Reconstituting Plant Secondary Metabolism in Saccharomyces cerevisiae for Production of High-Value Benzylisoquinoline Alkaloids.

Benzylisoquinoline alkaloids (BIAs) constitute a diverse class of plant secondary metabolites that includes the opiate analgesics morphine and codeine. Collectively, BIAs exhibit a myriad of pharmacological activities, including antimicrobial, antitussive, antispasmodic, and anticancer properties. Despite 2500 known BIA products, only a small proportion are currently produced though traditional crop-based manufacturing, as complex stereochemistry renders chemical synthesis of BIAs largely unfeasible. The advent of synthetic biology and sophisticated microbial engineering coupled with recent advances in the elucidation of plant BIA metabolic networks has provided growing motivation for producing high-value BIAs in microbial hosts. Here, we provide a technical basis for reconstituting BIA biosynthetic pathways in the common yeast Saccharomyces cerevisiae. Methodologies outlined in this chapter include fundamental techniques for expressing and assaying BIA biosynthetic enzymes, bioprospecting large libraries of BIA enzyme variants, and reconstituting and optimizing complete BIA formation pathways in yeast. To expedite construction of superior BIA-producing yeast strains, we emphasize high-throughput techniques. Finally, we identify fundamental challenges impeding deployment of yeast-based BIA production platforms and briefly outline future prospects to overcome such barriers.

New opportunities for protease ligand-binding site comparisons using SitesBase.

The rapid expansion of structural information for protein ligand-binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross-reactivity and toxicity. We have developed SitesBase, a comprehensive database of ligand-binding sites extracted automatically from the Macromolecular Structure Database. SitesBase is an easily accessible database which is simple to use and holds pre-calculated information about structural similarities between known ligand-binding sites. These similarities are presented to the wider community enabling binding-site comparisons for therapeutically interesting protein families, such as the proteases and for new proteins to enable the discovery of interesting new structure-function relationships. The database is available from http://www.modelling.leeds.ac.uk/sb/.

AI-based algorithms for protein surface comparisons.

Many current methods for protein analysis depend on the detection of similarity in either the primary sequence, or the overall tertiary structure (the Calpha atoms of the protein backbone). These common sequences or structures may imply similar functional characteristics or active properties. Active sites and ligand binding sites usually occur on or near the surface of the protein; so similarly shaped surface regions could imply similar functions. We investigate various methods for describing the shape properties of protein surfaces and for comparing them. Our current work uses algorithms from computer vision to describe the protein surfaces, and methods from graph theory to compare the surface regions. Early results indicate that we can successfully match a family of related ligand binding sites, and find their similarly shaped surface regions. This method of surface analysis could be extended to help identify unknown surface regions for possible ligand binding or active sites.