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Protein Electrostatic Properties are Fine-Tuned Through Evolution.

Mingzhe Shen1, Guy W Dayhoff1,2, Jana Shen1

  • 1Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, U.S.A.

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|May 9, 2025
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Summary
This summary is machine-generated.

Predicting protein ionization states is now possible using only primary sequence data. A new model, KaML-ESM, leverages evolutionary information for highly accurate pKa predictions, advancing protein electrostatics research.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Protein ionization states are crucial for structure, stability, solubility, and function.
  • Current prediction methods heavily rely on protein structural information.
  • Understanding protein electrostatics is essential for various biological processes.

Purpose of the Study:

  • To develop a novel method for predicting protein ionization states using only primary sequence data.
  • To assess the accuracy of sequence-based pKa predictions.
  • To provide a tool for mapping protein electrostatic landscapes.

Main Methods:

  • Development of KaML-ESM, a machine learning model pretrained on a synthetic pKa dataset.
  • Leveraging evolutionary representations from large-scale protein language models (ESMs).
  • Validation through external evaluations and proteome-wide analysis.

Main Results:

  • KaML-ESM achieves high accuracy in pKa predictions, with RMSEs near experimental limits for key residues (Asp, Glu, His, Lys).
  • Prediction errors for Cysteine residues were significantly reduced.
  • Demonstrated that protein primary sequence encodes electrostatic properties.

Conclusions:

  • Primary protein sequence alone enables accurate prediction of ionization states and pKa values.
  • Protein electrostatics may be co-optimized with structure and function through evolution.
  • The KaML platform facilitates diverse applications in drug design, protein engineering, and molecular simulations.