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In a broad genomics analysis to find novel protein targets for antibiotic discovery, MurF was identified as an essential gene product for Streptococcus pneumonia that catalyzes a critical reaction in the biosynthesis of the peptidoglycan in the formation of the cell wall. Lacking close relatives in mammalian biology, MurF presents attractive characteristics as a potential drug target. Initial screening of the Abbott small-molecule compound collection identified several compounds for further validation as pharmaceutical leads. Here we report the integrated efforts of NMR and X-ray crystallography, which reveal the multidomain structure of a MurF-inhibitor complex in a compact conformation that differs dramatically from related structures. The lead molecule is bound in the substrate-binding region and induces domain closure, suggestive of the domain arrangement for the as yet unobserved transition state conformation for MurF enzymes. The results form a basis for directed optimization of the compound lead by structure-based design to explore the suitability of MurF as a pharmaceutical target.

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Adenosine (ADO) is an endogenous homeostatic inhibitory neuromodulator that reduces cellular excitability at sites of tissue injury and inflammation. Inhibition of adenosine kinase (AK), the primary metabolic enzyme for ADO, selectively increases ADO concentrations at sites of tissue trauma and enhances the analgesic and antiinflammatory actions of ADO. Optimization of the high-throughput screening lead, 4-amino-7-aryl-substituted pteridine (5) (AK IC(50) = 440 nM), led to the identification of compound 21 (4-amino-5-(3-bromophenyl)-7-(6-morpholino-pyridin-3-yl)pyrido [2,3-d]pyrimidine, ABT-702), a novel, potent (AK IC(50) = 1.7 nM) non-nucleoside AK inhibitor with oral activity in animal models of pain and inflammation.

We present an approach to enhance wheeled planetary rover dead-reckoning localization performance by leveraging the use of zero-type constraint equations in the navigation filter. Without external aiding, inertial navigation solutions inherently exhibit cubic error growth. Furthermore, for planetary rovers that are traversing diverse types of terrain, wheel odometry is often unreliable for use in localization, due to wheel slippage. For current Mars rovers, computer vision-based approaches are generally used whenever there is a high possibility of positioning error; however, these strategies require additional computational power, energy resources, and significantly slow down the rover traverse speed. To this end, we propose a navigation approach that compensates for the high likelihood of odometry errors by providing a reliable navigation solution that leverages non-holonomic vehicle constraints as well as state-aware pseudo-measurements (e.g., zero velocity and zero angular rate) updates during periodic stops. By using this, computationally expensive visual-based corrections could be performed less often. Experimental tests that compare against GPS-based localization are used to demonstrate the accuracy of the proposed approach. The source code, post-processing scripts, and example datasets associated with the paper are published in a public repository.