Dr. Vasant Honavar Department of Computer Science honavar@cs ...

utilizing other sources of data when available – including predicted or known structures of the protein but not the
complex, evolutionary profiles, etc.) As part of this effort, we are assembling comprehensive databases of
protein-protein, protein-RNA, and protein-DNA interfaces, and developing software tools for identification of
protein-protein, protein-RNA and protein-DNA interface residues.
(c) Experimental validation of predicted protein-protein and protein-RNA interactions through a number of
collaborative projects e.g., understanding the significance of proline-mediated cis-trans conformational changes
in regulating protein function. Our hypothesis is that the "proline switch" may be widely exploited to regulate
protein-protein and protein-ligand interactions by controlling the transition between two alternative binding
surfaces.
Some results to date include a novel two-stage approach to identification of protein-protein interface residues from amino
acid sequence data (Yan, Dobbs and Honavar, 2004); an approach to improving the accuracy of protein-protein interface
identification using multiple sources of information (Sen et al., 2004), and new probabilistic graphical models and related
methods for assigning protein sequences to functional families (Andorf, Silvescu, and Honavar, 2004).
Data Driven Discovery and Refinement of Genetic Signaling Pathways and Regulatory Networks (in collaboration
with Heather Greenlee, and Jan Buss, funded in part by a National Institutes of Health Grant 5R03EY014931)
Retinal dystrophies (diseases that involve degeneration of photoreceptors in the retina) are a major cause of blindness.
Age related macular degeneration (AMD), which results in degeneration of cone photoreceptors dense macula, affects 10
million people in the United States alone. AMD is an extremely debilitating disease, which robs affected individuals of their
high acuity vision. Our research is aimed at understanding the mechanisms that control retinal development and
differentiation. Such understanding can lead to the development of therapies involving rehabilitation of retinal cells that
can be rescued from degeneration by the application of exogenous survival factors and transplantation of retinal
progenitor/stem cells into the degenerate retina.
The widely accepted model of retinal progenitor differentiation asserts that retinal progenitors pass through successive
intrinsic “competency states” in which they are capable of responding to extrinsic cues. The extrinsic signal can
encompass a whole host of soluble and cell-cell mediated factors. Intrinsic competence to respond to such external
factors involves expression of a number of transcription factors as well as expression and plasma membrane localization
of appropriate cell surface receptors that make it possible for the progenitors to respond to these cues from the extracellular
environment. Because the ability of a progenitor cell to respond to cues from its environment is determined to a
large extent by the proteins present in its proteome, we are investigating changes in expression among plasma
membrane proteins of the retinal progenitor cells from several different ages. This provides us with data on the changing
levels of protein expression in the developing retina which can be used to discover coordinated patterns of gene
expression changes.
Specific computational aims of this research include development and applications of novel computational approaches to:
data-driven inference genetic regulatory networks, and signaling pathways including:
(a) Discovery of co-expressed or co-regulated genes from gene expression patterns
(b) Construction and data-driven refinement of genetic networks from gene expression data
(c) Modeling and analysis of changes in gene expression patterns and the underlying genetic regulatory networks
and signaling pathways that control retinal development and differentiation
This work builds on our recent results on analysis of gene expression in chloroplast biogenesis in maize (Lonosky et al,
2004) and inference of temporal boolean network models of genetic networks from gene expression data (Silvescu and
Honavar, 2001).
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