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Cinder: keeping crystallographers app-y.

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Summary
This summary is machine-generated.

A new mobile app, Cinder, helps scientists quickly score crystallization images on smartphones. This accelerates data collection for machine learning, addressing a key bottleneck in protein structure determination.

Keywords:
Cindercrystallizationimagesmachine learningmobile appsscoring

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Area of Science:

  • Structural Biology
  • Biophysics
  • Crystallography

Background:

  • Protein crystallization is crucial for X-ray diffraction analysis but is a bottleneck due to manual image assessment.
  • Automated imagers capture trial images, but analysis remains a manual and time-consuming process.
  • Lack of reliable training data hinders the development of machine-learning tools for automated image analysis.

Purpose of the Study:

  • To develop a mobile application, Cinder, for rapid scoring of crystallization images.
  • To streamline the data collection process for crystallization trials.
  • To facilitate the creation of machine-learning datasets for automated image analysis.

Main Methods:

  • Development of the Cinder mobile application for scoring crystallization images on smartphones/tablets.
  • Integration of Cinder scores into a crystallization database.
  • Provision of a web interface for detailed data analysis.

Main Results:

  • A significant increase in the number of scored crystallization images was observed after Cinder's release.
  • The application enables quick and efficient scoring directly on mobile devices.
  • Generated data is readily available for training machine-learning algorithms.

Conclusions:

  • Cinder effectively addresses the bottleneck in assessing crystallization images.
  • The mobile application facilitates rapid data acquisition for machine learning.
  • Cinder enhances the efficiency of structural biology research by providing more data for analysis.