doi: bioRxiv preprint redundancy. In this study, we report a subset of E. coli amidases involved in cell separation during cell division are not redundant and instead are preferentially active during growth in distinct pH environments. Specifically, we discover E. coli amidases AmiB and AmiC are activated by acidic pH. Three semi-redundant periplasmic regulators-NlpD, EnvC, and YgeR-collectively mediate low pH-dependent stimulation of amidase activity. This discovery contributes to our understanding of how the cell wall remains robust across diverse environmental conditions and reveals opportunities for the development of condition-specific antimicrobial agents..

Nearly all bacteria are encased in a peptidoglycan cell wall, an essential crosslinked matrix of polysaccharide strands and short peptide stems. In the Gram-negative model organism Escherichia coli, more than forty cell wall synthases and autolysins coordinate the growth and division of the peptidoglycan sacculus in the periplasm. The precise contribution of many of these enzymes to cell wall metabolism remains unclear due to significant apparent redundancy, particularly among the cell wall autolysins. E. coli produces three major LytC-type-N-acetylmuramoyl-L-alanine amidases, which share a role in separating the newly formed daughter cells during cytokinesis. Here, we reveal two of the three amidases exhibit growth medium-dependent changes in activity. Specifically, we report acidic growth conditions stimulate AmiB—and to a lesser extent, AmiC—activity. Combining computational and genetic analysis, we demonstrate that low pH-dependent stimulation of AmiB requires three periplasmic amidase activators: EnvC, NlpD, and YgeR. Altogether, our findings support overlapping, but not redundant, roles for the E. coli amidases in cell separation and illuminate the physiochemical environment as an important mediator of cell wall enzyme activity.

pH regulates protein function and interactions by altering the charge of individual residues causing loss or gain of intramolecular noncovalent bonds, which may lead to structural rearrangements. While tools to analyze residue-specific charge distribution of proteins at a given pH exist, currently no tool is available to investigate noncovalent bond changes at two different pH values. To make protein pH sensitivity analysis more accessible, we developed patcHwork, a web server that combines the identification of amino acids undergoing a charge shift with the determination of affected noncovalent bonds at two user-defined pH values. At the sequence-only level, patcHwork applies the Henderson–Hasselbalch equation to determine pH-sensitive residues. When the 3D protein structure is available, patcHwork can be employed to gain mechanistic understanding of the effect of pH. This is achieved using the PDB2PQR and PROPKA tools and noncovalent bond determination algorithms. A user-friendly interface allows visualizing pH-sensitive residues, affected salt bridges, hydrogen bonds and aromatic (pi–pi and cation–pi) interactions. patcHwork can be used to identify patches, a new concept we propose of pH-sensitive residues in close proximity on the protein, which may have a major impact on function. We demonstrate the attractiveness of patcHwork studying experimentally investigated pH-sensitive proteins (https://patchwork.biologie.uni-freiburg.de/).

pH regulates protein function and interactions by altering the charge of individual residues causing the loss or gain of intra-molecular non-covalent bonds, which may additionally lead to structural rearrangements. While tools to analyze residue-specific charge distribution of protein sequences and structures at a given pH exist, currently no tool is available to investigate non-covalent bond changes at two different pH values. In an effort to make protein pH sensitivity analysis more accessible to researchers without computational structural biology background, we developed patcHwork, a web server that combines the identification of amino acids undergoing a charge shift with the determination of affected non-covalent bonds at two user-defined pH values. At the sequence-only level, patcHwork applies the Henderson-Hasselbalch equation to determine pH-sensitive residues. When the 3D protein structure is available, patcHwork can be employed to gain a deeper mechanistic understanding of the effect of pH on a protein of interest. This is achieved using the PDB2PQR and PROPKA tools and non-covalent bond determination algorithms. A user-friendly interface allows visualizing pH-sensitive residues as well as affected salt bridges, hydrogen bonds and aromatic (pi-pi and cation-pi) interactions. Importantly, patcHwork can be used to identify patches, a new concept we propose of pH-sensitive residues in close proximity on the protein structure, which may have a major impact on function. We demonstrate the attractiveness of patcHwork studying experimentally investigated pH-sensitive proteins. (Access:https://patchwork.biologie.uni-freiburg.de/)