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Masters thesis of engineering computational modelling of graphene oxide and amyloidogenic protein amylin

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Computational Modelling of Graphene oxide and Amyloidogenic Protein Amylin Computational Modelling of Graphene Oxide and Amyloidogenic Protein Amylin A thesis submitted in fulfilment of the requiremen[.]

Computational Modelling of Graphene Oxide and Amyloidogenic Protein Amylin A thesis submitted in fulfilment of the requirements for the degree of Master of Engineering Enxi Peng Bachelor of Chemical & Materials Engineering (Hons) University of Auckland School of Engineering College of Science, Technology, Engineering and Maths RMIT University November 2020 Declaration of Candidature I certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research program; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines have been followed I acknowledge the support I have received for my research through the provision of an Australian Government Research Training Program Scholarship Enxi Peng 6/11/2020 i Acknowledgments I dedicate this thesis to my mother As a single parent, she afforded me the opportunities of growing up, getting educated and thriving in a free country She endured the hardships of being an immigrant, and nurtured me through kindness, understanding & tolerance I would not be the person I am today without her To that, mum, I love you and thank you I would like to thank my primary and secondary supervisors, Prof Irene Yarovsky & Dr Nevena Todorova for their kind support throughout my academic journey, the good times and the bad To Irene, I would like to offer my genuine admirations towards your leadership, insights and grit These are the qualities that I hope one-day I would also be able to portray To Nevena, I would like to say a huge thank you for your advice & kindness, and always believing in me Thank you both for always challenging my understandings and expanding the ways that I think and approach problems I would also like to thank my fellow research group cohorts for providing the fun and stimulating discussions we have had together Thank you, Dr Andrew J Christofferson, Dr Adam Makarucha, Dr Mathew Penna, Dr George Yiapanis, Dr M Harunur Rashid, Dr Tu Le, Dr Patrick Charchar, Kamron Ley, Alan Bentvelzen, and Wenxuan Li I acknowledge the support I have received for my research through the provision of an Australian Government Research Training Program Scholarship I also acknowledge the generous allocation of high-performance computational resources from the Australian National Computational Infrastructure (NCI), the Pawsey Supercomputing Centre, and Melbourne Bioinformatics ii Table of Contents Declaration of Candidature .i Acknowledgments ii Introduction 1.1 Overview 1.2 Proteins: Structure & Folding 1.3 Protein self-association & aggregation 1.3.1 History of amyloidogenic diseases 1.3.2 Structure formations of amyloidogenic fibrils 1.3.3 Islet amyloid polypeptide 12 1.4 1.4.1 Graphene and Graphite 12 1.4.2 Graphene Oxide 13 1.5 Effects of Graphitic Nanomaterials on Amyloid Aggregation 12 Project aims 17 Computational Modelling Techniques 18 2.1 Overview 18 2.2 Introduction 18 iii 2.3 Ab-initio Methods 20 2.4 Classical Molecular Dynamics 21 2.5 Conformational sampling 21 2.6 Replica Exchange with Solute Tempering 23 2.7 Simulation Procedures 25 Effects of forcefield and sampling method in all-atom simulations of inherently disordered proteins: Application to conformational preferences of human amylin 28 Effects of Size and Functionalisation on the Structure and Properties of Graphene Oxide Nanoflakes: An In Silico Investigation 41 Conclusions and Future Work 48 References 50 Appendix 54 Publication List 54 Peer-reviewed Publications 54 Conference Presentation 55 Supplementary Data 57 Effects of Forcefield and Sampling Method in All-atom Simulations of Inherently Disordered Proteins: Application to Conformational Preferences of Human Amylin 57 Comparing to BEMD and REMD 57 iv Free-Energy Calculations 59 Modified TIP3P Water Simulations 62 Effects of Size and Functionalisation on the Structure and Properties of Graphene Oxide Nanoflakes: An In Silico Investigation 63 v Abstract Detailed understanding of the interactions between nanomaterials and biological molecules is crucial for the development of novel materials in modern medicine for targeted drug delivery and novel therapeutics On the other hand, the incorporation of various nanomaterials into everyday life is equally as progressive, which could lead to potential adverse implications on biological systems Recent studies have suggested that some carbon-based nanoparticles can promote, and others can inhibit fibril formation of amyloidogenic peptides and proteins These types of proteins can misfold and aggregate leading to the accumulation of insoluble fibril-like structures, which have been linked to diseases such as Alzheimer’s, atherosclerosis and type-II diabetes The mechanisms of interactions that render these