Abstract: Beginning with the sequencing of the human genome in the 1990s, major efforts have been underway to expand significantly upon our current working knowledge of proteins. Structural Genomics efforts have solved 15,000 protein structures, but most of these are of unknown or uncertain biochemical function or have an incorrect assigned putative function. With all these structures, methodology must be developed to functionally annotate these proteins in order to identify potential applications, such as bioremediation and understanding cellular processes. Enzymes such as those in the Haloacid Dehalogenase Superfamily (HADSF) could help solve environmental problems as some known members possess the ability to detoxify and degrade halogenated hydrocarbons. Computational methods, including the machine learning method Partial Order Optimum Likelihood (POOL) and Structurally Aligned Local Sites of Activity (SALSA), developed at Northeastern, are used to predict the biochemical function of proteins that lack validated function, using a local-structure-based approach with the functionally important residues designated by POOL. Of the 71 proteins studied, 41 predictions were made, in some cases correcting prior putative annotations, using our computation methodologies. These predictions are being experimentally validated by direct biochemical assay to establish the function of each protein and to verify our computational approach to function prediction.

Functional characterization of structural genomics proteins in the haloacid dehalogenase superfamily using computed chemical properties

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.