Computational physical organic chemistry using the empirical valence bond approach

There has been growing interest in applying the empirical valence bond approach to a range of (bio)chemical problems, primarily to study enzymatic and non-enzymatic catalysis, but also to studying other processes such as excited state chemistry and reaction dynamics. Despite its apparent theoretical simplicity, this approach is a powerful computational tool that can be used to reproduce and rationalize a wide range of experimental observables, such as linear free energy relationships, kinetic isotope effects, and temperature effects on reaction rates. We provide here both a theoretical background for this approach, as well as highlighting several of its broad applications in computational physical organic chemistry

Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution

Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies