Over the past two decades, advances in the crystallisation of soluble proteins, diffraction data collection and data analysis have made structure solution of soluble proteins almost routine. But crystallisation – remains a major bottleneck for membrane proteins. The additional challenge with membrane proteins is two-fold: first, the protein itself is hard to produce in large quantities and tends to be unstable. Second, the parameter space is even larger than for soluble proteins due to additional parameters: the detergents. The crystallisation process is thus complex and poorly understood.
Membrane proteins are currently crystallized by using brute-force screening to search this high-dimensional parameter space to find initial conditions, followed by trial-and-error optimisation to grow crystals suitable for diffraction studies. Though over 85% of drug targets are membrane proteins, fewer than 650 unique membrane protein structures have been determined. We urgently require better methods to crystallize membrane proteins. RAMP created a unique training network that brings together three strands: (1) development of a microfluidics-based technology to control membrane protein crystallisation, and the ability to sample the large parameter space of crystallisation conditions rapidly; (2) the introduction to membrane protein crystallisation optimisation of modelling of the phase diagram, a technique used with great success for soluble protein crystallisation; and (3) the application of these developments to medically and biologically important membrane protein targets.
RAMP also trained the students on two new and emerging techniques: 1) serial crystallographic methods. These are increasingly used at synchrotron sources as well as at rapidly developing ultra-bright free-electron laser sources, and require crystals in the 1-20 μm size to solve structure of previously intractable proteins. 2) Neutron protein crystallography, which requires large crystals (> 0.01 mm3). It is, however, the only way to visualise protons - important information for drug design.