Computing protein infrared spectroscopy with quantum chemistry

Author(s):  
Nicholas A Besley

Quantum chemistry is a field of science that has undergone unprecedented advances in the last 50 years. From the pioneering work of Boys in the 1950s, quantum chemistry has evolved from being regarded as a specialized and esoteric discipline to a widely used tool that underpins much of the current research in chemistry today. This achievement was recognized with the award of the 1998 Nobel Prize in Chemistry to John Pople and Walter Kohn. As the new millennium unfolds, quantum chemistry stands at the forefront of an exciting new era. Quantitative calculations on systems of the magnitude of proteins are becoming a realistic possibility, an achievement that would have been unimaginable to the early pioneers of quantum chemistry. In this article we will describe ongoing work towards this goal, focusing on the calculation of protein infrared amide bands directly with quantum chemical methods.

Author(s):  
Dmitrij Rappoport ◽  
Alan Aspuru-Guzik

Studying organic reaction mechanisms using quantum chemical methods requires from the researcher an extensive knowledge of both organic chemistry and first-principles computation. The need for empirical knowledge arises because any reasonably complete exploration of the potential energy surfaces (PES) of organic reactions is computationally prohibitive. We have previously introduced the Heuristically-Aided Quantum Chemistry (HAQC) approach to modeling complex chemical reactions, which abstracts the empirical knowledge in terms of chemical heuristics—simple rules guiding the PES exploration—and combines them with structure optimizations using quantum chemical methods. The HAQC approach makes use of heuristic kinetic criteria for selecting reaction paths that are not only plausible, that is, consistent with the empirical rules of organic reactivity, but also feasible under the reaction conditions. In this work, we develop heuristic kinetic feasilibity criteria, which correctly predict feasible reaction pathways for a wide range of simple polar (substitutions, additions, and eliminations) and pericyclic organic reactions (cyclizations, sigmatropic shifts, and cycloadditions). In contrast to knowledge-based reaction mechanism prediction methods, the same kinetic heuristics are successful in classifying reaction pathways as feasible or infeasible across this diverse set of reaction mechanisms. We discuss the energy profiles of HAQC and their potential applications in machine learning of chemical reactivity.<br>


2018 ◽  
Author(s):  
Dmitrij Rappoport ◽  
Alan Aspuru-Guzik

Studying organic reaction mechanisms using quantum chemical methods requires from the researcher an extensive knowledge of both organic chemistry and first-principles computation. The need for empirical knowledge arises because any reasonably complete exploration of the potential energy surfaces (PES) of organic reactions is computationally prohibitive. We have previously introduced the Heuristically-Aided Quantum Chemistry (HAQC) approach to modeling complex chemical reactions, which abstracts the empirical knowledge in terms of chemical heuristics—simple rules guiding the PES exploration—and combines them with structure optimizations using quantum chemical methods. The HAQC approach makes use of heuristic kinetic criteria for selecting reaction paths that are not only plausible, that is, consistent with the empirical rules of organic reactivity, but also feasible under the reaction conditions. In this work, we develop heuristic kinetic feasilibity criteria, which correctly predict feasible reaction pathways for a wide range of simple polar (substitutions, additions, and eliminations) and pericyclic organic reactions (cyclizations, sigmatropic shifts, and cycloadditions). In contrast to knowledge-based reaction mechanism prediction methods, the same kinetic heuristics are successful in classifying reaction pathways as feasible or infeasible across this diverse set of reaction mechanisms. We discuss the energy profiles of HAQC and their potential applications in machine learning of chemical reactivity.<br>


2016 ◽  
Vol 52 (86) ◽  
pp. 12761-12764 ◽  
Author(s):  
Shu-Xian Hu ◽  
John K. Gibson ◽  
Wan-Lu Li ◽  
Michael J. Van Stipdonk ◽  
Jonathan Martens ◽  
...  

A uranyl–di-15-crown-5 complex with a unique slipped sandwich structure was synthesized and characterized by infrared spectroscopy and quantum-chemical methods.


Hydrogen ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 101-121
Author(s):  
Sergey P. Verevkin ◽  
Vladimir N. Emel’yanenko ◽  
Riko Siewert ◽  
Aleksey A. Pimerzin

The storage of hydrogen is the key technology for a sustainable future. We developed an in silico procedure, which is based on the combination of experimental and quantum-chemical methods. This method was used to evaluate energetic parameters for hydrogenation/dehydrogenation reactions of various pyrazine derivatives as a seminal liquid organic hydrogen carriers (LOHC), that are involved in the hydrogen storage technologies. With this in silico tool, the tempo of the reliable search for suitable LOHC candidates will accelerate dramatically, leading to the design and development of efficient materials for various niche applications.


2017 ◽  
Vol 19 (34) ◽  
pp. 23176-23186 ◽  
Author(s):  
Mauritz Johan Ryding ◽  
Israel Fernández ◽  
Einar Uggerud

Reactions between water clusters containing the superoxide anion, O2˙−(H2O)n (n = 0–4), and formic acid, HCO2H, were studied experimentally in vacuo and modelled using quantum chemical methods.


Sign in / Sign up

Export Citation Format

Share Document