In silico characterization of missense mutations in infectious diseases: case studies of tuberculosis and COVID-19
- Authors: Barozi, Victor
- Date: 2023-10-13
- Subjects: Microbial mutation , COVID-19 (Disease) , Drug resistance in microorganisms , Antitubercular agents , Tuberculosis , Molecular dynamics , Single nucleotide polymorphisms
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/431626 , vital:72791 , DOI 10.21504/10962/431626
- Description: One of the greatest challenges facing modern medicine and the global public health today is antimicrobial drug resistance (AMR). This “silent pandemic,” as coined by the world health organization (WHO), is steadily increasing with an estimated 4.95 million mortalities attributed to AMR in 2019, 1.27 million of which were directly linked to AMR. Some of the contributors to AMR include self-prescription, drug overuse, sub-optimal drug prescriptions by health workers, and inaccessibility to drugs, especially in remote areas, which leads to poor adherence. The situation is aggravated by the upsurge of new zoonotic infections like the coronavirus disease 2019, which present unique challenges and take the bulk of resources hence stunting the fight against AMR. Quite alarming still is our current antimicrobial arsenal, which hasn’t had any novel antimicrobial drug discovery/addition, of a new class, since the 1980s. This puts a burden on the existing broad-spectrum antimicrobial drugs which are already struggling against multi-drug resistant strains like multi-drug resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). Besides the search for new antimicrobial agents, the other avenue for addressing AMR is studying drug resistance mechanisms, especially single nucleotide polymorphisms (SNPs), that change drug target characteristics. With the advancement of computational power and data storage resources, computational approaches can be applied in mutational studies to provide insight into the drug resistance mechanisms with an aim to inform future drug design and development. Therefore, in the first part of this thesis, we employ integrative in silico approaches, including 3D structure modeling, molecular dynamic (MD) simulations, comparative essential dynamics (ED), and protein network analysis approaches i.e., dynamic residue network (DRN) analysis to decipher drug resistance mechanisms in tuberculosis (TB). This involved an investigation of the drug resistance mutations in the catalase-peroxidase (KatG) and pyrazinamidase (MtPncA) enzymes which are responsible for activation of TB first-line drugs; Isoniazid (INH) and Pyrazinamide (PZA), respectively. In the case of KatG, eleven high confidence (HC) KatG mutations associated with a high prevalence of phenotypic INH resistance were identified and their 3D structures modeled before subjecting them to MD simulations. Global analysis showed an unstable KatG structure and active site environment in the mutants compared to the wildtype. Active site dynamics in the mutants compromised cofactor (heme) interactions resulting in less bonds/interactions compared to the wildtype. Given the importance of the heme, reduced interactions affect enzyme function. Trajectory analysis also showed asymmetric protomer behavior both in the wildtype and mutant systems. DRN analysis identified the KatG dimerization domain and C-terminal domain as functionally important and influential in the enzyme function as per betweenness centrality and eigenvector centrality distribution. In the case of the MtPncA enzyme, our main focus was on understanding the MtPncA binding ability of Nicotinamide (an analogue of PZA) in comparison to PZA, especially in the presence of 82 resistance conferring MtPncA mutations. Like in KatG, the mutant structures were modeled and subjected to MD simulations and analysis. Interestingly, more MtPncA mutants favored NAM interactions compared to PZA i.e., 34 MtPncA mutants steadily coordinated NAM compared to 21 in the case of PZA. Trajectory and ligand interaction analysis showed how increased active site lid loop dynamics affect the NAM binding, especially in the systems with the active site mutations i.e., H51Y, W68R, C72R, L82R, K96N, L159N, and L159R. This led to fewer protein-ligand interactions and eventually ligand ejection. Network analysis further identified the protein core, metal binding site (MBS), and substrate binding site as the most important regions of the enzyme. Furthermore, the degree of centrality analysis showed how specific MtPncA mutations i.e., C14H, F17D, and T412P, interrupt intra-protein communication from the MtPncA core to the MBS, affecting enzyme activity. The analysis of KatG and MtPncA enzyme mutations not only identified the effects of mutations on enzyme behaviour and communication, but also established a framework of computational approaches that can be used for mutational studies in any protein. Besides AMR, the continued encroachment of wildlife habitats due to population growth has exposed humans to wildlife pathogens leading to zoonotic diseases, a recent example being coronavirus disease 2019 (COVID-19). In the second part of the thesis, the established computational approaches in Part 1, were employed to investigate the changes in inter-protein interactions and communication patterns between the severe acute respiratory coronavirus 2 (SARS-CoV-2) with the human host receptor protein (ACE2: angiotensin-converting enzyme 2) consequent to mutations in