Ian Berry

A procedure for setting up high-throughput nanolitre crystallization experiments. Crystallization workflow for initial screening, automated storage, imaging and optimization

Crystallization trials at the Division of Structural Biology in Oxford are now almost exclusively carried out using a high‐throughput workflow implemented in the Oxford Protein Production Facility. Initial crystallization screening is based on nanolitre‐scale sitting‐drop vapour‐diffusion experiments (typically 100 nl of protein plus 100 nl of reservoir solution per droplet) which use standard crystallization screening kits and 96‐well crystallization plates. For 294 K crystallization trials the barcoded crystallization plates are entered into an automated storage system with a fully integrated imaging system. These plates are imaged in accordance with a pre‐programmed schedule and the resulting digital data for each droplet are harvested into a laboratory information‐management system (LIMS), scored by crystal recognition software and displayed for user analysis via a web‐based interface. Currently, storage for trials at 277 K is not automated and for imaging the crystallization plates are fed by hand into an imaging system from which the data enter the LIMS. The workflow includes two procedures for nanolitre‐scale optimization of crystallization conditions: (i) a protocol for variation of pH, reservoir dilution and protein:reservoir ratio and (ii) an additive screen. Experience based on 592 crystallization projects is reported.

Towards rationalization of crystallization screening for small- to medium-sized academic laboratories: the PACT/JCSG+ strategy

A crystallization screening process is presented that was developed for a small academic laboratory. Its underlying concept is to combine sparse-matrix screening with systematic screening in a minimum number of crystallization conditions. The sparse-matrix screen is the cherry-picked combination of conditions from the Joint Center for Structural Genomics (JCSG) extended using conditions from other screens. Its aim is to maximize the coverage of crystallization parameter space with no redundancy. The systematic screen, a pH-, anion- and cation-testing (PACT) screen, aims to decouple the components of each condition and to provide information about the protein, even in the absence of crystals, rather than cover a wide crystallization space. This screening strategy is combined with nanolitre-volume dispensing hardware and a small but practical experiment-tracking system. The screens have been tested both at the NKI and in other laboratories and it is concluded that they provide a useful minimal screening strategy.

SPINE high-throughput crystallization, crystal imaging and recognition techniques: current state, performance analysis, new technologies and future aspects

This paper reviews the developments in high-throughput and nanolitre-scale protein crystallography technologies within the remit of workpackage 4 of the Structural Proteomics In Europe (SPINE) project since the projects inception in October 2002. By surveying the uptake, use and experience of new technologies by SPINE partners across Europe, a picture emerges of highly successful adoption of novel working methods revolutionizing this area of structural biology. Finally, a forward view is taken of how crystallization methodologies may develop in the future.

SPINE bioinformatics and data-management aspects of high-throughput structural biology

SPINE (Structural Proteomics In Europe) was established in 2002 as an integrated research project to develop new methods and technologies for high‐throughput structural biology. Development areas were broken down into workpackages and this article gives an overview of ongoing activity in the bioinformatics workpackage. Developments cover target selection, target registration, wet and dry laboratory data management and structure annotation as they pertain to high‐throughput studies. Some individual projects and developments are discussed in detail, while those that are covered elsewhere in this issue are treated more briefly. In particular, this overview focuses on the infrastructure of the software that allows the experimentalist to move projects through different areas that are crucial to high‐throughput studies, leading to the collation of large data sets which are managed and eventually archived and/or deposited.

The use of gradient direction in pre-processing images from crystallization experiments

Robots are now used routinely to perform crystallization experiments and many laboratories now have imaging systems to record the results. These images must be evaluated rapidly and the results fed back into optimization procedures. Software to analyse the images is being developed; described here are methods to restrict the area of the image to be analysed in order to speed up processing. Properties of the gradient of greyscale images are used to identify first the well and then the crystallization drop for various crystallization trays and different imaging systems. Methods are discussed to identify artefacts in the images that are not related to the experimental outcome, but can cause problems for the machine-learning algorithms used in classification and waste time during analysis. Gradient angles are exploited to eliminate faults in the crystallization trays, bubbles and splatter droplets prior to analysis.

xtalPiMS: a PiMS-based web application for the management and monitoring of crystallization trials.

A major advance in protein structure determination has been the advent of nanolitre-scale crystallization and (in a high-throughput environment) the development of robotic systems for storing and imaging crystallization trials. Most of these trials are carried out in 96-well (or higher density) plates and managing them is a significant information management challenge. We describe xtalPiMS, a web-based application for the management and monitoring of crystallization trials. xtalPiMS has a user-interface layer based on the standards of the Protein Information Management System (PiMS) and a database layer which links the crystallization trial images to the meta-data associated with a particular crystallization trial. The user interface has been optimized for the efficient monitoring of high-throughput environments with three different automated imagers and work to support a fourth imager is in progress, but it can even be of use without robotics. The database can either be a PiMS database or a legacy database for which a suitable mapping layer has been developed.

IMAGE STORAGE FOR AUTOMATED CRYSTALLIZATION IMAGING SYSTEMS

To use crystallography for the determination of the three-dimensional structures of proteins, protein crystals need to be grown. Automated imaging systems are increasingly being used to monitor these crystallization experiments. These present problems of accessibility to the data, repeatability of any image analysis performed and the amount of storage required. Various image formats and techniques can be combined to provide effective solutions to high volume processing problems such as these, however lack of widespread support for the most effective algorithms, such as JPeg2000 which yielded a 64% improvement in file size over the bitmap, currently inhibits the immediate take up of this approach.

The Effect of Image Compression on Classification and Storage Requirements in a High-Throughput Crystallization System

High-throughput crystallization and imaging facilities can require a huge amount of disk space to keep images on-line. Although compressed images can look very similar to the human eye, the effect on the performance of crystal detection software needs to be analysed. This paper tests the use of common lossy and lossless compression algorithms on image file size and on the performance of the York University image analysis software by comparison of compressed Oxford images with their native, uncompressed bitmap images. This study shows that significant (approximately 4-fold) space savings can be gained with only a moderate effect on classification capability.