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:
Sequence, structure, dynamics, and substrate specificity analyses of bacterial Glycoside Hydrolase 1 enzymes from several activities
- Authors: Veldman, Wayde Michael
- Date: 2022-04-08
- Subjects: Glycosidases , Bioinformatics , Molecular dynamics , Ligands (Biochemistry) , Enzymes , Ligand binding (Biochemistry) , Sequence alignment (Bioinformatics) , Structural bioinformatics
- Language: English
- Type: Doctoral thesis , text
- Identifier: http://hdl.handle.net/10962/233805 , vital:50129 , DOI 10.21504/10962/233810
- Description: Glycoside hydrolase 1 (GH1) enzymes are a ubiquitous family of enzymes that hydrolyse the glycosidic bond between two or more carbohydrates, or between a carbohydrate and a non-carbohydrate moiety. Despite their conserved catalytic domain, these enzymes have many different enzyme activities and/or substrate specificities as a change of only a few residues in the active site can alter their function. Most GH1 active site residues are situated in loop regions, and it is known that enzymes are more likely to develop new functions (broad specificity) if they possess an active site with a high proportion of loops. Furthermore, the GH1 active site consists of several subsites and cooperative binding makes the binding affinity of sites difficult to measure because the properties of one subsite are influenced by the binding of the other subsites. Extensive knowledge of protein-ligand interactions is critical to the comprehension of biology at the molecular level. However, the structural determinants and molecular details of GH1 ligand specificity and affinity are very broad, highly complex, not well understood, and therefore still need to be clarified. The aim of this study was to computationally characterise the activity of three newly solved GH1 crystallographic structures sent to us by our collaborators, and to provide evidence for their ligand-binding specificities. In addition, the differences in structural and biochemical contributions to enzyme specificity and/or function between different GH1 activities/enzymes was assessed, and the sequence/structure/function relationship of several activities of GH1 enzymes was analysed and compared. To accomplish the research aims, sequence analyses involving sequence identity, phylogenetics, and motif discovery were performed. As protein structure is more conserved than sequence, the discovered motifs were mapped to 3D structures for structural analysis and comparisons. To obtain information on enzyme mechanism or mode of action, as well as structure-function relationship, computational methods such as docking, molecular dynamics, binding free energy calculations, and essential dynamics were implemented. These computational approaches can provide information on the active site, binding residues, protein-ligand interactions, binding affinity, conformational change, and most structural or dynamic elements that play a role in enzyme function. The three new structures received from our collaborators are the first GH1 crystallographic structures from Bacillus licheniformis ever determined. As phospho-glycoside compounds were unavailable for purchase for use in activity assays, and as the active sites of the structures were absent of ligand, in silico docking and MD simulations were performed to provide evidence for their GH1 activities and substrate specificities. First though, the amino acid sequences of all known characterised bacterial GH1 enzymes were retrieved from the CAZy database and compared to the sequences of the three new B. licheniformis crystallographic structures which provided evidence of the putative 6Pβ-glucosidase activity of enzyme BlBglH, and dual 6Pβ-glucosidase/6Pβ-galactosidase (dual-phospho) activity of enzymes BlBglB and BlBglC. As all three enzymes were determined to be putative 6Pβ-glycosidase activity enzymes, much of the thesis focused on the overall analysis and comparison of the 6Pβ-glucosidase, 6Pβ-galactosidase, and dual-phospho activities that make up the 6Pβ-glycosidases. The 6Pβ-glycosidase active site residues were identified through consensus of binding interactions using all known 6Pβ-glycosidase PDB structures complexed complete ligand substrates. With regards to the 6Pβ-glucosidase activity, it was found that the L8b loop is longer and forms extra interactions with the L8a loop likely leading to increased L8 loop rigidity which would prevent the displacement of residue Ala423 ensuring a steric clash with galactoconfigured ligands and may engender substrate specificity for gluco-configured ligands only. Also, during molecular dynamics simulations using enzyme BlBglH (6Pβ-glucosidase activity), it was revealed that the favourable binding of substrate stabilises the loops that surround and make up the enzyme active site. Using the BlBglC (dual-phospho activity) enzyme structure with either galacto- (PNP6Pgal) or gluco-configured (PNP6Pglc) ligands, MD simulations in triplicate revealed important details of the broad specificity of dual-phospho activity enzymes. The ligand O4 hydroxyl position is the only difference between PNP6Pgal and PNP6Pgal, and it was found that residues Gln23 and Trp433 bind strongly to the ligand O3 hydroxyl group