the SARS-CoV-2 receptor binding domain (RBD). Here, the focus was on RBD mutations of the Omicron sub-lineages. We identified four Omicron-sub lineages with RBD mutations i.e., BA.1, BA.2, BA.3 and BA.4. Each sub-lineage mutations were modeled into RBD structure in complex with the hACE2. MD analysis of the RBD-hACE2 complex highlighted how the RBD mutations change the conformational flexibility of both the RBD and hACE2 compared to the wildtype (WT). Furthermore, DRN analysis identified novel allosteric paths composed of residues with high betweenness and eigenvector centralities linking the RBD to the hACE2 in both the wildtype and mutant systems. Interestingly, these paths were modified with the progression of Omicron sub-lineages, highlighting how the virus evolution affects protein interaction. Lastly, the effect of mutations on S RBD and hACE2 interaction was investigated from the hACE2 perspective by focusing on mutations in the hACE2 protein. Here, naturally occurring hACE2 polymorphisms in African populations i.e., S19P, K26R, M82I, K341R, N546D, and D597Q, were identified and their effects on RBD-hACE2 interactions investigated in presence of the Omicron BA.4/5 RBD mutations. The hACE2 polymorphisms subtly affected the complex dynamics; however, RBD-hACE2 interaction analysis showed that hACE2 mutations effect the complex formation and interaction. Here, the K26R mutation favored RBD-hACE2 interactions, whereas S19P resulted in fewer inter-protein interactions than the reference system. The M82I mutation resulted in a higher RBD-hACE2 binding energy compared to the wildtype meaning that the mutation might not favor RBD binding to the hACE2. On the other hand, K341R had the most RBD-hACE2 interactions suggesting that it probably favors RBD binding to the hACE2. N546D and D597Q had diminutive differences to the reference system. Interestingly, the network of high betweenness centrality residues linking the two proteins, as seen in the previous paragraph, were maintained/modified in presence of hACE2 mutations. HACE2 mutations also changed the enzyme network patterns resulting in a concentration of high eigenvector centrality residues around the zinc-binding and active site region, ultimately influencing the enzyme functionality. Altogether, the thesis highlights fundamental structural and network changes consequent to mutations both in TB and COVID-19 proteins of interest using in silico approaches. These approaches not only provide a new context on impact of mutations in TB and COVID target proteins, but also presents a framework that be implemented in other protein mutation studies. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2023
- Full Text:
- Authors: Barozi, Victor
- Date: 2023-10-13
- Subjects: Microbial mutation , COVID-19 (Disease) , Drug resistance in microorganisms , Antitubercular agents , Tuberculosis , Molecular dynamics , Single nucleotide polymorphisms
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/431626 , vital:72791 , DOI 10.21504/10962/431626
- Description: One of the greatest challenges facing modern medicine and the global public health today is antimicrobial drug resistance (AMR). This “silent pandemic,” as coined by the world health organization (WHO), is steadily increasing with an estimated 4.95 million mortalities attributed to AMR in 2019, 1.27 million of which were directly linked to AMR. Some of the contributors to AMR include self-prescription, drug overuse, sub-optimal drug prescriptions by health workers, and inaccessibility to drugs, especially in remote areas, which leads to poor adherence. The situation is aggravated by the upsurge of new zoonotic infections like the coronavirus disease 2019, which present unique challenges and take the bulk of resources hence stunting the fight against AMR. Quite alarming still is our current antimicrobial arsenal, which hasn’t had any novel antimicrobial drug discovery/addition, of a new class, since the 1980s. This puts a burden on the existing broad-spectrum antimicrobial drugs which are already struggling against multi-drug resistant strains like multi-drug resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). Besides the search for new antimicrobial agents, the other avenue for addressing AMR is studying drug resistance mechanisms, especially single nucleotide polymorphisms (SNPs), that change drug target characteristics. With the advancement of computational power and data storage resources, computational approaches can be applied in mutational studies to provide insight into the drug resistance mechanisms with an aim to inform future drug design and development. Therefore, in the first part of this thesis, we employ integrative in silico approaches, including 3D structure modeling, molecular dynamic (MD) simulations, comparative essential dynamics (ED), and protein network analysis approaches i.e., dynamic residue network (DRN) analysis to decipher drug resistance mechanisms in tuberculosis (TB). This involved an investigation of the drug resistance mutations in the catalase-peroxidase (KatG) and pyrazinamidase (MtPncA) enzymes which are responsible for activation of TB first-line drugs; Isoniazid (INH) and Pyrazinamide (PZA), respectively. In the case of KatG, eleven high confidence (HC) KatG mutations associated with a high prevalence of phenotypic INH resistance were identified and their 3D structures modeled before subjecting them to MD simulations. Global analysis showed an unstable