The project is in the area of computational molecular biology; specifically it addresses problems in protein folding and the self-assembly of virus shells. The `grand challenge` problem associated with protein folding is to determine how a protein will fold in 3-dimensions when given only its amino acid sequence. An important first step in tackling the protein folding problem is a solution to the motif recognition problem (i.e., given a known 3D structure, or motif, determining whether this motif occurs in an unknown amino acid sequence). The goal of this effort is to develop new algorithms, based solely on the right pairwise correlation probabilities, which out perform existing motif recognition algorithms. An implementation of these algorithms, is being programmed. In fact, the package is aiding in the discovery of the coiled-coil motif in such viruses as Mollney murine leukemia, influenza hemagglutinin, and HIV. The coiled-coil motif is thought to be the mechanism by which proteins fuse with or bind to the cell membrane. This continuing research deals with recognizing motifs when less data is available, predicting inter-strand interactions in motifs, and finding new insights into the 3-dimensional structure of proteins. The second part of the project involves studying the self-assembly of virus shells. In particular, by computationally modeling how these shells are built, it is hopefully possible to suggest ways of interfering with the growth of these viruses and of causing their deformity. The Educational Component of this CAREER Grant includes: (a) Development of interdisciplinary (computer science, mathematics, and biology) undergraduate and graduate courses and curriculum in computational molecular biology, discrete mathematics and the theory of (biological) algorithms; (b) Development of both undergraduate and graduate seminars in computational molecular biology. Participation of undergraduate students in developing the extended system project and the Virus Shell Assembly project. (This is part of the Research Experiences for Undergraduates Program at NSF and Undergraduate Research Opportunities Program of MIT.)
