Guddat, L. | Search bioRxiv

bioRxiv2020

Structure and mechanism of potent bifunctional β-lactam- and homoserine lactone-degrading enzymes from marine microorganisms

Genes that confer antibiotic resistance can rapidly be disseminated from one microorganism to another by mobile genetic elements, thus transferring resistance to previously susceptible bacterial strains. The misuse of antibiotics in health care and agriculture has provided a powerful evolutionary pressure to accelerate the spread of resistance genes, including those encoding {beta}-lactamases. These are enzymes that are highly efficient in inactivating most of the commonly used {beta}-lactam antibiotics. However, genes that confer antibiotic resistance are not only associated with pathogenic microorganisms, but are also found in non-pathogenic (i.e. environmental) microorganisms. Two recent examples are metal-dependent {beta}-lactamases (MBLs) from the marine organisms Novosphingobium pentaromativorans and Simiduia agarivorans. Previous studies have demonstrated that their {beta}-lactamase activity is comparable to those of well-known MBLs from pathogenic sources (e.g. NDM-1, AIM-1) but that they also possess efficient lactonase activity, an activity associated with quorum sensing. Here, we probed the structure and mechanism of these two enzymes using crystallographic, spectroscopic and fast kinetics techniques. Despite highly conserved active sites both enzymes demonstrate significant variations in their reaction mechanisms, highlighting both the extraordinary ability of MBLs to adapt to changing environmental conditions and the rather promiscuous acceptance of diverse substrates by these enzymes.

biochemistry↗

bioRxiv2019

Identifying and engineering ancient variants of enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion ("indel") events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time. Author summaryMassive sequencing projects expose the extent of natural, genetic diversity. Here, we describe a method with capacity to perform ancestor sequence reconstruction from data sets in excess of 10,000 sequences, poised to recover ancestral diversity, including the evolutionary events that determine present-time biological function and structure. We introduce a novel strategy for suggesting "indel variants" that are distinct from, but can be explored alongside, substitution variants for creating ancestral libraries. We demonstrate how indels can be used as building blocks to form "hybrid ancestors"; based on this strategy, we synthesise ancestor variants, with varying enzymatic activities, for wide-ranging applications in the biotechnology sector.

bioinformatics↗