Protein surface analysis by dimension reduction with applications in functional annotation and drug target prediction

The protein structure initiatives have increased the number of experimentally determined protein tertiary structures, providing tremendous opportunities for detailed comparative analysis of proteins. Although protein structures provide the most exquisite type of molecular information that can yield mechanistic insights into how proteins function, there are still many protein structures with undetermined or poorly defined functions. Functional annotation from protein 3-D structures has attracted many researchers, with most approaches relying on structural superposition against well-characterized proteins. 3-D structure superposition is a complex and computationally demanding problem; forcing most available approaches to only consider backbone atoms for simplicity and efficiency. In this study, we propose protein surface as a more powerful representation of proteins than the traditional backbone or atomic representations. In order to efficiently analyze protein surfaces, we introduce a novel approach to reduce the 3-D surface to a 2-D image map and utilize image registration algorithms to compare these feature-rich images. Whereas the dimension reduction inherently captures the 3-D geometry of the surface patches, we enrich the image map with additional features known to be important for defining molecular activity of the proteins, such as curvature, electrostatic potential, hydrophobicity, and residue conservation. Comparison of these enriched surface maps using image registration methods allows us to find similar surface patches shared between proteins. While the computational challenges remain to scale our approach to study comparisons in the entire set of available protein structures, our novel approach provides unique advantages compared to other structure comparison methods. We show that our method is able to detect local similarities even when proteins lack a global structure similarity. We also demonstrate the utility of the image maps and their comparisons in functional annotation, and drug target prediction tasks.Ph.D., Biomedical Engineering -- Drexel University, 201