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 π-π stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu).

Inferring joint sequence-structural determinants of protein functional specificity

“…GNATs catalyze the transfer of a carboxylic acyl group from Coenzyme A (acyl-CoA) to a diversity of substrates. Previously, a BPPS analysis of glucosamine-6-phosphate N -acetyltransferase (Gna1) led to two observations ( Neuwald and Altschul, 2016a ) ( Figure 1 ): (1) Within the homodimeric structure of Gna1 (pdb: 4ag9) ( Dorfmueller et al, 2012 ), BPPS-defined residues for this family are contributed by both subunits to form the dimeric interface and the active site for each subunit. In contrast, within a single subunit most of these residues are far from the active site and face away from it.…”

“…This generates an MSA. (2) Bayesian Partitioning with Pattern Selection (BPPS) ( Neuwald, 2014a ; Neuwald, 2014b ; Neuwald and Altschul, 2016a ) is applied in three steps: (i) Step 1 uses Markov chain Monte Carlo (MCMC) sampling to partition the MSA into hierarchically-arranged subgroups based on the correlated residue patterns most distinctive of each subgroup. (ii) Step 2 converts the MSA into a hiMSA based on the BPPS hierarchy.…”

“…An optional Step E checks for consistency between BPPS Step 1 runs. Step 2 of BPPS ( Neuwald and Altschul, 2016a ) uses a combination of multiple sequence alignment and BPPS MCMC sampling. The Gi bbs S ampler for M ulti-alignment O ptimization (GISMO) ( Neuwald and Altschul, 2016b ) adjusts each sub-group’s alignment by adding regions conserved in the subgroup but not in the superfamily as a whole.…”

Initial Cluster Analysis

“…I n the study of proteins , a variety of methods derive, from analyzing a multiple alignment of related sequences or a corresponding phylogenetic tree, predictions that a specific set of residues within a particular protein are functionally important (Lichtarge et al, 1996 ; Mihalek et al, 2004 ; Fischer et al, 2008 ; Sankararaman and Sjölander, 2008 ; Janda et al, 2012 ; Neuwald and Altschul, 2016 ). When a three-dimensional structure is available for that protein, the residues so identified may appear to form one or more spatial clusters perhaps indicative of active sites or other functional modules.…”

Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity

“…Using Markov chain Monte Carlo sampling, BPPS partitions an MSA into hierarchically arranged subgroups, each defined by a corresponding conserved pattern that best distinguishes that subgroup from those further up the hierarchy. The overlap is typically weak between BPPS pattern residues and either FDRs 53 or high scoring DCA residue pairs 49 . Moreover, as illustrated here, BPPS enhances the utility of DCA by allowing characterization of direct couplings specific to a functionally-divergent subgroup.…”