Thesis
Extending peptide search space and self-assembly methods
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2023
- Thesis identifier
- T16600
- Person Identifier (Local)
- 201994175
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Over the years, there has been a general shift from reducing proteins to shorter functional sequences to design peptide nanomaterials from the bottom up. Shorter peptides are often cheaper and easier to produce while still possessing a large combinatorial chemical space that can be exploited for drug delivery systems, membrane bound pore formation and biocompatible nanoelectronics systems. Discovery of new useful self-assembling peptide nanomaterials has often been achieved by modifying existing known peptide structures found in nature or rigorous manual design based on design rules also derived from natural products. The latest strategies have been driven by experimental studies of peptides, computational simulations of peptide systems, and computational screening of large sequence spaces via coarse-grained simulations. Recently, screening approaches have been expanded to larger search spaces by developments in machine learning technology and to lateral space by different methods such as consideration of the effects of pH, non-gene encoded amino acids residues and application of protecting groups such as Fmoc to direct self-assembly. In this thesis the effects of different coarse-grained (versions of MARTINI) forcefields on the simulation of self-assembly of dipeptides were examined. This revealed that the use of the same beads to represent amino acids in short peptides and proteins leads to divergent results, such that the beads have been made less attractive with iterative developments of the forcefield leading to better simulations of proteins but worse simulations of short peptides. This informed the subsequent study and design of a coarse-grained model of graphene oxide that was simulated for co-assembly with intrinsically disordered elastin-like proteins, based on the natural elastin motif Val-Pro-Gly-X-Gly. This model was shown to shed light onto the mechanistic driving forces behind the experimental observation of the most complete reduction of graphene oxide occurring at 70 % ethanol rather than continuing to increase past this concentration which was previously not understood. Additionally, to further expand the search space beyond previous studies which employed coarse-grained molecular dynamics to test the aggregation propensity of all di- and tripeptides, the ability of machine learning to search the far greater hexapeptide (206) space was investigated. This methodology was validated by finding the best aggregators known from smaller search spaces with far fewer simulations performed than would be required with screening. To expand search space laterally, a constant pH molecular dynamics method, modified to include system charge neutralization, was developed. This new method was shown to better reproduce experimental results of the oleate micelle system than previous computation attempts and was used to reproduce dynamic pH dependent effects of several short experimental peptide systems. Within this investigation a coarse-grained representation of Fmoc is developed and is also shown to accurately reproduce pH dependent dynamic self-organization of Fmoc protected peptides. Subsequently, the two key developments, constant pH constant charge coarse-grained molecular dynamics and exhaustive screening of very large peptide search spaces are combined in the search for short pore-forming peptides. This accelerated discovery of new pore-forming octapeptides (208) is able to discover 71 new pore-forming peptides with experimental validation of predictions showing 13/13 tested conformed to whether or not they had been predicted to form pores or not in a model membrane.
- Advisor / supervisor
- Tuttle, Tell
- Murphy, John A., 1948-
- Resource Type
- Note
- This thesis was previously under moratorium from 01/06/2023 until 01/07/2024.
- DOI
- Funder
- Embargo Note
Las relaciones
Elementos
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File | 2023-06-06 | Embargo |