PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions.

Area of Science:

• Molecular Biology
• Computational Biology
• Genetics

Background:

• Protein-RNA interactions are vital for cellular functions like gene expression and protein synthesis.
• Missense mutations impacting these interactions are implicated in various diseases.
• Predicting mutation effects on protein-RNA binding is crucial for understanding disease mechanisms.

Purpose of the Study:

• To introduce PremPRI, a novel computational method for predicting the impact of single mutations on protein-RNA interactions.
• To quantitatively assess changes in binding affinity due to mutations in RNA-binding proteins.

Main Methods:

• PremPRI utilizes a multiple linear regression scoring function.
• The function incorporates three sequence-based and eight structure-based features.
• The model was trained and validated on 248 mutations from 50 protein-RNA complexes.

Main Results:

• PremPRI demonstrated strong agreement between predicted and experimental binding affinity changes (Pearson correlation coefficient of 0.72).
• The method achieved a root-mean-square error of 0.76 kcal·mol⁻¹.
• PremPRI outperformed three existing computational methods in prediction accuracy.

Conclusions:

• PremPRI accurately predicts the effects of mutations on protein-RNA binding affinity.
• The tool can identify functionally significant genetic variants.
• PremPRI aids in elucidating molecular mechanisms and designing novel protein-RNA interaction inhibitors.