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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable

Bryn Marie Reimer1,2, Ernest Awoonor-Williams1, Andrei A Golosov1

  • 1Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Biomedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

Journal of Chemical Information and Modeling
|January 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CovCysPredictor, a machine learning tool to identify "ligandable" cysteines for targeted covalent drug discovery. It accurately predicts cysteines likely to be selectively modified, advancing cancer drug development.

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Area of Science:

  • Drug Discovery and Development
  • Computational Chemistry
  • Structural Biology

Background:

  • Targeted covalent inhibition is a key strategy in drug discovery, particularly for challenging targets like mutant KRAS in cancer.
  • Identifying suitable cysteine residues for covalent modification remains a significant hurdle due to limitations in current experimental and computational tools.

Purpose of the Study:

  • To develop and validate a machine learning model for accurately predicting "ligandable" cysteines for targeted covalent drug discovery.
  • To identify cysteines that are more likely to be selectively modified, enhancing drug specificity and reducing off-target effects.

Main Methods:

  • Utilized the CovPDB and CovBinderInPDB databases to train and test interpretable machine learning models.
  • Explored various physicochemical features (e.g., pKa, solvent exposure, electrostatics) and protein-ligand pocket descriptors.
  • Developed a logistic regression model and evaluated its performance using F1 scores on held-out test sets.

Main Results:

  • The final logistic regression model achieved a median F1 score of 0.73 on independent test sets.
  • The model demonstrated reasonable performance on holo proteins, correctly identifying the most ligandable cysteine in most cases.
  • The developed tool, CovCysPredictor, can accurately predict potential ligandable cysteines for targeted covalent drug discovery.

Conclusions:

  • Machine learning models can effectively predict ligandable cysteines, prioritizing selectivity over mere reactivity.
  • CovCysPredictor offers a valuable tool for the scientific community to advance targeted covalent drug discovery.
  • This approach holds promise for developing novel therapeutics against difficult-to-treat diseases, including various human cancers.