KatG structure and active site environment in the mutants compared to the wildtype. Active site dynamics in the mutants compromised cofactor (heme) interactions resulting in less bonds/interactions compared to the wildtype. Given the importance of the heme, reduced interactions affect enzyme function. Trajectory analysis also showed asymmetric protomer behavior both in the wildtype and mutant systems. DRN analysis identified the KatG dimerization domain and C-terminal domain as functionally important and influential in the enzyme function as per betweenness centrality and eigenvector centrality distribution. In the case of the MtPncA enzyme, our main focus was on understanding the MtPncA binding ability of Nicotinamide (an analogue of PZA) in comparison to PZA, especially in the presence of 82 resistance conferring MtPncA mutations. Like in KatG, the mutant structures were modeled and subjected to MD simulations and analysis. Interestingly, more MtPncA mutants favored NAM interactions compared to PZA i.e., 34 MtPncA mutants steadily coordinated NAM compared to 21 in the case of PZA. Trajectory and ligand interaction analysis showed how increased active site lid loop dynamics affect the NAM binding, especially in the systems with the active site mutations i.e., H51Y, W68R, C72R, L82R, K96N, L159N, and L159R. This led to fewer protein-ligand interactions and eventually ligand ejection. Network analysis further identified the protein core, metal binding site (MBS), and substrate binding site as the most important regions of the enzyme. Furthermore, the degree of centrality analysis showed how specific MtPncA mutations i.e., C14H, F17D, and T412P, interrupt intra-protein communication from the MtPncA core to the MBS, affecting enzyme activity. The analysis of KatG and MtPncA enzyme mutations not only identified the effects of mutations on enzyme behaviour and communication, but also established a framework of computational approaches that can be used for mutational studies in any protein. Besides AMR, the continued encroachment of wildlife habitats due to population growth has exposed humans to wildlife pathogens leading to zoonotic diseases, a recent example being coronavirus disease 2019 (COVID-19). In the second part of the thesis, the established computational approaches in Part 1, were employed to investigate the changes in inter-protein interactions and communication patterns between the severe acute respiratory coronavirus 2 (SARS-CoV-2) with the human host receptor protein (ACE2: angiotensin-converting enzyme 2) consequent to mutations in the SARS-CoV-2 receptor binding domain (RBD). Here, the focus was on RBD mutations of the Omicron sub-lineages. We identified four Omicron-sub lineages with RBD mutations i.e., BA.1, BA.2, BA.3 and BA.4. Each sub-lineage mutations were modeled into RBD structure in complex with the hACE2. MD analysis of the RBD-hACE2 complex highlighted how the RBD mutations change the conformational flexibility of both the RBD and hACE2 compared to the wildtype (WT). Furthermore, DRN analysis identified novel allosteric paths composed of residues with high betweenness and eigenvector centralities linking the RBD to the hACE2 in both the wildtype and mutant systems. Interestingly, these paths were modified with the progression of Omicron sub-lineages, highlighting how the virus evolution affects protein interaction. Lastly, the effect of mutations on S RBD and hACE2 interaction was investigated from the hACE2 perspective by focusing on mutations in the hACE2 protein. Here, naturally occurring hACE2 polymorphisms in African populations i.e., S19P, K26R, M82I, K341R, N546D, and D597Q, were identified and their effects on RBD-hACE2 interactions investigated in presence of the Omicron BA.4/5 RBD mutations. The hACE2 polymorphisms subtly affected the complex dynamics; however, RBD-hACE2 interaction analysis showed that hACE2 mutations effect the complex formation and interaction. Here, the K26R mutation favored RBD-hACE2 interactions, whereas S19P resulted in fewer inter-protein interactions than the reference system. The M82I mutation resulted in a higher RBD-hACE2 binding energy compared to the wildtype meaning that the mutation might not favor RBD binding to the hACE2. On the other hand, K341R had the most RBD-hACE2 interactions suggesting that it probably favors RBD binding to the hACE2. N546D and D597Q had diminutive differences to the reference system. Interestingly, the network of high betweenness centrality residues linking the two proteins, as seen in the previous paragraph, were maintained/modified in presence of hACE2 mutations. HACE2 mutations also changed the enzyme network patterns resulting in a concentration of high eigenvector centrality residues around the zinc-binding and active site region, ultimately influencing the enzyme functionality. Altogether, the thesis highlights fundamental structural and network changes consequent to mutations both in TB and COVID-19 proteins of interest using in silico approaches. These approaches not only provide a new context on impact of mutations in TB and COVID target proteins, but also presents a framework that be implemented in other protein mutation studies. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2023
- Full Text:
Design, synthesis, manufacture, characterization and evaluation of lipid nanocapsules in chitosan-iota-carrageenan based hydrogel scaffold as a potential anti-Covid-19 drug delivery system
