Parisa Hosseinzadeh

Comprehensive computational design of ordered peptide macrocycles

Mixed chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to-date, but there is currently no way to systematically search through the structural space spanned by such compounds for new drug candidates.   Natural proteins do not provide a useful guide: peptide macrocycles lack regular secondary structures  and hydrophobic cores and have different backbone torsional preferences. Hence, the development of new peptide macrocycles has been approached by modifying natural products or by using library selection methods; the former is limited by the small number of known structures, and the latter by the limited numbers and diversity accessible through library-based methods. In this presentation, I will talk about the approach I took to overcome these limitations. /by developing new computational methods, I enumerated the stable structures that can be adopted by macrocyclic peptides composed of L- and D-amino acids and identified more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures.  NMR structures of nine out of twelve tested macrocycles were very close to the designed structure, indicating the robustness of this method.  These  results provide a nearly complete coverage of the rich space of structures possible for short peptide-based macrocycles unparalleled for other molecular systems, and vastly increase the available starting scaffolds for both rational drug design and library selection methods. I will then talk about current progress in computational design of macrocycles that target protein surfaces.