We live in times of overwhelmingly fast development of new genomics and proteomics methods bringing primary structures of a number of genomes.More than 20,000 protein structures are stored in the protein data bank (PDB),but it is less than one tenth of a number of already defined primary structures of proteins.Even using fastest throughput crystallization and X-ray crystallography methods scientists would not be able to solve 3D structures of all these proteins during the next decade.Medicinal chemistry is increasingly using 3D structures of proteins,DNA,and RNA for the development of drugs.There exist a number of successful stories of structure-based drug design.One can recall the development of HIV protease inhibitor made using 3D structure of HIV protease.At the same time it is obvious that scientists do not always have the solved 3D structures of biomolecules and have to use their structures predicted on the basis of their primary sequences. A number of predicted 3D structures of various proteins already are used in structure-based drug design.For example,simply identifying solvent exposed residues in some proteins saves enormous amount of work in revealing the sites critical for antibody binding or / and putative drugs binding to proteins.In many cases,like cytochrome P450 family of proteins, predictive modeling became a regular instrument for outlining the catalytic properties of various cytochromes and recommendation for possible structures of inhibitors of these enzymes. Construction of the template based pharmacophore models for further drug design on the basis of predicted protein structures gives a reasonable rate of success.At the same time prediction of RNA structures for this purpose has not developed sufficiently despite obvious opportunities to model specific shapes of RNA and consequently its binding to small compounds and docking to specific proteins. Currently protein structure prediction methods are becoming a part of broader genomic and proteomic studies.One of the promising directions here is using protein structure prediction to estimate the possible impact of single nucleotide polymorphisms (SNP)on the properties of specified proteins and subsequently on overall medical conditions of patients. Technologies for large-scale SNP genotyping are now affordable.At the same time current development of protein structure prediction technology also makes it possible to create high throughput prediction programs that would work in synchronized modes with large-scale SNP genotyping.This combination can bring enormous amount of data on possible impacts of SNPs in coding regions. Reliability of protein structure prediction methods is supported by a number of studies where authors made comprehensive point mutagenesis analysis of predicted structures and show very impressive correspondence of predicted 3D structures to mutagenesis results.Meanwhile I had to note that in so-called 'gray zones ' of low sequence identity reliability of protein structure prediction became lower and here we would need more support from experimental results.Nevertheless in many cases one does not need a prediction of the entire protein structure.Prediction of specific,functionally important regions can help in structure-based drug design even when we cannot create such a model of entire protein. Protein structure prediction programs develop very rapidly.A number of publications on this topic more than doubled during last 5 years.I think the methods of prediction would become a regular instrument in a number of high throughput methods of drug design.
Rebecca and John Moores UCSD Cancer Center San Diego Super Computer Center USA
Publication date: March 1, 2004
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