Purdue University is awarded a grant to develop novel computational methods that identify functional regions of proteins by revealing mutations patterns, which are constrained by functional requirements. Protein sequences and the tertiary structures have been accumulated in an exponential pace partly due to large-scale genome sequencing and structural genomics projects. An urgent task for bioinformatics includes the development of methods for annotating the flood of new sequences and structures with their functions and the location of sites where these functions occur. Two types of methods will be developed: The first type of methods examine amino acid mutations specific to known functional regions of proteins. The second type of methods identifies positions in proteins that mutate simultaneously in a mutually constrained fashion. A strong advantage of the methods is that it is general enough so that it can be easily extended to predict many types of functional sites and structure features of proteins. The project capitalizes on tremendous efforts and progress made by experimental sequence and structure determination by developing a new generation of computational tools that detect structured variation, rather than conventional conservation, in protein sequences.
The project incorporates the concept of the structured variation into the field of biological sequence analysis. Because the methodology is general and versatile, the method can be applied to other bioinformatics methods, such as sequence alignments, protein structure prediction methods, and single nucleotide polymorphism analysis. Graduate and undergraduate students in biological sciences and computer science will be trained in cross-listed courses among several departments. Several existing programs at Purdue for recruiting minority students and undergraduate students will contribute to broad participation in the project. Overall the proposed project leverages Purdue University's efforts in interdisciplinary computational life science and engineering. For further information about this project visit the PI's lab website at http://kiharalab.org.
