Max Hebditch

PhosIDP visualizes how phosphorylation affects charge and fold propensity in intrinsically disordered regions, aiding in functional interpretation of phosphorylation effects.

The paper introduces a tool to predict electrostatic frustration in proteins by analyzing protonatable groups, applied to coronaviruses to identify histidine residues affected by acidic pH.

The paper develops a computational model to analyze pH-stability of antibodies and applies it to proteases, identifying design principles for acid-stable proteins.

The paper develops machine learning models to predict antibody biophysical properties using sequence data, finding that charge and hydrophobicity are key features.

The paper develops a web server to predict protein surface polarity and pH/ionic strength dependence, showing antibody-antigen interfaces are as polar as Fab surfaces.