eSupervisor Detail

About me

Computational biophysical chemist (PhD thesis submitted, viva pending) with expertise in molecular dynamics, enhanced sampling, and peptide–receptor interactions. Author of 3 publications with 2 in progress, recognized with best poster awards. Experienced in research design, data analysis, and scientific communication, with college-level teaching experience. Committed to mentoring researchers and enabling rigorous, high-quality scientific outcomes.

Interests: My Ph.D. work explores the structural and dynamic principles of peptides, examining how they fold, stabilise, and interact with receptors, while employing molecular dynamics and enhanced-sampling techniques to guide the design of next-generation therapeutics. In my doctoral studies, (i) I focused on cyclotides, exceptionally stable plant-derived peptides characterised by a cystine-knot motif of three disulfide bonds. Using molecular dynamics simulations and Markov State Models, I investigated why nature favours the specific Cys I-IV, Cys II-V and Cys III-VI disulfide pattern and found these to be crucial for structural stability and folding efficiency. Interestingly, cyclotides fold through kinetically preferred pathways rather than purely thermodynamic ones. Markov State Model analysis showed that intramolecular disulfide pairing occurs rapidly, with a computed rate constant of ~10⁶ s⁻¹ consistent with experimental data from DsbD. Using rate networks and transition path theory, I identified the most probable pathway by which the unfolded peptide (D) evolves into the native state (N). I also studied the classification of cyclotides into bracelet and Möbius types. A unique backbone twist in Möbius cyclotides arises from a single proline adopting a rare cis conformation, likely stabilised by a neighbouring tryptophan residue. Well-Tempered Metadynamics revealed a smaller cis–trans energy difference in the wild-type, suggesting greater conformational flexibility, whereas the larger energy difference in the mutant with tryptophan replaced by alanine indicates stabilisation of the trans state, thereby reducing structural adaptability. This subtle local feature drives large-scale structural differences, highlighting nature’s fine-tuning of peptide folds and offering guiding principles for designing stable synthetic peptides. In this work, VMD, Discovery Studio Visualizer, Bio3D, PLUMED, and MATLAB were employed for structural characterisation, trajectory analysis, and data visualisation, while PyEMMA was used to construct the Markovian kinetic model. (ii) In parallel, I explored Brain Natriuretic Peptide (BNP), a key player in cardioprotection, but limited by its poor in vivo stability. As part of an interdisciplinary project, I applied Well-Tempered Metadynamics to compare the conformational free energy surfaces of wild-type BNP and engineered mutants, using distance and orientation angle as collective variables to identify mutations that enhance local stability. To complement this, Random Acceleration Molecular Dynamics simulations were used to estimate peptide–receptor residence times, providing quantitative insights into binding stability with Natriuretic Peptide Receptor-C. Together, these studies revealed strategies to improve BNP’s pharmacokinetics and therapeutic efficacy, paving the way for the rational design of next-generation peptide-based drugs for cardiovascular diseases. Moreover, I also have experience in Steered Molecular Dynamics (SMD) for investigating force-driven interactions between the poly-alanine and POPC membranes. I have recently begun exploring machine learning techniques to virtually screen potential ligands, harnessing the power of AI to accelerate drug discovery and design.