Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics...

Area of Science:

• Computational biology
• Neuroimmunology
• Drug discovery

Background:

• Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with no current cure.
• Existing MS treatments offer symptomatic relief but lack curative potential.
• Large-scale multiomics data and network theory present novel avenues for MS drug discovery.

Purpose of the Study:

• To develop a computational framework integrating biomolecular network modeling and structural dynamics for MS drug discovery.
• To identify novel therapeutic targets and potential drug candidates for multiple sclerosis.

Main Methods:

• Developed a shortest path-based algorithm for prioritizing differentially expressed genes in protein-protein interaction networks.
• Utilized pathway enrichment analysis and target druggability assessment to identify potential therapeutic targets.
• Performed druggability simulations and virtual screening to identify potential drug compounds.

Main Results:

• Identified TNF-α-induced protein 3 (TNFAIP3), involved in NF-κB signaling, as a potential therapeutic target for MS.
• Discovered two druggable sites on the TNFAIP3 dimer through simulations and mutation analysis.
• Identified 30 hit compounds with low energy scores via pharmacophore model-based virtual screening.

Conclusions:

• The novel computational framework systematically unravels MS disease mechanisms and links them to chemical space for treatment development.
• This approach, combining omics data analysis and druggability simulations, can be applied to discover treatments for other complex diseases.
• TNFAIP3 emerges as a promising therapeutic target for multiple sclerosis.