CrystPro: Spatiotemporal Analysis of Protein Crystallization Images.
CrystPro: Spatiotemporal Analysis of Protein Crystallization Images.
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DOI：
10.1021/acs.cgd.5b00714
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发表时间：
2015
影响因子：
3.8
通讯作者：
Aygun RS
中科院分区：
文献类型：
--
作者：
Sigdel M;Pusey ML;Aygun RS
建立了数千个对应于不同条件组合的实验，以确定成功蛋白质结晶的相关条件。近年来，已经开发了高通量的机器人装置来自动化蛋白质结晶实验，并利用成像技术来监测结晶过程。在实验过程中，图像被多次收集。海量的采集图像使得人工查看图像变得乏味和令人沮丧。本文利用微量荧光标记技术，通过对时间序列图像的分析，描述了一种用于监测结晶实验图像中蛋白质晶体生长的自动化系统CrystPro。在给定图像序列集合的情况下，目标是开发一种高效而可靠的系统来检测晶体生长变化，例如新晶体的形成和晶体尺寸的增加。CrystPro包括三个主要步骤--确定适合于时空分析的结晶试验、确定试验的时空分析和晶体生长分析。我们在3个结晶图像数据集(PCP-ILopt-11、PCP-ILopt-12和PCP-ILopt-13)上评估了我们的系统的性能，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)对时空分析试验的识别准确率为98.3%，灵敏度为.896；b)识别具有新晶体形成的晶体对的准确率为77.4%，灵敏度为.986；以及c)晶体尺寸增大检测的准确率为85.8%，灵敏度为0.667。结果表明，我们的方法是可靠和有效的跟踪晶体的生长和确定有用的图像序列，供结晶学家进一步审查。
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 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 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.