Target selection for structural genomics based on combining fold recognition and crystallisation prediction methods: application to the human proteome.

Abstract

The objective of this study is to automatically identify regions of the human proteome that are suitable for 3D structure determination by X-ray crystallography and to annotate them according to their likelihood to produce diffraction quality crystals. The results provide a powerful tool for structural genomics laboratories who wish to select human proteins based on the statistical likelihood of crystallisation success. Combining fold recognition and crystallisation prediction algorithms enables the efficient calculation of the crystallisability of the entire human proteome. This novel study estimates that there are approximately 40,000 crystallisable regions in the human proteome. Currently, only 15% of these regions (approx. 6,000 sequences) have been solved to at least 95% sequence identity. The remaining unsolved regions have been categorised into 5 crystallisation classes and an integral membrane protein (IMP) class, based on established structure prediction, crystallisation prediction and transmembrane (TM) helix prediction algorithms. Approximately 750 unsolved regions (2% of the proteome) have been identified as having a PDB fold representative (template) and an 'optimal' likelihood of crystallisation. At the other end of the spectrum, more than 10,500 non-IMP regions with a PDB template are classified as 'very difficult' to crystallise (26%) and almost 2,500 regions (6%) were predicted to contain at least 3 TM helices. The 3D-SPECS (3D Structural Proteomics Explorer with Crystallisation Scores) website contains crystallisation predictions for the entire human proteome and can be found at http://www.bioinformaticsplus.org/3dspecs.