MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins

Motivation: Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Results: Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts—around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. Availability and implementation: MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  T. Hwa,et al.  Identification of direct residue contacts in protein–protein interaction by message passing , 2009, Proceedings of the National Academy of Sciences.

[2]  David T. Jones,et al.  De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts , 2014, PloS one.

[3]  SödingJohannes.  Protein homology detection by HMM--HMM comparison , 2005 .

[4]  David C. Jones,et al.  CATH--a hierarchic classification of protein domain structures. , 1997, Structure.

[5]  Johannes Söding,et al.  Protein homology detection by HMM?CHMM comparison , 2005, Bioinform..

[6]  R. Jernigan,et al.  Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation , 1985 .

[7]  Burkhard Rost,et al.  FreeContact: fast and free software for protein contact prediction from residue co-evolution , 2014, BMC Bioinformatics.

[8]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[9]  A. Lesk,et al.  Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus. , 1987, Journal of Molecular Biology.

[10]  D. Thirumalai,et al.  Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes , 2008, Protein science : a publication of the Protein Society.

[11]  D T Jones,et al.  Prediction of novel and analogous folds using fragment assembly and fold recognition , 2005, Proteins.

[12]  Thomas A. Hopf,et al.  Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing , 2012, Cell.

[13]  A. Valencia,et al.  Emerging methods in protein co-evolution , 2013, Nature Reviews Genetics.

[14]  D. Baker,et al.  Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information , 2014, eLife.

[15]  Marcin J. Skwark,et al.  PconsFold: improved contact predictions improve protein models , 2014, Bioinform..

[16]  William R Taylor,et al.  Prediction of contacts from correlated sequence substitutions. , 2013, Current opinion in structural biology.

[17]  Osvaldo Graña,et al.  Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8 , 2009, Proteins.

[18]  Massimiliano Pontil,et al.  PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..

[19]  K. Dill,et al.  The Protein-Folding Problem, 50 Years On , 2012, Science.

[20]  Malgorzata Kotulska,et al.  Automated Procedure for Contact-Map-Based Protein Structure Reconstruction , 2014, The Journal of Membrane Biology.

[21]  Thomas A. Hopf,et al.  Protein 3D Structure Computed from Evolutionary Sequence Variation , 2011, PloS one.

[22]  E. Neher.  How frequent are correlated changes in families of protein sequences? , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Gregory B. Gloor,et al.  Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction , 2008, Bioinform..

[24]  D T Jones,et al.  Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.

[25]  Marcin J. Skwark,et al.  PconsC: combination of direct information methods and alignments improves contact prediction , 2013, Bioinform..

[26]  Timothy Nugent,et al.  Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis , 2012, Proceedings of the National Academy of Sciences.

[27]  A. Biegert,et al.  HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment , 2011, Nature Methods.

[28]  Pierre Baldi,et al.  Improved residue contact prediction using support vector machines and a large feature set , 2007, BMC Bioinformatics.

[29]  Markus Gruber,et al.  CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations , 2014, Bioinform..

[30]  Art Poon,et al.  The Rate of Compensatory Mutation in the DNA Bacteriophage φX174 , 2005, Genetics.