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

Area of Science:

• Computational Biology
• Bioinformatics
• Protein Evolution

Background:

• Homologous protein superfamilies evolve diverse functions from common structural cores.
• Sequence divergence reflects subgroup-specific residue patterns, forming complex probability distributions.

Purpose of the Study:

• To model protein sequence distributions using hierarchical interrelated hidden Markov models (hiHMMs).
• To identify functionally relevant properties and biochemical mechanisms that are not readily apparent from sequence or structure alone.

Main Methods:

• Modeling sequence correlations with hierarchical interrelated hidden Markov models (hiHMMs).
• Inferring hiHMM distributions from sequence data using Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling.
• Mapping correlated residue patterns to protein structures for hypothesis generation.

Main Results:

• The hiHMM approach successfully characterizes complex, high-dimensional sequence distributions.
• Application to N-acetyltransferases revealed previously unknown sequence and structural features.
• A putative coenzyme-A-induced-fit substrate binding mechanism involving arginine residue switching was identified.

Conclusions:

• hiHMMs provide a powerful framework for uncovering hidden functional properties in protein superfamilies.
• This method offers valuable insights even for proteins with minimal available data.
• The approach facilitates hypothesis generation regarding protein function and mechanism.