Designing Peptide Sequences in Flexible Chain Conformations to Bind RNA: A Search Algorithm Combining Monte Carlo, Self-Consistent Mean Field and Concerted Rotation Techniques

A search algorithm combining Monte Carlo, self-consistent mean field, and concerted rotation techniques was developed to discover peptide sequences that are reasonable HIV drug candidates due to their exceptional binding to human tRNA(UUU), the primer of HIV replication. The search algorithm allows for iteration between sequence mutations and conformation changes during sequence evolution. Searches conducted for different classes of peptides identified several potential peptide candidates. Analysis of the energy revealed that the asparagine and cysteine at residues 11 and 12 play important roles in recognizing tRNALys3 via van der Waals interactions, contributing to binding specificity. Arginines preferentially attract the phosphate linkage via chargecharge interaction, contributing to binding affinity. Evaluation of the RNA/peptide complexs structure revealed that adding conformation changes to the search algorithm yields peptides with better binding affinity and specificity to tRNALys3 than a previous mutation-only algorithm.