Comparative Density Functional Theory and Density Functional Tight Binding Study of Arginine and Arginine-Rich Cell-Penetrating Peptide TAT Adsorption on Anatase TiO2

TL;DR

This study compares DFT and DFTB methods for modeling arginine and TAT peptide adsorption on TiO2, showing DFTB's efficiency and accuracy in electronic structure predictions for large bio-inorganic systems.

Contribution

It demonstrates that DFTB can effectively model bio-inorganic interfaces, outperforming DFT with GGA functionals in band alignment accuracy for large molecules.

Findings

DFTB agrees well with DFT on structures and conformers.

DFTB provides better band alignment than DFT with PBE functional.

DFTB is computationally more efficient and accurate for large bio-inorganic systems.

Abstract

We present a comparative Density Functional Theory (DFT) and Density Functional Tight Binding (DFTB) study of geometries and electronic structures of arginine (Arg), arginine adsorbed on the anatase (101) surface of titania in several adsorption configurations, and of an arginine-rich cell penetrating peptide TAT and its adsorption on the surface of TiO2. Two DFTB parameterizations are considered, tiorg-0-1/mio-1-1 and matsci-0-3. While there is good agreement in the structures and relative energies of Arg and peptide conformers between DFT and DFTB, both adsorption geometries and energies are noticeably different for Arg adsorbed on TiO2. The tiorg-0-1/mio-1-1 parameterization performs better than matsci-0-3. We relate this difference to the difference in electronic structures resulting from the two methods (DFT and DFTB) and specifically to the band alignment between the molecule and the oxide. We show that the band alignment of TAT and of TiO2 modeled with DFTB is qualitatively correct but that with DFT using the PBE functional is not. This is specific to the modeling of large molecules where the HOMO is close to the conduction band of the oxide. We therefore report a case where the approximate DFT-based method - DFTB (with which the correct band structure can be effectively obtained) - performs better than the DFT itself with a functional approximation feasible for the modeling of large bio-inorganic interfaces, i.e. GGA (as opposed to hybrid functionals which are impractical at such a scale). Our results highlight the utility of the DFTB method for the modeling of bioinorganic interfaces not only from the CPU cost perspective but also from the accuracy point of view.