Ab initio protein modelling for automated X-ray crystal structure solution

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Proteins make up the functional machinery of all living beings. Their particular roles depend on their 3-dimensional structures which allow given proteins to interact specifically with other molecules in their environment. Some proteins - enzymes - go further and can transform certain compounds into others. To understand better how proteins work and be able to use them in industry and medicine, scientists are greatly interested in figuring out their 3-dimensional structures. There are various ways to do this, but the dominant technique is X-ray crystallography. In this, an intense beam of X-rays is fired at a protein crystal. The X-rays are diffracted when passing through the crystal, producing a pattern of rays that is characteristic of the protein under study. In order to elucidate the structure of the protein, information derived from multiple diffraction patterns obtained from the same protein but under different conditions must be drawn together. The acquisition of such extra diffraction patterns can be time consuming, expensive, and commonly involves hazardous chemicals. A technique exists, however, where computers substitute the additional experiments by estimating equivalent information from available structures of proteins similar to that under study. In this way, protein structures can be solved from one single diffraction pattern. This technique - called Molecular Replacement (MR) - is fast, economical, clean and often uncomplicated. However, since MR relies on pre-existing structures, it is not applicable to many proteins of interest, for which similar structures are simply not available. For many years, scientists have tried to develop computer methods to predict the structure of proteins, purely based on their sequences. These methods are generally called ab initio modelling methods. Over the past decade, these efforts have started to bear fruit. These predicted models are unlikely to substitute for crystal structures any time soon since they typically contain errors, but recent work has shown that they are sometimes close enough to the real structure for them to be used in the MR process. This is the main idea behind this proposal - to adapt current ab initio modelling procedures to the specific needs of MR. With ab initio modelling, it is generally the case that the more detailed (i.e. the longer) the computer calculation, the better the model you can make. Unfortunately, achieving the best models is so demanding that it often requires extensive calculation times or access to supercomputers or other vast computer resources. Few crystallographers have access to these facilities, making the modelling method impractical. We therefore propose a different approach, making efficient use of simpler models that can be easily obtained on typical computers. In our preliminary work, we have already proven that this approach can work successfully for MR. What we want to do now is find the best way to produce optimal models and to do this automatically. This effectively means adapting the method to meet the demands of modern X-ray crystallography, making it fast so that it can be used as a routine approach and accessible to other crystallographers without specialist knowledge of ab initio modelling. We then want to include the method in the MrBUMP program, which is a well-established package allowing for easy, automated MR. MrBUMP can be added to a software package called CCP4i that is widely used by crystallographers. By incorporating our processing method in a familiar program, we expect it to become widely used across the world. We expect that by extending the MR computational approach we will enable protein structures to be determined more quickly and cheaply. In this way, research in all sorts of areas that depend on protein structure information, like drug design, will proceed faster.