AI-based protein structure databases have the potential to accelerate rare diseases research: AlphaFoldDB and the case of IAHSP/Alsin

Abstract

Artificial intelligence (AI)-based protein structure databases are expected to have an impact on drug discovery. Here, we show how AlphaFold could support rare diseases research programs. We focus on Alsin, a protein responsible for rare motor neuron diseases, such as infantile-onset ascending hereditary spastic paralysis (IAHSP) and juvenile primary lateral sclerosis (JPLS), and involved in some cases of amyotrophic lateral sclerosis (ALS). First, we compared the AlphaFoldDB human Alsin model with homology models of Alsin domains. We then evaluated the flexibility profile of Alsin and of experimentally characterized mutants present in patients with IAHSP. Next, we compared preliminary models of dimeric/tetrameric Alsin responsible for its physiological action with hypothetical models reported in the literature. Finally, we suggest the best animal model for drug candidates testing. Overall, we computationally show that drug discovery efforts toward Alsin-involving diseases should be pursued.

Keywords: AI; AlphaFold; Alsin; Druggability; IAHSP; Protein modeling; Rare diseases.