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PRA-MutPred: Predicting the Effect of Point Mutations in Protein-RNA Complexes Using Structural Features.

K Harini1, M Sekijima2, M Michael Gromiha1,3

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036 Tamil Nadu, India.

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We developed a machine-learning model to predict changes in protein-RNA binding affinity upon mutation. This tool, PRA-MutPred, aids in understanding disease-related mutations by analyzing structural and sequence features.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Interactions

Background:

  • Protein-RNA interactions are crucial for cellular function.
  • Mutations in proteins can alter these interactions, leading to diseases.
  • Quantifying the change in binding affinity (ΔΔG) upon mutation is key to understanding these effects.

Purpose of the Study:

  • To develop a machine-learning model for predicting ΔΔG in protein-RNA complexes.
  • To identify key features influencing binding affinity changes.
  • To create a user-friendly web server for these predictions.

Main Methods:

  • Collected 710 experimentally determined ΔΔG values from 134 protein-RNA complexes.
  • Generated diverse sequence and structural features (conservation, residue, network, interface).
  • Developed a support vector regressor model.

Main Results:

  • The model achieved a correlation of 0.75 and a mean absolute error of 0.84 kcal/mol in jack-knife tests.
  • Structural features like contact potentials, interface atom contacts, and solvent accessibility were most influential.
  • A web server, PRA-MutPred, was created for predicting protein-RNA binding affinity changes.

Conclusions:

  • Machine learning effectively predicts mutation-induced changes in protein-RNA binding affinity.
  • Structural features play a significant role in determining these affinity changes.
  • PRA-MutPred provides a valuable tool for researchers studying protein-RNA interactions and associated diseases.