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:
An in-silico study of the type II NADH: Quinone Oxidoreductase (ndh2). A new anti-malaria drug target
- Authors: Baye, Bertha Cinthia
- Date: 2022-10-14
- Subjects: Malaria , Plasmodium , Molecular dynamics , Computer simulation , Quinone , Antimalarials , Molecules Models , Docking , Drugs Computer-aided design
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/365633 , vital:65767 , DOI https://doi.org/10.21504/10962/365633
- Description: Malaria is caused by Plasmodium parasites, spread to people through the bites of infected female Anopheles mosquitoes. This study focuses on all 5 (Plasmodium falciparum, Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax) parasites that cause malaria in humans. Africa is a developing continent, and it is the most affected with an estimation of 90% of more than 400 000 malaria-related deaths reported by the World Health Organization (WHO) report in 2020, in which 61% of that number are children under the ages of five. Malaria resistance was initially observed in early 1986 and with the progression of time anti-malarial drug resistance has only increased. As a result, there is a need to study the malarial proteins mechanism of action and identify alternative treatment strategies for this disease. Type II NADH: quinone oxidoreductase (NDH2) is a monotopic protein that catalyses the electron transfer from NADH to quinone via FAD without a proton-pumping activity, and functions as an initial enzyme, either in addition to or as an alternative to proton-pumping NADH dehydrogenase (complex I) in the respiratory chain of bacteria, archaea, and fungal and plant mitochondrial. The structures for the Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax were modelled from the crystal structure of Plasmodium falciparum (5JWA). Compounds from the South African natural compounds database (SANCDB) were docked against both the NDH2 crystal structure and modelled structures. By performing in silico screening the study aimed to find potential compounds that might interrupt the electron transfer to quinone therefore disturbing the enzyme‟s function and thereby possibly eliminating the plasmodium parasite. CHARMM-GUI was used to create the membrane (since this work is with membrane-bound proteins) and to orient the protein on the membrane using OPM server guidelines, the interface produced GROMACS topology files that were used in molecular dynamics simulations. Molecular dynamics simulations were performed in the Centre for high performance computing (CHPC) cluster under the CHEM0802 project and the trajectories produced were further analysed. In this work not only were hit compounds from SANCDB identified, but also differences in behaviour across species and in the presence or absence of the membrane were described. This highlights the need to include the correct protein environment when studying these systems. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Authors: Baye, Bertha Cinthia
- Date: 2022-10-14
- Subjects: Malaria , Plasmodium , Molecular dynamics , Computer simulation , Quinone , Antimalarials , Molecules Models , Docking , Drugs Computer-aided design
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/365633 , vital:65767 , DOI https://doi.org/10.21504/10962/365633
- Description: Malaria is caused by Plasmodium parasites, spread to people through the bites of infected female Anopheles mosquitoes. This study focuses on all 5 (Plasmodium falciparum, Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax) parasites that cause malaria in humans. Africa is a developing continent, and it is the most affected with an estimation of 90% of more than 400 000 malaria-related deaths reported by the World Health Organization (WHO) report in 2020, in which 61% of that number are children under the ages of five. Malaria resistance was initially observed in early 1986 and with the progression of time anti-malarial drug resistance has only increased. As a result, there is a need to study the malarial proteins mechanism of action and identify alternative treatment strategies for this disease. Type II NADH: quinone oxidoreductase (NDH2) is a monotopic protein that catalyses the electron transfer from NADH to quinone via FAD without a proton-pumping activity, and functions as an initial enzyme, either in addition to or as an alternative to proton-pumping NADH dehydrogenase (complex I) in the respiratory chain of bacteria, archaea, and fungal and plant mitochondrial. The structures for the Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax were modelled from the crystal structure of Plasmodium falciparum (5JWA). Compounds from the South African natural compounds database (SANCDB) were docked against both the NDH2 crystal structure and modelled structures. By performing in silico screening the study aimed to find potential compounds that might interrupt the electron transfer to quinone therefore disturbing the enzyme‟s function and thereby possibly eliminating the plasmodium parasite. CHARMM-GUI was used to create the membrane (since this work is with membrane-bound proteins) and to orient the protein on the membrane using OPM server guidelines, the interface produced GROMACS topology files that were used in molecular dynamics simulations. Molecular dynamics simulations were performed in the Centre for high performance computing (CHPC) cluster under the CHEM0802 project and the trajectories produced were further analysed. In this work not only were hit compounds from SANCDB identified, but also differences in behaviour across species and in the presence or absence of the membrane were described. This highlights the need to include the correct protein environment when studying these systems. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
Identification of novel compounds against Plasmodium falciparum Cytochrome bc1 Complex inhibiting the trans-membrane electron transfer pathway: an In Silico study
- Authors: Chebon, Lorna Jemosop
- Date: 2022-10-14
