Abstract

Protein functional constraints are manifest as superfamily and functional-subgroup conserved residues, and as pairwise correlations. Deep Analysis of Residue Constraints (DARC) aids the visualization of these constraints, characterizes how they correlate with each other and with structure, and estimates statistical significance. This can identify determinants of protein functional specificity, as we illustrate for bacterial DNA clamp loader ATPases. These load ring-shaped sliding clamps onto DNA to keep polymerase attached during replication and contain one δ, three γ, and one δ’ AAA+ subunits semi-circularly arranged in the order δ-γ1-γ2-γ3-δ’. Only γ is active, though both γ and δ’ functionally influence an adjacent γ subunit. DARC identifies, as functionally-congruent features linking allosterically the ATP, DNA, and clamp binding sites: residues distinctive of γ and of γ/δ’ that mutually interact in trans, centered on the catalytic base; several γ/δ’-residues and six γ/δ’-covariant residue pairs within the DNA binding N-termini of helices α2 and α3; and γ/δ’-residues associated with the α2 C-terminus and the clamp-binding loop. Most notable is a trans-acting γ/δ’ hydroxyl group that 99% of other AAA+ proteins lack. Mutation of this hydroxyl to a methyl group impedes clamp binding and opening, DNA binding, and ATP hydrolysis—implying a remarkably clamp-loader-specific function.

Highlights

  • An important question in biology is which sequence and structural features enable proteins sharing a common catalytic core to perform entirely different functions

  • Direct coupling analysis (DCA) focuses on predicting contacts between residue pairs based on correlated substitution patterns among homologous proteins: In order to maintain structural integrity, substitutions at one residue position often result in compensating substitutions at other positions over evolutionary time

  • Bayesian Partitioning with Pattern Selection (BPPS)[50,51,52], like DCA, identifies correlations among columns in an multiple sequence alignment (MSA), but unlike DCA, focuses on residues co-conserved among functionally related subgroups

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Summary

Introduction

An important question in biology is which sequence and structural features enable proteins sharing a common catalytic core to perform entirely different functions. They may appear as residues conserved in an entire superfamily or in functionally related protein subgroups (i.e., as correlations between sequence patterns and biochemical properties), as subtle pairwise correlations, or as correlations among these sequence features or with structural features. Investigated protein constraints include function determining residues (FDRs), “coevolving sectors”, directly coupled (DC) residue pairs, and subgroup-specific patterns. DCA focuses on predicting contacts between residue pairs based on correlated substitution patterns among homologous proteins: In order to maintain structural integrity, substitutions at one residue position often result in compensating substitutions at other positions over evolutionary time. Bayesian Partitioning with Pattern Selection (BPPS)[50,51,52], like DCA, identifies correlations among columns in an MSA, but unlike DCA, focuses on residues co-conserved among functionally related subgroups. DCA and BPPS are complementary, with a combined analysis often providing deeper biological insight

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