nanomaterials as inhibiting or promoting of amyloid formation remain unclear However, with the aid of advanced computational resources, it is now possible to explore the conformational features and dynamical interactions of proteins with complex nanomaterials at atomistic details and time scales inaccessible by experiments In order to explore the interactions of proteins with nanomaterials it is important we have a detailed understanding of the dynamical behaviour and properties of the individual systems first With this in mind, theoretical computational approaches were utilised to investigate the structures and dynamics of the amyloidogenic protein amylin and Graphene Oxide (GO) The thesis is organised as follows: Chapter comprises the literature review of protein structure, amyloid fibrils and of the effect of nanomaterials on protein aggregation Chapter reviews the computational methods used to perform simulations and analysis Chapter comprises my published article on benchmarking forcefields and sampling methods for disordered proteins using amylin as a case study vi Chapter comprises my published article on the effect of oxidation on the properties of graphene oxide flakes in solution Chapter summarises the conclusions and proposes future work based on these studies vii Chapter 1 Introduction 1.1 Overview Detailed in this chapter, is an overview of the structure and dynamics of proteins; specifically, the folding and misfolding of functional proteins into insoluble aggregates Section 1.2 details an introduction into proteins structures In section 1.3, a literature review on the current state of understanding of protein self-assembly and aggregation is represented A detailed review of the effect of nanomaterials on biological systems is represented under section 1.4 1.2 Proteins: Structure & Folding One crucial element of all organic life forms is presence of proteins, a biological macromolecule responsible for a wide array of roles inside living organisms The responsibilities of proteins include cell-signalling hormones, catalytic enzymes, immune responses, reproductive and metabolic cycles, as well as forming structural elements such as keratin or collagen Proteins are polypeptide chain consisting of amino acid residues, linked together by peptide bonds Although there are 22 different amino acids throughout known life, only 20 of which are present in the genetic code Each of these amino acids consist of a central carbon atom (C) attached to an amino group (-NH2), a carboxyl group (-COOH), a hydrogen atom (-H) and finally a unique side-chain group (-R) Due to the uniqueness of the side-chain group, each amino acid can be distinguished from each other, see Figure Individual amino acids are joined together via a condensation reaction where they are joined between the amino and carboxyl group, producing a single water molecule from the reaction Almost all amino acids, with the exception of Gly can form one of two enantiomers, which are mirror images of each other However, amino acids exist almost always in the L-configuration, while the D-configuration only exists in microorganisms and the cell walls of bacteria Figure Polypeptides are polymers composed of many amino acids linked together by peptide bonds Peptide bond forms between carboxyl group of one amino acid and amino group of another, and it is a dehydration reaction (Image: useruploads.socratic.org) There are 20 naturally occurring amino acids, where the side-chain group also attributes to various properties to the amino acids, which can be divided into three groups Hydrophobic amino acids are made up of hydrophobic side-chains, which consists of: Alanine (Ala), Isoleucine (Ile), Leucine (Leu), Methionine (Met), Phenylalanine (Phe), Tryptophan (Trp), Tyrosine (Tyr), Proline (Pro) and Valine (Val) The next group is composed of charged residues, Arginine (Arg), Lysine (Lys), Aspartic Acid (Asp) and Glutamic Acid (Glu), and those with polar side-chain groups; Serine (Ser), Threonine (Thr), Asparagine (Asp), Glutamine (Gln), Cysteine (Cys), Histidine (His), and Asparagine (Asn) The last of the 20 amino acids is Glycine (Gly), which only has a hydrogen atom as the side-chain group, thus this amino acid possesses special properties that usually classifies it as a part of the hydrophobic group One particular amino acid group of interest is that with aromatic groups on its side-chains, which includes: ... dynamics of the amyloidogenic protein amylin and Graphene Oxide (GO) The thesis is organised as follows: Chapter comprises the literature review of protein structure, amyloid fibrils and of the... nanoparticles can promote, and others can inhibit fibril formation of amyloidogenic peptides and proteins These types of proteins can misfold and aggregate leading to the accumulation of insoluble fibril-like... 62 Effects of Size and Functionalisation on the Structure and Properties of Graphene Oxide Nanoflakes: An In Silico Investigation 63 v Abstract Detailed understanding of the interactions

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