- Authors: Mukubwa, Grady Kathondo
- Date: 2022-10-14
- Subjects: Nanocapsules Design , Hydrogel , COVID-19 (Disease) , Characterization , Drug delivery systems
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/364955 , vital:65665
- Description: Covid-19 is a deadly viral disease that has been rampant around the world since 2019. Although the successful introduction of the vaccine has reduced the spread of covid-19, new cases and deaths are still being recorded. To date, no specific curative antiviral treatment has been approved for covid-19. However, many existing antiviral drugs have been and are still being studied against covid-19 and some of them, such as Remdesivir, have shown promise and could be repurposed to treat this infection. Unfortunately, antiviral drugs are prone to resistance as most of them have poor biopharmaceutical properties, including low solubility, permeability and bioavailability, which could hinder any clinical success. Recent advances in nanotechnology-based delivery systems have made it possible to improve the biopharmaceutical properties of many drugs, especially those of poorly water-soluble drugs, by formulating them as lipid nanoparticles (LNP). Thus, in order to contribute to the fight against covid-19, this work aimed to develop Lipid Nanocapsules (LNC), based on some natural raw materials, which could improve the biopharmaceutical properties of antiviral drugs. In addition, since covid-19 infection is mainly respiratory, this work also aimed to fabricate a targeted delivery system based on a hydrogel capable of entrapping LNC and ensuring their efficient deposition and release in the lungs. The LNC consisted of a mixture of medium-chain triglycerides oil (MCT oil), crude soy lecithin, tween 80, NaCl and water, while the hydrogel consisted of a chitosan-grafted-iota carrageenan-grafted-poly (acrylamide-co-acrylic acid) system (CS-iCar-p (AAm-Co-AA)). Efavirenz (EFV), a drug with very low water solubility that has recently been demonstrated to have the potential to influence sars-cov-2 life cycle through different targets (3CLP, RdRp, Hellicase, 3’to5’exonuclease, 2’-O-ribose methyltransferase and EndoRNAse), was chosen as the model drug to evaluate the developed delivery system. The combination of LNP and hydrogel results in a delivery system known as the LNP-hydrogel composite, an emerging area of research in the field of drug delivery. To date, no research has reported the design and fabrication of an LNC-CS-iCar-p (AAm-Co-AA) hydrogel composite that could effectively deliver an antiviral drug to the lungs in addition to its advantages in terms of biological activities. Prior to the design of experiment, EFV solubility was assessed in water, labrafac lipophile 1349 and MCT oil. After that, the Design Expert Software version 13 was used to design the different experiments performed in this work. The I-optimal mixture design of experiments was performed for both LNC preparation and CS-iCar-p (AAm-Co-AA) hydrogel synthesis to study the impact of raw materials on the characteristics of these delivery systems. LNC were prepared using the phase inversion method while the free radical precipitation graft copolymerization method was used to synthesize hydrogel. In order to build polynomial models that could predict the amount of drug both LNC and CS-iCar-p (AAm-Co-AA) hydrogel can entrap, a D-optimal (custom) randomized design was performed. Moreover, various characterization techniques were used to investigate the physicochemical properties of the developed delivery systems. Thereafter, drug release studies were performed using a 1% sodium lauryl sulfate solution adjusted to either pH 4 or 7. Solubility studies revealed that EFV was more soluble in labrafac lipophile 1349 and in MCT oil than in water; therefore, given its affordability, MCT oil was used for the LNC formulation. The design of experiment carried out allowed the construction of polynomial models that could predict, on the one hand, the droplet size, the polydispersity index and the Zeta potential of LNC, which were respectively around 50nm, below 0.2 and below -33. On the other hand, the model could predict the swelling capacity of the synthesized hydrogel, which was optimised to about 30,000% (300 g of water to 1 g of hydrogel). This turned out to be influenced by the proportion of polymers, the ratio of monomers as well as the concentration of the cross-linking agent. In addition, the characterization techniques further supported the improvement of EFV solubility by highlighting its conversion into its amorphous state after encapsulation in LNC. They also confirmed successful synthesis of CS-iCar-p (AAm-co-AA) hydrogel. LNC were able to encapsulate about 87% of EFV while the synthesized CS-iCar-p (AAm-co-AA) hydrogel entrapped around 53% of EFV encapsulated in LNC. While LNC were able to release 42% and 27% of EFV after 74 hours in a 1% sodium lauryl sulfate solution (SLS) at pH 7 and pH 4 respectively, the LNC-CS-iCar-p (AAm-co-AA) hydrogel composite released about 50% and 40% of the drug after 9 days in the same release medium. Interestingly, the chemical integrity of the drug was preserved throughout the manufacturing process up to after its release, suggesting that the developed LNC-CS-iCar-p (AAm-co-AA) hydrogel composite could be used as a novel potential anticovid-19 drugs delivery system. , Thesis (MSc) -- Faculty of Science, Chemistry, 2022
- Full Text:
- Authors: Mukubwa, Grady Kathondo
