Evolution , in its course , has found many different solutions to the same problem. Focusing on proteins, the amino acid sequence of protein domains performing the same function in URLanisms differs markedly. Since the structure and function of proteins are ultimately encoded in the amino acid sequence, multiple sequence alignments (MSA) of homologous protein domains can be used to provide information about this encoding. Regarding each sequence in an MSA as a solution of an optimisation problem, we exploit the MSA to infer the relevance of different positions, thereby identifying a hierarchy of relevant sites. Our method exploits information on coevolution going beyond pairwise correlations. This method , called Critical Variable Selection (CVS), affords predictions that are significantly different from those of methods based on pairwise correlations, and it recovers biologically relevant sites , including highly conserved ones. As compared to other methods based on pairwise correlations, we find, in the analysed cases, that CVS is more efficient in identifying the core of relevant sites , as well as most of the tightest contacts in the protein tertiary structure .
Evolution in its course found a variety of solutions to the same optimisation problem. The advent of high-throughput genomic sequencing has made available extensive data from which, in principle, one can infer the underlying structure on which biological functions rely. In this paper, we present a new method aimed at extracting sites encoding structural and func- tional properties from a set of protein primary sequences, namely a Multiple Sequence Alignment. The method, called Critical Variable Selection, is based on the idea that subsets of relevant sites cor- respond to subsequences that occur with a particularly broad frequency distribution in the dataset. By applying this algorithm to in silico sequences, to the Response Regulator Receiver and to the Voltage Sensor Domain of Ion Channels, we show that this procedure recovers not only information encoded in single site statistics and pairwise correlations but it also captures dependencies going beyond pairwise correlations. The method proposed here is complementary to Statistical Coupling Analysis, in that the most relevant sites predicted by the two methods markedly differ. We find robust and consistent results for datasets as small as few hundred sequences, that reveal a hidden hierarchy of sites that is consistent with present knowledge on biologically relevant sites and evo- lutionary dynamics. This suggests that Critical Variable Selection is able to identify in a Multiple Sequence Alignment a core of sites encoding functional and structural information .
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problem. Focusing on proteins, the amino acid sequence of protein domains performing the same function in URLanisms differs markedly. Since the structure and function of proteins are ultimately encoded in the amino acid sequence, multiple sequence alignments (MSA) of homologous protein domains can be used to provide information about this encoding. Regarding each sequence in an MSA as a solution of an optimisation problem, we exploit the MSA to infer the relevance of different positions, thereby identifying a hierarchy of relevant sites. Our method exploits information on coevolution
optimisation problem. The advent of high-throughput genomic sequencing has made available extensive data from which, in principle, one can infer the underlying structure on which biological functions rely. In this paper, we present a new method aimed at extracting sites encoding structural and func- tional properties from a set of protein primary sequences, namely a Multiple Sequence Alignment. The method, called Critical Variable Selection, is based on the idea that subsets of relevant sites cor- respond to subsequences that occur with a particularly broad frequency distribution in the dataset. By applying this algorithm to in silico sequences, to the Response Regulator Receiver and to the Voltage Sensor Domain of Ion Channels, we show that this procedure recovers not only information encoded in single site statistics and pairwise correlations but it also captures dependencies
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This method , called Critical Variable Selection (CVS), affords predictions that are significantly different from those of methods based on pairwise correlations, and it recovers
The method proposed here is complementary to Statistical Coupling Analysis, in that the most relevant sites predicted by the two methods markedly differ. We find robust and consistent results for datasets as small as few hundred sequences, that reveal a hidden hierarchy of sites that is consistent with present knowledge on
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, including highly conserved ones. As compared to other methods based on pairwise correlations, we find, in the analysed cases, that CVS is more efficient in identifying the core of relevant sites , as well as most of the tightest contacts in the protein tertiary structure
and evo- lutionary dynamics. This suggests that Critical Variable Selection is able to identify in a Multiple Sequence Alignment a core of sites encoding functional and structural information