Understanding and designing head‐to‐tail cyclic peptides

Abstract

Cyclic peptides (CPs) are an exciting class of molecules with a variety of applications. However, design strategies for CP therapeutics, for example, are generally limited by a poor understanding of their sequence-structure relationships. This knowledge gap often leads to a trial-and-error approach for designing CPs for a specific purpose, which is both costly and time-consuming. Herein, we describe the current experimental and computational efforts in understanding and designing head-to-tail CPs along with their respective challenges. In addition, we provide several future directions in the field of computational CP design to improve its accuracy, efficiency and applicability. These advances, combined with experimental techniques, shall ultimately provide a better understanding of these interesting molecules and a reliable working platform to rationally design CPs with desired characteristics.

Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations

“…However, the behavior of a peptide in a biological system can only be adequately predicted if we know its solution structural ensemble, necessitating solvated simulations, particularly using explicit solvent. Experiments and simulations both show that solvent plays a critical role in cyclic peptide structures; at times, even water molecules bridged or caged within a cyclic peptide have been observed. ,− Because of their small size, closed topology, and the abundance of solvent-exposed H-bond donors and acceptors, accurate modeling of cyclic peptides is difficult using implicit-solvent models, which do not account for these consequential and direct interactions with solvent molecules …”

Structure prediction of cyclic peptides by molecular dynamics + machine learning

“…The use of an explicit-solvent model is thus critical to accurately describe their energetics and structural preferences in solution. 30 To enable efficient simulations of cyclic peptides using explicit-solvent molecular dynamics (MD) simulations, we recently tailored an enhanced sampling method to cyclic peptides. 31 This method uses bias-exchange metadynamics 32,33 to target the essential transitional motions of cyclic peptides 31 and has enabled systematic studies of cyclic-peptide variants using explicit-solvent MD simulations to identify wellstructured cyclic peptides.…”

Exhaustive Exploration of the Conformational Landscape of Small Cyclic Peptides Using a Robotics Approach

“…This is mainly due to the ring-closure constraint, which leads to high energy barriers between the different meta-stable conformations. 11,33,34 Thus, it makes the conformational sampling very challenging. Actually, even for small sys-tems, achieving a complete exploration of the energy landscape requires long simulation times when using basic approaches.…”

“…Although molecular dynamics simulations and Monte Carlo methods can be used to explore the conformational space of linear peptides, , the application of these methods to cyclic peptides is less straightforward. This is mainly due to the ring-closure constraint, which leads to high energy barriers between the different metastable conformations. ,, Thus, it makes the conformational sampling very challenging. Actually, even for small systems, achieving a complete exploration of the energy landscape requires long simulation times when using basic approaches.…”