Modelling Fmoc-dipeptide nanostructures, the synergistic effect of combining computational and experimental methods

  • Ivan Ramos Sasselli

Student thesis: Doctoral Thesis


Nanomaterials based on aromatic peptide amphiphiles are interesting new materials with potential applications in the areas of biomedicine and nanotechnology. These natural based materials take advantage of the properties of the peptides, as it is the ability to form the final structures spontaneously without any external stimulus by self-assembly, or the high number of functionality available due to the 20 natural building blocks, amino acids, and their possible combinations. Although it is known that the functionality of these nanostructures is highly dependent on both, the chemical groups and the topology of the nanostructure, and both vary with the amino acids side chains, the relationship between nanostructure shape and peptide chain composition is still unknown. Understanding this is necessary to be able to design Fmoc-peptide nanostructures on demand. In this thesis a combination between these experimental techniques and computational methods, molecular dynamic (MD) simulations, is used to elucidate the self-assembly motifs for a set of model systems composed of Fmoc-dipeptides. The interpretation of the experimental spectroscopic characterization is improved by using enzymatic self-assembly under thermodynamic control Fmoc-dipeptide and side-by-side comparison of nanostructures using dynamic peptide libraries (DPLs). This approach allowed to resolve which features increase the self-assembly tendency of these molecules. Both MD and DPL approaches depend on the premise that gels can be at thermodynamic equilibrium, which is not clear in the literature. It has been argued that they represent metastable states, where crystals are suggested to represent the actual thermodynamically favoured structures. Hence, the study starts with a model proposed to demonstrate that nanofibrous gels can represent the thermodynamically favoured structure. This is achieved by using a packing model where self-assembling molecules are represented by prisms with faces of different nature, solvophilic andsolvophobic to mimic the amphiphilicity of these molecules as a key feature. This approach gives rise to a combination of solvophobic and solvophilic interactions where a level of solvent exposure is favourable. The model depends on parameters which can be related with features of the system and demonstrates that the amphiphilicity is key to allow 1D objects, fibres, to be more stable than 3D objects,crystals; and hence, that MD simulations and DPLs can be applied for their study.For MD simulations, the CHARMM force field is used because it has beenapplied and validated to a wide variety of peptide-based systems. However, this forcefield does not include parameters for the Fmoc moiety. Therefore, the second steps for this study was to develop an Fmoc parameterization for the CHARMM force field, in order to be able to run all atoms self-assembling Fmoc-peptides simulations, to improve the understanding of these nanostructures and their formation. The parameterization is based in the CHARMM protocol adapted due to the amphiphilic nature of the Fmoc moiety. Experimentally, in order to get more valuable information from the experimental characterization, the study of different Fmoc-dipeptides nanostructures with specific changes in their peptide chain are compared in order to understand how these specific changes affect the self-assembled structure: phenylalanine/leucinesubstitution to understand how the aromatic side chain affects; and amide/methyl esterC-terminus substitution, to understand the role of the possible extra hydrogen bondsof the amide group. Furthermore, DPLs are also applied to rationalize the influence of these changes in the self-assembling tendency of Fmoc-dipeptides.;Then, the experimental information is used to develop a model for Fmoc-TF-NH ₂ fibre and simulate
Date of Award8 Apr 2016
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SupervisorTell Tuttle (Supervisor) & Rein Ulijn (Supervisor)

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