Application of computer-aided drug design for identification of P. falciparum inhibitors
- Authors: Diallo, Bakary N’tji
- Date: 2021-10-29
- Subjects: Plasmodium falciparum , Malaria -- Chemotherapy , Molecular dynamics , Antimalarials , Cheminformatics , Drug development , Ligand binding (Biochemistry) , Plasmodium falciparum1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR) , South African Natural Compounds Database
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/192798 , vital:45265 , 10.21504/10962/192798
- Description: Malaria is a millennia-old disease with the first recorded cases dating back to 2700 BC found in Chinese medical records, and later in other civilizations. It has claimed human lives to such an extent that there are a notable associated socio-economic consequences. Currently, according to the World Health Organization (WHO), Africa holds the highest disease burden with 94% of deaths and 82% of cases with P. falciparum having ~100% prevalence. Chemotherapy, such as artemisinin combination therapy, has been and continues to be the work horse in the fight against the disease, together with seasonal malaria chemoprevention and the use of insecticides. Natural products such as quinine and artemisinin are particularly important in terms of their antimalarial activity. The emphasis in current chemotherapy research is the need for time and cost-effective workflows focussed on new mechanisms of action (MoAs) covering the target candidate profiles (TCPs). Despite a decline in cases over the past decades with, countries increasingly becoming certified malaria free, a stalling trend has been observed in the past five years resulting in missing the 2020 Global Technical Strategy (GTS) milestones. With no effective vaccine, a reduction in funding, slower drug approval than resistance emergence from resistant and invasive vectors, and threats in diagnosis with the pfhrp2/3 gene deletion, malaria remains a major health concern. Motivated by these reasons, the primary aim of this work was a contribution to the antimalarial pipeline through in silico approaches focusing on P. falciparum. We first intended an exploration of malarial targets through a proteome scale screening on 36 targets using multiple metrics to account for the multi-objective nature of drug discovery. The continuous growth of structural data offers the ideal scenario for mining new MoAs covering antimalarials TCPs. This was combined with a repurposing strategy using a set of orally available FDA approved drugs. Further, use was made of time- and cost-effective strategies combining QVina-W efficiency metrics that integrate molecular properties, GRIM rescoring for molecular interactions and a hydrogen mass repartitioning (HMR) molecular dynamics (MD) scheme for accelerated development of antimalarials in the context of resistance. This pipeline further integrates a complex ranking for better drug-target selectivity, and normalization strategies to overcome docking scoring function bias. The different metrics, ranking, normalization strategies and their combinations were first assessed using their mean ranking error (MRE). A version combining all metrics was used to select 36 unique protein-ligand complexes, assessed in MD, with the final retention of 25. From the 16 in vitro tested hits of the 25, fingolimod, abiraterone, prazosin, and terazosin showed antiplasmodial activity with IC50 2.21, 3.37, 16.67 and 34.72 μM respectively and of these, only fingolimod was found to be not safe with respect to human cell viability. These compounds were predicted active on different molecular targets, abiraterone was predicted to interact with a putative liver-stage essential target, hence promising as a transmission-blocking agent. The pipeline had a promising 25% hit rate considering the proteome-scale and use of cost-effective approaches. Secondly, we focused on Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR) using a more extensive screening pipeline to overcome some of the current in silico screening limitations. Starting from the ZINC lead-like library of ~3M, hierarchical ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches with molecular docking and re-scoring using eleven scoring functions (SFs) were used. Later ranking with an exponential consensus strategy was included. Selected hits were further assessed through Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), advanced MD sampling in a ligand pulling simulations and (Weighted Histogram Analysis Method) WHAM analysis for umbrella sampling (US) to derive binding free energies. Four leads had better predicted affinities in US than LC5, a 280 nM potent PfDXR inhibitor with ZINC000050633276 showing a promising binding of -20.43 kcal/mol. As shown with fosmidomycin, DXR inhibition offers fast acting compounds fulfilling antimalarials TCP1. Yet, fosmidomycin has a high polarity causing its short half-life and hampering its clinical