Inferring interaction partners from protein sequences.

Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data.

Folding LacZ in the periplasm of Escherichia coli.

Targeted, translational LacZ fusions provided the initial support for the signal sequence hypothesis in prokaryotes and allowed for selection of the mutations that identified the Sec translocon. Many of these selections relied on the fact that expression of targeted, translational lacZ fusions like malE-lacZ and lamB-lacZ42-1 causes lethal toxicity as folded LacZ jams the translocation pore. However, there is another class of targeted LacZ fusions that do not jam the translocon. These targeted, nonjamming fusions also show toxic phenotypes that may be useful for selecting mutations in genes involved in posttranslocational protein folding and targeting; however, they have not been investigated to the same extent as their jamming counterparts. In fact, it is still unclear whether LacZ can be fully translocated in these fusions. It may be that they simply partition into the inner membrane where they can no longer participate in folding or assembly. In the present study, we systematically characterize the nonjamming fusions and determine their ultimate localization. We report that LacZ can be fully translocated into the periplasm, where it is toxic. We show that this toxicity is likely due to LacZ misfolding and that, in the absence of the periplasmic disulfide bond catalyst DsbA, LacZ folds in the periplasm. Using the novel phenotype of periplasmic ß-galactosidase activity, we show that the periplasmic chaperone FkpA contributes to LacZ folding in this nonnative compartment. We propose that targeted, nonjamming LacZ fusions may be used to further study folding and targeting in the periplasm of Escherichia coli.

Predicting functionally informative mutations in Escherichia coli BamA using evolutionary covariance analysis.

The essential outer membrane ß-barrel protein BamA forms a complex with four lipoprotein partners BamBCDE that assembles ß-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 ß-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 ß-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.