Deepa Gopakumar

Computational Modeling and Simulation of Biomolecular Motors

Molecular motors can be considered as biological molecular devices that are indispensable agents or machines for movement in living organisms. The most common motor proteins are Myosin, Kinesin and Dynein which are responsible for nanoscale cellular and molecular movement. In this work, the above mentioned protein molecules have been subjected to structural and sequence analysis, modeling and molecular dynamics simulation to study their properties and control aspects. Structural and sequence studies, interactional analysis and thermodynamic characterization effectively support the possibility for designing of biomolecular systems with these molecular motors as the source of driving force. Most of these protein molecules are found to be thermodynamically stable keeping active sites for interaction with ligands. It was also found that most of the protein molecules from the kinesin family possessed an appreciably high stability even after the temperature evolution of the molecule. This suggests the possibility for designing kinesin-based biomolecular systems.

Insilico modeling and simulation of magnetic nanoparticles for the biological cell isolation technique

Magnetic nanoparticles (MNPs) can be used in a wide variety of biomedical applications like contrast agents for magnetic resonance imaging, magnetic labeling, controlled drug release, hyperthermia, and in cell isolation. Most of these applications need distinct and controllable interactions between the MNPs and living cells and can be made possible by a proper functionalization technique. This paper describes a computational approach for the identification of magnetic nanoparticles for the development, design, and demonstration of a novel, incorporated system for selective and rapid removal of biological, chemical, and radioactive biohazards from human body. The attraction between an external magnetic field and the MNPs facilitate separation of a wide variety of biological materials. This principle can be used for the isolation and aggregation of wandering cancer cells from the blood or the bone marrow to make a proper and early diagnosis of leukemia. Similarly, toxins, kidney stones, and other unwanted particles in the human body can be easily diagnosed and removed by the same technique. Nanoparticle-sized iron oxides have been studied in this work by computational modeling and molecular dynamic (MD) simulation techniques. Structural, thermodynamic, and magnetic properties have been formulated. In this work, nanoparticles of size varying from 0.5 to 2.5 nm have been analyzed. Cell isolation ability of the nanoparticles has been compared based on the computational results. MNPs are biologically activated and permitted to bind with the targeted cells through various pathways, thereby allowing certain cellular compartments to be specifically addressed. Once the cells are identified, the preferred cellular compartments can be magnetically isolated and removed with the help of an external magnetic field. Out of the iron oxides analyzed in this work, 1.1 nm Fe3O4 is found to be most interacting with leukemia protein. Hence, leukemia cells can be effectively targeted, separated, and removed using Fe3O4 of the suggested dimension.

Quantum Mechanical Characterization of Single Walled Carbon Nanotube (SWCNT) to Evaluate Stability and Conductivity

Single Walled Carbon Nanotube (SWCNT) is known to have unique thermodynamic and electrical properties which mainly depends upon the chiral index values (n, m). Quantum mechanical modeling and simulation studies were conducted for these samples to characterize the above properties. The energy gap of conducting carbon nano tubes has been found to be negligibly small. Armchair configuration with (n=m) is found to be highly stable. All these samples are found to be conducting. Structures with n and m values (8,7), (7,8), (7,6), (7,2), (6,5), (5,3) (4,5) and (3,5) are found to be unstable and are all semiconductors.

In‐Silico Characterization of Multi Walled Carbon Nanotubes (MWCNTS) to Develop Gas Sensors

This paper reports the computational modeling and simulation of ‘Multi walled carbon nanotube’ (MWCNT) to characterize the adsorption of gases. The computational results were properly evaluated experimentally. CNT is known to undergo electrical breakdown on exposure to gases. This unique property has been used in designing CNT‐based gas sensors. The electrical resistance of ‘large diameter MWCNT’ was found to decrease in the presence of air after experiencing electrical breakdown, while ‘pristine MWCNTs’ were not found to be appreciably sensitive. The deformation and the corresponding mechano electric effects of CNT have been well predicted. Composite electric field guided assembly (CEGA) method was used to locate a single MWCNT between electrodes. The electrical characteristics of the deposited MWCNTs were observed using I–V‐curves. The large‐diameter MWCNT showed better sensitivity as they possess more distorted shells that can create more adsorption sites for oxygen molecules. The oxidation of CNT begi...

Thermal Analysis of Nanofluids Using Modeling and Molecular Dynamics Simulation

Nanofluids are nanotechnology‐based heat transfer fluids obtained by suspending nanometer‐sized particles in conventional heat transfer fluids in a stable manner. In many of the physical phenomena such as boiling and properties such as latent heat, thermal conductivity and heat transfer coefficient, there is significant change on addition of nanoparticles. These exceptional qualities of Nanofluids mainly depend on the atomic level mechanisms, which in turn govern all mechanical properties like strength, Young’s modulus, Poisson’s ratio, compressibility etc. Control over the fundamental thermo physical properties of the working medium will help to understand these unique phenomena of nanofluids to a great extent. Macroscopic modeling approaches, which are based on conventional relations of thermodynamics, have been proved to be incompetent to explain this difference. Atomistic ‘modeling and simulation’ has been emerged out as an efficient alternative for this. The enhancement of thermal conductivity of wat...

Quantum Mechanical Modeling and Molecular Dynamic Simulation of Ruthenium (Ru) Polypyridyl Complexes to Study Feasibility of Artificial Photosynthesis

The photochemical reaction is initiated by a charge separation process in the reaction center (RC) complex. Major research in this regard is to analyze the light driven electron transfer and to study the response of the molecule in which the RC is embedded, stabilizing the charge separation process in photosynthesis. In research related to artificial photosynthesis, modeling and simulation of highly energetic photosensitizers have been always a choice. Ruthenium (II) polypyridyl complexes are widely used in this regard. In this work these complexes have been successfully designed in the computational manner with a quantum mechanical model in the density functional level of theory (DFT) based on the local density approximation energy expression augmented by BLYP corrections using the DND basis set with ‘all electron core’ treatment option. In the analysis, band energy, electronic population, vibrational frequency, thermodynamic functions and energies of Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) have been used. Molecular dynamic simulation studies were conducted to characterize the dynamic properties. In the analysis, the ‘metal ligand charge transfer transition’ (MLCT) in these complexes has been studied in detail. Thermodynamic stability of these complexes has been compared. The π-electron acceptor properties of the tetra cyano ruthenium poly pyridyl complexes has been found to be in the order of bpz (2,2’-bipyrazine)≫ bptz (3,6-bis-(2-pyridyl)-1,2,4,5-tetrazine) ≫ dpp (2,3-bis(2’-pyridyl) pyrazine) ≫ bpy (2,2’-pyridine). The possibility for these compounds to be used as photosynthetic targets will also follow the same order.