Research

The research in our computational and theory group is devoted on realistic modeling of biological systems, biologically inspired synthetic materials, and catalytic processes. We use atomistic and coarse grained classical molecular dynamics (MD), ground and excited state quantum chemistry calculations, quantum mechanics / molecular mechanics (QM/MM) methods, free energy methods, as well as analytic methods.

Inhibitor Designing for Efflux Pump

Efflux pumps are a subsection of RND superfamily which catalyse the extrusion process of foreign materials, thereby plays a mainstream role towards multidrug Resistance in gram negative bacteria. The cell wall of the gram negative bacteria consist of outer and inner membrane. In a way these membrane provides a structural rigidity to the efflux pumps. The pumps are tripartite in nature that consists of an inner membrane anchored protein, an outer membrane protein and an adapter protein that connects the two. The inner membrane anchored protein is homotrimeric and the conformational interplay between these there protomer results contraction and expansion of the distal binding pocket situated in this region. The contraction of the pocket force the drug to be pushed out of that region. Studies have shown that the energy required for the efflux pump to operate is supplied by the proton motive force. A large spectrum of antibiotics are driven out by these pumps thus it becomes a potential threat. To combat this situation molecules are designed which can occupy the binding sites of the pump and cease the conformational interconversion of the protomers, termed as efflux pump inhibitors (EPI).

Inhibitor Designing for Epidermal growth factor receptor (EGFR) Kinase

Epidermal Growth Factor Receptor belongs to the family of receptor tyrosine kinases (RTKs). RTKs are crucial in cell signalling and are involved in various cellular processes, including cell growth, proliferation, differentiation, and survival. Kinases are enzymes that catalyse phosphorylation, in which phosphate groups from ATP (adenosine triphosphate) are transferred to specific target proteins. The kinase activity is initiated when a particular ligand, such as epidermal growth factor (EGF), binds to the EGFR. EGFR undergoes a conformational change upon ligand binding, which is critical for its signalling activity. However, capturing the full range of conformational dynamics of EGFR kinase remains challenging. Overactive EGFR signalling can lead to uncontrolled cell growth and tumour formation, such as non-small cell lung cancer (NSCLC). Inhibitors, such as gefitinib and erlotinib, have been developed as targeted therapies for certain types of cancer, particularly those with EGFR mutations. These inhibitors block the ATP-binding site of the kinase domain, thereby preventing over-phosphorylation and downstream signalling. While EGFR kinase inhibitors have shown remarkable efficacy in certain cancers, resistance to these drugs eventually develops in many patients. Investigating the underlying mechanisms of drug resistance is crucial for developing strategies to overcome or prevent resistance. Allosteric modulation, which involves targeting alternative sites on the EGFR protein, can open up new possibilities for therapeutic design and lower the likelihood of resistance. Computational methods, including virtual screening, molecular docking and molecular dynamics can identify potential allosteric sites and design inhibitors that target them. Exploring allosteric modulation of EGFR kinase could open up novel therapeutic avenues.

Polypeptides as Therapeutics

Lipopeptides are amphiphilic molecules that contain a short peptide (acyclic or cyclic) attached to a hydrophobic alkyl chain. In addition to the 20 essential amino acids (AA), the peptide subpart may contain a D-isomer of AA, non-standard AA, and substituted AA. Common lipopeptides include Surfactin, Daptomycin, and Polymyxins. The interplay between the hydrophilic and hydrophobic residues allows lipopeptide to intercalate in various membranes enabling it to be an antiviral, antibacterial, antifungal, vaccine adjuvant, and anticancer therapeutic drug. In the lab, we perform the mechanistic study of lipopeptide (FADDI-019, Daptomycin, and C6-Pep) interaction with bacterial and cancer membranes using molecular dynamics (MD) , constant pH MD (CpHMD), and enhanced sampling methods. We are also developing new Daptomycin analogs for action against daptomycin-resistant gram-positive bacteria using AI (Deep and Graph Neural Networks).

Inhibitor Designing for Leucine-rich repeat kinase 2 (LLRK2)

Parkinson’s disease (PD) -- a neurodegenerative brain disorder is associated with the mutations (more than 40) in the Leucine-rich repeat kinase-2 (LRRK2) gene. LRRK2 has several domains, armadillo (ARM), an ankyrin (ANK), a leucine rich repeat (LRR), a GTPase (ROC and COR), a kinase and a WD40 domains with various mutations (Figure 1). The mutation G2019S in kinase domain is the most celebrated one among them which increases the kinase activity by 2- to 4-fold. States of kinase (active and inactive) are associated with the DYG/S (residue 2017-2019) motif of activation loop. The exchange in position between residues D2017 and Y2018 is one of the causes of change in states active (DYG-in) to inactive (DYG-out) and vice versa. G2019S mutation provide additional stabilization of DYG-in state of kinase through the formation of hydrogen bond with residues Q1919 and E1920. One of the aims of our work is to study of these phenomena in molecular level and explore the role of the residues Q1919 and E1920 in increasing kinase activity. As the LRRK2 inhibitors are reported to have the potential in the protection of dopaminergic neuron loss for PD animal models, the experimental and computational researches related to the discovery of safe, LRRK2 selective, potent and CNS penetrant drug molecules are the topic of great interest. So, another aspect of our research is to study of the interaction between different kinase inhibitors and LRRK2 kinase and further model the new type of drug molecules using computational tools.