- Subjects: Malaria , Plasmodium falciparum , Molecular dynamics , Antimalarials , Molecules Models , Docking , Cytochromes , Drug resistance , Computer simulation , Drugs Computer-aided design , System analysis
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/365666 , vital:65774 , DOI https://doi.org/10.21504/10962/365666
- Description: Malaria continues to be a burden globally with a myriad of challenges deterring eradication efforts. With most antimalarials facing drug resistance, such as atovaquone (ATQ), alternative compounds that can withstand resistance are warranted. The Plasmodium falciparum cytochrome b (PfCytb), a subunit of P. falciparum cytochrome bc1 complex, is a validated drug target. Structurally, cytochrome b, cytochrome c1, and iron sulphur protein (ISP) subunits form the catalytic domain of the protein complex having heme bL, heme bH and iron-sulphur [2FE-2S] cluster cofactors. These cofactos have redox centres to aid in the electron transfer (ET) process. These subunits promote ET mainly through the enzyme’s ubiquinol oxidation (Qo) and ubiquinone reduction (Qi) processes in the catalytic domain. ATQ drug has been used in the prevention and treatment of uncomplicated malaria by targeting PfCytb protein. Once the mitochondrial transmembrane ET pathway is inhibited, it causes a collapse in its membrane potential. Previously reported ATQ drug resistance has been associated with the point mutations Y268C, Y268N and Y268S. Thus, in finding alternatives to the ATQ drug, this research aimed to: i) employ in silico approaches incorporating protein into phospholipid bilayer for the first time to understand the parasites’ resistance mechanism; ii) determine any sequence and structural differences that could be explored in drug design studies; and iii) screen for PfCytb-iron sulphur protein (Cytb-ISP) hit compounds from South African natural compound database (SANCDB) and Medicines for Malaria Venture (MMV) that can withstand the identified mutations. Using computational tools, comparative sequence and structural analyses were performed on the cytochrome b protein, where the ultimate focus was on P. falciparum cytochrome b and its human homolog. Through multiple sequence alignment, motif discovery and phylogeny, differences between P. falciparum and H. sapiens cytochrome b were identified. Protein modelling of both P. falciparum and H. sapiens cytochrome b - iron sulphur protein (PfCytb-ISP and HsCytb-ISP) was performed. Results showed that at the sequence level, there were few amino acid residue differences because the protein is highly conserved. Important to note is the four-residue deletion in Plasmodium spp. absent in the human homolog. Motif analysis discovered five unique motifs in P. falciparum cytochrome b protein which were mapped onto the predicted protein model. These motifs were not in regions of functional importance; hence their function is still unknown. At a structural level, the four-residue deletion was observed to alter the Qo substrate binding pocket as reported in previous studies and confirmed in this study. This deletion resulted in a 0.83 Å structural displacement. Also, there are currently no in silico studies that have performed experiments with P. falciparum cytochrome b protein incorporated into a phospholipid bilayer. Using 350 ns molecular dynamics (MD) simulations of the holo and ATQ-bound systems, the study highlighted the resistance mechanism of the parasite protein where the loss of active site residue-residue interactions was identified, all linked to the three mutations. The identified compromised interactions are likely to destabilise the protein’s function, specifically in the Qo substrate binding site. This showed the possible effect of mutations on ATQ drug activity, where all three mutations were reported to share a similar resistance mechanism. Thereafter, this research work utilised in silico approaches where both Qo active site and interface pocket were targeted by screening the South African natural compounds database (SANCDB) and Medicines for Malaria Venture (MMV) compounds to identify novel selective hits. SANCDB compounds are known for their structural complexity that preserves the potency of the drug molecule. Both SANCDB and MMV compounds have not been explored as inhibitors against the PfCytb drug target. Molecular docking, molecular dynamics (MD) simulations, principal component, and dynamic residue network (DRN; global and local) analyses were utilised to identify and confirm the potential selective inhibitors. Docking results identified compounds that bound selectively onto PfCytb-ISP with a binding energy ≤ -8.7 kcal/mol-1. Further, this work validated a total of eight potential selective compounds to inhibit PfCytb-ISP protein (Qo active site) not only in the wild-type but also in the presence of the point mutations Y268C, Y268N and Y268S. The selective binding of these hit compounds could be linked to the differences reported at sequence/residue level in chapter 3. DRN and residue contact map analyses of the eight compounds in holo and ligand-bound systems revealed reduced residue interactions and decreased protein communication. This suggests that the eight compounds show the possibility of inhibiting the parasite and disrupting important residue-residue interactions. Additionally, 13 selective compounds were identified to bind at the protein’s heterodimer interface, where global and local analysis confirmed their effect on active site residues (distal location) as well as on the communication network. Based on the sequence differences between PfCytb and the human homolog, these findings suggest these selective compounds as potential allosteric modulators of the parasite enzyme, which may serve as possible replacements of the already resistant ATQ drug. Therefore, these findings pave the way for further in vitro studies to establish their anti-plasmodial inhibition levels. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Authors: Chebon, Lorna Jemosop
- Date: 2022-10-14
