Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 770

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Open access
Jones, David T., Buchan, Daniel W. A., Cozzetto, Domenico, Pontil, Massimiliano·2012·Bioinformatics
DOI 10.1093/bioinformatics/btr638
Open this paper in CoChat — free account

Abstract

Motivation: The accurate prediction of residue–residue contacts, critical for maintaining the native fold of a protein, remains an open problem in the field of structural bioinformatics. Interest in this long-standing problem has increased recently with algorithmic improvements and the rapid growth in the sizes of sequence families. Progress could have major impacts in both structure and function prediction to name but two benefits. Sequence-based contact predictions are usually made by identifying correlated mutations within multiple sequence alignments (MSAs), most commonly through the information-theoretic approach of calculating mutual information between pairs of sites in proteins. These predictions are often inaccurate because the true covariation signal in the MSA is often masked by biases from many ancillary indirect-coupling or phylogenetic effects. Here we present a novel method, PSICOV, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. Our method builds on work which had previously demonstrated corrections for phylogenetic and entropic correlation noise and allows accurate discrimination of direct from indirectly coupled mutation correlations in the MSA. Results: PSICOV displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks. For 118 out of 150 targets, the L/5 (i.e. top-L/5 predictions for a protein of length L) precision for long-range contacts (sequence separation >23) was ≥0.5, which represents an improvement sufficient to be of significant benefit in protein structure prediction or model quality assessment. Availability: The PSICOV source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/PSICOV Contact: d.jones@cs.ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

