Parameterization of the DFTB3 Method for Br, Ca, Cl, F, I, K, and Na in Organic and Biological Systems

Abstract

We present an extension to the recent 3OB parametrization of the Density Functional Tight Binding Model DFTB31,2 for biological and organic systems. Parameters for the halogens F, Cl, Br, and I have been developed for use in covalently bound systems and benchmarked on a test set of 106 molecules (the ‘OrgX’ set), using bonding distances, bonding angles, atomization energies, and vibrational frequencies to assess the performance of the parameters. Additional testing has been done with the X40 set of 40 supramolecular systems containing halogens,3 adding a simple correction for the halogen bonds that are strongly overbound in DFTB3. Furthermore, parameters for Ca, K, and Na as counterions in biological systems have been created. To benchmark geometries as well as ligand binding energies a test set ‘BioMe’ of 210 molecules has been created that cover coordination to various functional groups frequently occurring in biological systems. The new DFTB3/3OB parameter set outperforms DFT calculations with a double-ζ basis set in terms of energies and can reproduce DFT geometries, with some minor deviations in bond distances and angles due to the use of a minimal basis set.

QM/MM free energy simulations: recent progress and challenges

“…As reviewed elsewhere recently, 52–54 the latest version of the DFTB methodology, referred to as DFTB3, 55 provides encouraging results for a fairly broad class of systems of biological interest; the accuracy is often comparable to density functional theory with the generalized gradient approximation (DFT/GGA) and a double-zeta-plus-polarization quality basis set, while the computational cost is similar to conventional semi-empirical methods 56 such as AM1 and PM3, making it routine to conduct nano-second simulations based on DFTB3/MM potentials. For metalloenzyme applications, recent developments have led to promising parameterizations for several metal ions that include the alkali metals, 57 magnesium, zinc 58 and copper. 59 The DFTB3 method in the current form is most reliable for structural properties, including for fairly complex bi-metallic motifs in several enzymes; 40,41,58,60,61 for energetic properties, the results are less robust as compared to DFT/GGA 62,63 but can often be improved to satisfying accuracy with single point energy calculations at high QM(/MM) level, making DFTB3 a promising low-level approach in dual-level QM/MM free energy simulations, a topic that we will also discuss here.…”

Analysis of Phosphoryl-Transfer Enzymes with QM/MM Free Energy Simulations

“…Currently available NDDO methods are usually not sufficiently reliable for metal ions, especially transition-metal ions. DFTB, which is based on DFT rather than Hartree–Fock, appears more promising for the description of metal ions; specifically for DFTB3, parameterization has been done for several main group ions such as Na + , Mg 2+ , and Ca 2+ (Kubillus, Kubař, Gaus, Řezáč, & Elstner, 2015; Lu, Gaus, Elstner, & Cui, 2015), as well as for simpler transition-metal ions such as Zn 2+ (Lu et al, 2015) and Cu +/2+ (Gaus et al, 2015). The performance of DFTB3 is most impressive for structural properties of metal compounds, while the energetics are less accurate, especially for highly charged ligands, due presumably to the limited description of polarization and charge-transfer effects.…”

Prediction of pKa values for drug-like molecules using semiempirical quantum chemical methods

“…∆H • f (X) is computed using either PM6-DH+ (Korth, 2010), PM6 (Stewart, 2007), PM7 (Stewart, 2012), PM3 (Stewart, 1989), AM1 (Dewar et al, 1985), or DFTB3 (Gaus et al, 2011) (where the electronic energy is used instead of the heat of formation), while ∆G • solv (X) is computed using either the SMD (Marenich et al, 2009) or COSMO (Klamt andSchüürmann, 1993) solvation method. The SMD calculations are performed with the GAMESS program (Schmidt et al, 1993), the latter using the semiempirical PCM interface developed by Steinmann et al (2013) and the DFTB/PCM interface developed by Nishimoto (2016) and using version 3ob-3-1 of the 3OB parameter set (Gaus et al, 2011(Gaus et al, , 2014Lu et al, 2015;Kubillus et al, 2015), while the COSMO calculations are performed using MOPAC2016. A maximum of 200 optimization cycles are used for solution phase optimizations and a gradient convergence criterion (OPTTOL) of 5 × 10 −4 au and delocalized internal coordinates (Baker et al, 1996) are used for GAMESS-based optimization.…”