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

摘要

Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequencelevel, such divergence appears as correlations that arise from residue patterns distinct toeach 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 thesesequence correlations implicitly. By characterizing such correlations one may hope to obtaininformation 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 Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effectiveapproach for characterizing a complex, high dimensional distribution. Other routines thenmap correlated residue patterns to available structures with a view to hypothesis generation.When applied to N-acetyltransferases, this reveals sequence and structural features indicative of functionally important, yet generally unknown biochemical properties. Even for sets ofproteins for which nothing is known beyond unannotated sequences and structures, this canlead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge andπ-π stacking interactions. A suite of programs implementing this approach is available(psed.igs.umaryland.edu).