Abstract

In this work the quantum chemistry Tersoff potential in combination with classical trajectory calculations was used to investigate the interaction of the DNA molecule with a carbon nanotube (CNT). The so-called hybrid approach—the classical and quantum-chemical modeling, where the force fields and interaction between particles are based on a definite (but not unique) description method, has been outlined in some detail. In such approach the molecules are described as a set of spheres and springs, thereby the spheres imitate classical particles and the spring the interaction force fields provided by quantum chemistry laws. The Tersoff potential in hybrid molecular dynamics (MD) simulations correctly describes the nature of covalent bonding. The aim of the present work was to estimate the dynamical and structural behavior of the DNA-CNT system at ambient temperature conditions. The dynamical configurations were built up for the DNA molecule interacting with the CNT. The analysis of generated МD configurations for the DNA-CNT complex was carried out. For the DNA-CNT system the observations reveal an encapsulation-like behavior of the DNA chain inside the CNT chain. The discussions were made on Von Neumann’s Theory Projective Measurement Quantum Computational mining combined molecular docking and pharmacophore-based approach on Molecular Dynamics Simulations of the DNA-CNT Interaction Process to Hybrid Quantum Chemistry Potential and Classical Trajectory prediction strategies through a probabilistic fusion method for target ranking of anti-HIV-I P24-derived peptide mimic promising pharmacophores possible use of the DNA-CNT complex as a candidate material in drug delivery and related systems.

Keywords

Molecular Dynamics Simulations; DNA-CNT Interaction Process; Hybrid Quantum; Chemistry Potential; Classical Trajectory; Approach; Von Neumann’s Theory; Projective Measurement; Quantum Computation; Computational mining approach; combined molecular; docking-based; pharmacophore-based; target; prediction strategy; probabilistic fusion method; target ranking; anti-HIV-I P24-derived peptide; mimic promising pharmacophores;