The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The relationship between protein structure and function: a comprehensive survey with application to the yeast genome
Open accessAbstract
For most proteins in the genome databases, function is predicted via sequence comparison. In spite of the popularity of this approach, the extent to which it can be reliably applied is unknown. We address this issue by systematically investigating the relationship between protein function and structure. We focus initially on enzymes functionally classified by the Enzyme Commission (EC) and relate these to by structurally classified domains the SCOP database. We find that the major SCOP fold classes have different propensities to carry out certain broad categories of functions. For instance, alpha/beta folds are disproportionately associated with enzymes, especially transferases and hydrolases, and all-alpha and small folds with non-enzymes, while alpha+beta folds have an equal tendency either way. These observations for the database overall are largely true for specific genomes. We focus, in particular, on yeast, analyzing it with many classifications in addition to SCOP and EC (i.e. COGs, CATH, MIPS), and find clear tendencies for fold-function association, across a broad spectrum of functions. Analysis with the COGs scheme also suggests that the functions of the most ancient proteins are more evenly distributed among different structural classes than those of more modern ones. For the database overall, we identify the most versatile functions, i.e. those that are associated with the most folds, and the most versatile folds, associated with the most functions. The two most versatile enzymatic functions (hydro-lyases and O-glycosyl glucosidases) are associated with seven folds each. The five most versatile folds (TIM-barrel, Rossmann, ferredoxin, alpha-beta hydrolase, and P-loop NTP hydrolase) are all mixed alpha-beta structures. They stand out as generic scaffolds, accommodating from six to as many as 16 functions (for the exceptional TIM-barrel). At the conclusion of our analysis we are able to construct a graph giving the chance that a functional annotation can be reliably transferred at different degrees of sequence and structural similarity. Supplemental information is available from http://bioinfo.mbb.yale.edu/genome/foldfunc++ +.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Improved tools for biological sequence comparison.
Pearson, W. R., & Lipman, D. J. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.8.2444 · Cited by 11,227
The relation between the divergence of sequence and structure in proteins.
Chothia, C., & Lesk, A. M. (1986) · The EMBO Journal · doi:10.1002/j.1460-2075.1986.tb04288.x · Cited by 2,458
Cited by
What is Bioinformatics? A Proposed Definition and Overview of the Field
Luscombe, N. M., Greenbaum, D., & Gerstein, M. (2001) · Methods of Information in Medicine · doi:10.1055/s-0038-1634431 · Cited by 453
Self-citationWithin the twilight zone: a sensitive profile-profile comparison tool based on information theory
Yona, G., & Levitt, M. (2002) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.5293 · Cited by 287
Protein Function Prediction Using Local 3D Templates
Laskowski, R. A., Watson, J. D., & Thornton, J. M. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.05.067 · Cited by 213
Evolutionary innovations and the organization of protein functions in genotype space
Ferrada, E., & Wagner, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014172 · Cited by 51
Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding
(2025) · doi:10.1038/s42256-025-01059-x · Cited by 7
Metadata
- Authors
- Hegyi, H., & Gerstein, M.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 1999-04
- Volume
- 288
- Issue
- 1
- Pages
- 147-164
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W2143011057
- Cited by
- 421
- References
- 2
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The relationship between protein structure and function: a comprehensive survey with application to the yeast genome. https://app.cochat.ai/papers/10.1006/jmbi.1999.2661
- The paper
- Hegyi, H., & Gerstein, M. (1999). The relationship between protein structure and function: a comprehensive survey with application to the yeast genome. Journal of Molecular Biology. https://doi.org/10.1006/jmbi.1999.2661
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.