π-π stacking tackled with density functional theory

Through comparison with ab initio reference data, we have evaluated the performance of various density functionals for describing π-π interactions as a function of the geometry between two stacked benzenes or benzene analogs, between two stacked DNA bases, and between two stacked Watson–Crick pairs. Our main purpose is to find a robust and computationally efficient density functional to be used specifically and only for describing π-π stacking interactions in DNA and other biological molecules in the framework of our recently developed QM/QM approach "QUILD". In line with previous studies, most standard density functionals recover, at best, only part of the favorable stacking interactions. An exception is the new KT1 functional, which correctly yields bound π-stacked structures. Surprisingly, a similarly good performance is achieved with the computationally very robust and efficient local density approximation (LDA). Furthermore, we show that classical electrostatic interactions determine the shape and depth of the π-π stacking potential energy surface

HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD

A Model of the Chemical Bond Must Be Rooted in Quantum Mechanics, Provide Insight, and Possess Predictive Power

In this response to the preceding paper by Bader, we show that the core arguments and statements presented in the latter are flawed. We argue that it is insufficient for a model of the chemical bond to be rooted in quantum mechanics. A good model must in addition provide insight and possess predictive power. Our molecular orbital (MO) model of the chemical bond, in particular, the associated energy–decomposition approach satisfies all these conditions. On the other hand, Atoms-in-Molecules (AIM) theory is only rooted in quantum mechanics as far as its mathematical framework is concerned. The physical status of its central concepts is not so clear. In particular, “bond paths” and “bond critical points” are once more confirmed not to be indicators of a stabilizing interaction. Moreover, AIM theory lacks any predictive power. We also address specific questions raised in the preceding paper. Finally, interpreting chemical bonding implies choosing a perspective on this phenomenon. That there are many perspectives is a matter of fact and this is in no way unphysical. What is unscientific is to claim uniqueness and truth for one of these choices, namely AIM, and to dismiss on this ground all other approaches