PremPRI

PremPRI predicts changes in protein-RNA binding affinity caused by single mutations to quantify their impact on protein-RNA interactions.

Key Features:

• Quantitative Prediction: Calculates alterations in binding affinity due to specific mutations, providing a quantitative measure of their impact.
• Scoring Function: Employs a multiple linear regression scoring function that integrates 11 sequence- and structure-based features and is parameterized on data from 248 mutations across 50 protein-RNA complexes.
• Performance Metrics: Demonstrates a Pearson correlation coefficient of 0.72 and a root-mean-square error of 0.76 kcal mol⁻¹ for predicted binding affinity changes and outperforms three other existing methods.

Scientific Applications:

• Functional Variant Identification: Predicts the impact of mutations on protein-RNA interactions to aid identification of functionally significant variants.
• Molecular Mechanism Elucidation: Helps interpret how specific mutations affect molecular mechanisms of protein-RNA binding.
• Drug Design: Informs design strategies for inhibitors targeting protein-RNA interactions by predicting mutation-induced changes in binding affinity.

Methodology:

Requires the 3D structure of a protein-RNA complex as input and uses sequence and structural information with an 11-feature multiple linear regression model parameterized on 248 mutations from 50 protein-RNA complexes to predict the effects of single mutations on binding affinity.