- Subjects: Malaria , Plasmodium falciparum , Molecular dynamics , Antimalarials , Molecules Models , Docking , Cytochromes , Drug resistance , Computer simulation , Drugs Computer-aided design , System analysis
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/365666 , vital:65774 , DOI https://doi.org/10.21504/10962/365666
- Description: Malaria continues to be a burden globally with a myriad of challenges deterring eradication efforts. With most antimalarials facing drug resistance, such as atovaquone (ATQ), alternative compounds that can withstand resistance are warranted. The Plasmodium falciparum cytochrome b (PfCytb), a subunit of P. falciparum cytochrome bc1 complex, is a validated drug target. Structurally, cytochrome b, cytochrome c1, and iron sulphur protein (ISP) subunits form the catalytic domain of the protein complex having heme bL, heme bH and iron-sulphur [2FE-2S] cluster cofactors. These cofactos have redox centres to aid in the electron transfer (ET) process. These subunits promote ET mainly through the enzyme’s ubiquinol oxidation (Qo) and ubiquinone reduction (Qi) processes in the catalytic domain. ATQ drug has been used in the prevention and treatment of uncomplicated malaria by targeting PfCytb protein. Once the mitochondrial transmembrane ET pathway is inhibited, it causes a collapse in its membrane potential. Previously reported ATQ drug resistance has been associated with the point mutations Y268C, Y268N and Y268S. Thus, in finding alternatives to the ATQ drug, this research aimed to: i) employ in silico approaches incorporating protein into phospholipid bilayer for the first time to understand the parasites’ resistance mechanism; ii) determine any sequence and structural differences that could be explored in drug design studies; and iii) screen for PfCytb-iron sulphur protein (Cytb-ISP) hit compounds from South African natural compound database (SANCDB) and Medicines for Malaria Venture (MMV) that can withstand the identified mutations. Using computational tools, comparative sequence and structural analyses were performed on the cytochrome b protein, where the ultimate focus was on P. falciparum cytochrome b and its human homolog. Through multiple sequence alignment, motif discovery and phylogeny, differences between P. falciparum and H. sapiens cytochrome b were identified. Protein modelling of both P. falciparum and H. sapiens cytochrome b - iron sulphur protein (PfCytb-ISP and HsCytb-ISP) was performed. Results showed that at the sequence level, there were few amino acid residue differences because the protein is highly conserved. Important to note is the four-residue deletion in Plasmodium spp. absent in the human homolog. Motif analysis discovered five unique motifs in P. falciparum cytochrome b protein which were mapped onto the predicted protein model. These motifs were not in regions of functional importance; hence their function is still unknown. At a structural level, the four-residue deletion was observed to alter the Qo substrate binding pocket as reported in previous studies and confirmed in this study. This deletion resulted in a 0.83 Å structural displacement. Also, there are currently no in silico studies that have performed experiments with P. falciparum cytochrome b protein incorporated into a phospholipid bilayer. Using 350 ns molecular dynamics (MD) simulations of the holo and ATQ-bound systems, the study highlighted the resistance mechanism of the parasite protein where the loss of active site residue-residue interactions was identified, all linked to the three mutations. The identified compromised interactions are likely to destabilise the protein’s function, specifically in the Qo substrate binding site. This showed the possible effect of mutations on ATQ drug activity, where all three mutations were reported to share a similar resistance mechanism. Thereafter, this research work utilised in silico approaches where both Qo active site and interface pocket were targeted by screening the South African natural compounds database (SANCDB) and Medicines for Malaria Venture (MMV) compounds to identify novel selective hits. SANCDB compounds are known for their structural complexity that preserves the potency of the drug molecule. Both SANCDB and MMV compounds have not been explored as inhibitors against the PfCytb drug target. Molecular docking, molecular dynamics (MD) simulations, principal component, and dynamic residue network (DRN; global and local) analyses were utilised to identify and confirm the potential selective inhibitors. Docking results identified compounds that bound selectively onto PfCytb-ISP with a binding energy ≤ -8.7 kcal/mol-1. Further, this work validated a total of eight potential selective compounds to inhibit PfCytb-ISP protein (Qo active site) not only in the wild-type but also in the presence of the point mutations Y268C, Y268N and Y268S. The selective binding of these hit compounds could be linked to the differences reported at sequence/residue level in chapter 3. DRN and residue contact map analyses of the eight compounds in holo and ligand-bound systems revealed reduced residue interactions and decreased protein communication. This suggests that the eight compounds show the possibility of inhibiting the parasite and disrupting important residue-residue interactions. Additionally, 13 selective compounds were identified to bind at the protein’s heterodimer interface, where global and local analysis confirmed their effect on active site residues (distal location) as well as on the communication network. Based on the sequence differences between PfCytb and the human homolog, these findings suggest these selective compounds as potential allosteric modulators of the parasite enzyme, which may serve as possible replacements of the already resistant ATQ drug. Therefore, these findings pave the way for further in vitro studies to establish their anti-plasmodial inhibition levels. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
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