Center for High-Throughput Structural Biology:


Automated crystallography image classification

Center for High-Throughput Structural Biology

We have expanded and improved our approach to automated crystallography image classification [1-3]. We have moved from a Matlab code that implemented 840 image features and linear discriminant analysis for image classification into two classes, into a C++ code that computes 12,375 basic features, and Bayesian classifier system that recognizes 10 image classes.

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Center for High-Throughput Structural Biology

Center for High-Throughput Structural Biology

The Center for High-Throughput Structural Biology (CHTSB) - http://www.chtsb.org/

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Chemical space mapping to interpret crystallization screening results

Center for High-Throughput Structural Biology

Mapping crystallization results in chemical space helps to correlate seemingly distant relationships between crystallization conditions, point to possible optimization strategies and reveal un-sampled areas of crystallization space.

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Crystallization additives

Center for High-Throughput Structural Biology

Certain chemical compounds or small molecules may have dramatic effects on the success with which individual proteins crystallize. While additives, as they are often called, can be decisive in macromolecular crystallization, their greater use has suffered from lack of any compelling, rational basis for their inclusion in mother liquors. Based on a hypothesis that various small molecules might establish stabilizing, intermolecular, non-covalent crosslinks in protein crystals promoting lattice formation hundreds of compounds were systematically studied, and crystallization results verified through X-ray diffraction analysis.

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Establishing a crystallization image training set

Center for High-Throughput Structural Biology

A training set of crystallization outcomes has been established.

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