Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena.

One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators.

Enzymes are biological catalysts with many industrial applications, but natural enzymes are usually unsuitable for industrial processes because they are not optimized for the process conditions. The properties of enzymes can be improved by directed evolution, which involves multiple rounds of mutagenesis and screening. By using mathematical models to predict the structure-activity relationship of an enzyme, and by defining the optimal combination of mutations in silico, we can significantly reduce the number of bench experiments needed, and hence the time and investment required to develop an optimized product.

New insights into GluT1 mechanics during glucose transfer.

Glucose plays a crucial role in the mammalian cell metabolism. In the erythrocytes and endothelial cells of the blood-brain barrier, glucose uptake is mediated by the glucose transporter type 1 (GluT1). GluT1 deficiency or mutations cause severe physiological disorders.

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.

Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants.

Application of fourier transform and proteochemometrics principles to protein engineering.

Background: Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.

Comparative genomics of odorant binding proteins in Anopheles gambiae, Aedes aegypti, and Culex quinquefasciatus.

About 1 million people in the world die each year from diseases spread by mosquitoes, and understanding the mechanism of host identification by the mosquitoes through olfaction is at stake. The role of odorant binding proteins (OBPs) in the primary molecular events of olfaction in mosquitoes is becoming an important focus of biological research in this area. Here, we present a comprehensive comparative genomics study of OBPs in the three disease-transmitting mosquito species Anopheles gambiae, Aedes aegypti, and Culex quinquefasciatus starting with the identification of 110 new OBPs in these three genomes.