Project description
An important technique for characterising proteins gets real-time process monitoring
Proteins are one of nature's most important polymers, ubiquitous in living organisms. Their critical functions are tightly linked to their 3D structures. Characterising these 3D structures is thus fundamental to numerous applications in biomedicine and also to environmental sensing, wastewater treatment and, increasingly, natural solutions in fields ranging from energy to organic electronics. X-ray protein crystallography is an important technique used to determine the 3D position of every atom in a protein from a high concentration of purified protein that is then crystallised. The first and perhaps most difficult step is obtaining crystals of the protein of interest. Typically, many crystallisation conditions must be tried. The EU-funded Fail Fast project is developing methodology to characterise nanocrystal formation before the crystals have grown, putting an end to trial and error for significant benefits in time, money and outcome.
Objective
High-resolution protein structures obtained by X-ray diffraction require the production of well-ordered proteins crystals of high quality. However, to produce well-ordered protein crystals, several bottlenecks must be addressed to screen and optimize the thousands of conditions that can lead to crystallization. In this project, we will accelerate the process of protein crystallization by detecting early, i.e. assessing nano-crystal formation and providing immediate feedback, instead of waiting for crystals to grow, thereby saving time and money. We will use nonlinear light scattering that is highly sensitive to crystal formation. In combination with machine learning analysis, we will develop optimization routines to predict the conditions that likely lead to crystallization in a very short time scale, hours, instead of weeks.
Fields of science
Programme(s)
Funding Scheme
ERC-POC - Proof of Concept GrantHost institution
1015 Lausanne
Switzerland