in the PNP6Pgal-enzyme complex, but to the ligand O4 hydroxyl group in the PNP6Pglc-enzyme complex. Also, His124 formed many hydrogen bonds with the PNP6Pgal O3 hydroxyl group but had none with PNP6Pglc. Alternatively, residues Tyr173, Tyr301, Gln302 and Thr321 formed hydrogen bonds with PNP6Pglc but not PNP6Pgal. Lastly, using multiple 3D structures from various GH1 activities, a large network of conserved interactions between active site residues (and other important residues) was uncovered, which most likely stabilise the loop regions that contain these residues, helping to retain their positions needed for binding molecules. Alternatively, there exists several differing residue-residue interactions when comparing each of the activities which could contribute towards individual activity substrate specificity by causing slightly different overall structure and malleability of the active site. Altogether, the findings in this thesis shed light on the function, mechanisms, dynamics, and ligand-binding of GH1 enzymes – particularly of the 6Pβ-glycosidase activities. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
- Authors: Veldman, Wayde Michael
- Date: 2022-04-08
- Subjects: Glycosidases , Bioinformatics , Molecular dynamics , Ligands (Biochemistry) , Enzymes , Ligand binding (Biochemistry) , Sequence alignment (Bioinformatics) , Structural bioinformatics
- Language: English
- Type: Doctoral thesis , text
- Identifier: http://hdl.handle.net/10962/233805 , vital:50129 , DOI 10.21504/10962/233810
- Description: Glycoside hydrolase 1 (GH1) enzymes are a ubiquitous family of enzymes that hydrolyse the glycosidic bond between two or more carbohydrates, or between a carbohydrate and a non-carbohydrate moiety. Despite their conserved catalytic domain, these enzymes have many different enzyme activities and/or substrate specificities as a change of only a few residues in the active site can alter their function. Most GH1 active site residues are situated in loop regions, and it is known that enzymes are more likely to develop new functions (broad specificity) if they possess an active site with a high proportion of loops. Furthermore, the GH1 active site consists of several subsites and cooperative binding makes the binding affinity of sites difficult to measure because the properties of one subsite are influenced by the binding of the other subsites. Extensive knowledge of protein-ligand interactions is critical to the comprehension of biology at the molecular level. However, the structural determinants and molecular details of GH1 ligand specificity and affinity are very broad, highly complex, not well understood, and therefore still need to be clarified. The aim of this study was to computationally characterise the activity of three newly solved GH1 crystallographic structures sent to us by our collaborators, and to provide evidence for their ligand-binding specificities. In addition, the differences in structural and biochemical contributions to enzyme specificity and/or function between different GH1 activities/enzymes was assessed, and the sequence/structure/function relationship of several activities of GH1 enzymes was analysed and compared. To accomplish the research aims, sequence analyses involving sequence identity, phylogenetics, and motif discovery were performed. As protein structure is more conserved than sequence, the discovered motifs were mapped to 3D structures for structural analysis and comparisons. To obtain information on enzyme mechanism or mode of action, as well as structure-function relationship, computational methods such as docking, molecular dynamics, binding free energy calculations, and essential dynamics were implemented. These computational approaches can provide information on the active site, binding residues, protein-ligand interactions, binding affinity, conformational change, and most structural or dynamic elements that play a role in enzyme function. The three new structures received from our collaborators are the first GH1 crystallographic structures from Bacillus licheniformis ever determined. As phospho-glycoside compounds were unavailable for purchase for use in activity assays, and as the active sites of the structures were absent of ligand, in silico docking and MD simulations were performed to provide evidence for their GH1 activities and substrate specificities. First though, the amino acid sequences of all known characterised bacterial GH1 enzymes were retrieved from the CAZy database and compared to the sequences of the three new B. licheniformis crystallographic structures which provided evidence of the putative 6Pβ-glucosidase activity of enzyme BlBglH, and dual 6Pβ-glucosidase/6Pβ-galactosidase (dual-phospho) activity of enzymes BlBglB and BlBglC. As all three enzymes were determined to be putative 6Pβ-glycosidase activity enzymes, much of the thesis focused on the overall analysis and comparison of the 6Pβ-glucosidase, 6Pβ-galactosidase, and dual-phospho activities that make up the 6Pβ-glycosidases. The 6Pβ-glycosidase active site residues were identified through consensus of binding interactions using all known 6Pβ-glycosidase PDB structures complexed complete ligand substrates. With regards to the 6Pβ-glucosidase activity, it was found that the L8b loop is longer and forms extra interactions with the L8a loop likely leading to increased L8 loop rigidity which would prevent the displacement of residue Ala423 ensuring a steric clash with galactoconfigured ligands and may engender substrate specificity for gluco-configured ligands only. Also, during molecular dynamics simulations using enzyme BlBglH (6Pβ-glucosidase activity), it was revealed that the favourable binding of substrate stabilises the loops that surround and make up the enzyme active site. Using the BlBglC (dual-phospho activity) enzyme structure with either galacto- (PNP6Pgal) or gluco-configured (PNP6Pglc) ligands, MD simulations in triplicate revealed important details of the broad specificity of dual-phospho activity enzymes. The ligand O4 hydroxyl position is the only difference between PNP6Pgal and PNP6Pgal, and it was found that residues Gln23 and Trp433 bind strongly to the ligand O3 hydroxyl group in the PNP6Pgal-enzyme complex, but to the ligand O4 hydroxyl group in the PNP6Pglc-enzyme complex. Also, His124 formed many hydrogen bonds with the PNP6Pgal O3 hydroxyl group but had none with PNP6Pglc. Alternatively, residues Tyr173, Tyr301, Gln302 and Thr321 formed hydrogen bonds with PNP6Pglc but not PNP6Pgal. Lastly, using multiple 3D structures from various GH1 activities, a large network of conserved interactions between active site residues (and other important residues) was uncovered, which most likely stabilise the loop regions that contain these residues, helping to retain their positions needed for binding molecules. Alternatively, there exists several differing residue-residue interactions when comparing each of the activities which could contribute towards individual activity substrate specificity by causing slightly different overall structure and malleability of the active site. Altogether, the findings in this thesis shed light on the function, mechanisms, dynamics, and ligand-binding of GH1 enzymes – particularly of the 6Pβ-glycosidase activities. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2022
- Full Text:
Analysis of bacterial Mur amide ligase enzymes for the identification of inhibitory compounds by in silico methods
- Chamboko, Chiratidzo Respina
- Authors: Chamboko, Chiratidzo Respina
- Date: 2020
- Subjects: Mur amide ligases , Ligases , Ligand binding (Biochemistry) , Antibacterial agents
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/163430 , vital:41036
- Description: An increased emergence of resistant pathogenic bacterial strains over the years has resulted in many people dying of untreatable infections. This has become one of the most critical global public health problems, as resistant strains are complicating treatment of infectious diseases, increasing human morbidity, mortality, and health care costs. A very limited amount of effective antibiotics is currently available, but the development of novel classes of antibacterial agents is becoming a priority. Mur amide ligases are enzymes that have been identified as potentially good targets for antibiotics, as they are uniquely found in bacteria. They are responsible for the formation of peptide bonds in a growing peptidoglycan structure for bacterial cell walls. The current work presented here focused on characterizing these Mur amide ligase enzymes and obtaining inhibitory compounds that could potentially be of use in drug discovery of antibacterial agents. To do this, multiple sequence alignment, motif analysis and phylogenetic tree constructions were carried out, followed by docking studies and molecular dynamic simulations. Prior to docking, homology modelling of missing residues in the MurF structure (PDB 1GG4) was performed. Characterization results revealed the Mur amide ligase enzymes contained defined conservation in limited regions, that ultimately mapped towards the central domain responsible for ATP binding (presence of a conserved GKT motif). Further analysis of results further unraveled the unique patterns observed within each group of the family of enzymes. As a result of these findings, docking studies were carried out on each Mur amide ligase structure. At most, two ligands were identified to be sufficiently inhibiting each Mur amide ligase. The ligands obtained were SANC00574 and SANC00575 for MurC, SANC00290 and SANC00438 for MurD, SANC00290 and SANC00525 for MurE and SANC00290 and SANC00434 for MurF. The two best ligands identified for each enzyme had docked in the active site of their respective proteins, passed Lipinski’s rule of five and had substantially low binding energies. Molecular dynamic simulations were then performed to analyze the behavior of the proteins and protein-ligand complexes, to confirm the lead compounds as good inhibitors of the Mur amide ligases. In the case of MurC, MurD and MurE complexes, the identified ligands clearly impacted the behavior of the protein, as the ligand bound proteins became more compact and stable, while flexibility decreased. There was however an opposite effect on MurF complexes, that resulted in identified inhibitors being discarded. As a potential next step, in vivo and in vitro experiments can be performed with identified ligands from this research, to further support the information presented.
