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Predicting how mutations alter protein interactions is key to understanding diseases. This review covers computational methods, data challenges, and future AI-driven improvements for mutation effect prediction.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Genomics

Background:

  • Genetic variations impact biomolecular interactions, affecting biological processes and disease.
  • Accurate prediction of mutation-induced binding free energy changes (ΔΔGs) is vital for disease mechanism elucidation and therapeutic development.

Purpose of the Study:

  • To provide a comprehensive review of recent advancements in predicting mutation effects on protein interactions.
  • To evaluate physicochemical-based, machine learning, and deep learning predictive approaches.

Main Methods:

  • Review of computational methods for predicting mutation-induced changes in protein binding free energy.
  • Analysis of strengths and limitations of various predictive approaches.
  • Discussion of challenges in mutational data and tool development.

Main Results:

  • Physicochemical, machine learning, and deep learning methods show promise but have limitations.
  • Challenges include data biases, quality, and size, hindering accurate prediction tool development.
  • Artificial intelligence offers potential for significant improvements.

Conclusions:

  • Accurate prediction of mutation effects on protein interactions is crucial for biological and medical research.
  • Addressing data limitations and leveraging AI are key future directions.
  • Advancements in computational tools will enhance understanding of disease mechanisms and drug discovery.