Rit Pratik Mishra (Supervisor: Prof Gaurav Goel)

Protein aggregation is one of the primary reasons for the degradation of protein and has also been linked to several diseases like Alzheimer’s. Importance of understanding the kinetics of protein

aggregation is fundamental in devising ways to mitigate the degradation. In recent years, with the development of high performance computing techniques, it has been possible to understand

aggregate formation at atomic level and the factors leading to aggregation which were difficult to capture with experimental techniques. These breakthroughs, coupled with understanding gained from solution experiments, can enable us to build strategies to combat aggregation, including the design and evaluation of peptides and small molecules that inhibit the growth or that facilitate the dissociation of aggregates.

However, the time-scales and length-scales relevant for aggregation makes unfeasible the use of accurate all-atom (AA) explicit solvent simulations for prediction of all steps of aggregation thermodynamics and/or kinetics. This calls the need for a multi-scale modelling which enables us to use different computational techniques for different stages of aggregation. The choice of computational technique used in our multi-scale modelling was done by finding a proper balance between “loss in resolution/accuracy while modelling the protein” and “the computational cost”. In this study, we have considered insulin as the therapeutic protein owing to its low molecular weight and significant importance in the pharmaceutical industry. We have modelled aggregation kinetics of the insulin using a widely used extended Lumry-Eyring Model [1]. The model can be briefly summarised as a native insulin monomer undergoing conformational changes, to form a aggregation prone partially folded intermediate (PFI) insulin species, which acts as a precursor for oligomer growth till the nucleus formation which is the rate limiting step. Broadly, the process can be divided into native insulin unfolding to PFI, followed by oligomer growth till the formation of nucleus and finally fibril growth and each step is modelled at a different resolution.

 In an earlier work [2] we had used AA explicit solvent simulations to identify aggregation-prone PFIs of insulin. We then used a structural bioinformatics approach to identify most stable N-PFI insulin homo-dimers, a putative aggregation pathway complex. In contrast to functional protein-receptor association, there can be multiple aggregation pathway complexes. To identify such complexes, a set of most probable conformations of the N-PFI complex were first obtained using rigid-body docking coupled with MMPBSA energies for ranking of complexes. These docked complexes were used as starting structures for several long coarse-grained molecular dynamics (CGMD) simulations to obtain an ensemble of aggregation pathway complexes. The physics based MARTINI forcefield, appropriate for non-native protein-protein interactions relevant for aggregation, was used for this set of simulations. We then used linear integer programming to identify the smallest number of distinct N-PFI complexes sufficient to represent all 38 dimers obtained from CGMD simulations. We hypothesise that this small set of N-PFI complexes can be taken as configurational-diffusion-limited transient state for insulin homo-dimer on aggregation pathway. The dimerisation rate was then calculated using the transition-complex theory on this set of N-PFI complexes. Insulin unfolding and dimerisation rates were then used as input to mesoscale Population Balance Method (PBM) based on extended Lumry-Eyring kinetics. Predictions of nucleation time and oligomeric species concentration from our multi-scale model were compared to experimental data on insulin aggregation kinetics. We are presently working on employing the developed multi-scale model to investigate effect of temperature, co-solvent, and specific residue mutations on insulin aggregation kinetics.