Streamlining Protein Crystallography Automation from Sample Prep to Imaging

The Enduring Value of Protein Crystallography

X-ray crystallography generates high-resolution electron-density maps that support accurate protein structure determination and ligand placement. It is a fundamental technique in various life sciences and biotechnology applications. A principal example is drug discovery, where it remains a workhorse for high-throughput protein-ligand structure determination, guiding 80% of lead optimization projects and supporting a growing number of FDA submissions.  As cryo-electron microscopy expands the range of biological targets that can be studied, X-ray crystallography remains essential for obtaining high-resolution structural data for lead optimization, fragment screening, and biologics development, among other applications. Rather than replacing X-ray crystallography, the growth of cryo-electron microscopy is increasing the need for crystallography as more targets enter structural biology workflows.

Its ongoing significance is reflected in the rise in worldwide X-ray crystallography structure submissions to the Protein Data Bank, from 9,566 in 2015 to 10,865 in 2025. Protein crystallography’s role in drug development is set to grow, with a projected market expansion from USD 1.8 billion in 2025 to USD 3.0 billion by 2032. This growing demand is increasing the need for process optimization to improve accuracy, reduce manual workload, and support scalable operations. Automation has emerged as a key approach to addressing these workflow challenges and represents a natural evolution in protein crystallography as teams seek to increase throughput while improving accuracy and reproducibility.

The Evolution of Protein Crystallography Automation

In the early 2000s, crystallography workflows were largely manual, limiting throughput to around 100 pipetted conditions per day. Automated liquid handlers introduced in 2003 substantially increased capacity, while automated imaging in 2007 reduced the need for time-consuming microscope inspection. Remote-access and mail-in synchrotron services followed in 2011, broadening access to high-quality X-ray diffraction, and AlphaFold transformed structure prediction and model building in 2020. Automated data-processing pipelines also accelerated molecular replacement, model building, and structural refinement. By 2026, these advances are converging into integrated workflows spanning sample preparation, crystallization, imaging, data collection, and AI-assisted crystallography.

Statistics that illustrate the progress over the last quarter-century include:

• 25 nL minimum drop volumes vs 1-5 μL with manual approaches
• 1000X more conditions screened per day
• >95% reduction in data collection time

Key business benefits of these improvements include:

• First crystal structure in one month vs 18 months
• 15 times more structures per dollar
• Faster, more cost-effective drug development
• Greater accessibility for smaller companies

Identifying Bottlenecks in the Protein Crystallography Workflow

Protein crystallography is a complex, multistep process in which inefficiencies or bottlenecks at any stage can delay downstream activities and extend development timelines. This section outlines the key workflow steps and highlights where bottlenecks can arise.

Protein Production and Purification

This step produces the high-purity protein required for crystallization and downstream analysis. However, certain targets, including membrane proteins, can be difficult to isolate in sufficient quantities and suitable conformations. Bottlenecks can emerge when low yields, instability, or lengthy purification steps delay crystallization or limit the number of conditions that can be tested.

Crystal Production

This step involves combining purified protein with screening solutions to form crystallization drops and testing a broad range of conditions to identify those that produce diffraction-quality crystals. Throughput can become a major bottleneck because hundreds or thousands of conditions may require screening and pre-use aliquoting.

Crystal Monitoring

This step involves inspecting each drop to identify crystal formation and assess crystal quality and suitability for diffraction. Manual inspection can limit throughput, while repeatedly transferring large numbers of plates between incubators and imaging systems can create additional delays.

Crystal Optimization

This step involves refining crystallization conditions to improve crystal size and diffraction quality. Historically, throughput was constrained by 24-well trays, larger drops requiring substantially more protein, and the manual preparation of custom optimization solutions.

When Does Automation Become Necessary?

Automation has significantly improved the accuracy, throughput, and scalability of protein crystallography. However, deciding when to implement automation, and which stage of the workflow to target, remains a complex decision shaped by time requirements, reproducibility, and future capacity. A fully automated workflow can free up researcher time and support greater scale, but instrumentation also brings substantial upfront and ongoing operational costs. This can make it difficult to identify the point at which automation becomes the most strategic investment. It is also important to consider the opportunities automation enables, including testing more conditions, taking on additional projects, expanding operations, and pursuing more ambitious funding opportunities. Ultimately, while investment timing and specific needs will vary by laboratory, consistent reproducibility, time gains, and real scalability can only be achieved through automation.

Automation in Action: Key Insights from Crystallography at Helix

Automated protein crystallography workflows allow teams to progress more quickly with large-scale screening but also provide advantages for more challenging projects as the following real-world case studies demonstrate.

Large Scale Efficiency

Using manual methods, setting up more than 45,000 crystallization drops would require nearly eight months of laboratory work. By leveraging automation, Helix was able to screen more than 45,000 drops and deliver 11 structures in just 11 weeks, ultimately completing 20 structures for the project. This demonstrates how automation can make large-scale crystallography projects more feasible while substantially reducing timelines, with downstream benefits for the cost and efficiency of drug development.

Overcoming Challenging Proteins

Low-yielding and conformationally unstable proteins have long been a major challenge in protein crystallography workflows. Helix worked with a particularly challenging kinase that required ligands to maintain solubility and had to be purified within a single day using multiple chromatography steps, only to yield 1.5 mg from a 20 L culture. Despite the limited protein available, Helix generated four plates comprising 384 conditions and 1,152 drops, ultimately producing a crystal structure with a ligand of interest bound. Achieving this combination of speed and experimental scale would have been extremely difficult with manual methods. This real-world example highlights how automation enables discovery in addition to increasing throughput.

Hudson Lab Automation Solutions for Automating Protein Crystallography

Hudson Lab Automation provides solutions that address specific workflow bottlenecks, improve efficiency and scalability, as well as supporting complete end-to-end automation. Below are the key points where our instruments can integrate into the crystallization workflow.