Recent studies have highlighted the important role of protein unfolding thermodynamics and reconfiguration kinetics for protein aggregation. Further, it has been shown that identification of short-lived aggregation prone species can help in design of strategies to inhibit aggregation. These earliest events in the aggregation process are difficult to characterize in experiments. In this talk, I will present our work on using molecular simulations to probe short timescale, rare events associated with protein unfolding. This knowledge was subsequently used to design peptides that significantly inhibit insulin aggregation. We have used an enhanced sampling molecular dynamics technique, namely bias-exchange metadynamics (BEMD), coupled with a Markovian kinetic transition network model to construct a detailed thermodynamic and kinetic model of insulin folding. We find that metastable states identified in our study compare well with experimental data obtained in spectroscopic, crystallographic, and calorimetric measurements during early stages of insulin aggregation. We then identified metastable states with a high propensity for aggregation using a combination of protein-protein docking and scoring. Hot spot residues for insulin self-association were identified and used as a template to design a small peptide mimicking a significant number of interactions made by these binding interface residues. We incubated insulin at 60<sup>o</sup>C by adding designed peptides to formulation buffer and found up to 7 times reduction in monomer loss as quantified by size exclusion chromatography. ITC measurements were used to determine enthalpy of peptide binding to insulin at non-amyloidogenic conditions. Finally, binding of insulin to its receptor in presence of peptides was confirmed using SPR.

Insulin folding landscape and in-silico design of peptide excipients to inhibit aggregation

255th National Meeting (2018)

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