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To increase the efficiency of diffraction data collection for protein crystallographic studies, an automated system designed to store frozen protein crystals, mount them sequentially, align them to the X-ray beam, collect complete data sets, and return the crystals to storage has been developed. Advances in X-ray data collection technology including more brilliant X-ray sources, improved focusing optics, and faster-readout detectors have reduced diffraction data acquisition times from days to hours at a typical protein crystallography laboratory [1, 2]. In addition, the number of high-brilliance synchrotron X-ray beam lines dedicated to macromolecular crystallography has increased significantly, and data collection times at these facilities can be routinely less than an hour per crystal. Because the number of protein crystals that may be collected in a 24 hr period has substantially increased, unattended X-ray data acquisition, including automated crystal mounting and alignment, is a desirable goal for protein crystallography. The ability to complete X-ray data collection more efficiently should impact a number of fields, including the emerging structural genomics field [3], structure-directed drug design, and the newly developed screening by X-ray crystallography [4], as well as small molecule applications.

Investigation continues of detergent foam images as analogue/digital (A/D) model fluids. Attention turns to the radial distribution function G(r) and how it is partitioned in terms of individual neighborships:first, second, third, etc. Data are illustrated for liquid- and gaslike systems, and key features of the neighborship partition are enumerated. Data are compared with mathematical constructs offered by the ideal gas, hard disk, and hexagonal crystalline models. Such comparison points to an indirect method for partitioning G(r) into neighborship distributions in agreement with experiment. Notably, the method requires no specification of particle shapes and potentials. The indirect method is applied to liquid sodium using X-ray data of previous researchers. Structures of liquid sodium are offered via an alternative, empirical way.

Detergent foam images are investigated as analogue/digital (A/D) models for simple fluids. Results of foam preparation/image processing experiments with quasi-two-dimensional systems are illustrated. In the analysis, the pair and triplet correlation functions, g(r) and ξ(ϑ,s;r), are measured for the vertexes of plateau border networks. The phase morphology is characterized by the amplitude and extent of oscillations in g(r). Effective pair potentials u(r) are calculated via the lowest order equation in the Born−Yvon−Green hierarchy. The u(r) are further investigated via Monte Carlo calculations and the Morse function. The experimental data are used to calculate the pair correlation entropy and the compressibility factor as functions of particle number density. These and other observables enable comparisons between the model systems and real atomic fluids. Overall, the results show foam images to mimic fluid structure at the atomic level in several respects. For the experimental conditions discussed, succe...

Background: Human urokinase-type plasminogen activator has been implicated in the regulation and control of basement membrane and interstitial protein degradation. Because of its role in tissue remodeling, urokinase is a central player in the disease progression of cancer, making it an attractive target for design of an anticancer clinical agent. Few urokinase inhibitors have been described, which suggests that discovery of such a compound is in the early stages. Towards integrating structural data into this process, a new human urokinase crystal form amenable to structure-based drug design has been used to discover potent urokinase inhibitors. Results: On the basis of crystallographic data, 2-naphthamidine was chosen as the lead scaffold for structure-directed optimization. This co-crystal structure shows the compound binding at the primary specificity pocket of the trypsin-like protease and at a novel binding subsite that is accessible from the 8-position of 2-napthamidine. This novel subsite was characterized and used to design two compounds with very different 8-substituents that inhibit urokinase with K i values of 30–40 nM. Conclusions: Utilization of a novel subsite yielded two potent urokinase inhibitors even though this site has not been widely used in inhibitor optimization with other trypsin-like proteases, such as those reported for thrombin or factor Xa. The extensive binding pockets present at the substrate-binding groove of these other proteins are blocked by unique insertion loops in urokinase, thus necessitating the utilization of additional binding subsites. Successful implementation of this strategy and characterization of the novel site provides a significant step towards the discovery of an anticancer clinical agent.