Gadija Abdullah
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The detection of meningococcal disease through identification of antimicrobial peptides using an in silico model creation. A thesis submitted in fulfilment of the requirement for the degree of Philosophiae Doctor In the Department of Biotechnology Faculty of Natural Sciences, University of the Western Cape Gadija Abdullah Supervisor: Dr Ashley Pretorius Co-supervisor: Dr Riaan Den Haan May 2019 http://etd.uwc.ac.za/ DECLARATION I declare that this thesis is a presentation of my original research work. I have written the enclosed PhD Thesis, ‘The detection of meningococcal disease through identification of antimicrobial peptides using an in silico model creation’ completely by myself and have documented all sources and material used. This thesis was not previously presented to another examination board in any other university and has not been published. Gadija Abdullah May 2019 ……………………… Signature ii http://etd.uwc.ac.za/ ACKNOWLEGDEMENTS To ALLAH (swt), the Cherisher and Sustainer of the Worlds for His Infinite Blessings and Guidance in my life and that of my family, in whom I trust, for all these doors did not open by accident. Shukran (Thank You). I sincerely thank Associate Professor A. Pretorius, Chairperson of the Science Faculty, Assessment Committee and Principle Investigator of the Bioinformatics Research Group on Disease Diagnostics at the University of the Western Cape, South Africa. I am grateful and appreciative to him for the constant assistance, guidance and help in developing my research skills. A special Shukran goes to Shuaib Abdullah, a loving husband for his support. I would not have survived these years of study without his constant encouragement and unconditional love. He has taught me that I could be anything in spite of whatever circumstances in which I find myself. He has in every sense earned this honour more than me. And to my wonderful kids, Yaghya, Yusuf, Noorah and baby Nuhaa, who throughout my studies somehow understood and complained far less. To my mom, thank you for all the support with the kids. I thank the University of the Western Cape for this opportunity to further my studies. I would like to thank my fellow students at the Bioinformatics Research Group (BRG) of the University of the Western Cape for their insights, suggestions and training on various databases which assisted me in this research study. iii http://etd.uwc.ac.za/ ABSTRACT The detection of meningococcal disease through identification of antimicrobial peptides using an in silico model creation. G. Abdullah Ph.D. thesis, Department of Biotechnology, Faculty of Natural Sciences, University of the Western Cape Neisseria meningitidis (the meningococcus), the causative agent of meningococcal disease (MD) was identified in 1887 and despite effective antibiotics and partially effective vaccines, Neisseria meningitidis (N. meningitidis) is the leading cause worldwide of meningitis and rapidly fatal sepsis usually in otherwise healthy individuals. Over 500 000 meningococcal cases occur every year. These numbers have made bacterial meningitis a top ten infectious cause of death worldwide. MD primarily affects children under 5 years of age, although in epidemic outbreaks there is a shift in disease to older children, adolescents and adults. MD is also associated with marked morbidity including limb loss, hearing loss, cognitive dysfunction, visual impairment, educational difficulties, developmental delays, motor nerve deficits, seizure disorders and behavioural problems. Antimicrobial peptides (AMPs) are molecules that provide protection against environmental pathogens, acting against a large number of microorganisms, including bacteria, fungi, yeast and virus. AMPs production is a major component of innate immunity against infection. The chemical properties of AMPs allow them to insert into the anionic cell wall and phospholipid membranes of microorganisms or bind to the bacteria making it easily detectable for diagnostic purposes. AMPs can be exploited for the generation of novel antibiotics, as biomarkers in the diagnosis of inflammatory conditions, for the manipulation of the inflammatory process, wound iv http://etd.uwc.ac.za/ healing, autoimmunity and in the combat of tumour cells. Due to the severity of meningitis, early detection and identification of the strain of N. meningitidis is vital. Rapid and accurate diagnosis is essential for optimal management of patients and a major problem for MD is its diagnostic difficulties and experts conclude that with an early intervention the patient’ prognosis will be much improved. It is becoming increasingly difficult to confirm the diagnosis of meningococcal infection by conventional methods. Although polymerase chain reaction (PCR) has the