Fitness of unregulated human Ras mutants modeled by implementing computational mutagenesis and machine learning...

Area of Science:

• Molecular Biology
• Computational Biology
• Oncogenesis

Background:

• Ras proteins are key oncogenes regulating cell growth, differentiation, and proliferation.
• Understanding mutations in Ras proteins is crucial for cancer research and therapeutic development.

Purpose of the Study:

• To develop predictive models for H-Ras protein fitness using experimental data and AI.
• To assess the accuracy of computational mutagenesis and machine learning algorithms in predicting protein variant fitness.
• To explore the potential of these models in understanding oncogenesis and designing cancer therapies.

Main Methods:

• Utilized experimental fitness data and computational mutagenesis to generate feature vectors of protein structural changes.
• Implemented machine learning algorithms, including random forest classification and tree regression.
• Employed cross-validation and control experiments to evaluate model performance and statistical significance.

Main Results:

• Developed models that reliably predict fitness for single residue mutants of H-Ras p21.
• Achieved high accuracy in classification models (balanced accuracy up to 82%, MCC 0.63, AUC 0.90) and regression models (Pearson's correlation 0.79).
• Demonstrated superior performance compared to state-of-the-art methods and random guessing on control data.

Conclusions:

• The study presents a successful proof-of-principle for using AI and computational mutagenesis to predict protein fitness.
• The developed models offer a complementary approach to understanding oncogenesis mechanisms in Ras proteins and related isoforms.
• Findings provide valuable insights for designing future diagnostic and therapeutic strategies for cancers involving Ras mutations.