Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis.

The search for compounds active against Mycobacterium tuberculosis is reliant upon high-throughput screening (HTS) in whole cells. We have used Bayesian machine learning models which can predict anti-tubercular activity to filter an internal library of over 150,000 compounds prior to in vitro testing. We used this to select and test 48 compounds in vitro; 11 were active with MIC values ranging from 0.4 µM to 10.2 µM, giving a high hit rate of 22.9%. Among the hits, we identified several compounds belonging to the same series including five quinolones (including ciprofloxacin), three molecules with long aliphatic linkers and three singletons. This approach represents a rapid method to prioritize compounds for testing that can be used alongside medicinal chemistry insight and other filters to identify active molecules. Such models can significantly increase the hit rate of HTS, above the usual 1% or lower rates seen. In addition, the potential targets for the 11 molecules were predicted using TB Mobile and clustering alongside a set of over 740 molecules with known M. tuberculosis target annotations. These predictions may serve as a mechanism for prioritizing compounds for further optimization.

Development of a high density hemagglutinin protein microarray to determine the breadth of influenza antibody responses.

We have developed an influenza hemagglutinin protein microarray to assess humoral recognition of diverse influenza strains induced by vaccination and infection. Each array consists of controls and 127 hemagglutinin antigens from 60 viruses, spotted in replicates to generate a single array of 1296 spots. Six arrays are configured on a single slide, which in the following analysis was probed simultaneously with 2 isotype-specific fluorescent secondary antibodies yielding over 15,000 data points per slide. Here we report the use of this system to evaluate mouse, ferret, and human sera. The array allows simultaneous examination of the magnitude of antibody responses, the isotype of such antibodies, and the breadth of influenza strain recognition. We are advancing this technology as a platform for rapid, simple, high-throughput assessment of homologous and heterologous antibody responses to influenza disease and vaccination.