- Date: 2022-10-14
- Subjects: Nanocapsules Design , Hydrogel , COVID-19 (Disease) , Characterization , Drug delivery systems
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/364955 , vital:65665
- Description: Covid-19 is a deadly viral disease that has been rampant around the world since 2019. Although the successful introduction of the vaccine has reduced the spread of covid-19, new cases and deaths are still being recorded. To date, no specific curative antiviral treatment has been approved for covid-19. However, many existing antiviral drugs have been and are still being studied against covid-19 and some of them, such as Remdesivir, have shown promise and could be repurposed to treat this infection. Unfortunately, antiviral drugs are prone to resistance as most of them have poor biopharmaceutical properties, including low solubility, permeability and bioavailability, which could hinder any clinical success. Recent advances in nanotechnology-based delivery systems have made it possible to improve the biopharmaceutical properties of many drugs, especially those of poorly water-soluble drugs, by formulating them as lipid nanoparticles (LNP). Thus, in order to contribute to the fight against covid-19, this work aimed to develop Lipid Nanocapsules (LNC), based on some natural raw materials, which could improve the biopharmaceutical properties of antiviral drugs. In addition, since covid-19 infection is mainly respiratory, this work also aimed to fabricate a targeted delivery system based on a hydrogel capable of entrapping LNC and ensuring their efficient deposition and release in the lungs. The LNC consisted of a mixture of medium-chain triglycerides oil (MCT oil), crude soy lecithin, tween 80, NaCl and water, while the hydrogel consisted of a chitosan-grafted-iota carrageenan-grafted-poly (acrylamide-co-acrylic acid) system (CS-iCar-p (AAm-Co-AA)). Efavirenz (EFV), a drug with very low water solubility that has recently been demonstrated to have the potential to influence sars-cov-2 life cycle through different targets (3CLP, RdRp, Hellicase, 3’to5’exonuclease, 2’-O-ribose methyltransferase and EndoRNAse), was chosen as the model drug to evaluate the developed delivery system. The combination of LNP and hydrogel results in a delivery system known as the LNP-hydrogel composite, an emerging area of research in the field of drug delivery. To date, no research has reported the design and fabrication of an LNC-CS-iCar-p (AAm-Co-AA) hydrogel composite that could effectively deliver an antiviral drug to the lungs in addition to its advantages in terms of biological activities. Prior to the design of experiment, EFV solubility was assessed in water, labrafac lipophile 1349 and MCT oil. After that, the Design Expert Software version 13 was used to design the different experiments performed in this work. The I-optimal mixture design of experiments was performed for both LNC preparation and CS-iCar-p (AAm-Co-AA) hydrogel synthesis to study the impact of raw materials on the characteristics of these delivery systems. LNC were prepared using the phase inversion method while the free radical precipitation graft copolymerization method was used to synthesize hydrogel. In order to build polynomial models that could predict the amount of drug both LNC and CS-iCar-p (AAm-Co-AA) hydrogel can entrap, a D-optimal (custom) randomized design was performed. Moreover, various characterization techniques were used to investigate the physicochemical properties of the developed delivery systems. Thereafter, drug release studies were performed using a 1% sodium lauryl sulfate solution adjusted to either pH 4 or 7. Solubility studies revealed that EFV was more soluble in labrafac lipophile 1349 and in MCT oil than in water; therefore, given its affordability, MCT oil was used for the LNC formulation. The design of experiment carried out allowed the construction of polynomial models that could predict, on the one hand, the droplet size, the polydispersity index and the Zeta potential of LNC, which were respectively around 50nm, below 0.2 and below -33. On the other hand, the model could predict the swelling capacity of the synthesized hydrogel, which was optimised to about 30,000% (300 g of water to 1 g of hydrogel). This turned out to be influenced by the proportion of polymers, the ratio of monomers as well as the concentration of the cross-linking agent. In addition, the characterization techniques further supported the improvement of EFV solubility by highlighting its conversion into its amorphous state after encapsulation in LNC. They also confirmed successful synthesis of CS-iCar-p (AAm-co-AA) hydrogel. LNC were able to encapsulate about 87% of EFV while the synthesized CS-iCar-p (AAm-co-AA) hydrogel entrapped around 53% of EFV encapsulated in LNC. While LNC were able to release 42% and 27% of EFV after 74 hours in a 1% sodium lauryl sulfate solution (SLS) at pH 7 and pH 4 respectively, the LNC-CS-iCar-p (AAm-co-AA) hydrogel composite released about 50% and 40% of the drug after 9 days in the same release medium. Interestingly, the chemical integrity of the drug was preserved throughout the manufacturing process up to after its release, suggesting that the developed LNC-CS-iCar-p (AAm-co-AA) hydrogel composite could be used as a novel potential anticovid-19 drugs delivery system. , Thesis (MSc) -- Faculty of Science, Chemistry, 2022
- Full Text:
