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Related Experiment Video

Updated: Nov 6, 2025

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Published on: March 10, 2023

Polo: an open-source graphical user interface for crystallization screening.

Ethan T Holleman¹, Erica Duguid^1,2, Lisa J Keefe^1,2

¹Hauptman-Woodward Medical Research Institute, 700 Ellicott Street, Buffalo, NY 14203, USA.

Journal of Applied Crystallography

|May 6, 2021

Summary

This summary is machine-generated.

Polo software streamlines crystal growth image analysis for researchers. This tool enhances efficiency and accuracy in identifying successful crystallization conditions by automating image review.

Keywords:

crystal imaging crystallization machine learning open-source graphical user interfaces

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Last Updated: Nov 6, 2025

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Published on: March 10, 2023

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Automated Protocols for Macromolecular Crystallization at the MRC Laboratory of Molecular Biology

Automated Protocols for Macromolecular Crystallization at the MRC Laboratory of Molecular Biology

Published on: January 24, 2018

Area of Science:

Biophysics
Structural Biology
Computational Biology

Background:

Automated analysis of crystallization images is crucial for efficient drug discovery and materials science.
Manual review of high-throughput crystallization screening images is time-consuming and prone to error.

Purpose of the Study:

To introduce Polo, a Python-based graphical user interface (GUI) for streamlined viewing and analysis of crystal growth images.
To enhance the efficiency and accuracy of identifying positive crystallization conditions in high-throughput screening.

Main Methods:

Development of a user-friendly GUI with one-click integration for the Machine Recognition of Crystallization Outcomes (MARCO) convolutional neural network.
Implementation of tools for image viewing, scoring, comparison of crystallization conditions, and collaborative review.

Main Results:

Polo significantly reduces the time and effort required for manual inspection of crystallization images.
The software facilitates improved identification of optimal crystallization conditions through automated classification and comparative analysis.

Conclusions:

Polo offers a powerful and accessible solution for researchers involved in crystal growth monitoring and analysis.
The software is freely available for academic and commercial use, promoting wider adoption and collaboration in the field.