title: Computational re-engineering of Amylin sequence with reduced amyloidogenic potential.
abstract: BACKGROUND
  The aggregation of amyloid proteins into fibrils is associated with neurodegenerative diseases such as Alzheimer's and Type II Diabetes. Different methods have explored ways to impede and inhibit amyloid aggregation. Most attempts in the literature involve applying stress to the environment around amyloids. Varying pH levels, modifying temperature, applying pressure through protein crowding and ligand docking are classical examples of these methods. However, environmental stress usually affects molecular pathways and protein functions in the cell and is challenging to construct in vivo. In this paper, we explore destabilizing amyloid proteins through the manipulation of genetic code to create beneficial substitute molecules for patients with certain deficiencies.
  
  
  RESULTS
  To unravel sequence mutations that destabilize amyloid fibrils yet simultaneously conserve native fold, we analyze the structural landscape of amyloid proteins and search for potential areas that could be exploited to weaken aggregation. Our tool, FibrilMutant, analyzes these regions and studies the effect of amino acid point mutations on nucleation and aggregation. This multiple objective approach impedes aggregation without stressing the cellular environment. We identified six main regions in amyloid proteins that contribute to structural stability and generated amino acid mutations to destabilize those regions. Full length fibrils were built from the mutated amyloid monomers and a dipolar-solvent model capturing the effect of dipole-dipole interactions between water and very large molecular systems to assess their aqueous stability was used to generate energy plots.
  
  
  CONCLUSIONS
  Our results are in agreement with experimental studies and suggest novel targeted single point mutations in the Amylin protein, potentially creating a better therapeutic agent than the currently administered Pramlintide drug for diabetes patients.
authors.affiliation: School of Computer Science, McGill University, Montreal, H3A OC6, Canada. mohamed.smaoui@mail.mcgill.ca.|McGill Center for Bioinformatics, McGill University, Montreal, H3A OC6, Canada. mohamed.smaoui@mail.mcgill.ca.
authors.forename: Mohamed R
authors.initials: MR
authors.lastname: Smaoui
authors.affiliation: School of Computer Science, McGill University, Montreal, H3A OC6, Canada. jeromew@cs.mcgill.ca.|McGill Center for Bioinformatics, McGill University, Montreal, H3A OC6, Canada. jeromew@cs.mcgill.ca.
authors.forename: Jérôme
authors.initials: J
authors.lastname: Waldispühl
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D007004:Hypoglycemic Agents / Q000494:pharmacology; D058228:Islet Amyloid Polypeptide / Q000737:chemistry* / Q000235:genetics* / Q000494:pharmacology; D008958:Models, Molecular; D017354:Point Mutation; D066329:Protein Aggregates / Q000235:genetics; D017510:Protein Folding; D055550:Protein Stability; D012984:Software
chemical_list: D007004:Hypoglycemic Agents; D058228:Islet Amyloid Polypeptide; D066329:Protein Aggregates; C105254:pramlintide