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Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural

Cecilia Pennica¹, Gordon Hanna¹, Suhail A Islam¹

¹Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.

Journal of Molecular Biology

|June 25, 2023

Summary

We developed Missense3D-PPI, a new tool to predict damaging missense variants at protein-protein interaction sites. This method improves upon existing tools for analyzing protein networks and variant effects.

Keywords:

disease prediction protein structure protein-protein interaction variants

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Area of Science:

Structural bioinformatics
Computational biology
Genomics

Background:

Missense variants can disrupt protein structure and function.
Existing tools like Missense3D analyze single protein structures but may miss variants affecting protein-protein interactions (PPIs).

Purpose of the Study:

To develop and evaluate Missense3D-PPI, a computational tool for predicting the impact of missense variants specifically at PPI interfaces.
To assess the performance of Missense3D-PPI against existing methods for variant effect prediction.

Main Methods:

Developed Missense3D-PPI to analyze structural features of missense variants at PPI sites.
Utilized a dataset of 1,279 missense variants across 434 proteins and 545 PPI complex structures.
Benchmarked Missense3D-PPI against Missense3D and other state-of-the-art tools (BeAtMuSiC, mCSM-PPI2, MutaBind2).

Main Results:

Missense3D-PPI demonstrated superior sensitivity (44% vs 8%) and accuracy (58% vs 40%) compared to Missense3D for predicting damaging variants at PPIs (p < 10^-16).
While specificity was lower than Missense3D (84% vs 98%), Missense3D-PPI showed significantly higher accuracy than BeAtMuSiC, mCSM-PPI2, and MutaBind2.
Analysis focused on structural disruptions like altered interchain bonds and unbalanced charged residues at interfaces.

Conclusions:

Missense3D-PPI is a valuable tool for predicting the structural consequences of missense variants within protein interaction networks.
The tool enhances the analysis of missense variants impacting protein-protein interactions and is accessible via a web portal.