Tools to Ease the Choice and Design of Protein Crystallisation Experiments

20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening

Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which remains a primary bottleneck in crystal-based structure determination. The National High-Throughput Crystallization Center at Hauptman-Woodward Medical Research Institute has focused on overcoming obstacles to crystallization through a combination of robotics-enabled high-throughput screening and advanced imaging to increase the success of finding crystallization conditions. This paper will describe the lessons learned from over 20 years of operation of our high-throughput crystallization services. The current experimental pipelines, instrumentation, imaging capabilities and software for image viewing and crystal scoring are detailed. New developments in the field and opportunities for further improvements in biomolecular crystallization are reflected on.

Data- and diversity-driven development of a Shotgun crystallization screen using the Protein Data Bank

Protein crystallization has for decades been a critical and restrictive step in macromolecular structure determination via X-ray diffraction. Crystallization typically involves a multi-stage exploration of the available chemical space, beginning with an initial sampling (screening) followed by iterative refinement (optimization). Effective screening is important for reducing the number of optimization rounds required, reducing the cost and time required to determine a structure. Here, an initial screen (Shotgun II) derived from analysis of the up-to-date Protein Data Bank (PDB) is proposed and compared with the previously derived (2014) Shotgun I screen. In an update to that analysis, it is clarified that the Shotgun approach entails finding the crystallization conditions that cover the most diverse space of proteins by sequence found in the PDB, which can be mapped to the well known maximum coverage problem in computer science. With this realization, it was possible to apply a more effective algorithm for selecting conditions. In-house data demonstrate that compared with alternatives, the Shotgun I screen has been remarkably successful over the seven years that it has been in use, indicating that Shotgun II is also likely to be a highly effective screen.

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

A multi-platform open-source Python-based graphical user interface has been developed to provide access to automated classification and data management tools for biomolecular crystallization screening.

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.

IceBear: an intuitive and versatile web application for research-data tracking from crystallization experiment to PDB deposition

The web-based IceBear software is a versatile tool to monitor the results of crystallization experiments and is designed to facilitate supervisor and student communications. It also records and tracks all relevant information from crystallization setup to PDB deposition in protein crystallography projects. Fully automated data collection is now possible at several synchrotrons, which means that the number of samples tested at the synchrotron is currently increasing rapidly. Therefore, the protein crystallography research communities at the University of Oulu, Weizmann Institute of Science and Diamond Light Source have joined forces to automate the uploading of sample metadata to the synchrotron. In IceBear, each crystal selected for data collection is given a unique sample name and a crystal page is generated. Subsequently, the metadata required for data collection are uploaded directly to the ISPyB synchrotron database by a shipment module, and for each sample a link to the relevant ISPyB page is stored. IceBear allows notes to be made for each sample during cryocooling treatment and during data collection, as well as in later steps of the structure determination. Protocols are also available to aid the recycling of pins, pucks and dewars when the dewar returns from the synchrotron. The IceBear database is organized around projects, and project members can easily access the crystallization and diffraction metadata for each sample, as well as any additional information that has been provided via the notes. The crystal page for each sample connects the crystallization, diffraction and structural information by providing links to the IceBear drop-viewer page and to the ISPyB data-collection page, as well as to the structure deposited in the Protein Data Bank.