The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Accurate modeling of protein conformation by automatic segment matching
Open accessAbstract
Segment match modeling uses a data base of highly refined known protein X-ray structures to build an unknown target structure from its amino acid sequence and the atomic coordinates of a few of its atoms (generally only the C alpha atoms). The target structure is first broken into a set of short segments. The data base is then searched for matching segments, which are fitted onto the framework of the target structure. Three criteria are used for choosing a matching data base segment: amino acid sequence similarity, conformational similarity (atomic co-ordinates), and compatibility with the target structure (van der Waals' interactions). The new method works surprisingly well: for eight test proteins ranging in size from 46 to 323 residues, the all-atom root-mean-square deviation of the modeled structures is between 0.93 A and 1.73 A (the average is 1.26 A). Deviations of this magnitude are comparable with those found for protein co-ordinates before and after refinement against X-ray data or for co-ordinates of the same protein in different crystal packings. These results are insensitive to errors in the C alpha positions or to missing C alpha atoms: accurate models can be built with C alpha errors of up to 1 A or by using only half the C alpha atoms. The fit to the X-ray structures is improved significantly by building several independent models based on different random choices and then averaging co-ordinates; this novel concept has general implications for other modeling tasks. The segment match modeling method is fully automatic, yields a complete set of atomic co-ordinates without any human intervention and is efficient (14 s/residue on the Silicon Graphics 4D/25 Personal Iris workstation.
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
A lattice model for protein structure prediction at low resolution.
Hinds, D. A., & Levitt, M. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.7.2536 · Cited by 217
Self-citation
Cited by
Comparative Protein Structure Modeling of Genes and Genomes
Martí-Renom, M. A., Stuart, A. C., Fiser, A., Sánchez, R., Melo, F., & Šali, A. (2000) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.29.1.291 · Cited by 3,169
Modeling of loops in protein structures
Fiser, A., Do, R. K. G., & Šali, A. (2000) · Protein Science · doi:10.1110/ps.9.9.1753 · Cited by 2,116
Protein Structure Prediction and Structural Genomics
Baker, D., & Sali, A. (2001) · Science · doi:10.1126/science.1065659 · Cited by 1,587
Comparative protein structure modeling using MODELLER
Webb, B., & Sali, A. (2016) · Current Protocols in Protein Science · doi:10.1002/cpps.20 · Cited by 730
“The basis of modeling by coordinate reconstruction is the finding that most hexapeptide segments of protein structure can be clustered into only 100 structurally different classes (Jones and Thirup, 1986; Claessens et al., 1989; Unger et al., 1989; Levitt, 1992; Bystroff and Baker, 1998).”
BackgroundMethodThe Basic and Clinical Pharmacology of Nonpeptide Vasopressin Receptor Antagonists
Thibonnier, M., Coles, P., Thibonnier, A., & Shoham, M. (2001) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev.pharmtox.41.1.175 · Cited by 146
Metadata
- Authors
- Levitt, M.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 1992-07
- Volume
- 226
- Issue
- 2
- Pages
- 507-533
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W1970197953
- Cited by
- 558
- References
- 1
- FWCI
- 4.58
- 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
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.