- Full Text:
- Authors: Chamboko, Chiratidzo Respina
- Date: 2020
- Subjects: Mur amide ligases , Ligases , Ligand binding (Biochemistry) , Antibacterial agents
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/163430 , vital:41036
- Description: An increased emergence of resistant pathogenic bacterial strains over the years has resulted in many people dying of untreatable infections. This has become one of the most critical global public health problems, as resistant strains are complicating treatment of infectious diseases, increasing human morbidity, mortality, and health care costs. A very limited amount of effective antibiotics is currently available, but the development of novel classes of antibacterial agents is becoming a priority. Mur amide ligases are enzymes that have been identified as potentially good targets for antibiotics, as they are uniquely found in bacteria. They are responsible for the formation of peptide bonds in a growing peptidoglycan structure for bacterial cell walls. The current work presented here focused on characterizing these Mur amide ligase enzymes and obtaining inhibitory compounds that could potentially be of use in drug discovery of antibacterial agents. To do this, multiple sequence alignment, motif analysis and phylogenetic tree constructions were carried out, followed by docking studies and molecular dynamic simulations. Prior to docking, homology modelling of missing residues in the MurF structure (PDB 1GG4) was performed. Characterization results revealed the Mur amide ligase enzymes contained defined conservation in limited regions, that ultimately mapped towards the central domain responsible for ATP binding (presence of a conserved GKT motif). Further analysis of results further unraveled the unique patterns observed within each group of the family of enzymes. As a result of these findings, docking studies were carried out on each Mur amide ligase structure. At most, two ligands were identified to be sufficiently inhibiting each Mur amide ligase. The ligands obtained were SANC00574 and SANC00575 for MurC, SANC00290 and SANC00438 for MurD, SANC00290 and SANC00525 for MurE and SANC00290 and SANC00434 for MurF. The two best ligands identified for each enzyme had docked in the active site of their respective proteins, passed Lipinski’s rule of five and had substantially low binding energies. Molecular dynamic simulations were then performed to analyze the behavior of the proteins and protein-ligand complexes, to confirm the lead compounds as good inhibitors of the Mur amide ligases. In the case of MurC, MurD and MurE complexes, the identified ligands clearly impacted the behavior of the protein, as the ligand bound proteins became more compact and stable, while flexibility decreased. There was however an opposite effect on MurF complexes, that resulted in identified inhibitors being discarded. As a potential next step, in vivo and in vitro experiments can be performed with identified ligands from this research, to further support the information presented.
- Full Text:
Analysis of bacterial Mur amide ligase enzymes for the identification of inhibitory compounds by in silico methods
- Chamboko, Chiratidzo Respina
- Authors: Chamboko, Chiratidzo Respina
- Date: 2020
- Subjects: Pathogenic microorganisms -- Analysis , Drug resistance in microorganisms , Microorganisms -- Effect of drugs on , Antibiotics -- Effectiveness , Pathogenic bacteria , Drug tolerance , Enzymes -- Analysis , Peptide antibiotics
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/161911 , vital:40690
- Description: An increased emergence of resistant pathogenic bacterial strains over the years has resulted in many people dying of untreatable infections. This has become one of the most critical global public health problems, as resistant strains are complicating treatment of infectious diseases, increasing human morbidity, mortality, and health care costs. A very limited amount of effective antibiotics is currently available, but the development of novel classes of antibacterial agents is becoming a priority. Mur amide ligases are enzymes that have been identified as potentially good targets for antibiotics, as they are uniquely found in bacteria. They are responsible for the formation of peptide bonds in a growing peptidoglycan structure for bacterial cell walls. The current work presented here focused on characterizing these Mur amide ligase enzymes and obtaining inhibitory compounds that could potentially be of use in drug discovery of antibacterial agents. To do this, multiple sequence alignment, motif analysis and phylogenetic tree constructions were carried out, followed by docking studies and molecular dynamic simulations. Prior to docking, homology modelling of missing residues in the MurF structure (PDB 1GG4) was performed. Characterization results revealed the Mur amide ligase enzymes contained defined conservation in limited regions, that ultimately mapped towards the central domain responsible for ATP binding (presence of a conserved GKT motif). Further analysis of results further unraveled the unique patterns observed within each group of the family of enzymes. As a result of these findings, docking studies were carried out on each Mur amide ligase structure. At most, two ligands were identified to be sufficiently inhibiting each Mur amide ligase. The ligands obtained were SANC00574 and SANC00575 for MurC, SANC00290 and SANC00438 for MurD, SANC00290 and SANC00525 for MurE and SANC00290 and SANC00434 for MurF. The two best ligands identified for each enzyme had docked in the active site of their respective proteins, passed Lipinski’s rule of five and had substantially low binding energies. Molecular dynamic simulations were then performed to analyze the behavior of the proteins and protein-ligand complexes, to confirm the lead compounds as good inhibitors of the Mur amide ligases. In the case of MurC, MurD and MurE complexes, the identified ligands clearly impacted the behavior of the protein, as the ligand bound proteins became more compact and stable, while flexibility decreased. There was however an opposite effect on MurF complexes, that resulted in identified inhibitors being discarded. As a potential next step, in vivo and in vitro experiments can be performed with identified ligands from this research, to further support the information presented.