In silico substrate binding profiling for SARS-COV-2 main protease (mpro) using hexapeptide substrates
- Authors: Zabo, Sophakama
- Date: 2022-10-14
- Subjects: COVID-19 (Disease) , Peptides , Chymotrypsin like , Chymotrypsin , Proteases , Proteolytic enzymes
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/365566 , vital:65760
- Description: COVID-19, as a disease resulting from SARS-CoV-2 infection, and a pandemic has had a devastating effect on the world. There are limited effective measures that control the spread and treatment of COVID-19 illness. The homodimeric cysteine main protease (Mpro) is crucial to the life cycle of the virus, as it cleaves the large polyproteins 1a and 1ab into matured, functional non-structural proteins. The Mpro exhibits high degrees of conservation in sequence, structure and specificity across coronavirus species, making it an ideal drug target. The Mpro substrate-binding profiles remain, despite the resolution of its recognition sequence and cleavage points (Leu-Gln↓(Ser/Ala/Gly)). In this study, a series of hexapeptide sequences containing the appropriate recognition sequence and cleavage points were generated and screened against the Mpro to study these binding profiles, and to further be the basis for efficiency-driven drug design. A multi-conformer hexapeptide substrate library comprising optimised 81000 models of 810 unique sequences was generated using RDKit within the context of python. Terminal capping with ACE and NMe was effected using SMILES and SMARTS matching. Multiple hexapeptides were complexed with chain B of crystallographic Mpro (PDS ID: 6XHM), following the validation of chain B for this purpose using AutoDock Vina at high levels of exhaustiveness (480). The resulting Vina scores ranged between -8.7 and -7.0 kcal.mol-1, and the reproducibility of best poses was validated through redocking. Ligand efficiency indices were calculated to identify substrate residues with high binding efficiency at their respective positions, revealing Val (P3), Ala (P1′); and Gly and Ala (P2′ and P3′) as leading efficient binders. Binding efficiencies were lowered by molecular weight. Substrate recognition was assessed by mapping of binding subsites, and Mpro specificity was evaluated through the resolution of intermolecular interaction at the binding interface. Molecular dynamics simulations for 20 ns were performed to assess the stability and behaviour of 132 Mpro systems complexed with KLQ*** substrates. Principal component analysis (PCA), was performed to assess II protein motions and conformational changes during the simulations. A strategy was formulated to classify and evaluate relations in the Mpro PCA motions, revealing four main clades of similarity. Similarity within a clade (Group 2) and dissimilarity between clades were confirmed. Trajectory visualisation revealed complex stability, substrate unbinding and dimer dissociation for various Mpro systems. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Authors: Zabo, Sophakama
- Date: 2022-10-14
- Subjects: COVID-19 (Disease) , Peptides , Chymotrypsin like , Chymotrypsin , Proteases , Proteolytic enzymes
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/365566 , vital:65760
- Description: COVID-19, as a disease resulting from SARS-CoV-2 infection, and a pandemic has had a devastating effect on the world. There are limited effective measures that control the spread and treatment of COVID-19 illness. The homodimeric cysteine main protease (Mpro) is crucial to the life cycle of the virus, as it cleaves the large polyproteins 1a and 1ab into matured, functional non-structural proteins. The Mpro exhibits high degrees of conservation in sequence, structure and specificity across coronavirus species, making it an ideal drug target. The Mpro substrate-binding profiles remain, despite the resolution of its recognition sequence and cleavage points (Leu-Gln↓(Ser/Ala/Gly)). In this study, a series of hexapeptide sequences containing the appropriate recognition sequence and cleavage points were generated and screened against the Mpro to study these binding profiles, and to further be the basis for efficiency-driven drug design. A multi-conformer hexapeptide substrate library comprising optimised 81000 models of 810 unique sequences was generated using RDKit within the context of python. Terminal capping with ACE and NMe was effected using SMILES and SMARTS matching. Multiple hexapeptides were complexed with chain B of crystallographic Mpro (PDS ID: 6XHM), following the validation of chain B for this purpose using AutoDock Vina at high levels of exhaustiveness (480). The resulting Vina scores ranged between -8.7 and -7.0 kcal.mol-1, and the reproducibility of best poses was validated through redocking. Ligand efficiency indices were calculated to identify substrate residues with high binding efficiency at their respective positions, revealing Val (P3), Ala (P1′); and Gly and Ala (P2′ and P3′) as leading efficient binders. Binding efficiencies were lowered by molecular weight. Substrate recognition was assessed by mapping of binding subsites, and Mpro specificity was evaluated through the resolution of intermolecular interaction at the binding interface. Molecular dynamics simulations for 20 ns were performed to assess the stability and behaviour of 132 Mpro systems complexed with KLQ*** substrates. Principal component analysis (PCA), was performed to assess II protein motions and conformational changes during the simulations. A strategy was formulated to classify and evaluate relations in the Mpro PCA motions, revealing four main clades of similarity. Similarity within a clade (Group 2) and dissimilarity between clades were confirmed. Trajectory visualisation revealed complex stability, substrate unbinding and dimer dissociation for various Mpro systems. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
