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Basis for substrate recognition and distinction by matrix metalloproteinases.


ABSTRACT: Genomic sequencing and structural genomics produced a vast amount of sequence and structural data, creating an opportunity for structure-function analysis in silico [Radivojac P, et al. (2013) Nat Methods 10(3):221-227]. Unfortunately, only a few large experimental datasets exist to serve as benchmarks for function-related predictions. Furthermore, currently there are no reliable means to predict the extent of functional similarity among proteins. Here, we quantify structure-function relationships among three phylogenetic branches of the matrix metalloproteinase (MMP) family by comparing their cleavage efficiencies toward an extended set of phage peptide substrates that were selected from ? 64 million peptide sequences (i.e., a large unbiased representation of substrate space). The observed second-order rate constants [k(obs)] across the substrate space provide a distance measure of functional similarity among the MMPs. These functional distances directly correlate with MMP phylogenetic distance. There is also a remarkable and near-perfect correlation between the MMP substrate preference and sequence identity of 50-57 discontinuous residues surrounding the catalytic groove. We conclude that these residues represent the specificity-determining positions (SDPs) that allowed for the expansion of MMP proteolytic function during evolution. A transmutation of only a few selected SDPs proximal to the bound substrate peptide, and contributing the most to selectivity among the MMPs, is sufficient to enact a global change in the substrate preference of one MMP to that of another, indicating the potential for the rational and focused redesign of cleavage specificity in MMPs.

SUBMITTER: Ratnikov BI 

PROVIDER: S-EPMC4210027 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Basis for substrate recognition and distinction by matrix metalloproteinases.

Ratnikov Boris I BI   Cieplak Piotr P   Gramatikoff Kosi K   Pierce James J   Eroshkin Alexey A   Igarashi Yoshinobu Y   Kazanov Marat M   Sun Qing Q   Godzik Adam A   Osterman Andrei A   Stec Boguslaw B   Strongin Alex A   Smith Jeffrey W JW  

Proceedings of the National Academy of Sciences of the United States of America 20140922 40


Genomic sequencing and structural genomics produced a vast amount of sequence and structural data, creating an opportunity for structure-function analysis in silico [Radivojac P, et al. (2013) Nat Methods 10(3):221-227]. Unfortunately, only a few large experimental datasets exist to serve as benchmarks for function-related predictions. Furthermore, currently there are no reliable means to predict the extent of functional similarity among proteins. Here, we quantify structure-function relationshi  ...[more]

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