use. These leads scaffolds are different from fosmidomycin and hence may offer better pharmacokinetic and pharmacodynamic properties and may also be promising for lead optimization. A combined analysis of residues’ contributions to the free energy of binding in MM-PBSA and to steered molecular dynamics (SMD) Fmax indicated GLU233, CYS268, SER270, TRP296, and HIS341 as exploitable for compound optimization. Finally, we updated the SANCDB library with new NPs and their commercially available analogs as a solution to NP availability. The library is extended to 1005 compounds from its initial 600 compounds and the database is integrated to Mcule and Molport APIs for analogs automatic update. The new set may contribute to virtual screening and to antimalarials as the most effective ones have NP origin. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2021
- Full Text:
- Date Issued: 2021-10-29
- Authors: Diallo, Bakary N’tji
- Date: 2021-10-29
- Subjects: Plasmodium falciparum , Malaria -- Chemotherapy , Molecular dynamics , Antimalarials , Cheminformatics , Drug development , Ligand binding (Biochemistry) , Plasmodium falciparum1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR) , South African Natural Compounds Database
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/192798 , vital:45265 , 10.21504/10962/192798
- Description: Malaria is a millennia-old disease with the first recorded cases dating back to 2700 BC found in Chinese medical records, and later in other civilizations. It has claimed human lives to such an extent that there are a notable associated socio-economic consequences. Currently, according to the World Health Organization (WHO), Africa holds the highest disease burden with 94% of deaths and 82% of cases with P. falciparum having ~100% prevalence. Chemotherapy, such as artemisinin combination therapy, has been and continues to be the work horse in the fight against the disease, together with seasonal malaria chemoprevention and the use of insecticides. Natural products such as quinine and artemisinin are particularly important in terms of their antimalarial activity. The emphasis in current chemotherapy research is the need for time and cost-effective workflows focussed on new mechanisms of action (MoAs) covering the target candidate profiles (TCPs). Despite a decline in cases over the past decades with, countries increasingly becoming certified malaria free, a stalling trend has been observed in the past five years resulting in missing the 2020 Global Technical Strategy (GTS) milestones. With no effective vaccine, a reduction in funding, slower drug approval than resistance emergence from resistant and invasive vectors, and threats in diagnosis with the pfhrp2/3 gene deletion, malaria remains a major health concern. Motivated by these reasons, the primary aim of this work was a contribution to the antimalarial pipeline through in silico approaches focusing on P. falciparum. We first intended an exploration of malarial targets through a proteome scale screening on 36 targets using multiple metrics to account for the multi-objective nature of drug discovery. The continuous growth of structural data offers the ideal scenario for mining new MoAs covering antimalarials TCPs. This was combined with a repurposing strategy using a set of orally available FDA approved drugs. Further, use was made of time- and cost-effective strategies combining QVina-W efficiency metrics that integrate molecular properties, GRIM rescoring for molecular interactions and a hydrogen mass repartitioning (HMR) molecular dynamics (MD) scheme for accelerated development of antimalarials in the context of resistance. This pipeline further integrates a complex ranking for better drug-target selectivity, and normalization strategies to overcome docking scoring function bias. The different metrics, ranking, normalization strategies and their combinations were first assessed using their mean ranking error (MRE). A version combining all metrics was used to select 36 unique protein-ligand complexes, assessed in MD, with the final retention of 25. From the 16 in vitro tested hits of the 25, fingolimod, abiraterone, prazosin, and terazosin showed antiplasmodial activity with IC50 2.21, 3.37, 16.67 and 34.72 μM respectively and of these, only fingolimod was found to be not safe with respect to human cell viability. These compounds were predicted active on different molecular targets, abiraterone was predicted to interact with a putative liver-stage essential target, hence promising as a transmission-blocking agent. The pipeline had a promising 25% hit rate considering the proteome-scale and use of cost-effective approaches. Secondly, we focused on Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR) using a more extensive screening pipeline to overcome some of the current in silico screening limitations. Starting from the ZINC lead-like library of ~3M, hierarchical ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches with molecular docking and re-scoring using eleven scoring functions (SFs) were used. Later ranking with an exponential consensus strategy was included. Selected hits were further assessed through Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), advanced MD sampling in a ligand pulling simulations and (Weighted Histogram Analysis Method) WHAM analysis for umbrella sampling (US) to derive binding free energies. Four leads had better predicted affinities in US than LC5, a 280 nM potent PfDXR inhibitor with ZINC000050633276 showing a promising binding of -20.43 kcal/mol. As shown with fosmidomycin, DXR inhibition offers fast acting compounds fulfilling antimalarials TCP1. Yet, fosmidomycin has a high polarity causing its short half-life and hampering its clinical use. These leads scaffolds are different from fosmidomycin and hence may offer better pharmacokinetic and pharmacodynamic properties and may also be promising for lead optimization. A combined analysis of residues’ contributions to the free energy of binding in MM-PBSA and to steered molecular dynamics (SMD) Fmax indicated GLU233, CYS268, SER270, TRP296, and HIS341 as exploitable for compound optimization. Finally, we updated the SANCDB library with new NPs and their commercially available analogs as a solution to NP availability. The library is extended to 1005 compounds from its initial 600 compounds and the database is integrated to Mcule and Molport APIs for analogs automatic update. The new set may contribute to virtual screening and to antimalarials as the most effective ones have NP origin. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2021
- Full Text:
- Date Issued: 2021-10-29
Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1
- Sheik Amamuddy, Olivier Serge André
- Authors: Sheik Amamuddy, Olivier Serge André
- Date: 2020
- Subjects: Machine learning , Molecules -- Models , Data mining , Neural networks (Computer science) , Antiretroviral agents , Protease inhibitors , Drug resistance , Multidrug resistance , Molecular dynamics , Renin-angiotensin system , HIV (Viruses) -- South Africa , HIV (Viruses) -- Social aspects -- South Africa , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/115964 , vital:34282
- Description: Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis.
- Full Text:
- Date Issued: 2020
- Authors: Sheik Amamuddy, Olivier Serge André
- Date: 2020
- Subjects: Machine learning , Molecules -- Models , Data mining , Neural networks (Computer science) , Antiretroviral agents , Protease inhibitors , Drug resistance , Multidrug resistance , Molecular dynamics , Renin-angiotensin system , HIV (Viruses) -- South Africa , HIV (Viruses) -- Social aspects -- South Africa , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/115964 , vital:34282
- Description: Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis.
- Full Text:
- Date Issued: 2020
Cyclooxygenase-1 as an anti-stroke target: potential inhibitor identification and non-synonymous single nucleotide polymorphism analysis
- Authors: Muronzi, Tendai
- Date: 2020
- Subjects: Cerebrovascular disease , Cerebrovascular disease -- Treatment , Cerebrovascular disease -- Chemotherapy , Cyclooxygenases , High throughput screening (Drug development) , Drug development , Molecular dynamics , South African Natural Compounds Database , ZINC database
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/143404 , vital:38243
- Description: Stroke is the third leading cause of death worldwide, with 87% of cases being ischemic stroke. The two primary therapeutic strategies to reduce post-ischemic brain damage are cellular and vascular approaches. The vascular strategy aims to rapidly re-open obstructed blood vessels, while the cellular approach aims to interfere with the signalling pathways that facilitate neuron damage and death. Unfortunately, popular vascular treatments have adverse side effects, necessitating the need for alternative chemotherapeutics. In this study, cyclooxygenase-1 (COX-1), which plays a significant role in the post- ischemic neuroinflammation and neuronal death, was targeted for identification of novel drug compounds and to assess the effect of nsSNPs on its structure and function. In a drug discovery part, ligands from the South African Natural Compounds Database (SANCDB-https://sancdb.rubi.ru.ac.za/) and ZINC database (http://zinc15.docking.org/) were used for high-throughput virtual screening (HVTS) against COX-1. Additionally, five nsSNPs were being investigated to assess their impact on protein structure and function. Three of these SNPs were in the COX-1 dimer interface. Molecular docking and molecular dynamics simulations revealed asymmetric nature of the protein. Several ligands, peculiar to each monomer, exhibited favourable binding energies in the respective active sites. SNP analysis indicated effects on inter-monomer interactions and protein stability.
