» Articles » PMID: 22685552

A Coevolutionary Residue Network at the Site of a Functionally Important Conformational Change in a Phosphohexomutase Enzyme Family

Overview
Journal PLoS One
Date 2012 Jun 12
PMID 22685552
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Coevolution analyses identify residues that co-vary with each other during evolution, revealing sequence relationships unobservable from traditional multiple sequence alignments. Here we describe a coevolutionary analysis of phosphomannomutase/phosphoglucomutase (PMM/PGM), a widespread and diverse enzyme family involved in carbohydrate biosynthesis. Mutual information and graph theory were utilized to identify a network of highly connected residues with high significance. An examination of the most tightly connected regions of the coevolutionary network reveals that most of the involved residues are localized near an interdomain interface of this enzyme, known to be the site of a functionally important conformational change. The roles of four interface residues found in this network were examined via site-directed mutagenesis and kinetic characterization. For three of these residues, mutation to alanine reduces enzyme specificity to ~10% or less of wild-type, while the other has ~45% activity of wild-type enzyme. An additional mutant of an interface residue that is not densely connected in the coevolutionary network was also characterized, and shows no change in activity relative to wild-type enzyme. The results of these studies are interpreted in the context of structural and functional data on PMM/PGM. Together, they demonstrate that a network of coevolving residues links the highly conserved active site with the interdomain conformational change necessary for the multi-step catalytic reaction. This work adds to our understanding of the functional roles of coevolving residue networks, and has implications for the definition of catalytically important residues.

Citing Articles

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function.

Swint-Kruse L, Fenton A J Biol Chem. 2024; 300(3):105736.

PMID: 38336297 PMC: 10914490. DOI: 10.1016/j.jbc.2024.105736.


General strategies for using amino acid sequence data to guide biochemical investigation of protein function.

Kennedy E, Foster C, Barr S, Bourret R Biochem Soc Trans. 2022; 50(6):1847-1858.

PMID: 36416676 PMC: 10257402. DOI: 10.1042/BST20220849.


Rheostat positions: A new classification of protein positions relevant to pharmacogenomics.

Fenton A, Page B, Spellman-Kruse A, Hagenbuch B, Swint-Kruse L Med Chem Res. 2020; 29(7):1133-1146.

PMID: 32641900 PMC: 7276102. DOI: 10.1007/s00044-020-02582-9.


Data on the phosphorylation state of the catalytic serine of enzymes in the α-D-phosphohexomutase superfamily.

Lee Y, Furdui C, Beamer L Data Brief. 2017; 10:398-405.

PMID: 28050582 PMC: 5192239. DOI: 10.1016/j.dib.2016.12.017.


Using Evolution to Guide Protein Engineering: The Devil IS in the Details.

Swint-Kruse L Biophys J. 2016; 111(1):10-8.

PMID: 27410729 PMC: 4945580. DOI: 10.1016/j.bpj.2016.05.030.


References
1.
Zhou D, Stephens D, Gibson B, Engstrom J, McAllister C, Lee F . Lipooligosaccharide biosynthesis in pathogenic Neisseria. Cloning, identification, and characterization of the phosphoglucomutase gene. J Biol Chem. 1994; 269(15):11162-9. View

2.
Dickson R, Wahl L, Fernandes A, Gloor G . Identifying and seeing beyond multiple sequence alignment errors using intra-molecular protein covariation. PLoS One. 2010; 5(6):e11082. PMC: 2893159. DOI: 10.1371/journal.pone.0011082. View

3.
Buck M, Atchley W . Networks of coevolving sites in structural and functional domains of serpin proteins. Mol Biol Evol. 2005; 22(7):1627-34. DOI: 10.1093/molbev/msi157. View

4.
Regni C, Tipton P, Beamer L . Crystallization and initial crystallographic analysis of phosphomannomutase/phosphoglucomutase from Pseudomonas aeruginosa. Acta Crystallogr D Biol Crystallogr. 2000; 56(Pt 6):761-2. DOI: 10.1107/s0907444900004431. View

5.
Kose F, Weckwerth W, Linke T, Fiehn O . Visualizing plant metabolomic correlation networks using clique-metabolite matrices. Bioinformatics. 2001; 17(12):1198-208. DOI: 10.1093/bioinformatics/17.12.1198. View