Profiling Rhodes University students’ substance use during the Covid-19 pandemic lockdown: comparing the AUDIT and CCAPS-62 substance abuse sub-scale
- Authors: Goosen, Jeslyn Chrismaré
- Date: 2022-10-14
- Subjects: College students Substance use South Africa Makhanda , College students Alcohol use South Africa Makhanda , College students Mental health South Africa Makhanda , College students Attitudes , College students Economic conditions , COVID-19 (Disease) , Rhodes University
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/405974 , vital:70224
- Description: Students are vulnerable to academic distress and mental health concerns. Many struggle to effectively cope with the many demands placed on them from various factions; included but not limited to institutional demands, financial concerns, and parental expectations. With the most recent outbreak of the SARS-Co V-2 (better known as the COVID-19 pandemic) many students have struggled to effectively cope with the changes relating to the nationwide lockdown. Universities had to change the way in which they provide students with the necessary academic material, and many had to return to their familial homes. This had a deleterious effect on the way students performed their daily activities and coping. A rise in impaired mental health was noted. Many students used alcohol as a means of coping during this tumultuous and unprecedented time. Undergraduate students at Rhodes University were asked to complete a survey questionnaire via SurveyMonkey, an online survey service. Data was collected over a ten-day period during July 2020. The AUDIT and the CCAPS-62 Substance Use subscale were used to measure their alcohol intake during lockdown and results was compared. Results indicated a significant positive correlation between the CCAPS-62 substance use subscale and the AUDIT (r = 0.80, n = 930, p < 0.01). Outcomes identified that men tend to drink more than females, and white students tend to drink more than black students. Findings suggests that the CCAPS-62 a multidimensional instrument measuring general distress among students could positively contribute to the reliability and validity of the measure used in a multicultural and multilingual society such as South Africa. , Thesis (MA) -- Faculty of Humanities, Psychology, 2022
- Full Text:
- Authors: Goosen, Jeslyn Chrismaré
- Date: 2022-10-14
- Subjects: College students Substance use South Africa Makhanda , College students Alcohol use South Africa Makhanda , College students Mental health South Africa Makhanda , College students Attitudes , College students Economic conditions , COVID-19 (Disease) , Rhodes University
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/405974 , vital:70224
- Description: Students are vulnerable to academic distress and mental health concerns. Many struggle to effectively cope with the many demands placed on them from various factions; included but not limited to institutional demands, financial concerns, and parental expectations. With the most recent outbreak of the SARS-Co V-2 (better known as the COVID-19 pandemic) many students have struggled to effectively cope with the changes relating to the nationwide lockdown. Universities had to change the way in which they provide students with the necessary academic material, and many had to return to their familial homes. This had a deleterious effect on the way students performed their daily activities and coping. A rise in impaired mental health was noted. Many students used alcohol as a means of coping during this tumultuous and unprecedented time. Undergraduate students at Rhodes University were asked to complete a survey questionnaire via SurveyMonkey, an online survey service. Data was collected over a ten-day period during July 2020. The AUDIT and the CCAPS-62 Substance Use subscale were used to measure their alcohol intake during lockdown and results was compared. Results indicated a significant positive correlation between the CCAPS-62 substance use subscale and the AUDIT (r = 0.80, n = 930, p < 0.01). Outcomes identified that men tend to drink more than females, and white students tend to drink more than black students. Findings suggests that the CCAPS-62 a multidimensional instrument measuring general distress among students could positively contribute to the reliability and validity of the measure used in a multicultural and multilingual society such as South Africa. , Thesis (MA) -- Faculty of Humanities, Psychology, 2022
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Curriculum adjustment and adaptive leadership in two service-learning courses at Rhodes University as a consequence of the COVID-19 pandemic
- Authors: Khuhlane, Heide Nozuko
- Date: 2021-10-29
- Subjects: COVID-19 (Disease) , Curriculum planning South Africa Makhanda , Rhodes University , Service learning South Africa Makhanda , Educational leadership South Africa Makhanda , Educational change South Africa Makhanda , Adaptive leadership
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/191192 , vital:45069