potential advantage of providing more rapid confirmation of the presence of the bacterium than culturing, it is still time consuming as well as costly. Introduction of AMPs to bind to N. meningitidis receptors could provide a less costly and time consuming solution to the current diagnostic problems. World Health Organization (WHO) meningococcal meningitis program activities encourage laboratory strengthening to ensure prompt and accurate diagnosis to rapidly confirm the presence of MD. This study aimed to identify a list of putative AMPs showing antibacterial activity to N. meningitidis to be used as ligands against receptors uniquely expressed by the bacterium and for the identified AMPs to be used in a Lateral Flow Device (LFD) for the rapid and accurate diagnosis of MD. Various computational AMP databases such as APD, CAMP, DRAMP and DBAASP were explored to identify a list of experimentally validated AMPs against N. meningitidis. The identified peptides were used to construct probabilistic models; using an in silico mathematical algorithm Hidden Markov Models (HMM) to be used in the scanning of various genome sequences to identify putative AMPs for N. meningitidis. The predicted AMPs were selected based on their E-values and having a single domain. In this study, nine AMPs (YYNN1 – YYNN9) were identified as possible anti-N. meningitidis peptides displaying E-values < 0.01, with the smallest E-values seen for YYNN8 and then YYNN9. v http://etd.uwc.ac.za/ Various search engines were accessed to identify receptors in the outer membrane of N. meningitidis which will serve as targets to the putative AMPs. Three N. meningitidis proteins were shortlisted as receptors for the identified peptides namely NhhA, Opc and PorA. The major considerations for using the aforementioned proteins was based on their unique expression in N. meningitidis as to ensure selectivity of the bacterium during diagnosis as well the nature of expression of these proteins being extracellular, to ensure their availability for binding during diagnosis. The physicochemical properties of the identified peptides were determined using APD and Bactibase to determine whether these peptides conformed to known AMPs since these peptides were considered putative/novel. Furthermore, the 3D structures of the AMPs and N. meningitidis receptors were modelled using I-TASSER. The 3D modelling results revealed that, all the AMPs as well as the receptors were of good models structurally based on their TM, RMSD and C-score values. The AMPs showed secondary structures including α-helices and extended shapes similar to other known AMPs whereas the receptors showed structures as seen within the literature. The 3D structures of the AMPs were docked against the 3D structures of the N. meningitidis receptors using PatchDock, to determine their binding affinity as well as binding orientation. The results showed that AMP YYNN5 has the highest binding affinity score of 15396 when bound to PorA as well as the highest binding affinity score when docked to NhhA with a score of 11904, whilst AMP YYNN8 showed the highest binding affinity score when docked to Opc, with a binding score of 11546 as calculated by PatchDock. vi http://etd.uwc.ac.za/ Lastly, identification of mutation sensitive or “hotspot” amino acid residues that are involved in forming the interface between the AMPs and the receptor proteins were identified using KFC. Non “hotspot” residues were changed using site directed mutagenesis with the parental AMPs as templates to generate derivative AMPs that displayed increased predicted binding affinity for the NhhA, Opc and PorA proteins. Derivative AMP YYNN2c had an increase in binding affinity from 14446 to 15072 with a net positive percentage of 4.3% when bound to the PorA protein, resulting in this AMP as having the second highest binding affinity score after YYNN5 (15396) bound to PorA although the binding affinity of YYNN5 did not increase following site directed mutagenesis. To ensure that site directed mutagenesis did not alter the physicochemical properties or the structure of the derived AMPs, APD and Bactibase were used once more as well as I- TASSER. From the results obtained, the derived AMPs still conformed to known AMPs as well as displaying similar α-helical secondary structures as their parental counterparts, with slight variation in partial α-helical structure for certain derived AMPs. Taken together the results of this work indicates that YYNN5 bound to the N. meningitidis receptor PorA is the most likely AMP to be used in a LFD for the accurate and sensitive diagnosis of MD within patient samples such as blood. This is the first report on the identification of AMPs, using in silico analysis, as ligands to the bacteria N. meningitidis receptor proteins and can be a key step in the