EigenTHREADER: analogous protein fold recognition by efficient contact map threading

Motivation: Protein fold recognition when appropriate, evolutionarily‐related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can be detected, fold recognition is a notoriously difficult problem (Moult et al., 2014). Here we present EigenTHREADER, a novel fold recognition method capable of identifying folds where no homologous structures can be identified. EigenTHREADER takes a query amino acid sequence, generates a map of intra‐residue contacts, and then searches a library of contact maps of known structures. To allow the contact maps to be compared, we use eigenvector decomposition to resolve the principal eigenvectors these can then be aligned using standard dynamic programming algorithms. The approach is similar to the Al‐Eigen approach of Di Lena et al. (2010), but with improvements made both to speed and accuracy. With this search strategy, EigenTHREADER does not depend directly on sequence homology between the target protein and entries in the fold library to generate models. This in turn enables EigenTHREADER to correctly identify analogous folds where little or no sequence homology information is. Results: EigenTHREADER outperforms well‐established fold recognition methods such as pGenTHREADER and HHSearch in terms of True Positive Rate in the difficult task of analogous fold recognition. This should allow template‐based modelling to be extended to many new protein families that were previously intractable to homology based fold recognition methods. Availability and implementation: All code used to generate these results and the computational protocol can be downloaded from https://github.com/DanBuchan/eigen_scripts. EigenTHREADER, the benchmark code and the data this paper is based on can be downloaded from: http://bioinfadmin.cs.ucl.ac.uk/downloads/eigenTHREADER/. Contact: d.t.jones@ucl.ac.uk

[1]  David T. Jones,et al.  MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins , 2014, Bioinform..

[2]  Feng Zhao,et al.  Protein threading using context-specific alignment potential , 2013, Bioinform..

[3]  Christos H. Papadimitriou,et al.  Algorithmic aspects of protein structure similarity , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[4]  Nathaniel E. Helwig,et al.  An Introduction to Linear Algebra , 2006 .

[5]  D. Eisenberg.  Proteins. Structures and molecular properties, T.E. Creighton. W. H. Freeman and Company, New York (1984), 515, $36.95 , 1985 .

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

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

[8]  David T. Jones,et al.  pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination , 2009, Bioinform..

[9]  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.

[10]  Richard Healey,et al.  How Quantum Theory Helps Us Explain , 2011, The British Journal for the Philosophy of Science.

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

[12]  Serge Lang.  Introduction to Linear Algebra 2nd edition , 1970 .

[13]  Sandra D. Mitchell,et al.  After Fifty Years, Why Are Protein X-ray Crystallographers Still in Business? , 2015, The British Journal for the Philosophy of Science.

[14]  Christus,et al.  A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins , 2022 .

[15]  Hongyi Zhou,et al.  Fold recognition by combining sequence profiles derived from evolution and from depth‐dependent structural alignment of fragments , 2004, Proteins.

[16]  N. Grishin,et al.  Assessment of CASP11 contact‐assisted predictions , 2016, Proteins.

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

[18]  Andrzej Kloczkowski,et al.  BioShell-Threading: versatile Monte Carlo package for protein 3D threading , 2014, BMC Bioinformatics.

[19]  Sitao Wu,et al.  MUSTER: Improving protein sequence profile–profile alignments by using multiple sources of structure information , 2008, Proteins.

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

[21]  B. Rost,et al.  Effective use of sequence correlation and conservation in fold recognition. , 1999, Journal of molecular biology.

[22]  Peter B. McGarvey,et al.  UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches , 2014, Bioinform..

[23]  S. Bryant,et al.  Critical assessment of methods of protein structure prediction (CASP): Round II , 1997, Proteins.

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

[25]  Jinbo Xu,et al.  A multiple‐template approach to protein threading , 2011, Proteins.

[26]  Piero Fariselli,et al.  Fast overlapping of protein contact maps by alignment of eigenvectors , 2010, Bioinform..

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

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

[29]  Zhiyong Wang,et al.  MRFalign: Protein Homology Detection through Alignment of Markov Random Fields , 2014, PLoS Comput. Biol..

[30]  B. Rost,et al.  Protein fold recognition by prediction-based threading. , 1997, Journal of molecular biology.

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

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

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

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