Heuristic energy-based cyclic peptide design

Rational computational design is crucial to the pursuit of novel drugs and therapeutic agents. Meso-scale cyclic peptides, which consist of 7-40 amino acid residues, are of particular interest due to their conformational rigidity, binding specificity, degradation resistance, and potential cell permeability. Because there are few natural cyclic peptides, de novo design involving non-canonical amino acids is a potentially useful goal. Here, we develop an efficient pipeline (CyclicChamp) for cyclic peptide design. After converting the cyclic constraint into an error function, we employ a variant of simulated annealing to search for low-energy peptide backbones while maintaining peptide closure. Compared to the previous random sampling approach, which was capable of sampling conformations of cyclic peptides of up to 14 residues, our method both greatly accelerates the computation speed for sampling conformations of small macrocycles (ca. 7 residues), and addresses the high-dimensionality challenge that large macrocycle designs often encounter. As a result, CyclicChamp makes conformational sampling tractable for 15- to 24-residue cyclic peptides, thus permitting the design of macrocycles in this size range. Microsecond-length molecular dynamics simulations on the resulting 15, 20, and 24 amino acid cyclic designs identify designs with kinetic stability. To test their thermodynamic stability, we perform additional replica exchange molecular dynamics simulations and generate free energy surfaces. Three 15-residue designs, one 20-residue and one 24-residue design emerge as promising candidates.Author summary: Cyclic peptides are circular chains of amino acid residues that are promising candidates for new therapeutic drugs. Current FDA approved cyclic peptide-based drugs are mostly derived from natural sources. However, recent work has enabled the computational design of new cyclic peptide drugs. Current de novo computational design methods can handle sizes of 7 to 13 residues without conformational constraints. As size increases, the exponentially growing conformational space makes conformational sampling intractable. The literature’s prevalent approach of random sampling finds poor configurations, with the result that the success rate of finding a stable design is very low. Here, we develop an efficient search algorithm by combining tailored optimization algorithms with established energy models. Our heuristic design pipeline, CyclicChamp, produces stable cyclic peptide designs of 7, 15, 20, and 24 amino acids as validated by algorithmically-independent molecular dynamics simulations. This pipeline not only expands the structural variety for future drug development, but also paves the way for potential cyclic peptide-based enzyme design.