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TrIPP: a trajectory iterative pKa predictor.

Christos Matsingos1,2, Ka Fu Man1, Arianna Fornili1

  • 1Department of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.

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|February 12, 2026
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Summary
This summary is machine-generated.

Protein residue ionization states (pK a) change with their environment. TrIPP (Trajectory Iterative pK a Predictor) analyzes these pK a shifts during molecular dynamics simulations, linking them to protein structure and function.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Protein function is modulated by the ionization state of residues.
  • Changes in local environments significantly impact residue protonation.
  • Protein dynamics are intrinsically linked to pK a values, crucial for pH regulation.

Purpose of the Study:

  • Introduce TrIPP, a novel Python tool for analyzing pK a variations in proteins.
  • Track and analyze changes in ionizable residue pK a along molecular dynamics (MD) trajectories.
  • Correlate pK a shifts with local and global environmental changes in proteins.

Main Methods:

  • Development of the TrIPP (Trajectory Iterative pK a Predictor) software.
  • Utilizing MD simulations to generate protein trajectory data.
  • Analysis of pK a values for ionizable residues within protein structures.

Main Results:

  • Demonstrated TrIPP's capability to track dynamic pK a variations.
  • Identified residues exhibiting physiologically relevant pK a changes during simulations.
  • Linked observed pK a shifts to specific alterations in the protein's microenvironment.

Conclusions:

  • Residue protonation propensity is sensitive to environmental changes.
  • TrIPP facilitates the study of pK a dynamics and its functional implications.
  • Understanding pK a variations is key to elucidating pH-dependent protein behavior.