title: Reducing the false positive rate in the non-parametric analysis of molecular coevolution.
abstract: BACKGROUND
  The strength of selective constraints operating on amino acid sites of proteins has a multifactorial nature. In fact, amino acid sites within proteins coevolve due to their functional and/or structural relationships. Different methods have been developed that attempt to account for the evolutionary dependencies between amino acid sites. Researchers have invested a significant effort to increase the sensitivity of such methods. However, the difficulty in disentangling functional co-dependencies from historical covariation has fuelled the scepticism over their power to detect biologically meaningful results. In addition, the biological parameters connecting linear sequence evolution to structure evolution remain elusive. For these reasons, most of the evolutionary studies aimed at identifying functional dependencies among protein domains have focused on the structural properties of proteins rather than on the information extracted from linear multiple sequence alignments (MSA). Non-parametric methods to detect coevolution have been reported to be especially susceptible to produce false positive results based on the properties of MSAs. However, no formal statistical analysis has been performed to definitively test the differential effects of these properties on the sensitivity of such methods.
  
  
  RESULTS
  Here we test the effect that variations on the MSA properties have over the sensitivity of non-parametric methods to detect coevolution. We test the effect that the size of the MSA (number of sequences), mean pairwise amino acid distance per site and the strength of the coevolution signal have on the ability of non-parametric methods to detect coevolution. Our results indicate that all three factors have significant effects on the accuracy of non-parametric methods. Further, introducing statistical filters improves the sensitivity and increases the statistical power of the methods to detect functional coevolution. Statistical analysis of the physico-chemical properties of amino acid sites in the context of the protein structure reveals striking dependencies among amino acid sites. Results indicate a covariation trend in the hydrophobicities and molecular weight characteristics of amino acid sites when analysing a non-redundant set of 8000 protein structures. Using this biological information as filter in coevolutionary analyses minimises the false positive rate of these methods. Application of these filters to three different proteins with known functional domains supports the importance of using biological filters to detect coevolution.
  
  
  CONCLUSIONS
  Coevolutionary analyses using non-parametric methods have proved difficult and highly prone to provide spurious results depending on the properties of MSAs and on the strength of coevolution between amino acid sites. The application of statistical filters to the number of pairs detected as coevolving reduces significantly the number of artifactual results. Analysis of the physico-chemical properties of amino acid sites in the protein structure context reveals their structure-dependent covariation. The application of this known biological information to the analysis of covariation greatly enhances the functional coevolutionary signal and removes historical covariation. Simultaneous use of statistical and biological data is instrumental in the detection of functional amino acid sites dependencies and compensatory changes at the protein level.
authors.affiliation: Evolutionary Genetics and Bioinformatics Laboratory, Department of Genetics, Smurfit Institute of Genetics, University of Dublin, Trinity College, Dublin, Ireland. codonerf@tcd.ie
authors.forename: Francisco M
authors.initials: FM
authors.lastname: Codoñer
authors.forename: Shirley
authors.initials: S
authors.lastname: O'Dea
authors.forename: Mario A
authors.initials: MA
authors.lastname: Fares
mesh_terms: D000595:Amino Acid Sequence; D018834:Chaperonin 60 / Q000235:genetics; D003198:Computer Simulation; D019143:Evolution, Molecular; D005819:Genetic Markers / Q000235:genetics; D018841:HSP90 Heat-Shock Proteins / Q000235:genetics; D008957:Models, Genetic; D018709:Statistics, Nonparametric; D054299:env Gene Products, Human Immunodeficiency Virus / Q000235:genetics
chemical_list: D018834:Chaperonin 60; D005819:Genetic Markers; D018841:HSP90 Heat-Shock Proteins; D054299:env Gene Products, Human Immunodeficiency Virus