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/bioinformatics/btr638

Cites

2 of 2 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

Show all 2 references

Research works printed but not linked: 40

  1. Ashkenazy, 2010, Reducing phylogenetic bias in correlated mutation analysis, Protein Eng. Des. Sel., 23, 321, 10.1093/protein/gzp078 · DOI
  2. Banerjee, 2008, Model selection through sparse maximum likelihood estimation, J. Mach. Learn. Res., 9, 485
  3. Berman, 2000, The protein data bank, Nucleic Acids Res., 28, 235, 10.1093/nar/28.1.235 · DOI · Paper page
  4. Bühlmann, 2011, Statistics for High-Dimensional Data: Methods, Theory and Applications., 10.1007/978-3-642-20192-9 · DOI
  5. Burger, 2010, Disentangling direct from indirect co-evolution of residues in protein alignments, PLoS Comput. Biol., 6, e1000633, 10.1371/journal.pcbi.1000633 · DOI
  6. Buslje, 2009, Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information, Bioinformatics, 25, 1125, 10.1093/bioinformatics/btp135 · DOI
  7. Dunn, 2008, Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction, Bioinformatics, 24, 333, 10.1093/bioinformatics/btm604 · DOI
  8. Ezkurdia, 2009, Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8, Proteins, 77, 196, 10.1002/prot.22554 · DOI
  9. Fariselli, 2001, Prediction of contact maps with neural networks and correlated mutations, Protein Eng., 14, 835, 10.1093/protein/14.11.835 · DOI
  10. Fischer, 2001, CAFASP2: the second critical assessment of fully automated structure prediction methods, Proteins, 45, 171, 10.1002/prot.10036 · DOI
  11. Friedman, 2008, Sparse inverse covariance estimation with the graphical Lasso, Biostatistics, 9, 432, 10.1093/biostatistics/kxm045 · DOI · Paper page
  12. Gobel, 1994, Correlated mutations and residue contacts in proteins, Proteins, 18, 309, 10.1002/prot.340180402 · DOI · Paper page
  13. Graña, 2005, CASP6 assessment of contact prediction, Proteins, 61, 214, 10.1002/prot.20739 · DOI
  14. Graña, 2005, EVAcon: a protein contact prediction evaluation service, Nucleic Acids Res., 33, W347, 10.1093/nar/gki411 · DOI
  15. Gromiha, 2004, Inter-residue interactions in protein folding and stability, Prog. Biophys. Mol. Biol., 86, 235, 10.1016/j.pbiomolbio.2003.09.003 · DOI · Paper page
  16. Halabi, 2009, Protein sectors: evolutionary units of three-dimensional structure, Cell, 138, 774, 10.1016/j.cell.2009.07.038 · DOI · Paper page
  17. Hamilton, 2008, An introduction to protein contact prediction, Methods Mol. Biol., 453, 87, 10.1007/978-1-60327-429-6_3 · DOI
  18. Hamilton, 2004, Protein contact prediction using patterns of correlation, Proteins, 56, 679, 10.1002/prot.20160 · DOI
  19. Henikoff, 1992, Amino acid substitution matrices from protein blocks, Proc. Natl Acad. Sci. USA, 89, 10915, 10.1073/pnas.89.22.10915 · DOI · Paper page
  20. Henikoff, 1994, Position-based sequence weights, J. Mol. Biol., 243, 574, 10.1016/0022-2836(94)90032-9 · DOI · Paper page
  21. Horner, 2008, Correlated substitution analysis and the prediction of amino acid structural contacts, Brief. Bioinform., 9, 46, 10.1093/bib/bbm052 · DOI
  22. Ledoit, 2003, Improved estimation of the covariance matrix of stock returns with an application to portfolio selection, J. Empir. Finance, 10, 603, 10.1016/S0927-5398(03)00007-0 · DOI · Paper page
  23. Lena, 2011, Is there an optimal substitution matrix for contact prediction with correlated mutations?, IEEE/ACM Trans. Comput. Biol. Bioinform., 8, 1017, 10.1109/TCBB.2010.91 · DOI
  24. Ma, 2007, Supervised group Lasso with applications to microarray data analysis, BMC Bioinformatics, 8, 60, 10.1186/1471-2105-8-60 · DOI
  25. MacCallum, 2004, Striped sheets and protein contact prediction, Bioinformatics, 20, i224, 10.1093/bioinformatics/bth913 · DOI
  26. Magrane, 2011, UniProt knowledgebase: a hub of integrated protein data, Database, 2011, bar009, 10.1093/database/bar009 · DOI
  27. Martin, 2005, Using information theory to search for co-evolving residues in proteins, Bioinformatics, 21, 4116, 10.1093/bioinformatics/bti671 · DOI
  28. McLachlan, 1971, Tests for comparing related amino-acid sequences. cytochrome c and cytochrome c 551, J. Mol. Biol., 61, 409, 10.1016/0022-2836(71)90390-1 · DOI
  29. Meinshausen, 2006, High dimensional graphs and variable selection with the Lasso, Ann. Stat., 34, 1436, 10.1214/009053606000000281 · DOI · Paper page
  30. Miller, 2008, Using inferred residue contacts to distinguish between correct and incorrect protein models, Bioinformatics, 24, 1575, 10.1093/bioinformatics/btn248 · DOI
  31. Neher, 1994, How frequent are correlated changes in families of protein sequences?, Proc. Natl Acad. Sci. USA, 91, 98, 10.1073/pnas.91.1.98 · DOI
  32. Olmea, 1997, Improving contact predictions by the combination of correlated mutations and other sources of sequence information, Fold Des., 2, S25, 10.1016/S1359-0278(97)00060-6 · DOI
  33. Pollastri, 2002, Prediction of contact maps by giohmms and recurrent neural networks using lateral propagation from all four cardinal corners, Bioinformatics, 18, S62, 10.1093/bioinformatics/18.suppl_1.S62 · DOI
  34. Pollock, 1997, Effectiveness of correlation analysis in identifying protein residues undergoing correlated evolution, Protein Eng., 10, 647, 10.1093/protein/10.6.647 · DOI
  35. Punta, 2005, PROFcon: novel prediction of long-range contacts, Bioinformatics, 21, 2960, 10.1093/bioinformatics/bti454 · DOI
  36. Shao, 2003, Predicting interresidue contacts using templates and pathways, Proteins, 53, 497, 10.1002/prot.10539 · DOI
  37. Weigt, 2009, Identification of direct residue contacts in protein-protein interaction by message passing, Proc. Natl Acad. Sci. USA, 106, 67, 10.1073/pnas.0805923106 · DOI · Paper page
  38. Xue, 2009, Predicting residue-residue contact maps by a two-layer, integrated neural-network method, Proteins, 76, 176, 10.1002/prot.22329 · DOI
  39. Yuan, 2005, Better prediction of protein contact number using a support vector regression analysis of amino acid sequence, BMC Bioinformatics, 6, 248, 10.1186/1471-2105-6-248 · DOI
  40. Yuan, 2007, Model selection and estimation in the gaussian graphical model, Biometrika, 91, 19, 10.1093/biomet/asm018 · DOI · Paper page

Cited by

Metadata

Authors
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M.
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2011-11-17
Volume
28
Issue
2
Pages
184-190
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btr638
OpenAlex
W2166701319
Cited by
770
References
2
FWCI
12.58
Type
journal article
Language
en
Open access
bronze

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.