Richard J. Fox

Improving catalytic function by ProSAR-driven enzyme evolution

We describe a directed evolution approach that should find broad application in generating enzymes that meet predefined process-design criteria. It augments recombination-based directed evolution by incorporating a strategy for statistical analysis of protein sequence activity relationships (ProSAR). This combination facilitates mutation-oriented enzyme optimization by permitting the capture of additional information contained in the sequence-activity data. The method thus enables identification of beneficial mutations even in variants with reduced function. We use this hybrid approach to evolve a bacterial halohydrin dehalogenase that improves the volumetric productivity of a cyanation process ∼4,000-fold. This improvement was required to meet the practical design criteria for a commercially relevant biocatalytic process involved in the synthesis of a cholesterol-lowering drug, atorvastatin (Lipitor), and was obtained by variants that had at least 35 mutations.

Enzyme optimization: moving from blind evolution to statistical exploration of sequence–function space

Directed evolution is a powerful tool for the creation of commercially useful enzymes, particularly those approaches that are based on in vitro recombination methods, such as DNA shuffling. Although these types of search algorithms are extraordinarily efficient compared with purely random methods, they do not explicitly represent or interrogate the genotype-phenotype relationship and are essentially blind in nature. Recently, however, researchers have begun to apply multivariate statistical techniques to model protein sequence-function relationships and guide the evolutionary process by rapidly identifying beneficial diversity for recombination. In conjunction with state-of-the-art library generation methods, the statistical approach to sequence optimization is now being used routinely to create enzymes efficiently for industrial applications.

A two-sample Bayesian t-test for microarray data

BackgroundDetermining whether a gene is differentially expressed in two different samples remains an important statistical problem. Prior work in this area has featured the use of t-tests with pooled estimates of the sample variance based on similarly expressed genes. These methods do not display consistent behavior across the entire range of pooling and can be biased when the prior hyperparameters are specified heuristically.ResultsA two-sample Bayesian t-test is proposed for use in determining whether a gene is differentially expressed in two different samples. The test method is an extension of earlier work that made use of point estimates for the variance. The method proposed here explicitly calculates in analytic form the marginal distribution for the difference in the mean expression of two samples, obviating the need for point estimates of the variance without recourse to posterior simulation. The prior distribution involves a single hyperparameter that can be calculated in a statistically rigorous manner, making clear the connection between the prior degrees of freedom and prior variance.ConclusionThe test is easy to understand and implement and application to both real and simulated data shows that the method has equal or greater power compared to the previous method and demonstrates consistent Type I error rates. The test is generally applicable outside the microarray field to any situation where prior information about the variance is available and is not limited to cases where estimates of the variance are based on many similar observations.

Catalytic effectiveness, a measure of enzyme proficiency for industrial applications

Recent attention has been paid to the inadequacy of using the ratio Vmax/KM as a measure of enzyme performance, particularly in the context of industrial biocatalysis. This can lead to misleading expectations of enzyme performance and can be troublesome when used to select among different variants for scale-up evaluation under process conditions. To address these issues, we derive the average velocity based on the time-integrated behavior of the enzyme over the course of the reaction. The resulting expression, deemed catalytic effectiveness, captures important features of the system that have heretofore been ignored (such as highly variable substrate and/or product concentrations and inhibition) and offers a rigorous way to compare enzymes for their capacity to carry out industrial transformations.