"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."
Purdue University is awarded a grant to develop novel computational algorithms and software for building protein structure models, which can identify and utilize very distantly related template structures for the modeling. Considering the growing number of protein structures produced by structural genomics projects, it is important for protein structure prediction methods to take full advantage of those structures as templates in modeling. However, existing methods rely too heavily on sequence similarity in searching template structures and computing alignments with templates. The proposed project is aimed to push the limit of template searching and structure modeling by extensive use of structural information in computing target-template alignments and by introducing a refinement procedure, which uses an advanced ab initio folding. Key innovations of this project include (1) novel threading algorithms with a probabilistic handling of residue contacts in template proteins, (2) use of suboptimal alignments and multiple templates to improve accuracy of a model, and (3) use of an ab initio folding method for refining the model, which takes estimated errors at each region of the model into account. Improvement of recognition of a template for modeling a target protein will leverage the value of experimentally solved structures and will also lead to a significant reduction of the cost of structural genomics projects by reducing the number of template structures needed for modeling the structures of proteome of organisms. The proposed project is carried out in international collaboration with world experts in the protein structure prediction field. 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. Software tools developed under this funding may be accessed from the PI's lab website at http://dragon.bio.purdue.edu/.
