Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing “Omics Data” to Druggability Simulations

Multiple
sclerosis (MS) is the most common chronic inflammatory
demyelinating disease of the central nervous system. While the drugs
currently available for MS provide symptomatic benefit, there is no
curative treatment. The emergence of large-scale multiomics data and
network theory provide new opportunities for drug discovery in MS,
as these are promising strategies for developing novel drugs. In this
study, we proposed a computational framework that combined biomolecular
network modeling and structural dynamics analysis to facilitate the
discovery of new drugs with potential activity in MS. First, we developed
a new shortest path-based algorithm that prioritized differentially
expressed genes using a newly topological and functional exploration
of protein–protein interaction network. Then, pathway enrichment
analysis and an assessment of target druggability suggested that TNF-α-induced
protein 3 (TNFAIP3), which is involved in NF-κ
B signaling, could be a potential therapeutic target for MS. Finally,
druggability simulations and mutation enrichment analysis of the TNFAIP3
dimer presented two druggable sites. Follow-up pharmacophore model-based
virtual screening of the two sites yielded 30 hit compounds with low
energy scores. In summary, this novel method based on analyzing “omics
data” and performing druggability simulations, is a systematic
approach that unravels disease mechanisms and links them to the chemical
space to develop treatments and can be applied to other complex diseases.