Combinatorial Mutasynthesis of Scrambled Beauvericins,Cyclooligomer Depsipeptide Cell Migration Inhibitors from Beauveria bassiana

Fungal cyclooligomer depsipeptides such as beauvericin, bassianolide, and enniatins display antibiotic, antifungal, insecticidal, broad‐spectrum cancer cell antiproliferative, and cell migration inhibitory activities. We have identified a gene encoding a novel enzyme, ketoisovalerate reductase (KIVR), which is the sole provider of D ‐hydroxyisovalerate (D ‐Hiv), a common precursor for cyclooligomer depsipeptide biosynthesis in Beauveria bassiana. KIVR and related hypothetical oxidoreductases encoded in fungal genomes are similar to ketopantoate reductases but not to D ‐hydroxycarboxylate dehydrogenases. We demonstrate that a KIVR knockout B. bassiana strain can be used for the efficient mutasynthesis of unnatural beauvericin congeners. Simultaneous feeding of precursor analogues enabled the combinatorial mutasynthesis of scrambled beauvericins, some assembled entirely from unnatural precursors. The effects of the introduced structural changes on the antiproliferative and cell migration inhibitory activities of these analogues were evaluated.     

Functional Chromatography Reveals Three Natural Products that Target the Same Protein with Distinct Mechanisms of Action

Access to lead compounds with defined molecular targets continues to be a barrier to the translation of natural product resources. As a solution, we developed a system that uses discrete, recombinant proteins as the vehicles for natural product isolation. Here, we describe the use of this functional chromatographic method to identify natural products that bind to the AAA+ chaperone, p97, a promising cancer target. Application of this method to a panel of fungal and plant extracts identified rheoemodin, 1‐hydroxydehydroherbarin, and phomapyrrolidone A as distinct p97 modulators. Excitingly, each of these molecules displayed a unique mechanism of p97 modulation. This discovery provides strong support for the application of functional chromatography to the discovery of protein modulators that would likely escape traditional high‐throughput or phenotypic screening platforms.     

Feature-level and decision-level fusion of noncoincidently sampledsensors for land mine detection

We present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the full benefits of fusion. A novel algorithm for feature-level fusion of noncommensurate, noncoincidently sampled data is described, in which a model is fitted to the sensor data and the model parameters are used as features. Formulations for both feature-level and decision-level fusion are described, along with some practical simplifications. A closed-form expression is available for feature-level fusion of normally distributed data and this expression is used with simulated data to study requirements for sample position accuracy in multisensor data. The performance of feature-level and decision-level fusion algorithms are compared for experimental data acquired by a metal detector, a ground-penetrating radar, and an infrared camera at a challenging test site containing surrogate mines. It is found that fusion of binary decisions does not perform significantly better than the best available sensor. The performance of feature-level fusion is significantly better than the individual sensors, as is decision-level fusion when detection confidence information is also available (“soft-decision” fusion)