Abstract. Monoclonal antibodies (mAbs) as a class of therapeutic molecules are finding an increasing demand in the biotechnology industry for the treatment of diseases like cancer and multiple sclerosis. A key challenge associated to successful commercialization of mAbs is that from the various physical and chemical instabilities that are inherent to these molecules. Out of all probable instabilities, aggregation of mAbs has been a major problem that has been associated with a change in the protein structure and is a hurdle in various upstream and downstream processes. It can stimulate immune response causing protein misfolding having deleterious and harmful effects inside a cell. Also, the extra cost incurred to remove aggregated mAbs from the rest of the batch is huge. Size exclusion chromatography (SEC) is a major technique for characterizing aggregation in mAbs where change in the aggregates' size over time is estimated. The current project is an attempt to understand the rate and mechanism of formation of higher order oligomers when subjected to different environmental conditions such as buffer type, temperature, pH, and salt concentration. The results will be useful in avoiding the product exposure to conditions that can induce aggregation during upstream, downstream, and storage process. Extended Lumry-Eyring model (ELE), Lumry-Eyring Native Polymerization model (LENP), and Finke-Watzky model (F-W) have been employed in this work to fit the aggregation experimental data and results are compared to find the best fit model for mAb aggregation to connect the theoretical dots with the reality.

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Earliest events in the aggregation process, such as single molecule reconfiguration, are extremely important and the most difficult to characterize in experiments. To this end, we have used well-tempered bias exchange metadynamics simulations to determine the equilibrium ensembles of an insulin molecule under amyloidogenic conditions of low pH and high temperature. A bin-based clustering method that uses statistics accumulated in bias exchange metadynamics trajectories was employed to construct a detailed thermodynamic and kinetic model of insulin folding. The highest lifetime, lowest free-energy ensemble identified consisted of native conformations adopted by a folded insulin monomer in solution, namely, the R-, the R-, and the T-states of insulin. The lowest free-energy structure had a root mean square deviation of only 0.15 nm from native x-ray structure. The second longest-lived metastable state was an unfolded, compact monomer with little similarity to the native structure. We have identified three additional long-lived, metastable states from the bin-based model. We then carried out an exhaustive structural characterization of metastable states on the basis of tertiary contact maps and per-residue accessible surface areas. We have also determined the lowest free-energy path between two longest-lived metastable states and confirm earlier findings of non-two-state folding for insulin through a folding intermediate. The ensemble containing the monomeric intermediate retained 58% of native hydrophobic contacts, however, accompanied by a complete loss of native secondary structure. We have discussed the relative importance of nativelike versus nonnative tertiary contacts for the folding transition. We also provide a simple measure to determine the importance of an individual residue for folding transition. Finally, we have compared and contrasted this intermediate with experimental data obtained in spectroscopic, crystallographic, and calorimetric measurements during early stages of insulin aggregation. We have also determined stability of monomeric insulin by incubation at a very low concentration to isolate protein-protein interaction effects.

We report a structure-based approach to design peptides that can bind to aggregation-prone, partially folded intermediates (PFI) of insulin, thereby inhibiting early stages of aggregation nucleation. We account for the important role of the modular architecture of protein–protein binding interfaces and tertiary structure heterogeneity of the PFIs in the design of peptide inhibitors. The determination of association hotspots revealed that two interface segments are required to capture majority contribution to insulin homodimer binding energy. The selection of peptides that will have a high probability to inhibit insulin self-association was done on the basis of similarity in binding interface coverage of PFI residues in the peptide–PFI complex and the native–PFI dimer. Data on aggregate growth rate and secondary structure for formulations incubated under amyloidogenic conditions show that designed peptides inhibit insulin aggregation in a concentration-dependent manner. The mechanism of aggregation inhibition was probed by determining the enthalpy of peptide–insulin binding and peptide micellization using isothermal titration calorimetry. Finally, the effect of designed peptides on insulin activity was quantified using a spectrophotometric assay for glucose uptake by HepG2 cells.

Benzothiazole-2-carboxyarylalkylamides are reported as a new class of potent anti-mycobacterial agents.