Effect-specific analysis of pathogenic SNVs in human interactome: Leveraging edge-based network robustness

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Understanding the functional impact of genetic variants on molecular networks is crucial.
• Current methods often lack comprehensive functional information regarding variant-induced network rewiring.

Purpose of the Study:

• To develop and apply a structure-based prediction tool for assessing the network rewiring effects of genetic variants.
• To analyze the impact of genetic variants on the human interactome and understand network robustness.

Main Methods:

• Utilized large-scale homology modeling and extracted native protein structures from the Protein Data Bank (PDB).
• Employed a novel tool, SNP-IN, for structure-based prediction of genetic variant effects on molecular networks.
• Analyzed network rewiring using the edge-based network robustness concept.

Main Results:

• The SNP-IN tool successfully predicted network rewiring effects for a significantly larger number of variants compared to experimental methods.
• Confirmed widespread perturbations within the human interactome attributable to genetic variants.
• Characterized network rewiring behavior through the lens of edge-based network robustness.

Conclusions:

• Structure-based prediction offers a scalable approach to functionally annotate genetic variants in molecular networks.
• Genetic variants induce substantial rewiring in the human interactome, impacting network robustness.
• This study provides a framework for investigating variant-specific network perturbations and their functional consequences.