Abstract
Polo is a Python-based graphical user interface designed to streamline viewing and analysis of images to monitor crystal growth, with a specific target to enable users of the High-Throughput Crystallization Screening Center at Hauptman-Woodward Medical Research Institute (HWI) to efficiently inspect their crystallization experiments. Polo aims to increase efficiency, reducing time spent manually reviewing crystallization images, and to improve the potential of identifying positive crystallization conditions. Polo provides a streamlined one-click graphical interface for the Machine Recognition of Crystallization Outcomes (MARCO) convolutional neural network for automated image classification, as well as powerful tools to view and score crystallization images, to compare crystallization conditions, and to facilitate collaborative review of crystallization screening results. Crystallization images need not have been captured at HWI to utilize Polo's basic functionality. Polo is free to use and modify for both academic and commercial use under the terms of the copyleft GNU General Public License v3.0.
Highlights
Growing high-quality protein crystals is a major bottleneck in crystal-based structure determination, which accounts for $90% of all structures in the Protein Data Bank (PDB) (Derewenda, 2011; Fazio et al, 2014; Lynch et al, 2020)
At the Crystallization Center at Hauptman-Woodward Medical Research Institute (HWI), researchers ship their macromolecular samples to the facility where experimental setup is handled on-site
The Machine Recognition of Crystallization Outcomes (MARCO) model is open source, requiring TensorFlow or TensorFlow Lite (Vanhoucke, 2018), but implementation of the algorithm in many crystal screening laboratories is challenging, which has limited the practical use of the MARCO model
Summary
Growing high-quality protein crystals is a major bottleneck in crystal-based structure determination, which accounts for $90% of all structures in the Protein Data Bank (PDB) (Derewenda, 2011; Fazio et al, 2014; Lynch et al, 2020) This bottleneck is a result of both the inherent difficulty of crystallizing macromolecular samples and the labor-intensive process of traditional experimental protocols which depend on the manual preparation and examination of dozens to hundreds of crystallization conditions (McPherson & Gavira, 2014). This creates a challenge, as even in HT experiments most chemical conditions will still fail to produce macromolecular crystals This results in a needle-inthe-haystack problem requiring researchers to carefully and manually review potentially tens of thousands of images to identify successful crystallization conditions (Luft et al, 2011). Polo is free to use and modify for both academic and commercial use under the terms of the permissive GPL v3.0 software license
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