Modelling solution structures of cyclic peptides - How good are we?

Abstract

Cyclic peptides (CPs) are macrocycles made from amino acids that can bind to classically undruggable drug targets. Controlling pharmacokinetic properties of these macrocyclic peptides is an open question, limited by having precise knowledge of the underlying solution structures. CP solution dynamics are complex: individual structures are often separated by large energy barriers; the presence of conformers is environment dependent and solution conformers can be different from bioactive structures. Specialised simulation methods for CPs have previously been designed to reproduce available x-ray structures, but it is often unknown how well solution structures are reproduced. Here, we report the largest cyclic peptide solution NMR dataset “MacroConf”, which we built from existing literature. We use a range of molecular dynamics methods and specialised cheminformatics methods to model the solution structures of over 30 cyclic peptides from this dataset. We explore (a) how different methods perform relative to one another and to the experimental reference structures and (b) whether computationally cheaper methods can reach the accuracy of gold-standard accelerated molecular dynamics simulations to allow for fast prediction of solution ensembles. By providing automated computational workflows, this work is easily extendable, fully reproducible, and is built as a reference point to guide development of future conformer generators for cyclic peptides.