β-Amino acids containing peptides and click-cyclized peptide as β-turn mimics: a comparative study with ‘conventional’ lactam- and disulfide-bridged hexapeptides

Secondary Structure Stabilization of Macrocyclic Antimicrobial Peptides via Cross-Link Swapping

Lactam cross-links have been employed to stabilize the helical secondary structure and enhance the activity and physiological stability of antimicrobial peptides; however, stabilization of β-sheets via lactamization has not been observed. In the present study, lactams between the side chains of C- and N-terminal residues have been used to stabilize the β-sheet conformation in a short ten-residue analogue of chicken angiogenin-4. Designed using a combination of molecular dynamics simulations and Markov state models, the lactam cross-linked peptides are shown to adopt stabilized β-sheet conformations consistent with simulated structures. Replacement of the peptide side-chain Cys-Cys disulfide by a lactam cross-link enhanced the broad-spectrum antibacterial activity compared to the parent peptide and exhibited greater propensity to induce proinflammatory activity in macrophages. The combination of molecular simulations and conformational and biological analyses of the synthetic peptides provides a useful paradigm for the rational design of therapeutically active peptides with constrained β-sheet structures.

Computational Opportunities and Challenges in Finding Cyclic Peptide Modulators of Protein-Protein Interactions

Peptide cyclization can improve stability, conformational constraint, and compactness. However, apart from beta-turn structures, which are well incorporated into cyclic peptides (CPs), many primary peptide structures and functions are markedly altered by cyclization. Accordingly, to mimic linear peptide interfaces with cyclic peptides, it can be beneficial to screen combinatorial cyclic peptide libraries. Computational methods have been developed to screen CPs, but face a number of challenges. Here, we review methods to develop in silico computational libraries, and the potential for screening naturally occurring libraries of CPs. The simplest and most rapid computational pharmacophore methods that estimate peptide three-dimensional structures to be screened versus targets are relatively easy to implement, and while the constraint on structure imposed by cyclization makes them more effective than the same approaches with linear peptides, there are a large number of limiting assumptions. In contrast, full molecular dynamics simulations of cyclic peptide structures not only are costly to implement, but also require careful attention to interpretation, so that not only is the computation time rate limiting, but the interpretation time is also rate limiting due to the analysis of the typically complex underlying conformational space of CPs. A challenge for the field of computational cyclic peptide screening is to bridge this gap effectively. Natural compound libraries of short cyclic peptides, and short cyclized regions of proteins, encoded in the genomes of many organisms present a potential treasure trove of novel functionality which may be screened via combined computational and experimental screening approaches.

Computational approaches to developing short cyclic peptide modulators of protein-protein interactions

Cyclic peptides are a promising class of bioactive molecules potentially capable of modulating "difficult" targets, such as protein-protein interactions. Cyclic peptides have long been used as therapeutics derived from natural product derivatives, but remain an underexplored class of compounds from the perspective of rational drug design, possibly due to the known weaknesses of peptide drugs in general. While cyclic peptides are non"druglike" by the accepted empirical rules, their unique structure may lend itself to both membrane permeability and proteolytic resistance-the main barriers to oral delivery. The constrained shape of cyclic peptides also lends itself better to virtual screening approaches, and new tools and successes in this area have been recently noted. An increasing number of strategies are available, both to generate and screen cyclic peptide libraries, and best practises and current successes are described within. This chapter will describe various computational strategies for virtual screening cyclic peptides, along with known implementations and applications. We will explore the generation and screening of diverse combinatorial virtual libraries, incorporating a range of cyclization strategies and structural modifications. More advanced approaches covered include evolutionary algorithms designed to aid in screening large structural libraries, machine learning approaches, and harnessing bioinformatics resources to bias cyclic peptide virtual libraries towards known bioactive structures.