Protein crystallization analysis on the World Community Grid

We have developed an image-analysis and classification system for automatically scoring images from high-throughput protein crystallization trials. Image analysis for this system is performed by the Help Conquer Cancer (HCC) project on the World Community Grid. HCC calculates 12,375 distinct image features on microbatch-under-oil images from the Hauptman-Woodward Medical Research Institute’s High-Throughput Screening Laboratory. Using HCC-computed image features and a massive training set of 165,351 hand-scored images, we have trained multiple Random Forest classifiers that accurately recognize multiple crystallization outcomes, including crystals, clear drops, precipitate, and others. The system successfully recognizes 80% of crystal-bearing images, 89% of precipitate images, and 98% of clear drops

Establishing a training set through the visual analysis of crystallization trials. Part II: crystal examples

As part of a training set for automated image analysis, crystallization screening experiments for 269 different macromolecules were visually analyzed and a set of crystal images extracted. Outcomes and trends are analyzed

Help Conquer Cancer: Using GPUs to Accelerate Protein Crystallography Image Analysis

As part of the greater effort to find a cure for cancer, Igor Jurisica’s group at the Ontario Cancer Institute (OCI) is running a project to improve the throughput of protein crystallography [4, 5]. When proteins crystallize, their structure can be determined by observin