Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations.

ABSTRACT: Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly. By characterizing such correlations one may hope to obtain information regarding functionally-relevant properties that have thus far evaded detection. To do so, we infer a hiHMM distribution from sequence data using Bayes' theorem and

SUBMITTER: Neuwald AF 

PROVIDER: S-EPMC5225019 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature