TrIPP: a trajectory iterative pKa predictor.

Christos Matsingos, Ka Fu Man, Arianna Fornili

Abstract read

In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Christos MatsingosDepartment of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.ORCID 0009-0003-6431-9053

Ka Fu ManDepartment of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.

Arianna ForniliDepartment of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.ORCID 0000-0001-6554-3979

Funding

Biotechnology and Biological Sciences Research Council BB/M009513/1Engineering and Physical Sciences Research Council EP/T518086/1Engineering and Physical Sciences Research Council EP/X035603/1UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim

6 · The paper itself

Abstract

summaryThe 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.

Indexed as

Computational BiologyMolecular Dynamics SimulationProteinsSoftwareHydrogen-Ion ConcentrationProteins

Identifiers

PMID41678666

DOI10.1093/bioinformatics/btag063

PMCPMC12930849

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.