Evaluation of Theoretical Descriptors for NM
Overview
The MOPAC program performs semi-empirical (quantum mechanical) calculations for the evaluation of theoretical descriptors (molecular and electronic properties) of NMs to tackle the size of NMs efficiently, i.e. the large number of atoms in a NM. The effect of solvents can be captured by the COSMO method implemented in MOPAC. To validate the results obtained from MOPAC, the NWChem program package is proposed, applying DFT and wave function based ab initio methods for considerably smaller size of NMs. MD simulations with the GROMACS software for an extensive size of NMs are feasible and faster simulations are guaranteed by making use of GPUs. ESPResSo software performs many-particle simulations of coarse-grained atomistic or bead-spring models for a variety of systems such as polymers, liquid crystals, colloids, polyelectrolytes, and biological systems, such as proteins, DNA, and lipid membranes and therefore a valuable application to investigate bionano interactions of NMs. ESPResSo is free software licensed under the GNU Lesser General Public License. The calculation of interface properties, such as the adsorption free energies at the coarse-grained level are derived using a united atom model, a SmartNanoTox multiscale technique.
SmartNanoTox modelling tools, http://www.smartnanotox.eu/?page_id=143, were developed by H2020 project SmartNanoTox for the prediction of the biological activity of NMs. The tools include NM and biomolecule coarse-graining using Python scripts, evaluation of parameters of interactions such as potentials of mean force for biomolecular segments at the surface of NMs (using freeware Gromacs package), and prediction of protein 3D structure using free iTasser tool. SNT-MT are open source free software licensed under the GNU Lesser General Public License.
For the calculation of the theoretical descriptors, listed in Table 1, with the tools (software) available to provide NanoCommons services to potential users/stakeholders (subject to an application process), essential inputs to utilise the software capabilities are: 1) the chemical information of the NMs research study, 2) datasets (experimental or theoretical), if available, 3) the descriptor name to be evaluated and, 4) the chemical structure (molecular or crystal), if available, i.e., preferably, the cartesian coordinates (xyz atomic structure) of the NMs, as well as, if applicable, the cartesian coordinates of their adsorbates (e.g. proteins or lipids).
Table 1: Methodology and software tools for the calculation of theoretical descriptors of NMs
Descriptor name | Method | Simulation package |
Band gap | PM6, DFT | MOPAC, NWChem |
Ionization potential | PM6, HF, DFT, TDDFT | MOPAC, NWChem |
Density of states | PM6, HF, DFT | MOPAC, NWChem |
Hydrophobicity | CG | SNT-MT |
Dipole moment | PM6 (COSMO), DFT | MOPAC, NWChem |
Atomic charge | PM6, DFT | MOPAC, NWChem |
Total charge | PM6, DFT | MOPAC, NWChem |
Electronegativity | PM6, DFT, LDM | MOPAC, NWChem |
Absorption spectra | CIS, TDDFT | MOPAC, NWChem |
Binding energies | PM6, DFT, DFTB | MOPAC, GPAW, DFTB+ |
vdW energy | CG, PM7, DFT | SNT-MT, MOPAC, NWChem, SIESTA |
Corona kinetics | CG | ESPResSo |
Binding affinities | CG | SNT-MT |
Hamaker constants | CG, Lifschitz theory | SNT-MT |
Adsorption energies for proteins | CG, DFT, DFTB | SNT-MT, GROMACS, SIESTA, DFTB+ |
Vibrational
frequencies |
PM6, MD | MOPAC, GROMACS |
Abbreviations
- CG: Coarse Grained
- CIS: Configuration Interaction Singles
- COSMO: Conductor-like Screening Model
- DFT: Density Functional Theory
- DFTB: Density Functional Tight-Binding
- DNA: Deoxyribo Nucleic Acid
- ESPResSo: Extensible Simulation Package for Research on Soft Matter
- GROMACS: GROningen MAchine for Chemical Simulations
- HCNP: Hydrophobic Charged Nanoparticles
- HF: Hartree-Fock
- iTasser: Iterative Threading Assembly Refinement
Further information
- Provided by: University College Dublin
- Type: ENM theoretical descriptors
- Applicability domain: Engineered Nanomaterials, Nanomedicine, Nanosafety
- Topic: Predictive modelling
- Contact: Konstantinos Kotsis
- For researchers
- For industry