Sigdel Madhav, Pusey Marc L, Aygun Ramazan S
University of Alabama in Huntsville, Huntsville, Alabama 35899, United States, iXpressGenes, Inc., 601 Genome Way, Huntsville, Alabama 35806, United States, and University of Alabama in Huntsville, Huntsville, Alabama 35899, United States.
Cryst Growth Des. 2015;15(11):5254-5262. doi: 10.1021/acs.cgd.5b00714. Epub 2015 Sep 16.
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 set-ups 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. Huge number of collected images make manual review of images tedious and discouraging. In this paper, utilizing , 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 spatio-temporal analysis, spatio-temporal analysis of identified trials, and crystal growth analysis. We evaluated the performance of our system on 3 crystallization image datasets (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 .896 sensitivity on identification of trials for spatio-temporal analysis, b) 77.4% accuracy and .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.
为了确定蛋白质成功结晶的相关条件，人们进行了数千次对应不同条件组合的实验。近年来，已开发出高通量机器人装置来自动化蛋白质结晶实验，并使用成像技术来监测结晶过程。在实验过程中会多次采集图像。大量采集的图像使得人工查看图像变得繁琐且令人沮丧。在本文中，我们利用[具体方法]，描述了一个名为CrystPro的自动化系统，该系统通过分析时间序列图像来监测结晶试验图像中的蛋白质晶体生长。给定图像序列集，目标是开发一个高效且可靠的系统，以检测晶体生长变化，如新晶体形成和晶体尺寸增加。CrystPro由三个主要步骤组成——识别适合时空分析的结晶试验、对已识别试验进行时空分析以及晶体生长分析。我们在3个结晶图像数据集（PCP - ILopt - 11、PCP - ILopt - 12和PCP - ILopt - 13）上评估了我们系统的性能，并将我们的结果与专家评分进行了比较。我们的结果表明：a）在识别适合时空分析的试验方面，准确率为98.3%，灵敏度为0.896；b）在识别有新晶体形成的晶体对方面，准确率为77.4%，灵敏度为0.986；c）在检测晶体尺寸增加方面，准确率为85.8%，灵敏度为0.667。结果表明，我们的方法在跟踪晶体生长以及确定有用的图像序列以供晶体学家进一步查看方面是可靠且高效的。
