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Quantum Mechanical Insights into Biological Processes at the Electronic Level

Chapter
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)

Abstract

The realm of biology is always governed by underlying electronic effects. These effects are often treated implicitly and may go nearly unnoticed in classical biomolecular simulations, such as Monte Carlo or molecular dynamics. It is important to remember, however, that these classical methods always operate on the single, ground electronic potential energy surface (PES). Furthermore, classical methods assume the classical behavior of the atomic nuclei, and thus rely on the so-called Born–Oppenheimer approximation (BAO) heavily used in quantum mechanics, as discussed in detail below. Due to the BAO, the ground PES can be obtained by finding the optimal electronic solution for every position of stationary classical nuclei. The combined electronic and nuclear energy as a function of nuclear coordinates in the PES. The Born–Oppenheimer PES is usually very close to the chemical reality. Parameters of classical force fields are optimized to reproduce this ground PES, either calculated quantum mechanically or derived from the experiment. Thus, electronic structure is always an active player in classical simulations through the parameters of the force field in use. However, when it comes to the assessment of the mechanism of a biochemical reaction that involves breaking and forming of covalent bonds, quantum mechanics is an almost exclusive reliable approach, with a prominent classical exception being the empirical valence bond method. Furthermore, there is a large class of biological processes that simply cannot be assessed without explicit quantum mechanical treatment. An obvious example is electron transfer in enzymes or DNA that plays a pivotal role in every oxidation or reduction event in living cells.

Keywords

Density Functional Theory Monte Carlo Potential Energy Surface Configuration Interaction Slater Determinant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Chemistry and BiochemistryUniversity of California, Los AngelesLos AngelesUSA

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