3D-Gateway - Gateway to protein structure and function

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Proteins comprise long chains of organic molecules that fold into compact globular 3-dimensional structures. Knowing this structure can give very valuable insights into the clefts, pockets or other surface features important for binding other molecules in the cell eg small molecules or proteins. Knowledge of the structure is also essential for designing drugs that bind to these features and inhibit the protein and can also help in understanding whether mutations in the protein's residues affect its stability or function, leading to disease. Experimentally determining the structure can be challenging, which is why only a small percentage of known proteins (~145,000 out of 120 million) have been characterised. However, powerful computational methods have been developed that predict protein structures by inheriting structural information from evolutionary related proteins whose structures are known. These prediction techniques have been made even more powerful, recently, as new ways of exploiting the evolutionary data have been found that more accurately constrain contacts in the protein. Applying these techniques, structures can be predicted for a large proportion of uncharacterised proteins. For example, for human proteins about 5% of the structures are known but a further 88% can be modelled, some to very high accuracy, thereby providing important frameworks for designing drugs to treat human diseases. When inheriting structural data between distant relatives one has to be much more cautious and most prediction methods return a confidence score for the models produced. This project will build an infrastructure (3D-Beacons) that aggregates experimentally determined structures with predicted structures generated by groups applying different algorithms. This will be done for proteins from selected organisms relevant to food security and human health - some will be pathogenic bacteria that threaten humans or animals/crops. We will use this data to annotate proteins in the UniProt resource, widely used by more than 750,000 unique users each month. Since the prediction methods reside in many different labs, by pooling the data in this way we can significantly increase the number of proteins with structural data. In addition, combining models built by independent algorithms allows us to compare 3D-models to find which parts agree regardless of method and which parts vary between methods and are clearly harder to model. Therefore, we will use this aggregated data to research the best strategies for calculating model quality at each position in the protein.We will build web pages to display the known and predicted structures for a given protein. It can be difficult to determine the structure of the whole protein so, where appropriate, we will display both experimental and predicted structures, taking great care to label the structures with information on the source (eg method used) and reliability of the data (eg confidence).We will also use our 3D-Beacons infrastructure to aggregate information on known and predicted functional sites on the protein structure and display this data on web pages, together with information on source and confidence. The site data mapped onto structure will be particularly helpful for developing rules that allow us to gauge whether a protein with no experimental characterisation has the same function as an evolutionary related protein with experimental characterisation. Relatives sharing the same function should have the same key functional site residues. With these rules we will be able to provide structural and functional annotations for millions of proteins in UniProt. The new data will represent a tenfold or more increase in the number of UniProt sequences which have structural and functional site information. UniProt is also widely used by researchers in industry and thus this expansion in information will have a very significant impact.