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1. Self-assembly of the PIC dye molecules
1. Self-assembly of the PIC dye molecules
Our aim in this project is to investigate the aggregation behavior of pseudoisocyanine chloride (PIC) dyestuff, which is recognized for its tendency to form fibril-like structures called J-aggregates. These J-aggregates bear resemblance to the amyloid formations observed in proteins known to exhibit amyloidogenic behavior. The previous studies suggested that the polyethylene glycol hinders and Ficoll-400 promotes the self-assembly of PIC dyes. To gain a deeper insight at the molecular level, we employed an enhanced umbrella sampling method using molecular dynamics simulations to investigate the impact of aqueous ethylene glycol on the solvation thermodynamics of PIC dyes that form H-type and J-type oligomers.
2. Polymer architecture and charged molecular crowders
2. Polymer architecture and charged molecular crowders
our aim of this project is to understand the collapse equlibria of the polymer with different architectures, in the presence of charged crowders concentration. As the investigations into a polymer chain has drawn considerable attention, largely due to its similarity to the two-state conformational scenario observed in protein. Emerging biomedical applications of these polymer-based smart materials include controlled drug-delivery and release systems, smart surfaces for cell adhesion and migration tissue engineering, and bioseparation. The insights from these polymers have implications for designing crowding-sensitive smart materials for tailored biomedical applications.
3. β-hairpin peptide and charged molecular crowders
3. β-hairpin peptide and charged molecular crowders
We build on insights obtained from simple Lennard-Jones (LJ) polymer models and extend these concepts to biological systems. So, we study the structural response of a β-hairpin peptide in the presence of charged crowders. This system provides a realistic and stringent test of how electrostatic crowding affects peptide stability and folding behavior, allowing us to examine whether the mechanisms learned from simple polymer models can explain conformational changes in peptides.
4. Automation of MD simulations
4. Automation of MD simulations
This project focuses on the automation of molecular dynamics (MD) simulations using AI-driven agentic frameworks. The goal is to develop intelligent workflows that can autonomously set up, execute, and analyze large-scale MD simulations with minimal human intervention.
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