Over the last years, genome-sequencing projects have provided new information about genes. While all these data have proven very useful to understand and exploit the information encoded in a genome, the knowledge of the 3D structures, functions and interactions of the coded proteins is required in order to integrate all this information in relevant areas such as drug design.
The wealth of genomic sequence information opens the way for a systematic program of high-throughput structure determination, driven mainly by the idea of producing a representative set of protein folds that could be used as templates for comparative modelling purposes.
Within their frameworks of structural genomics projects, X-ray crystallography and NMR techniques are used to provide high-resolution protein structures at large scale. Despite the advantages and developments in X-ray crystallography and NMR, the structural and functional assignment for the large number of known protein sequences remains a pressing problem for the genomic era of biology.
Computational structure prediction methods can provide valuable information for the fraction of sequences whose structures cannot be determined experimentally. The recognition of a protein fold provides valuable information about its function. While many sequence-based homology prediction methods exist, an important challenge remains: two highly dissimilar sequences can have similar folds.
How can this be quickly detected in the context of structural genomics? High-throughput NMR experiment s coupled with novel computational algorithms can address this challenge. The main idea here is based on protein 'fold recognition' using 15N-1H RDCs, together with sequence information, as query on a fold library of solved structures. For the matches found, more accurate 3D models will be obtained.
Finally, the knowledge obtained will be used to determine 3D structures of dimers, using SAXS data and information on interacting sites derived from the sequence.
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