Cited by 188Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

Domain size distributions can predict domain boundaries

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Wheelan, S. J., Marchler-Bauer, A., Bryant, S. H.·2000·Bioinformatics

Abstract

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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Crossref · 10.1093/bioinformatics/16.7.613

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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.”

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Authors
Wheelan, S. J., Marchler-Bauer, A., & Bryant, S. H.
Affiliations
National Institutes of Health (US)
Venue
Bioinformatics
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
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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