Structure and singly occupied molecular orbital analysis of anionic tautomers of guanine

Recently we reported the discovery of adiabatically bound anions of guanine which might be involved in the processes of DNA damage by low-energy electrons and in charge transfer through DNA. These anions correspond to some tautomers that have been ignored thus far. They were identified using a hybrid quantum mechanical-combinatorial approach in which an energy-based screening was performed on the library of 499 tautomers with their relative energies calculated with quantum chemistry methods. In the current study we analyze the adiabatically bound anions of guanine in two aspects: 1) the geometries and excess electron distributions are analyzed and compared with anions of the most stable neutrals to identify the sources of stability; 2) the chemical space of guanine tautomers is explored to verify if these new tautomers are contained in a particular subspace of the tautomeric space. The first task involves the development of novel approaches – the quantum chemical data like electron density, orbital and information on its bonding/antibonding character are coded into holograms and analyzed using chemoinformatics techniques. The second task is completed using substructure analysis and clustering techniques performed on molecules represented by 2D fingerprints. The major conclusion is that the high stability of adiabatically bound anions originates from the bonding character of the pi orbital occupied by the excess electron. This compensates for the antibonding character that usually causes significant buckling of the ring. Also the excess electron is more homogenously distributed over both rings than in the case of anions of the most stable neutral species. In terms of 2D substructure, the most stable anionic tautomers generally have additional hydrogen atoms at C8 and/or C2 and they don’t have hydrogen atoms attached to C4, C5 and C6. They also form an “island of stability” in the tautomeric space of guanine

Combinatorial–computational–chemoinformatics (C3) approach to finding and analyzing low-energy tautomers

Finding the most stable tautomer or a set of low-energy tautomers of molecules is critical in many aspects of molecular modelling or virtual screening experiments. Enumeration of low-energy tautomers of neutral molecules in the gas-phase or typical solvents can be performed by applying available organic chemistry knowledge. This kind of enumeration is implemented in a number of software packages and it is relatively reliable. However, in esoteric cases such as charged molecules in uncommon, non-aqueous solvents there is simply not enough available knowledge to make reliable predictions of low energy tautomers. Over the last few years we have been developing an approach to address the latter problem and we successfully applied it to discover the most stable anionic tautomers of nucleic acid bases that might be involved in the process of DNA damage by low-energy electrons and in charge transfer through DNA. The approach involves three steps: (1) combinatorial generation of a library of tautomers, (2) energy-based screening of the library using electronic structure methods, and (3) analysis of the information generated in step (2). In steps 1–3 we employ combinatorial, computational and chemoinformatics techniques, respectively. Therefore, this hybrid approach is named “Combinatorial*Computational*Chemoinformatics”, or just abbreviated as C3 (or C-cube) approach. This article summarizes our developments and most interesting methodological aspects of the C3 approach. It can serve as an example how to identify the most stable tautomers of molecular systems for which common chemical knowledge had not been sufficient to make definite predictions

Chemical Hieroglyphs: Abstract Depiction of Complex Void Space Topology of Nanoporous Materials

In general, most porous materials are so complex that structural information cannot be easily observed with 3D visualization tools. To address this problem, we have developed a special abstract 2D representation to depict all important topological features and geometrical parameters. Our approach involves reducing these structures based on symmetry and perceived building blocks to a compressed, graph representation that allows for quick structure analysis, classification, and comparison. © 2010 American Chemical Society

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application

Progresses in Ab Initio QM/MM Free Energy Simulations of Electrostatic Energies in Proteins: Accelerated QM/MM Studies of pKa, Redox Reactions and Solvation Free Energies

Hybrid quantum mechanical / molecular mechanical (QM/MM) approaches have been used to provide a general scheme for chemical reactions in proteins. However, such approaches still present a major challenge to computational chemists, not only because of the need for very large computer time in order to evaluate the QM energy but also because of the need for propercomputational sampling. This review focuses on the sampling issue in QM/MM evaluations of electrostatic energies in proteins. We chose this example since electrostatic energies play a major role in controlling the function of proteins and are key to the structure-function correlation of biological molecules. Thus, the correct treatment of electrostatics is essential for the accurate simulation of biological systems. Although we will be presenting here different types of QM/MM calculations of electrostatic energies (and related properties), our focus will be on pKa calculations. This reflects the fact that pKa of ionizable groups in proteins provide one of the most direct benchmarks for the accuracy of electrostatic models of macromolecules. While pKa calculations by semimacroscopic models have given reasonable results in many cases, existing attempts to perform pKa calculations using QM/MM-FEP have led to large discrepancies between calculated and experimental values. In this work, we accelerate our QM/MM calculations using an updated mean charge distribution and a classical reference potential. We examine both a surface residue (Asp3) of the bovine pancreatic trypsin inhibitor, as well as a residue buried in a hydrophobic pocket (Lys102) of the T4-lysozyme mutant. We demonstrate that by using this approach, we are able to reproduce the relevant sidechain pKas with an accuracy of 3 kcal/mol. This is well within the 7 kcal/mol energy difference observed in studies of enzymatic catalysis, and is thus sufficient accuracy to determine the main contributions to the catalytic energies of enzymes. We also provide an overall perspective of the potential of QM/MM calculations in general evaluations of electrostatic free energies, pointing out that our approach should provide a very powerful and accurate tool to predict the electrostatics of not only solution but also enzymatic reactions, as well as the solvation free energies of even larger systems, such as nucleic acid bases incorporated into DNA

Fluorescence Efficiency and Visible Re-emission Spectrum of Tetraphenyl Butadiene Films at Extreme Ultraviolet Wavelengths

A large number of current and future experiments in neutrino and dark matter detection use the scintillation light from noble elements as a mechanism for measuring energy deposition. The scintillation light from these elements is produced in the extreme ultraviolet (EUV) range, from 60 - 200 nm. Currently, the most practical technique for observing light at these wavelengths is to surround the scintillation volume with a thin film of Tetraphenyl Butadiene (TPB) to act as a fluor. The TPB film absorbs EUV photons and reemits visible photons, detectable with a variety of commercial photosensors. Here we present a measurement of the re-emission spectrum of TPB films when illuminated with 128, 160, 175, and 250 nm light. We also measure the fluorescence efficiency as a function of incident wavelength from 120 to 250 nm.Comment: 15 pages, 9 figures, Submitted to Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipmen