Implementing serial crystallography as a routine data collection tool at SARomics Biostructures
|Coordinator||SARomics Biostructures AB|
|Funding from Vinnova||SEK 429 000|
|Project duration||November 2018 - November 2019|
|Venture||Research infrastructure - utilisation and collaboration|
|Call||Research infrastructure - utilisation and collaboration: Industrial pilot projects for neutron and photon experiments at large scale research infrastructures|
Purpose and goal
About half of all drugs on the market today have membrane-bound proteins as targets. Information on these proteins´ three-dimensional structures is important for the development of new medicines. These proteins are difficult to study with the most common method, protein crystallography, as they usually only give rise to very small crystals. New methods have been developed at synchrotrons such as MAX IV in Lund to study these small crystals. The methods are under constant development, and the goal of the current project is for SARomics Biostructures staff to learn the new techniques.
Expected results and effects
As a result, SARomics Biostructures staff will learn the latest state-of-the-art methods for data collection from microcrystals, which is generally called "serial crystallography". This will give SARomics the possibility to engage with a wider range of projects on more challenging target proteins in the future, which will in turn increase the company´s competitiveness in the global market of early stage drug discovery.
Planned approach and implementation
SARomics will produce a large number of small crystals of a model protein that we will analyse together with staff at the BioMAX beamline of MAX IV in Lund. Serial crystallography involves exposing many such small crystals to X-rays at room temperature. We will investigate the best way to do this using the two most common methods under development today, i.e. streaming the crystals past the X-ray beam or spreading them out on a solid support. We will evaluate which of these works best for our system and also learn how to process the large amounts of data that result from such experiments.