Abstract
BackgroundIn order to find correlated pairs of positions between proteins, which are useful in predicting interactions, it is necessary to concatenate two large multiple sequence alignments such that the sequences that are joined together belong to those that interact in their species of origin. When each protein is unique then the species name is sufficient to guide this match, however, when there are multiple related sequences (paralogs) in each species then the pairing is more difficult. In bacteria a good guide can be gained from genome co-location as interacting proteins tend to be in a common operon but in eukaryotes this simple principle is not sufficient.ResultsThe methods developed in this paper take sets of paralogs for different proteins found in the same species and make a pairing based on their evolutionary distance relative to a set of other proteins that are unique and so have a known relationship (singletons). The former constitute a set of unlabelled nodes in a graph while the latter are labelled. Two variants were tested, one based on a phylogenetic tree of the sequences (the topology-based method) and a simpler, faster variant based only on the inter-sequence distances (the distance-based method). Over a set of test proteins, both gave good results, with the topology method performing slightly better.ConclusionsThe methods develop here still need refinement and augmentation from constraints other than the sequence data alone, such as known interactions from annotation and databases, or non-trivial relationships in genome location. With the ever growing numbers of eukaryotic genomes, it is hoped that the methods described here will open a route to the use of these data equal to the current success attained with bacterial sequences.
Highlights
In order to find correlated pairs of positions between proteins, which are useful in predicting interactions, it is necessary to concatenate two large multiple sequence alignments such that the sequences that are joined together belong to those that interact in their species of origin
The analysis of large multiple sequence alignments to reveal positions that co-vary has recently become a powerful method to identify pairs of interacting positions that can be used as constraints in the construction of molecular models
The genes of proteins that interact are often co-expressed and found close in the genome sequence on an operon in which all the genes are under common expression control
Summary
In order to find correlated pairs of positions between proteins, which are useful in predicting interactions, it is necessary to concatenate two large multiple sequence alignments such that the sequences that are joined together belong to those that interact in their species of origin. In bacteria a good guide can be gained from genome co-location as interacting proteins tend to be in a common operon but in eukaryotes this simple principle is not sufficient. Correlated substitution analysis can be used to find pairs of interacting positions between proteins if the multiple sequence alignments for two or more proteins are concatenated and processed as a single joint alignment. For this to work, requires that each pair of concatenated sequences coexist in the same organism (or a close relative) and have been subject to mutual evolutionary selection pressures. A way to do this is to note the difference in the gene identifier that are assigned sequentially along the genome
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