutes of Health (US)
- ORCID
- Wheelan, S. J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-07-01
- Volume
- 16
- Issue
- 7
- Pages
- 613-618
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2116160060
- Cited by
- 182
- References
- 20
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 6 · 2022: 7 · 2021: 6 · 2020: 2 · 2019: 5 · 2018: 1 · 2017: 6 · 2016: 7 · 2015: 4 · 2014: 8 · 2013: 8 · 2012: 8
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein domain boundaries, domain size distribution, protein structure prediction, threading, protein domains
- MeSH terms
- Algorithms, Models, Molecular, Probability, Proteins, Protein Structure, Tertiary
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