Enzyme Catalysis

Catechol O-methyltransferase (COMT) is a SAM- and Mg2+-dependent methyltransferase that regulates neurotransmitters through methylation. We perform longer dynamics (up to 350 ns) to quantify interconversion between bidentate and monodentate binding poses. We provide a systematic determination of the relative free energy of the monodentate and bidentate structures in order to identify whether structural differences alter the nature of the methyl transfer mechanism and source of enzymatic rate enhancement. We demonstrate that the bidentate and monodentate binding modes are close in energy and analysis of interactions in the two binding modes reveals that the driving force for monodentate catecholate orientations in classical molecular dynamics simulations is derived from stronger electrostatic stabilization afforded by alternate Mg2+ coordination with strongly charged active site carboxylates. Mixed semi-empirical-classical (SQM/MM) substrate C-O distances (2.7 Å) for the bidentate case are in excellent agreement with COMT X-ray crystal structures, as long as charge transfer between the substrates, Mg2+, and surrounding ligands is permitted. SQM/MM free energy barriers for methyl transfer from bidentate and monodentate catecholate configurations are comparable at around 21–22 kcal/mol, in good agreement with experiment (18–19 kcal/mol).

Self-assembly of Nanoscale Systems

We use atomistic molecular dynamics (MD) simulations to show that water nanodroplets can activate and guide the folding of planar graphene nanostructures. We found that nanodroplets can induce rapid bending, folding, sliding, rolling, and zipping of the planar nanostructures, which can lead to the assembly of nanoscale sandwiches, capsules, knots, and rings. We also show that carbon nanotubes can activate and guide on their surfaces and in their interiors the self-assembly of planar graphene nanostructures of various sizes and shapes.

Self-assembly of Patchy Particles as a Route to Complex Lattice Structure

We propose a strategy for robust high-quality self-assembly of nontrivial periodic structures out of patchy particles and investigate it with Brownian dynamics simulations. Its first element is the use of specific patch-patch and shell-shell interactions between the particles, which can be implemented through differential functionalization of patched and shell regions with specific DNA strands. The other key element of our approach is the use of a layer-by-layer protocol that allows one to avoid the formation of undesired random aggregates. As an example, we design and self-assemble in silico a version of a double diamond lattice in which four particle types are arranged into bcc crystal made of four fcc sublattices. The lattice can be further converted to cubic diamond by selective removal of the particles of certain types. Our results demonstrate that by combining the directionality, selectivity of interactions, and the layer-by-layer protocol, a high-quality robust self-assembly can be achieved. We develop a molecular dynamics software for the simulation of patchy particles. We implement a new force field analogous to Kern-Frenkel model but suitable for MD simulations.

Semi-classical and ab-initio Calculations of Carbonaceous Species

We investigate in details the nucleation processes of carbon and/or hydrogen atoms in interstellar medium by molecular dynamics simulation using adaptive intermolecular bond-order potential (AIREBO). We study the influence of temperature, particle density and hydrogen atoms addition on the structure of clusters. We use ab-initio molecular dynamics simulations to show that the polarization of graphene flakes by halide ions can lead to the formation of covalent bonds between the ions and graphene. We show that the covalent bonding depends on the size and shape of the graphene nanostructure as well as the type of halide ion. By determining the carbon-halide bond distance, bond energy and potential energy surface, we show that, unlike chloride ion, fluoride ion can form covalent bond with any graphene flake irrespective of the size and shape, with the polarization and charge distribution playing a distinct role in the interaction process. We found that bonding at the zigzag edge is the most energetically preferable however the ion can bind to any carbon atom on the surface when it is placed at a sufficiently close distance.

Self-assembly of Large Molecular Nanosystems

Collaborate with the experimental group, we examine how the chemical character of the synthesized amphiphilic dendrons affects the resulting self-assembled nanostructures. We model a variety of dendrons to examine 1) their conformations and 2) assemblies in aqueous solutions. We calculate a number of details about the dendron assemblies of significant interests for the experimentalists (hydrophobic core size, thickness of the PEG corona, etc.). We show by coarse-grained MD simulation that lipid micelles with encapsulated hydrophobic molecules (drugs) can be controllably prepared in aqueous solutions with the assistance of carbon nanotubes. First, we fill the nanotube with hydrophobic molecules and cover its surface with amphiphilic lipids. Once the molecules inside the nanotube are pressurized and the lipids on its surface are translated by the solution flowing around it, micelles or bicelles with encapsulated molecules are sequentially formed at the nanotube tip.