- Full Text:
- Authors: Chamboko, Chiratidzo Respina
- Date: 2020
- Subjects: Pathogenic microorganisms -- Analysis , Drug resistance in microorganisms , Microorganisms -- Effect of drugs on , Antibiotics -- Effectiveness , Pathogenic bacteria , Drug tolerance , Enzymes -- Analysis , Peptide antibiotics
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/161911 , vital:40690
- Description: An increased emergence of resistant pathogenic bacterial strains over the years has resulted in many people dying of untreatable infections. This has become one of the most critical global public health problems, as resistant strains are complicating treatment of infectious diseases, increasing human morbidity, mortality, and health care costs. A very limited amount of effective antibiotics is currently available, but the development of novel classes of antibacterial agents is becoming a priority. Mur amide ligases are enzymes that have been identified as potentially good targets for antibiotics, as they are uniquely found in bacteria. They are responsible for the formation of peptide bonds in a growing peptidoglycan structure for bacterial cell walls. The current work presented here focused on characterizing these Mur amide ligase enzymes and obtaining inhibitory compounds that could potentially be of use in drug discovery of antibacterial agents. To do this, multiple sequence alignment, motif analysis and phylogenetic tree constructions were carried out, followed by docking studies and molecular dynamic simulations. Prior to docking, homology modelling of missing residues in the MurF structure (PDB 1GG4) was performed. Characterization results revealed the Mur amide ligase enzymes contained defined conservation in limited regions, that ultimately mapped towards the central domain responsible for ATP binding (presence of a conserved GKT motif). Further analysis of results further unraveled the unique patterns observed within each group of the family of enzymes. As a result of these findings, docking studies were carried out on each Mur amide ligase structure. At most, two ligands were identified to be sufficiently inhibiting each Mur amide ligase. The ligands obtained were SANC00574 and SANC00575 for MurC, SANC00290 and SANC00438 for MurD, SANC00290 and SANC00525 for MurE and SANC00290 and SANC00434 for MurF. The two best ligands identified for each enzyme had docked in the active site of their respective proteins, passed Lipinski’s rule of five and had substantially low binding energies. Molecular dynamic simulations were then performed to analyze the behavior of the proteins and protein-ligand complexes, to confirm the lead compounds as good inhibitors of the Mur amide ligases. In the case of MurC, MurD and MurE complexes, the identified ligands clearly impacted the behavior of the protein, as the ligand bound proteins became more compact and stable, while flexibility decreased. There was however an opposite effect on MurF complexes, that resulted in identified inhibitors being discarded. As a potential next step, in vivo and in vitro experiments can be performed with identified ligands from this research, to further support the information presented.
