TrIPP: a trajectory iterative pKa predictor

<h4>Summary</h4>The protonation propensity of ionizable residues in proteins can change in response to changes in the local residue environment. The link between protein dynamics and pKa is particularly important in pH regulation of protein structure and function. Here, we introduce TrIPP (Trajectory Iterative pKa Predictor), a Python tool to track and analyze changes in the pKa of ionizable residues along Molecular Dynamics trajectories of proteins. We show how TrIPP can be used to identify residues with physiologically relevant variations in their predicted pKa values during the simulations and link them to changes in the local and global environment.<h4>Availability and implementation</h4>TrIPP is available at https://github.com/fornililab/TrIPP.