Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 778

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

LOMETS: A local meta-threading-server for protein structure prediction

Open access
Wu, Sitao, Zhang, Yang·2007·Nucleic Acids Research

Abstract

We developed LOMETS, a local threading meta-server, for quick and automated predictions of protein tertiary structures and spatial constraints. Nine state-of-the-art threading programs are installed and run in a local computer cluster, which ensure the quick generation of initial threading alignments compared with traditional remote-server-based meta-servers. Consensus models are generated from the top predictions of the component-threading servers, which are at least 7% more accurate than the best individual servers based on TM-score at a t-test significance level of 0.1%. Moreover, side-chain and C-alpha (C(alpha)) contacts of 42 and 61% accuracy respectively, as well as long- and short-range distant maps, are automatically constructed from the threading alignments. These data can be easily used as constraints to guide the ab initio procedures such as TASSER for further protein tertiary structure modeling. The LOMETS server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/LOMETS.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkm251

Cites

3 of 3 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Research works printed but not linked: 33

  1. Lundstrom, 2001, Pcons: a neural-network-based consensus predictor that improves fold recognition, Protein Sci, 10, 2354, 10.1110/ps.08501 · DOI · Paper page
  2. Ginalski, 2003, 3D-Jury: a simple approach to improve protein structure predictions, Bioinformatics, 19, 1015, 10.1093/bioinformatics/btg124 · DOI · Paper page
  3. Fischer, 2003, 3D-SHOTGUN: a novel, cooperative, fold-recognition meta-predictor, Proteins, 51, 434, 10.1002/prot.10357 · DOI
  4. Kurowski, 2003, GeneSilico protein structure prediction meta-server, Nucleic Acids Res, 31, 3305, 10.1093/nar/gkg557 · DOI · Paper page
  5. Fischer, 2003, CAFASP3: the third critical assessment of fully automated structure prediction methods, Proteins, 53, 503, 10.1002/prot.10538 · DOI
  6. Rychlewski, 2003, LiveBench-6: large-scale automated evaluation of protein structure prediction servers, Proteins, 53, 542, 10.1002/prot.10535 · DOI
  7. Skolnick, 2000, Structural genomics and its importance for gene function analysis, Nat. Biotechnol, 18, 283, 10.1038/73723 · DOI
  8. Baker, 2001, Protein structure prediction and structural genomics, Science, 294, 93, 10.1126/science.1065659 · DOI · Paper page
  9. Jaroszewski, 2005, FFAS03: a server for profile–profile sequence alignments, Nucleic Acids Res, 33, W284, 10.1093/nar/gki418 · DOI · Paper page
  10. Sali, 1993, Comparative protein modelling by satisfaction of spatial restraints, J. Mol. Biol, 234, 779, 10.1006/jmbi.1993.1626 · DOI · Paper page
  11. Simons, 1997, Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions, J. Mol. Biol, 268, 209, 10.1006/jmbi.1997.0959 · DOI · Paper page
  12. Zhang, 2004, Automated structure prediction of weakly homologous proteins on a genomic scale, Proc. Natl Acad. Sci. USA, 101, 7594, 10.1073/pnas.0305695101 · DOI · Paper page
  13. Shi, 2001, FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties, J. Mol. Biol, 310, 243, 10.1006/jmbi.2001.4762 · DOI · Paper page
  14. Soding, 2005, Protein homology detection by HMM-HMM comparison, Bioinformatics (Oxford, England), 21, 951, 10.1093/bioinformatics/bti125 · DOI · Paper page
  15. Xu, 2000, Protein threading using PROSPECT: design and evaluation, Proteins, 40, 343, 10.1002/1097-0134(20000815)40:3<343::AID-PROT10>3.0.CO;2-S · DOI
  16. Karplus, 2003, Combining local-structure, fold-recognition, and new fold methods for protein structure prediction, Proteins, 53, 491, 10.1002/prot.10540 · DOI · Paper page
  17. Zhou, 2004, Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition, Proteins, 55, 1005, 10.1002/prot.20007 · DOI
  18. Zhou, 2005, Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments, Proteins, 58, 321, 10.1002/prot.20308 · DOI · Paper page
  19. Mizuguchi, 1998, HOMSTRAD: a database of protein structure alignments for homologous families, Protein Sci, 7, 2469, 10.1002/pro.5560071126 · DOI · Paper page
  20. Rabiner, 1989, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. of the IEEE, 77, 257, 10.1109/5.18626 · DOI · Paper page
  21. Jones, 1999, Protein secondary structure prediction based on position-specific scoring matrices, J. Mol. Biol, 292, 195, 10.1006/jmbi.1999.3091 · DOI · Paper page
  22. Domingues, 2000, Structure-based evaluation of sequence comparison and fold recognition alignment accuracy, J. Mol. Biol, 297, 1003, 10.1006/jmbi.2000.3615 · DOI
  23. Karplus, 1998, Hidden Markov models for detecting remote protein homologies, Bioinformatics, 14, 846, 10.1093/bioinformatics/14.10.846 · DOI · Paper page
  24. Henikoff, 1994, Position-based sequence weights, J. Mol. Biol, 243, 574, 10.1016/0022-2836(94)90032-9 · DOI · Paper page
  25. Xu, 2003, RAPTOR: optimal protein threading by linear programming, J. Bioinform. Comput. Biol, 1, 95, 10.1142/S0219720003000186 · DOI · Paper page
  26. Zhang, 2004, Scoring function for automated assessment of protein structure template quality, Proteins, 57, 702, 10.1002/prot.20264 · DOI · Paper page
  27. Hobohm, 1994, Enlarged representative set of protein structures, Protein Sci, 3, 522, 10.1002/pro.5560030317 · DOI · Paper page
  28. Wu, 2007, Ab initio modeling of small proteins by iterative TASSER simulations, BMC Biology, 10.1186/1741-7007-5-17 · DOI · Paper page
  29. Zhang, 2007, Template-based modeling and free modeling by I-TASSER in CASP7, Proteins, 10.1002/prot.21702 · DOI · Paper page
  30. Zhang, 2002, Local energy landscape flattening: Parallel hyperbolic Monte Carlo sampling of protein folding, Proteins, 48, 192, 10.1002/prot.10141 · DOI
  31. Wallner, 2005, Pcons5: combining consensus, structural evaluation and fold recognition scores, Bioinformatics, 21, 4248, 10.1093/bioinformatics/bti702 · DOI
  32. Zhang, 2003, TOUCHSTONE II: A new approach to ab initio protein structure prediction, Biophys. J, 85, 1145, 10.1016/S0006-3495(03)74551-2 · DOI · Paper page
  33. Zhang, 2004, Tertiary structure predictions on a comprehensive benchmark of medium to large size proteins, Biophys. J, 87, 2647, 10.1529/biophysj.104.045385 · DOI

Cited by

See all 778

Metadata

Authors
Wu, S., & Zhang, Y.
Venue
Nucleic Acids Research
Published
2007-05
Volume
35
Issue
10
Pages
3375-3382
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
OpenAlex
W2126677834
Cited by
778
References
3
FWCI
7.30
Type
journal article
Language
en
Open access
gold

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.