CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments.

IF 2.6 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Oliver N F King, Karl E Levik, James Sandy, Mark Basham
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引用次数: 0

Abstract

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

CHiMP:在蛋白质结晶显微照片上训练的深度学习工具,实现实验自动化。
为了分析英国钻石光源(DLS)同步加速器蛋白质结晶实验的显微照片,我们创建了一组三个深度学习工具,统称为 CHiMP(Crystal Hits in My Plate)。第一个工具是一个分类网络,将图像分配到与实验结果相关的类别中。另外两个工具是同时执行对象检测和实例分割的网络,在第一种情况下可生成单个晶体的掩膜,在第二种情况下除晶体外还可生成结晶液滴的掩膜,从而记录这些实体的位置和大小。这些工具的创建使用了迁移学习,即以预先训练好的深度学习网络的权重为起点,通过在相对较小的数据集上进行进一步训练来重新使用。其中两个工具现已集成到 DLS 的 VMXi 大分子晶体学光束线,在那里,无论是监测结晶实验还是触发原位数据收集,它们都有可能免除用户输入的需要。第三个系统正在被集成到同样位于 DLS 的 XChem 片段药物发现筛选平台中,以便将声学化合物自动分配到结晶液滴中。
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来源期刊
Acta Crystallographica. Section D, Structural Biology
Acta Crystallographica. Section D, Structural Biology BIOCHEMICAL RESEARCH METHODSBIOCHEMISTRY &-BIOCHEMISTRY & MOLECULAR BIOLOGY
CiteScore
4.50
自引率
13.60%
发文量
216
期刊介绍: Acta Crystallographica Section D welcomes the submission of articles covering any aspect of structural biology, with a particular emphasis on the structures of biological macromolecules or the methods used to determine them. Reports on new structures of biological importance may address the smallest macromolecules to the largest complex molecular machines. These structures may have been determined using any structural biology technique including crystallography, NMR, cryoEM and/or other techniques. The key criterion is that such articles must present significant new insights into biological, chemical or medical sciences. The inclusion of complementary data that support the conclusions drawn from the structural studies (such as binding studies, mass spectrometry, enzyme assays, or analysis of mutants or other modified forms of biological macromolecule) is encouraged. Methods articles may include new approaches to any aspect of biological structure determination or structure analysis but will only be accepted where they focus on new methods that are demonstrated to be of general applicability and importance to structural biology. Articles describing particularly difficult problems in structural biology are also welcomed, if the analysis would provide useful insights to others facing similar problems.
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