- Description: The COVID-19 global pandemic altered many aspects of learning. Learning through service, a component of community engagement in higher education linking academic learning and the community was no exception. Informed by Experiential Learning Theory, this study investigated the curriculum adjustment of two service-learning courses at Rhodes University and the leadership development of those who lead the courses as a consequence of COVID-19. The study’s initial goal was to highlight the position of service-learning as a component of academic learning. With the onset of the COVID-19 pandemic the goal was extended to understanding the impact of the pandemic not only on service-learning, but on leadership as well. Furthermore, the study sought to determine the responsiveness of service-learning policies at Rhodes University at a time of crisis. The study was designed as an interpretivist case study with four participants and one secondary participant. The study employed document analysis, individual interviews and a focus group interview to collect data. Data analysis took the form of content analysis and coding, through the lens of Experiential Learning Theory and an alternative service-based model. The study findings revealed that as a result of the COVID-19 global pandemic both service-learning courses had to be adapted to ensure successful completion. The adaptations included attention to scaffolded learning, assessment and course outcomes; in one course the service engagement aspect with the community was lost entirely to ensure the saftey of students through adherence to COVID-19 safety regulations. The study also found that the participants developed adaptive leadership competencies and skills, technological and collaboration skills as well as a heightened regard for pastoral care and social justice. However, it was evident that the pandemic revealed gaps in the conceptual understanding of service-learning in the context of the two courses, a need for responsive policy, and practical strategies to implement those policies in smaller units in the institution. The study thus recommends an alternative service-based model approach to service-learning, increased policy responsiveness to issues posed by the ‘new normal’ to support adaptive leadership development, re-defining of the university-community partnership and the identification of opportunities for innovation and collaboration intra-departmentally through service-learning. , Thesis (MEd) -- Faculty of Education, Education, 2021
- Full Text:
- Authors: Khuhlane, Heide Nozuko
- Date: 2021-10-29
- Subjects: COVID-19 (Disease) , Curriculum planning South Africa Makhanda , Rhodes University , Service learning South Africa Makhanda , Educational leadership South Africa Makhanda , Educational change South Africa Makhanda , Adaptive leadership
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/191192 , vital:45069
- Description: The COVID-19 global pandemic altered many aspects of learning. Learning through service, a component of community engagement in higher education linking academic learning and the community was no exception. Informed by Experiential Learning Theory, this study investigated the curriculum adjustment of two service-learning courses at Rhodes University and the leadership development of those who lead the courses as a consequence of COVID-19. The study’s initial goal was to highlight the position of service-learning as a component of academic learning. With the onset of the COVID-19 pandemic the goal was extended to understanding the impact of the pandemic not only on service-learning, but on leadership as well. Furthermore, the study sought to determine the responsiveness of service-learning policies at Rhodes University at a time of crisis. The study was designed as an interpretivist case study with four participants and one secondary participant. The study employed document analysis, individual interviews and a focus group interview to collect data. Data analysis took the form of content analysis and coding, through the lens of Experiential Learning Theory and an alternative service-based model. The study findings revealed that as a result of the COVID-19 global pandemic both service-learning courses had to be adapted to ensure successful completion. The adaptations included attention to scaffolded learning, assessment and course outcomes; in one course the service engagement aspect with the community was lost entirely to ensure the saftey of students through adherence to COVID-19 safety regulations. The study also found that the participants developed adaptive leadership competencies and skills, technological and collaboration skills as well as a heightened regard for pastoral care and social justice. However, it was evident that the pandemic revealed gaps in the conceptual understanding of service-learning in the context of the two courses, a need for responsive policy, and practical strategies to implement those policies in smaller units in the institution. The study thus recommends an alternative service-based model approach to service-learning, increased policy responsiveness to issues posed by the ‘new normal’ to support adaptive leadership development, re-defining of the university-community partnership and the identification of opportunities for innovation and collaboration intra-departmentally through service-learning. , Thesis (MEd) -- Faculty of Education, Education, 2021
- Full Text:
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