|Title||Predicting functionally informative mutations in Escherichia coli BamA using evolutionary covariance analysis.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Dwyer, RS, Ricci, DP, Colwell, LJ, Silhavy, TJ, Wingreen, NS|
|Date Published||2013 Oct|
|Keywords||Amino Acid Substitution, Bacterial Outer Membrane Proteins, Computational Biology, Escherichia coli, Escherichia coli Proteins, Evolution, Molecular, Mutation, Protein Conformation|
The essential outer membrane β-barrel protein BamA forms a complex with four lipoprotein partners BamBCDE that assembles β-barrel proteins into the outer membrane of Escherichia coli. Detailed genetic studies have shown that BamA cycles through multiple conformations during substrate assembly, suggesting that a complex network of residues may be involved in coordinating conformational changes and lipoprotein partner function. While genetic analysis of BamA has been informative, it has also been slow in the absence of a straightforward selection for mutants. Here we take a bioinformatic approach to identify candidate residues for mutagenesis using direct coupling analysis. Starting with the BamA paralog FhaC, we show that direct coupling analysis works well for large β-barrel proteins, identifying pairs of residues in close proximity in tertiary structure with a true positive rate of 0.64 over the top 50 predictions. To reduce the effects of noise, we designed and incorporated a novel structured prior into the empirical correlation matrix, dramatically increasing the FhaC true positive rate from 0.64 to 0.88 over the top 50 predictions. Our direct coupling analysis of BamA implicates residues R661 and D740 in a functional interaction. We find that the substitutions R661G and D740G each confer OM permeability defects and destabilize the BamA β-barrel. We also identify synthetic phenotypes and cross-suppressors that suggest R661 and D740 function in a similar process and may interact directly. We expect that the direct coupling analysis approach to informed mutagenesis will be particularly useful in systems lacking adequate selections and for dynamic proteins with multiple conformations.