The marine natural product, marinopyrrole A (1), was previously shown to have significant antibiotic activity against Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus (MRSA). Although compound (1) exhibits a significant reduction in MRSA activity in the presence of human serum, we have identified key modifications that partially restore activity. We previously reported our discovery of a chloro-derivative of marinopyrrole A (1a) featuring a 2-4 fold improved minimum inhibitory concentration (MIC) against MRSA, significantly less susceptibility to serum inhibition and rapid and concentration-dependent killing of MRSA. Here, we report a novel fluoro-derivative of marinopyrrole A (1e) showing an improved profile of potency, less susceptibility to serum inhibition, as well as rapid and concentration-dependent killing of MRSA.

In every corner of machine learning and statistics, there is a need for estimators that work not just in an idealized model, but even when their assumptions are violated. Unfortunately, in high dimensions, being provably robust and being efficiently computable are often at odds with each other. We give the first efficient algorithm for estimating the parameters of a high-dimensional Gaussian that is able to tolerate a constant fraction of corruptions that is independent of the dimension. Prior to our work, all known estimators either needed time exponential in the dimension to compute or could tolerate only an inverse-polynomial fraction of corruptions. Not only does our algorithm bridge the gap between robustness and algorithms, but also it turns out to be highly practical in a variety of settings.