An investigation of computational techniques for the conformational determination of therapeutically relevant cyclic peptides

Abstract

Peptides are an important class of biomolecules made up of short chains of amino acids. In particular, cyclic peptides are of great interest as therapeutic molecules due to their restrained conformational flexibility. However, even with this structural confinement, cyclic peptides can adopt multiple conformations in solution which are difficult to predict using experimental methods. Computational modelling provides the ability to model the dynamics of peptides in solution and so can provide insight into the conformational ensembles. Understanding the conformations adopted by a peptide is essential in understanding its bioactivity and so is an important step in the development of peptide therapeutics. The aim of this thesis was to develop methods for the computational prediction of peptide conformational ensembles. This would provide a starting point for the development of novel peptide therapeutics with specifically determined conformational profiles. The REST2 enhanced sampling methodology using both AMBER14SB and CHARMM36m forcefields was compared to the Rosetta modelling suite in the generation of cyclic peptide conformational ensembles. Computationally derived structures were validated against experimental 1D and 2D NMR data. Furthermore, collision cross section data from Ion Mobility Spectrometry experiments was investigated as a potential source of conformational information. It was found that the CHARMM36m forcefield generally outperformed the AMBER14SB forcefield, however there were exceptions to this trend. The Rosetta ensembles consistently outperformed both of the REST2 ensembles for all of the peptides studied when validated against NMR observables. The Ion Mobility Spectrometry data showed no clear evidence that it contained solution state conformational information. The results of this work pave the way for further investigation into the conformational dynamics of therapeutically relevant peptides. This also provides the necessary first steps in the design of novel therapeutics with specific binding geometries, using computational methodologies.

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Identifiers

ORCID for Jonathan Essex: orcid.org/0000-0003-2639-2746

ORCID for Ali Tavassoli: orcid.org/0000-0002-7420-5063

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