Improving protein fold recognition with hybrid profiles combining sequence and structure evolution

MOTIVATION Template-based modeling, the most successful approach for predicting protein 3D structure, often requires detecting distant evolutionary relationships between the target sequence and proteins of known structure. Developed for this purpose, fold recognition methods use elaborate strategies to exploit evolutionary information, mainly by encoding amino acid sequence into profiles. Since protein structure is more conserved than sequence, the inclusion of structural information can improve the detection of remote homology. RESULTS Here, we present ORION, a new fold recognition method based on the pairwise comparison of hybrid profiles that contain evolutionary information from both protein sequence and structure. Our method uses the 16-state structural alphabet Protein Blocks, which provides an accurate 1D description of protein structure local conformations. ORION systematically outperforms PSI-BLAST and HHsearch on several benchmarks, including target sequences from the modeling competitions CASP8, 9 and 10, and detects ∼10% more templates at fold and superfamily SCOP levels. AVAILABILITY Software freely available for download at http://www.dsimb.inserm.fr/orion/. CONTACT jean-christophe.gelly@univ-paris-diderot.fr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  Peter B. McGarvey,et al.  UniRef: comprehensive and non-redundant UniProt reference clusters , 2007, Bioinform..

[2]  A. Godzik,et al.  Comparison of sequence profiles. Strategies for structural predictions using sequence information , 2008, Protein science : a publication of the Protein Society.

[3]  Anna R Panchenko,et al.  Finding weak similarities between proteins by sequence profile comparison. , 2003, Nucleic acids research.

[4]  C Venclovas,et al.  Processing and analysis of CASP3 protein structure predictions , 1999, Proteins.

[5]  Johannes Söding,et al.  Protein sequence comparison and fold recognition: progress and good-practice benchmarking. , 2011, Current opinion in structural biology.

[6]  Sarah Rauscher,et al.  Molecular mechanism of β‐sheet self‐organization at water‐hydrophobic interfaces , 2011, Proteins.

[7]  Arne Elofsson,et al.  A study on protein sequence alignment quality , 2002, Proteins.

[8]  Liam J. McGuffin.  Protein Fold Recognition and Threading , 2008 .

[9]  D. T. Jones,et al.  A new approach to protein fold recognition , 1992, Nature.

[10]  B. Rost.  Twilight zone of protein sequence alignments. , 1999, Protein engineering.

[11]  Sean R. Eddy,et al.  Profile hidden Markov models , 1998, Bioinform..

[12]  W. Pearson.  Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms. , 1991, Genomics.

[13]  S. Henikoff,et al.  Position-based sequence weights. , 1994, Journal of molecular biology.

[14]  A G Murzin,et al.  SCOP: a structural classification of proteins database for the investigation of sequences and structures. , 1995, Journal of molecular biology.

[15]  Narayanaswamy Srinivasan,et al.  iPBA: a tool for protein structure comparison using sequence alignment strategies , 2011, Nucleic Acids Res..

[16]  Anna Tramontano,et al.  Critical assessment of methods of protein structure prediction (CASP) — round x , 2014, Proteins.

[17]  Jean-François Gibrat,et al.  Statistical Significance of Threading Scores , 2012, J. Comput. Biol..

[18]  Liisa Holm,et al.  Picasso: generating a covering set of protein family profiles , 2001, Bioinform..

[19]  Ryan Day,et al.  A consensus view of fold space: Combining SCOP, CATH, and the Dali Domain Dictionary , 2003, Protein science : a publication of the Protein Society.

[20]  A. Elofsson,et al.  Structure is three to ten times more conserved than sequence—A study of structural response in protein cores , 2009, Proteins.

[21]  John P. Overington,et al.  HOMSTRAD: A database of protein structure alignments for homologous families , 1998, Protein science : a publication of the Protein Society.

[22]  T L Blundell,et al.  FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. , 2001, Journal of molecular biology.

[23]  Randy J Read,et al.  Assessment of CASP7 predictions in the high accuracy template‐based modeling category , 2007, Proteins.

[24]  Ulrich H. E. Hansmann,et al.  LOCUSTRA: Accurate Prediction of Local Protein Structure Using a Two-Layer Support Vector Machine Approach , 2008, J. Chem. Inf. Model..

[25]  T. Hubbard,et al.  Critical assessment of methods of protein structure prediction (CASP): Round III , 1999, Proteins.

[26]  D. Eisenberg,et al.  A method to identify protein sequences that fold into a known three-dimensional structure. , 1991, Science.

[27]  A. Krogh.  Hidden Markov Models in Computational Biology Applications to Protein Modeling UCSC CRL , 1993 .

[28]  A. D. McLachlan,et al.  Profile analysis: detection of distantly related proteins. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[29]  M. Sternberg,et al.  Enhanced genome annotation using structural profiles in the program 3D-PSSM. , 2000, Journal of molecular biology.

[30]  B. Rost,et al.  Critical assessment of methods of protein structure prediction—Round VIII , 2009, Proteins.

[31]  Anna Tramontano,et al.  Critical assessment of methods of protein structure prediction—Round VII , 2007, Proteins.

[32]  Jorja G. Henikoff,et al.  Using substitution probabilities to improve position-specific scoring matrices , 1996, Comput. Appl. Biosci..

[33]  Yaoqi Zhou,et al.  Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates , 2011, Bioinform..

[34]  A. Tramontano,et al.  Critical assessment of methods of protein structure prediction (CASP)—round IX , 2011, Proteins.

[35]  A Elofsson,et al.  Assessing the performance of fold recognition methods by means of a comprehensive benchmark. , 1996, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

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

[37]  D. Haussler,et al.  Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods. , 1998, Journal of molecular biology.

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

[39]  Arne Elofsson,et al.  Profile–profile methods provide improved fold‐recognition: A study of different profile–profile alignment methods , 2004, Proteins.

[40]  Dong Xu,et al.  FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking , 2014, Bioinform..

[41]  Bohdan Schneider,et al.  A short survey on protein blocks , 2010, Biophysical Reviews.

[42]  Richard Hughey,et al.  Hidden Markov models for detecting remote protein homologies , 1998, Bioinform..

[43]  George Karypis,et al.  svmPRAT: SVM-based Protein Residue Annotation Toolkit , 2009, BMC Bioinformatics.

[44]  J. Skolnick,et al.  TM-align: a protein structure alignment algorithm based on the TM-score , 2005, Nucleic acids research.

[45]  C. Chothia,et al.  Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[46]  M. Sternberg,et al.  Benchmarking PSI-BLAST in genome annotation. , 1999, Journal of molecular biology.

[47]  Nick V. Grishin,et al.  Probabilistic scoring measures for profile-profile comparison yield more accurate short seed alignments , 2003, Bioinform..

[48]  A. Hagler,et al.  Predicted three‐dimensional structure of the protease inhibitor domain of the Alzheimer's disease β‐amyloid precursor , 1991, Proteins.

[49]  Ben M. Webb,et al.  Comparative Protein Structure Modeling Using Modeller , 2006, Current protocols in bioinformatics.

[50]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[51]  Measures of Central Tendency in Rehabilitation Research: What Do They Mean? , 2001, American journal of physical medicine & rehabilitation.

[52]  C. Etchebest,et al.  Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks , 2000, Proteins.

[53]  Yang Zhang,et al.  Scoring function for automated assessment of protein structure template quality , 2004, Proteins.