Automated robotic harvesting of protein crystals-addressing a critical bottleneck or instrumentation overkill?

The low-cost Shifter microscope stage transforms the speed and robustness of protein crystal harvesting

Despite the tremendous success of X-ray cryo-crystallography in recent decades, the transfer of crystals from the drops in which they are grown to diffractometer sample mounts remains a manual process in almost all laboratories. Here, the Shifter, a motorized, interactive microscope stage that transforms the entire crystal-mounting workflow from a rate-limiting manual activity to a controllable, high-throughput semi-automated process, is described. By combining the visual acuity and fine motor skills of humans with targeted hardware and software automation, it was possible to transform the speed and robustness of crystal mounting. Control software, triggered by the operator, manoeuvres crystallization plates beneath a clear protective cover, allowing the complete removal of film seals and thereby eliminating the tedium of repetitive seal cutting. The software, either upon request or working from an imported list, controls motors to position crystal drops under a hole in the cover for human mounting at a microscope. The software automatically captures experimental annotations for uploading to the user's data repository, removing the need for manual documentation. The Shifter facilitates mounting rates of 100-240 crystals per hour in a more controlled process than manual mounting, which greatly extends the lifetime of the drops and thus allows a dramatic increase in the number of crystals retrievable from any given drop without loss of X-ray diffraction quality. In 2015, the first in a series of three Shifter devices was deployed as part of the XChem fragment-screening facility at Diamond Light Source, where they have since facilitated the mounting of over 120 000 crystals. The Shifter was engineered to have a simple design, providing a device that could be readily commercialized and widely adopted owing to its low cost. The versatile hardware design allows use beyond fragment screening and protein crystallography.

Using sound pulses to solve the crystal-harvesting bottleneck

Crystal harvesting has proven to be difficult to automate and remains the rate-limiting step for many structure-determination and high-throughput screening projects. This has resulted in crystals being prepared more rapidly than they can be harvested for X-ray data collection. Fourth-generation synchrotrons will support extraordinarily rapid rates of data acquisition, putting further pressure on the crystal-harvesting bottleneck. Here, a simple solution is reported in which crystals can be acoustically harvested from slightly modified MiTeGen In Situ-1 crystallization plates. This technique uses an acoustic pulse to eject each crystal out of its crystallization well, through a short air column and onto a micro-mesh (improving on previous work, which required separately grown crystals to be transferred before harvesting). Crystals can be individually harvested or can be serially combined with a chemical library such as a fragment library.

An Introduction to Programming for Bioscientists: A Python-Based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language’s usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a “variable,” the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.

Contemporary biology has largely become computational biology, whether it involves applying physical principles to simulate the motion of each atom in a piece of DNA, or using machine learning algorithms to integrate and mine “omics” data across whole cells (or even entire ecosystems). The ability to design algorithms and program computers, even at a novice level, may be the most indispensable skill that a modern researcher can cultivate. As with human languages, computational fluency is developed actively, not passively. This self-contained text, structured as a hybrid primer/tutorial, introduces any biologist—from college freshman to established senior scientist—to basic computing principles (control-flow, recursion, regular expressions, etc.) and the practicalities of programming and software design. We use the Python language because it now pervades virtually every domain of the biosciences, from sequence-based bioinformatics and molecular evolution to phylogenomics, systems biology, structural biology, and beyond. To introduce both coding (in general) and Python (in particular), we guide the reader via concrete examples and exercises. We also supply, as Supplemental Chapters, a few thousand lines of heavily-annotated, freely distributed source code for personal study.

