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Comprehensive computational design of ordered peptide macrocycles

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Science  15 Dec 2017:
Vol. 358, Issue 6369, pp. 1461-1466
DOI: 10.1126/science.aap7577

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Macrocycles by design

Macrocyclic peptides have diverse properties, including antibiotic and anticancer activities. This makes them good therapeutic leads, but screening libraries only cover a fraction of the sequence space available to peptides comprising D and L amino acids. Hosseinzadeh et al. achieved near-complete coverage in sampling the sequence space for 7- to 10-residue cyclic peptides and identified more than 200 designs predicted to fold into stable structures. Of 12 structures determined, nine were close to the computational models. They also sampled and designed 11- to 14-residue macrocycles, but without complete coverage. The designed macrocycles provide a path forward for engineering new therapeutics.

Science, this issue p. 1461

Abstract

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 the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with l-amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of l- and d-amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods.

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