(347e) Computational Design of Peptides That Bind to the Boxb RNA with High Affinity Conference: AIChE Annual MeetingYear: 2014Proceeding: 2014 AIChE Annual MeetingGroup: Food, Pharmaceutical & Bioengineering DivisionSession: Protein Engineering II: Rational and Computational Techniques Time: Tuesday, November 18, 2014 - 1:48pm-2:06pm Authors: Xiao, X., North Carolina State University Marcus, M. E., Leonard, J. N., Northwestern University Hall, C. K., North Carolina State University Therapeutic RNA delivery technology is a promising method for treating chronic or acute diseases, which works by enabling cell-based therapeutics to directly reprogram gene expression in host cells. This RNA delivery strategy, however, faces several major barriers to clinical treatment. One of these barriers is the difficulty of identifying appropriate RNA binding peptides to “load” cargo (therapeutic) RNA. In this work, our focus is on the complex formed by the λ N36 peptide and boxB RNA, because a specific recognition of boxB RNA by λ N36 peptide and a high affinity of the complex (Kd = 1.3 nM) has been found. Additionally, our collaborator Joshua Leonard of Northwestern University is using this complex as a model system to engineer exosomes with the ultimate goal of therapeutic RNA delivery. We have developed a computational search algorithm to design RNA binding peptides that mimic the λ N36 peptide’s ability to bind selectively to the boxB RNA (cargo RNA). Our search algorithm involves the concerted rotation move (CONROT) and Monte Carlo (MC) techniques. The CONROT technique is employed to move the peptide during the search for conformation candidates. When changing the peptide sequence, a new energy minimization strategy is performed to optimize the configuration of the side chains on the trial amino acids. We calculate the score for these new attempted sequences and conformations, and then employ the MC technique to accept or reject the attempted sequences and conformations based on the Metropolis sampling method. Through performing the search algorithm, we generated a library of good RNA binding peptides that are capable of recognizing and binding the boxB RNA with a range of affinities. The best RNA binding peptide sequence was identified among these designed peptides; it exhibits a higher binding affinity to boxB RNA (score: -144.32 kcal/mol) than λ N36 peptide (score: -138.81 kcal/mol). A further structural and energetic analysis reveals that the best peptide has 6 identical residues at the same sites as the λ N36 peptide, and its binding specificity to boxB RNA becomes strengthened as the inter-chain van der Waals energy decreases (becomes more negative).