Approaches to automated protein crystal harvesting

The harvesting of protein crystals is almost always a necessary step in the determination of a protein structure using X-ray crystallographic techniques. However, protein crystals are usually fragile and susceptible to damage during the harvesting process. For this reason, protein crystal harvesting is the single step that remains entirely dependent on skilled human intervention. Automation has been implemented in the majority of other stages of the structure-determination pipeline, including cloning, expression, purification, crystallization and data collection. The gap in automation between crystallization and data collection results in a bottleneck in throughput and presents unfortunate opportunities for crystal damage. Several automated protein crystal harvesting systems have been developed, including systems utilizing microcapillaries, microtools, microgrippers, acoustic droplet ejection and optical traps. However, these systems have yet to be commonly deployed in the majority of crystallography laboratories owing to a variety of technical and cost-related issues. Automation of protein crystal harvesting remains essential for harnessing the full benefits of fourth-generation synchrotrons, free-electron lasers and microfocus beamlines. Furthermore, automation of protein crystal harvesting offers several benefits when compared with traditional manual approaches, including the ability to harvest microcrystals, improved flash-cooling procedures and increased throughput.

CrystalDirect™: a novel approach for automated crystal harvesting based on photoablation of thin films

The last years have seen a major development in automation for protein production, crystallization, and X-ray diffraction data collection, which has contributed to accelerate the pace of structure solution and to facilitate the study of ever more challenging targets through macromolecular crystallography. This has led to a considerable increase in the numbers of crystals produced and analyzed. However the process of recovering crystals from crystallization supports and mounting them in X-ray data collection pins remains a manual and delicate operation. Here we present a novel approach enabling full automation of the crystal mounting process and describe the operation of the first-automated CrystalDirect harvesting unit. Implications for crystallography applications and for the future operational integration of automated crystallization and data collection resources are discussed.

A microfluidic approach for protein structure determination at room temperature via on-chip anomalous diffraction

We report a microfluidic approach for de novo protein structure determination via crystallization screening and optimization, as well as on-chip X-ray diffraction data collection. The structure of phosphonoacetate hydrolase (PhnA) has been solved to 2.11 Åvia on-chip collection of anomalous data that has an order of magnitude lower mosaicity than what is typical for traditional structure determination methods.

First experiences with semi-autonomous robotic harvesting of protein crystals

The demonstration unit of the Universal Micromanipulation Robot (UMR) capable of semi-autonomous protein crystal harvesting has been tested and evaluated by independent users. We report the status and capabilities of the present unit scheduled for deployment in a high-throughput protein crystallization center. We discuss operational aspects as well as novel features such as micro-crystal handling and drip-cryoprotection, and we extrapolate towards the design of a fully autonomous, integrated system capable of reliable crystal harvesting. The positive to enthusiastic feedback from the participants in an evaluation workshop indicates that genuine demand exists and the effort and resources to develop autonomous protein crystal harvesting robotics are justified.

Lessons from high-throughput protein crystallization screening: 10 years of practical experience

INTRODUCTION

X-ray crystallography provides the majority of our structural biological knowledge at a molecular level and, in terms of pharmaceutical design, is a valuable tool to accelerate discovery. It is the premier technique in the field, but its usefulness is significantly limited by the need to grow well-diffracting crystals. It is for this reason that high-throughput crystallization has become a key technology that has matured over the past 10 years through the field of structural genomics. Areas covered : The authors describe their experiences in high-throughput crystallization screening in the context of structural genomics and the general biomedical community. They focus on the lessons learnt from the operation of a high-throughput crystallization-screening laboratory, which to date has screened over 12,500 biological macromolecules. They also describe the approaches taken to maximize the success while minimizing the effort. Through this, the authors hope that the reader will gain an insight into the efficient design of a laboratory and protocols to accomplish high-throughput crystallization on a single-, multiuser laboratory or industrial scale. Expert opinion : High-throughput crystallization screening is readily available but, despite the power of the crystallographic technique, getting crystals is still not a solved problem. High-throughput approaches can help when used skillfully; however, they still require human input in the detailed analysis and interpretation of results to be more successful.

To automate or not to automate: this is the question

New protocols and instrumentation significantly boost the outcome of structural biology, which has resulted in significant growth in the number of deposited Protein Data Bank structures. However, even an enormous increase of the productivity of a single step of the structure determination process may not significantly shorten the time between clone and deposition or publication. For example, in a medium size laboratory equipped with the LabDB and HKL-3000 systems, we show that automation of some (and integration of all) steps of the X-ray structure determination pathway is critical for laboratory productivity. Moreover, we show that the lag period after which the impact of a technology change is observed is longer than expected.