Bioinformatics in structural genomics - Editorial

Editorial BIOINFORMATICS IN STRUCTURAL GENOMICS The goals of structural genomics initiatives are to significantly expand the structural 'coverage' of sequence space. A number of specific objectives have been included within this broad definition. These include, the determination of the detailed three-dimensional structure for at least one representative of each protein fold that occurs in nature, the determination of enough structures so that all others can be built with homology models and the determination of enough structures so that functional information can be inferred from models of all others. Projects are being launched at different pace and with very different scopes in the USA, Japan and Europe (Table). The initial phase has shifted focus slightly away from 'determining as many structures as possible' to 'finding large-scale semi-automatic solutions for the immediate technical bottlenecks'. These currently appear to be protein expression, purification and crystallisation. The different initiatives have adopted various approaches to the problems of how to select the experimental targets, how to analyse the resulting structures, and how to optimally benefit from the structural information generated to learn about function. Obviously, many of the initial tasks of structural genomics may profit from exploring the potential of bioinformatics. However, the precise way in which bioinformatics is embedded into existing initiatives and the percentage of resources allocated to bioinformatics activities differs substantially between the efforts. For example, the NorthEast Structural Genomics (NESG) consortium relies entirely on bioinformatics to select the targets that are experimentally pursued. In contrast, the first European structural genomics project hardly explored the potentials of bioinformatics. The most obvious initial application of bioinformatics tools to aid structural genomics has been the task of ranking the experimental targets. The task at hand is to somehow cluster all known proteins into families and to label all those families for which we do not yet have high-resolution information about the associated structures. However, computational biology is also required to optimally explore the information gained by solving a single structure. Examples are the transfer of structural information to homologues through comparative modelling and/or threading techniques. Another set of tools that aim to profit from the wealth of structures added through structural genomics aims at predicting aspects of function and at identifying functionally similar proteins. The problem of how functional specificity relates to protein structure is of very complex nature. Studying this adaptation requires combining experimental and predic-tive methods. A particular example of such combinations …