Kadukova, M. | Search bioRxiv

bioRxiv2020

Hydroxylation of antitubercular drug candidate, SQ109, by mycobacterial cytochrome P450

Spreading of the multidrug-resistant (MDR) strains of the deadliest pathogen Mycobacterium tuberculosis (Mtb) generates the need for new effective drugs. SQ109 showed activity against resistant Mtb and already advanced to Phase II/III clinical trials. Fast SQ109 degradation is attributed to the human liver Cytochrome P450s (CYPs). However, no information is available about interactions of the drug with Mtb CYPs. Here, we show that Mtb CYP124, previously assigned as a methyl-branched lipid monooxygenase, binds and hydroxylates SQ109 in vitro. A 1.25 [A]-resolution crystal structure of the CYP124-SQ109 complex unambiguously shows two conformations of the drug, both positioned for hydroxylation of the {omega}-methyl group in the trans position. The hydroxylated SQ109 presumably forms stabilizing H-bonds with its target, the Mycobacterial membrane protein Large 3 (MmpL3). We anticipate that Mtb CYPs could function as analogs of drug-metabolizing human CYPs affecting pharmacokinetics and pharmacodynamics of antitubercular (anti-TB) drugs.

biochemistry↗

bioRxiv2020

VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

MotivationEffective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. ResultsFor the first time we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows to efficiently introduce both convolution and pooling operations of the network. We trained our model, called VoroCNN, to predict local qualities of 3D protein folds. The prediction results are competitive to the state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in the recognition of protein binding interfaces. AvailabilityThe model, data, and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. Contactceslovas.venclovas@bti.vu.lt, sergei.grudinin@inria.fr

bioinformatics↗