Paper Info
Title
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 Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effective approach for characterizing a complex, high dimensional distribution. Other routines then map 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 of proteins for which nothing is known beyond unannotated sequences and structures, this can lead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge and pi-pi stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu).
Year
DOI
Venue
2016
10.1371/journal.pcbi.1005294
PLOS COMPUTATIONAL BIOLOGY
Field
DocType
Volume
Sequence alignment,Population,Alignment-free sequence analysis,Markov chain Monte Carlo,Biology,Protein superfamily,Bioinformatics,Hidden Markov model,Sequence analysis,Bayes' theorem
Journal
12
Issue
ISSN
Citations 
12
1553-7358
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Andrew F. Neuwald1354.83
Stephen F Altschul218026.55