Type IIA topoisomerase inhibition by a new class of antibacterial agents.

Despite the success of genomics in identifying new essential bacterial genes, there is a lack of sustainable leads in antibacterial drug discovery to address increasing multidrug resistance. Type IIA topoisomerases cleave and religate DNA to regulate DNA topology and are a major class of antibacterial and anticancer drug targets, yet there is no well developed structural basis for understanding drug action. Here we report the 2.1 A crystal structure of a potent, new class, broad-spectrum antibacterial agent in complex with Staphylococcus aureus DNA gyrase and DNA, showing a new mode of inhibition that circumvents fluoroquinolone resistance in this clinically important drug target. The inhibitor 'bridges' the DNA and a transient non-catalytic pocket on the two-fold axis at the GyrA dimer interface, and is close to the active sites and fluoroquinolone binding sites. In the inhibitor complex the active site seems poised to cleave the DNA, with a single metal ion observed between the TOPRIM (topoisomerase/primase) domain and the scissile phosphate. This work provides new insights into the mechanism of topoisomerase action and a platform for structure-based drug design of a new class of antibacterial agents against a clinically proven, but conformationally flexible, enzyme class.

MAIP: a web service for predicting blood-stage malaria inhibitors.

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/ . MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

Theoretical and experimental investigations on the morphology of pharmaceutical crystals.

We investigated the morphologies of three polymorphs of 1,3-di(cyclopropylmethyl)-8-aminoxanthine, a compound of pharmaceutical importance. We compared the experimental morphologies with those predicted by theoretical methods. We also predicted the elastic constants of the three polymorphs. These results are used to help assess the stabilities of the three polymorphs and compare the abilities of theoretical methods to reproduce experimental morphologies.