Work

TrIPP: a trajectory iterative p Ka predictor

Year: 2026

Type: article

Abstract: SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: TrIPP is available at https://github.com/fornililab/TrIPP.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btag063

Authors: Christos Matsingos, Ka Fu Man, Arianna Fornili

Institutions: Queen Mary University of London, Université Paris Cité, SPPIN - Saints-Pères Paris Institute for Neurosciences

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,989

Funders: Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council