CrystPro: Spatiotemporal Analysis of Protein Crystallization Images

Abstract

Thousands of experiments corresponding to different combinations of conditions are set up to determine the relevant conditions for successful protein crystallization. In recent years, high-throughput robotic setups have been developed to automate the protein crystallization experiments, and imaging techniques are used to monitor the crystallization progress. Images are collected multiple times during the course of an experiment. A huge number of collected images make manual review of images tedious and discouraging. In this work, utilizing trace fluorescence labeling, we describe an automated system called CrystPro for monitoring the protein crystal growth in crystallization trial images by analyzing the time sequence images. Given the sets of image sequences, the objective is to develop an efficient and reliable system to detect crystal growth changes such as new crystal formation and increase of crystal size. CrystPro consists of three major steps—identification of crystallization trials proper for spatiotemporal analysis, spatiotemporal analysis of identified trials, and crystal growth analysis. We evaluated the performance of our system on three crystallization image data sets (PCP-ILopt-11, PCP-ILopt-12, and PCP-ILopt-13) and compared our results with expert scores. Our results indicate (a) 98.3% accuracy and 0.896 sensitivity on identification of trials for spatiotemporal analysis, (b) 77.4% accuracy and 0.986 sensitivity of identifying crystal pairs with new crystal formation, and (c) 85.8% accuracy and 0.667 sensitivity on crystal size increase detection. The results show that our method is reliable and efficient for tracking growth of crystals and determining useful image sequences for further review by the crystallographers.