- Full Text:
- Date Issued: 2020
- Authors: Muronzi, Tendai
- Date: 2020
- Subjects: Cerebrovascular disease , Cerebrovascular disease -- Treatment , Cerebrovascular disease -- Chemotherapy , Cyclooxygenases , High throughput screening (Drug development) , Drug development , Molecular dynamics , South African Natural Compounds Database , ZINC database
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/143404 , vital:38243
- Description: Stroke is the third leading cause of death worldwide, with 87% of cases being ischemic stroke. The two primary therapeutic strategies to reduce post-ischemic brain damage are cellular and vascular approaches. The vascular strategy aims to rapidly re-open obstructed blood vessels, while the cellular approach aims to interfere with the signalling pathways that facilitate neuron damage and death. Unfortunately, popular vascular treatments have adverse side effects, necessitating the need for alternative chemotherapeutics. In this study, cyclooxygenase-1 (COX-1), which plays a significant role in the post- ischemic neuroinflammation and neuronal death, was targeted for identification of novel drug compounds and to assess the effect of nsSNPs on its structure and function. In a drug discovery part, ligands from the South African Natural Compounds Database (SANCDB-https://sancdb.rubi.ru.ac.za/) and ZINC database (http://zinc15.docking.org/) were used for high-throughput virtual screening (HVTS) against COX-1. Additionally, five nsSNPs were being investigated to assess their impact on protein structure and function. Three of these SNPs were in the COX-1 dimer interface. Molecular docking and molecular dynamics simulations revealed asymmetric nature of the protein. Several ligands, peculiar to each monomer, exhibited favourable binding energies in the respective active sites. SNP analysis indicated effects on inter-monomer interactions and protein stability.
- Full Text:
- Date Issued: 2020
Structure and interaction studies of beta-amyloid in the search for new lead compounds for the treatment of Alzheimer’s disease
- Authors: Mtini, Onke
- Date: 2020
- Subjects: Alzheimer's disease -- Chemotherapy , Alzheimer's disease -- Treatment , Amyloid beta-protein , Oxidative stress , Protein binding , South African Natural Compounds Database
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/167574 , vital:41493
- Description: Alzheimer’s disease (AD) is the most devastating neurodegenerative disorder that effects the aging population worldwide. In this study three hypotheses of AD are explored, the β-amyloid cascade hypothesis, the β-amyloid metal binding hypothesis and the oxidative stress hypothesis are explored. In the first case compounds from the South African Natural Compounds Database (SANCDB) are docked to models of β-amyloid fibrils and the properties of these fibrils under pulling simulations are compared to a known small molecule disruptor of β-amyloid, wgx-50. In these simulations SANCDB compounds are identified that disrupt β-amyloid in a similar manner to wgx-50. In these simulations the disruption to the free energy of binding of chains to the fibrils is quantified. For metal binding and oxidative stress hypotheses, problems in simulation arise due to only fragments of β-amyloid being present in the Research Collaboratory for Structural Bioinformatics protein data bank (RCSB PDB), as determined from NMR experiments. In this work, β-amyloid is set up under periodic boundary conditions to simulate a fibril under reasonable computational time. Within these periodic boundary conditions, β-amyloid has been solvated in copper and zinc rich environments and diffusion of these metals around the fibrils has been explored. The localization of these metals (in simulation only using van der Waal’s and electrostatic terms) around the fibril has led us to explore other possible metal binding sites. Metal bound to the infinite fibril has been optimized at the QM/MM level and some of the reactive oxygen species in the presence of the fibril are quantified.
