We propose to develop and apply methods for docking ligand fragments into families of proteins, for two specific purposes. The first of these is to develop methods to identify patterns which are unique to a specific family member, to avoid cross-reactivity in the design of highly specific drugs. The second is to develop methods to identify patterns of similarity, which can be used when multiple alleles of a target protein are known to help avoid the development of resistant strains of infectious disease organisms. The work is an extension of and suggested by our current efforts, funded by a separate Phase II SBIR, to develop methods for comparing surface features of proteins within and across families. The work proposed entails the development of (a) new docking methods which can directly use feature comparisons within families; (b) novel data segmentation tools based on unsupervised learning methods to assess similarity and differences obtained from docking results on individual family members; and (c) the development of new classification methods based on Vector Support Machines and other methods for optimizing separation functions to distinguish correctly docked from incorrectly docked structures within the families. While the work is exploratory, if successful it can provide a focussed set of powerful drug-discovery tools to take advantage of the increasingly rich amount of information on protein sequence and structure emerging from genomics and structural genomics efforts. Software developed will be offered for sale, and applied to available crystal structures to develop specialized databases which can be of immediate use in drug discovery.
The goals of the grant are to address key issues in current drug discovery, namely, specificity, cross-reactivity, and avoiding resistance in infectious disease organisms to new drugs. The methods are strongly enabled by the accumulation of knowledge from genomics and structural genomics efforts. We believe that these and similar methods will be necessary to reap the commercial and sociological benefit promised by this extraordinary accumulation of knowledge.
