WEB 2646
Supervisor: Dr Tamara Bidone
My professional background lies in Computational Biophysics and Biochemistry, with experience in biomolecular modeling and molecular dynamics simulations. My PhD research (2019-22) at the University of Leeds, UK, involved modeling the complete human T cell antigen receptor and the tyrosine kinase protein, Lck, and studying their molecular mechanisms in initiating intracellular signaling in T cells using molecular dynamics simulations in both atomistic and coarse-grained resolutions. The highlight of my PhD research was that it presented the first complete models of the T cell receptor and Lck associated with a realistic T cell membrane model. Following my PhD, I pursued postdoctoral research (2022-24) at the University of Oxford, UK, which primarily involved modeling and performing large-scale coarse-grained simulations of the E. coli bacterial outer membrane, which is an asymmetric membrane containing lipopolysaccharides and phospholipids. With the help of Archer2 (the national supercomputing facility in the UK), my research provided novel molecular-level insights into what happens when a realistic concentration of antibiotic molecules, such as polymyxin, associates with the E. coli outer membrane – the first layer of bacterial defense, with a wider implication in developing antimicrobial peptides to combat antibiotic resistance in Gram-negative bacteria. My PhD work (particularly on the human T cell receptor) and some collaborative work (on SPNS2, a lysolipid transporter in humans) during my postdoctoral appointment in Oxford also familiarized me with research on intrinsically disordered proteins.
I am currently a Postdoctoral Research Associate in Dr Tamara Bidone’s research group at the SCI Institute, primarily working on developing coarse-grained models to decipher the molecular mechanisms of microtubules. My work would also include studying the dynamics of adhesion proteins from all-atom molecular simulations. Overall, my responsibilities at the SCI involve applying coarse-graining, machine learning, and enhanced sampling methods to conduct computational research on biomolecules.