- Full Text:
Identification of SANCDB compounds against G2019S and I2020T variants of leucine-rich repeat Kinase 2 (LRRK2) for the development of drugs against Parkinson’s Disease
- Authors: Baye, Bertha Cinthia
- Date: 2020
- Subjects: Antiparkinsonian agents , Parkinson's disease -- Treatment , Protein kinases , Parkinson's disease -- Chemotherapy , Molecules -- Models
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/138764 , vital:37671
- Description: Parkinson’s disease is a type of movement disorder that occurs when nerve cells in the brain stop producing dopamine. It is the second neurodegenerative disease affecting 1-2% of people above the ages of 65 years old. There is a worldwide prevalence of 7 to 10 million affected people of all cultures and race. Studies have shown that mutation that causes Parkinson’s disease result in increased kinase activity. The c.6055 G > A in exon 41 is the most prevalent LRRK2 variation which causes a substitution of glycine to serine in G2019S in the highly activated loop of its MAP kinase domain. The LRRK2 G2019S variant is the most common genetic determinant of Parkinson’s disease identified to date. This work focused on building accurate 3D models of the LRRK2 kinase domain, that were used for large-scale in silico docking against South African natural compounds from the South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/). Molecular docking was performed to identify compounds that formed interactions with the active site of the protein and had the lowest binding energy scores. Molecular dynamics simulations showed different movements of the protein-ligand complexes and behavioural difference of the wildtype and the variants, all three structures proved to be compact. Network analysis was done to study residue interactions, contact maps, dynamic cross correlations, average BC and average L were used to study the residue interactions and general residue contribution to the functioning of the protein..
- Full Text:
- Authors: Baye, Bertha Cinthia
- Date: 2020
- Subjects: Antiparkinsonian agents , Parkinson's disease -- Treatment , Protein kinases , Parkinson's disease -- Chemotherapy , Molecules -- Models
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/138764 , vital:37671
- Description: Parkinson’s disease is a type of movement disorder that occurs when nerve cells in the brain stop producing dopamine. It is the second neurodegenerative disease affecting 1-2% of people above the ages of 65 years old. There is a worldwide prevalence of 7 to 10 million affected people of all cultures and race. Studies have shown that mutation that causes Parkinson’s disease result in increased kinase activity. The c.6055 G > A in exon 41 is the most prevalent LRRK2 variation which causes a substitution of glycine to serine in G2019S in the highly activated loop of its MAP kinase domain. The LRRK2 G2019S variant is the most common genetic determinant of Parkinson’s disease identified to date. This work focused on building accurate 3D models of the LRRK2 kinase domain, that were used for large-scale in silico docking against South African natural compounds from the South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/). Molecular docking was performed to identify compounds that formed interactions with the active site of the protein and had the lowest binding energy scores. Molecular dynamics simulations showed different movements of the protein-ligand complexes and behavioural difference of the wildtype and the variants, all three structures proved to be compact. Network analysis was done to study residue interactions, contact maps, dynamic cross correlations, average BC and average L were used to study the residue interactions and general residue contribution to the functioning of the protein..
- Full Text:
Identification of possible natural compounds as potential inhibitors against Plasmodium M1 alanyl aminopeptidase
- Soliman, Omar Samir Abdel Ghaffar
- Authors: Soliman, Omar Samir Abdel Ghaffar
- Date: 2019
- Subjects: Plasmodium , Malaria -- Chemotherapy , Plasmodium -- Inhibitors , Drug resistance in microorganisms , Aminopeptidases
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/72284 , vital:30026
- Description: Malaria is a major tropical health problem with a 29% mortality rate among people of all ages; it also affects 35% of the children. Despite the decrease in mortality rate in recent years, malaria still results in around 2000 deaths per day. Malaria is caused by Plasmodium parasites and is transmitted to humans via the bites from infected female Anopheles mosquitoes during blood meals. There are five different Plasmodium species that can cause human malaria, which include Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale and Plasmodium knowlesi. Among these five species, the most pathogenic ones are Plasmodium falciparum and Plasmodium vivax. Malaria is usually hard to diagnose because the symptoms are not exclusive to malaria and very similar to flu, e.g., fever, muscle pain, and chills, which lead to the misdiagnosis of malaria cases. Malaria is lethal if not treated because it can cause severe complications in the respiratory tract, liver, metabolic acidosis, and hypoglycemia. The malaria parasite life cycle includes two types of hosts, i.e., a human host and female Anopheles mosquito host. Malaria continuously develops resistance to the available drugs, which is one of the major challenges in disease control. This situation confirms the need to develop new drugs that target virulence factors of malaria. The malarial parasite has three main life cycle stages, which include the host liver stage, host blood stage and vector stage. In the blood stage, parasites degrade hemoglobin to amino acids, which is important as these parasites cannot produce their own amino acids. Different proteases are involved in this hemoglobin degradation process. M1 alanyl aminopeptidase is one of these proteases involved at the end of hemoglobin degradation. This study focused on M1 alanyl aminopeptidase as a potential drug target. M1 alanyl aminopeptidase consists of four domains: N-terminal domain, catalytic domain, middle domain and C-terminal domain. The catalytic domain remains conserved among different Plasmodium species. Inhibition of this enzyme might prevent Plasmodium growth as it can’t produce its own amino acids. In this study, sequence analysis was carried out in both human and Plasmodium M1 alanyl aminopeptidase to identify conserved and divergent regions between them. 3D protein models of the M1 alanyl aminopeptidase from Plasmodium species were built and validated. Then the generated models were used for virtual screening against 623 compounds retrieved from the South African Natural Compounds Database (SANCDB, https://sancdb.rubi.ru.ac.za/). Virtual screening was done using blind and targeted docking methods. Docking was used to identify compounds with selective high binding affinity to the active site of the parasite protein. In this study, one SANCDB compound was selected for each protein: SANC00531 was selected against P. falciparum M1 alanyl aminopeptidase, SANC00469 against P. knowlesi, SANC00660 against P. vivax, SANC00144 against P. ovale and SANC00109 against P. malariae. It was found that Plamsodium M1 alanyl aminopeptidase can be used as a potential drug target as it showed selective binding against different inhibitor compounds. This result will be investigated in future work though molecular dynamic analysis to investigate the stability of protein-ligand complexes.
