(317c) De Novo Multifunctional Peptide Design Using Sequence Similarity Metrics Conference: AIChE Annual MeetingYear: 2014Proceeding: 2014 AIChE Annual MeetingGroup: Computing and Systems Technology DivisionSession: Complex and Networked Chemical and Biochemical Systems Time: Tuesday, November 18, 2014 - 1:06pm-1:24pm Authors: Boone, K., University of Kansas Camarda, K. V., University of Kansas Tamerler, C., University of Kansas Sequence similarity is a design principle for de novo peptide development. Given peptides with known functions, sequence similarity may be used to create new sequences with the same functions or a combination of those functions. In previous work1, a method for finding scoring matrices in pairwise global sequence alignments between peptide groups of varied level of function was used to maximize the level of that function with de novo peptides. This work proposes a way of designing multifunctional peptides. Peptides with known therapeutic or other property values are evaluated in terms of sequence similarity, with the goal of creating structure-activity relationships for the novel design of peptides within a molecular design framework. The linked-antimicrobial peptide database (LAMP)2 provides a set of known sequences which serve as a base set to create the property prediction model. The peptides in this database either have shown antibacterial or antifungal properties, or both. A paired-alignment tree for peptides in each of these classes was constructed to extract common sequence features. The molecular design problem was then formulated as a graph maximization, and a Tabu search algorithm was then applied to find peptides that align well with multiple sequence features from the trees of subsequences representing antimicrobial properties. These novel peptides were then compared quantitatively with those peptides known to be both antibiotic and antifungal, to validate the design methodology. Oren, E.E., Tamerler, C., Sahin, D., Hnilova, M., Seker, U.O.S., Sarikaya, M. Samudrala, R., 2007. A novel knowledge-based approach to design inorganic-binding peptides. Bioinformatics, 23(21). Zhao, X., Wu H., Lu H., Li G., Huang Q., 2013. A database linking antimicrobial peptides. PLoS ONE, 8(6).