Speaker
Description
Cellular interiors are densely crowded with proteins and other macromolecules, leading to excluded-volume and weak enthalpic effects that can strongly modulate protein folding, binding, and aggregation. To study these phenomena, we are developing a combined computational-experimental framework to quantify crowding effects using a well-defined “model cytosol” and a sequence-aware coarse-grained (CG) molecular dynamics (MD) model.
On the computational side, we are adapting an in-house Cα dual-basin CG model to simulate multi-protein mixtures with explicit inter-chain interactions. Two new sequence-guided terms have been implemented: (i) a short-range effective hydrophobic attraction and (ii) a Debye-screened electrostatic potential with pH-dependent residue charges. In parallel, we are building a six-protein experimental mixture (2:2:3:2:1:1 of horse myoglobin, bovine β-lactoglobulin, chicken albumin, human hemoglobin, horse hemoglobin, and bovine serum albumin) and using resultant data from dynamic light scattering experiments to calibrate model parameters.
This model will be used to investigate the fold-switching dynamics of human chemokine XCL1 (lymphotactin), a metamorphic protein involved in the immune response that transitions between two distinct native folds depending on local conditions. We aim to quantify how cytosol-like crowding and weak enthalpic interactions modulate XCL1 fold-switching kinetics using CG MD simulations, with key predictions testable by NMR. More broadly, the framework offers a sequence-informed way to predict nonspecific association and early aggregation risk in crowded environments, with implications for therapeutic developability and formulation.
| Keyword-1 | Molecular Dynamics |
|---|---|
| Keyword-2 | XCL1 |
| Keyword-3 | Dynamic Light Scattering |