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

DOI: 10.1107/S2059798324009276
Data DOI: 10.5281/zenodo.11110372

Authors: Oliver N. F. King (Diamond Light Source) , Karl E. Levik (Diamond Light Source) , James Sandy (Diamond Light Source) , Mark Basham (Diamond Light Source; Rosalind Franklin Institute)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Acta Crystallographica Section D Structural Biology , VOL 80 , PAGES 744 - 764

State: Published (Approved)
Published: October 2024

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.

Subject Areas: Information and Communication Technology, Biology and Bio-materials, Medicine


Instruments: VMXi-Versatile Macromolecular Crystallography in situ

Added On: 04/10/2024 08:42

Discipline Tags:

Artificial Intelligence Health & Wellbeing Information & Communication Technologies Structural biology Drug Discovery Life Sciences & Biotech

Technical Tags:

Diffraction Macromolecular Crystallography (MX)