- Full Text:
- Date Issued: 2020
- Authors: Mtini, Onke
- Date: 2020
- Subjects: Alzheimer's disease -- Chemotherapy , Alzheimer's disease -- Treatment , Amyloid beta-protein , Oxidative stress , Protein binding , South African Natural Compounds Database
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/167574 , vital:41493
- Description: Alzheimer’s disease (AD) is the most devastating neurodegenerative disorder that effects the aging population worldwide. In this study three hypotheses of AD are explored, the β-amyloid cascade hypothesis, the β-amyloid metal binding hypothesis and the oxidative stress hypothesis are explored. In the first case compounds from the South African Natural Compounds Database (SANCDB) are docked to models of β-amyloid fibrils and the properties of these fibrils under pulling simulations are compared to a known small molecule disruptor of β-amyloid, wgx-50. In these simulations SANCDB compounds are identified that disrupt β-amyloid in a similar manner to wgx-50. In these simulations the disruption to the free energy of binding of chains to the fibrils is quantified. For metal binding and oxidative stress hypotheses, problems in simulation arise due to only fragments of β-amyloid being present in the Research Collaboratory for Structural Bioinformatics protein data bank (RCSB PDB), as determined from NMR experiments. In this work, β-amyloid is set up under periodic boundary conditions to simulate a fibril under reasonable computational time. Within these periodic boundary conditions, β-amyloid has been solvated in copper and zinc rich environments and diffusion of these metals around the fibrils has been explored. The localization of these metals (in simulation only using van der Waal’s and electrostatic terms) around the fibril has led us to explore other possible metal binding sites. Metal bound to the infinite fibril has been optimized at the QM/MM level and some of the reactive oxygen species in the presence of the fibril are quantified.
- Full Text:
- Date Issued: 2020
Targeting allosteric sites of Escherichia coli heat shock protein 70 for antibiotic development
- Authors: Okeke, Chiamaka Jessica
- Date: 2019
- Subjects: Heat shock proteins , Escherichia coli , Allosteric proteins , Antibiotics , Molecular chaperones , Ligands (Biochemistry) , Molecular dynamics , Principal components analysis , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/115998 , vital:34287
- Description: Hsp70s are members of the heat shock proteins family with a molecular weight of 70-kDa and are the most abundant group in bacterial and eukaryotic systems, hence the most extensively studied ones. These proteins are molecular chaperones that play a significant role in protein homeostasis by facilitating appropriate folding of proteins, preventing proteins from aggregating and misfolding. They are also involved in translocation of proteins into subcellular compartments and protection of cells against stress. Stress caused by environmental or biological factors affects the functionality of the cell. In response to these stressful conditions, up-regulation of Hsp70s ensures that the cells are protected by balancing out unfolded proteins giving them ample time to repair denatured proteins. Hsp70s is connected to numerous illnesses such as autoimmune and neurodegenerative diseases, bacterial infection, cancer, malaria, and obesity. The multi-functional nature of Hsp70s predisposes them as promising therapeutic targets. Hsp70s play vital roles in various cell developments, and survival pathways, therefore targeting this protein will provide a new avenue towards the discovery of active therapeutic agents for the treatment of a wide range of diseases. Allosteric sites of these proteins in its multi-conformational states have not been explored for inhibitory properties hence the aim of this study. This study aims at identifying allosteric sites that inhibit the ATPase and substrate binding activities using computational approaches. Using E. coli as a model organism, molecular docking for high throughput virtual screening was carried out using 623 compounds from the South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/) against identified allosteric sites. Ligands with the highest binding affinity (good binders) interacting with critical allosteric residues that are druggable were identified. Molecular dynamics (MD) simulation was also performed on the identified hits to assess for protein-inhibitor complex stability. Finally, principal component analysis (PCA) was performed to understand the structural dynamics of the ligand-free and ligand-bound structures during MD simulation.
- Full Text:
- Date Issued: 2019
- Authors: Okeke, Chiamaka Jessica
- Date: 2019
- Subjects: Heat shock proteins , Escherichia coli , Allosteric proteins , Antibiotics , Molecular chaperones , Ligands (Biochemistry) , Molecular dynamics , Principal components analysis , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/115998 , vital:34287
- Description: Hsp70s are members of the heat shock proteins family with a molecular weight of 70-kDa and are the most abundant group in bacterial and eukaryotic systems, hence the most extensively studied ones. These proteins are molecular chaperones that play a significant role in protein homeostasis by facilitating appropriate folding of proteins, preventing proteins from aggregating and misfolding. They are also involved in translocation of proteins into subcellular compartments and protection of cells against stress. Stress caused by environmental or biological factors affects the functionality of the cell. In response to these stressful conditions, up-regulation of Hsp70s ensures that the cells are protected by balancing out unfolded proteins giving them ample time to repair denatured proteins. Hsp70s is connected to numerous illnesses such as autoimmune and neurodegenerative diseases, bacterial infection, cancer, malaria, and obesity. The multi-functional nature of Hsp70s predisposes them as promising therapeutic targets. Hsp70s play vital roles in various cell developments, and survival pathways, therefore targeting this protein will provide a new avenue towards the discovery of active therapeutic agents for the treatment of a wide range of diseases. Allosteric sites of these proteins in its multi-conformational states have not been explored for inhibitory properties hence the aim of this study. This study aims at identifying allosteric sites that inhibit the ATPase and substrate binding activities using computational approaches. Using E. coli as a model organism, molecular docking for high throughput virtual screening was carried out using 623 compounds from the South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/) against identified allosteric sites. Ligands with the highest binding affinity (good binders) interacting with critical allosteric residues that are druggable were identified. Molecular dynamics (MD) simulation was also performed on the identified hits to assess for protein-inhibitor complex stability. Finally, principal component analysis (PCA) was performed to understand the structural dynamics of the ligand-free and ligand-bound structures during MD simulation.
