AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

Abstract

AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal datacollection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the samplestorage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.

Automated data collection in single particle electron microscopy

“…Integration of these physical condition monitors with automation software could aid in reproducibility and reduce downtime during collection and may also be used to identify issues that limit image quality. Finally, the eventual aim of EM automation will be to make the entire process from grid preparation to 3D structure determination fully automated, in a similar fashion to the automated pipelines developed for protein crystallography [69,70]. Automated data processing is already becoming a reality with software that can acquire EM images and process them into electron density maps semi-automatically, such as Appion [54], EMAN2 [71] and Relion [72].…”

Recent improvements to the automatic characterization and data collection algorithms on MASSIF-1

“…However, despite these advances, a human presence is still required to sequence actions. Pioneering beamlines that fully automated the process, such as LRL-CAT at the APS (Wasserman et al, 2012) and the SSRL MX beamlines (Tsai et al, 2013), removed the need for human presence, but as they rely on optical loop centring this means that restrictions have to be placed on the size of crystals and tend to be for robust, well diffracting samples, generally those for proprietary research for the pharmaceutical industry.…”

“…However, despite these advances, a human presence is still required to sequence actions. Pioneering beamlines that fully automated the process, such as LRL-CAT at the APS (Wasserman et al, 2012) and the SSRL MX beamlines (Tsai et al, 2013), removed the need for human presence, but as they rely on optical loop centring this means that restrictions have to be placed on the size of crystals and tend to be for robust, well diffracting samples, generally those for proprietary research for the pharmaceutical industry.In 2014 the ESRF beamline MASSIF-1 opened to users as the first beamline to fully automate MX data collection, including sample location and complex decision making algorithms (Svensson et al, 2015). The combination of sample location and characterisation allows even the smallest and weakly diffracting samples to be treated .…”

Fully automatic macromolecular crystallography: the impact of MASSIF-1 on the optimum acquisition and quality of data

“…Many synchrotrons offer 'mail-in' services, where a dedicated operator collects data for users [33][34][35] but this service has remained largely the proviso of the pharmaceutical industry. Lilly research laboratories collaborative access team at the advanced photon source and the stanford synchrotron radiation laboratory have led the way in truly automatic data collection [36,37] but as the services rely on optical centring, they tend to be restrictive and are again mostly used for proprietary research. This leaves a niche for a fully automatic system that is able to completely replace a human operator, in that it can work with the most challenging systems and make complex decisions based on the analysis of the data.…”