- Full Text:
- Authors: Soliman, Omar Samir Abdel Ghaffar
- Date: 2019
- Subjects: Plasmodium , Malaria -- Chemotherapy , Plasmodium -- Inhibitors , Drug resistance in microorganisms , Aminopeptidases
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/72284 , vital:30026
- Description: Malaria is a major tropical health problem with a 29% mortality rate among people of all ages; it also affects 35% of the children. Despite the decrease in mortality rate in recent years, malaria still results in around 2000 deaths per day. Malaria is caused by Plasmodium parasites and is transmitted to humans via the bites from infected female Anopheles mosquitoes during blood meals. There are five different Plasmodium species that can cause human malaria, which include Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale and Plasmodium knowlesi. Among these five species, the most pathogenic ones are Plasmodium falciparum and Plasmodium vivax. Malaria is usually hard to diagnose because the symptoms are not exclusive to malaria and very similar to flu, e.g., fever, muscle pain, and chills, which lead to the misdiagnosis of malaria cases. Malaria is lethal if not treated because it can cause severe complications in the respiratory tract, liver, metabolic acidosis, and hypoglycemia. The malaria parasite life cycle includes two types of hosts, i.e., a human host and female Anopheles mosquito host. Malaria continuously develops resistance to the available drugs, which is one of the major challenges in disease control. This situation confirms the need to develop new drugs that target virulence factors of malaria. The malarial parasite has three main life cycle stages, which include the host liver stage, host blood stage and vector stage. In the blood stage, parasites degrade hemoglobin to amino acids, which is important as these parasites cannot produce their own amino acids. Different proteases are involved in this hemoglobin degradation process. M1 alanyl aminopeptidase is one of these proteases involved at the end of hemoglobin degradation. This study focused on M1 alanyl aminopeptidase as a potential drug target. M1 alanyl aminopeptidase consists of four domains: N-terminal domain, catalytic domain, middle domain and C-terminal domain. The catalytic domain remains conserved among different Plasmodium species. Inhibition of this enzyme might prevent Plasmodium growth as it can’t produce its own amino acids. In this study, sequence analysis was carried out in both human and Plasmodium M1 alanyl aminopeptidase to identify conserved and divergent regions between them. 3D protein models of the M1 alanyl aminopeptidase from Plasmodium species were built and validated. Then the generated models were used for virtual screening against 623 compounds retrieved from the South African Natural Compounds Database (SANCDB, https://sancdb.rubi.ru.ac.za/). Virtual screening was done using blind and targeted docking methods. Docking was used to identify compounds with selective high binding affinity to the active site of the parasite protein. In this study, one SANCDB compound was selected for each protein: SANC00531 was selected against P. falciparum M1 alanyl aminopeptidase, SANC00469 against P. knowlesi, SANC00660 against P. vivax, SANC00144 against P. ovale and SANC00109 against P. malariae. It was found that Plamsodium M1 alanyl aminopeptidase can be used as a potential drug target as it showed selective binding against different inhibitor compounds. This result will be investigated in future work though molecular dynamic analysis to investigate the stability of protein-ligand complexes.
- Full Text:
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