- Full Text:
- Date Issued: 2019
In silico study of Plasmodium 1-deoxy-dxylulose 5-phosphate reductoisomerase (DXR) for identification of novel inhibitors from SANCDB
- Authors: Diallo, Bakary N'tji
- Date: 2018
- Subjects: Plasmodium 1-deoxy-dxylulose 5-phosphate reductoisomerase , Isoprenoids , Plasmodium , Antimalarials , Malaria -- Chemotherapy , Molecules -- Models , Molecular dynamics , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/64012 , vital:28523
- Description: Malaria remains a major health concern with a complex parasite constantly developing resistance to the different drugs introduced to treat it, threatening the efficacy of the current ACT treatment recommended by WHO (World Health Organization). Different antimalarial compounds with different mechanisms of action are ideal as this decreases chances of resistance occurring. Inhibiting DXR and consequently the MEP pathway is a good strategy to find a new antimalarial with a novel mode of action. From literature, all the enzymes of the MEP pathway have also been shown to be indispensable for the synthesis of isoprenoids. They have been validated as drug targets and the X-ray structure of each of the enzymes has been solved. DXR is a protein which catalyses the second step of the MEP pathway. There are currently 255 DXR inhibitors in the Binding Database (accessed November 2017) generally based on the fosmidomycin structural scaffold and thus often showing poor drug likeness properties. This study aims to research new DXR inhibitors using in silico techniques. We analysed the protein sequence and built 3D models in close and open conformations for the different Plasmodium sequences. Then SANCDB compounds were screened to identify new potential DXR inhibitors with new chemical scaffolds. Finally, the identified hits were submitted to molecular dynamics studies, preceded by a parameterization of the manganese atom in the protein active site.
- Full Text:
- Date Issued: 2018
- Authors: Diallo, Bakary N'tji
- Date: 2018
- Subjects: Plasmodium 1-deoxy-dxylulose 5-phosphate reductoisomerase , Isoprenoids , Plasmodium , Antimalarials , Malaria -- Chemotherapy , Molecules -- Models , Molecular dynamics , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/64012 , vital:28523
- Description: Malaria remains a major health concern with a complex parasite constantly developing resistance to the different drugs introduced to treat it, threatening the efficacy of the current ACT treatment recommended by WHO (World Health Organization). Different antimalarial compounds with different mechanisms of action are ideal as this decreases chances of resistance occurring. Inhibiting DXR and consequently the MEP pathway is a good strategy to find a new antimalarial with a novel mode of action. From literature, all the enzymes of the MEP pathway have also been shown to be indispensable for the synthesis of isoprenoids. They have been validated as drug targets and the X-ray structure of each of the enzymes has been solved. DXR is a protein which catalyses the second step of the MEP pathway. There are currently 255 DXR inhibitors in the Binding Database (accessed November 2017) generally based on the fosmidomycin structural scaffold and thus often showing poor drug likeness properties. This study aims to research new DXR inhibitors using in silico techniques. We analysed the protein sequence and built 3D models in close and open conformations for the different Plasmodium sequences. Then SANCDB compounds were screened to identify new potential DXR inhibitors with new chemical scaffolds. Finally, the identified hits were submitted to molecular dynamics studies, preceded by a parameterization of the manganese atom in the protein active site.
- Full Text:
- Date Issued: 2018
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