Towards a possible future solution against Multidrug Resistance: An in silico exploration of the Multidrug and Toxic compound Extrusion (MATE) transporter proteins as potential antimicrobial drug targets
- Authors: Damji, Amira Mahamood
- Date: 2024-04-04
- Subjects: Multidrug resistance , Multidrug and toxic compound extrusion family, eukaryotic , Docking , Molecular dynamics , Drug development , Transmembrane protein
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/435009 , vital:73123
- Description: The rise of multidrug resistance (MDR) has become a pressing global issue, hindering the treatment of cancers and infectious diseases, and imposing a burden on healthcare systems and the economy. The Multidrug and Toxic compound Extrusion (MATE) superfamily of membrane efflux transporters is one of the key players contributing to MDR due to their ability to export a wide range of cationic and hydrophilic xenobiotics, including treatment drugs, from cells, diminishing their efficacy. Targeting MATE transporters holds great promise in achieving some cellular control over MDR, but first, a deeper understanding of their structure-function-dynamics link is required. This study aimed to explore the MATE transporters as potential antimicrobial drug targets using a two-fold in silico approach. First, virtual screening of compounds from the South African Natural Compounds Database (SANCDB) was performed to identify prospective lead inhibitory compounds against the MATE transporters using molecular docking, and top hits were selected based on their binding energy and interaction with the active site on the N-lobe of the protein. Second, to investigate the molecular-level dynamics of their extrusion mechanism, the MATE transporter structures were embedded in a POPC membrane bilayer using the CHARMM-GUI online tool and then subjected to MD simulations for 100 ns with the CHARMM 36m force field using GROMACS. The resulting trajectories were evaluated using three standard metrics – RMSD, RMSF, and Rg; significant global structural changes were observed and key functional regions in both membrane- and non-membrane transmembrane (TM) segments were identified, containing more dynamic and flexible residues than other regions. Furthermore, the MATE transporters showed more of a loosely-packed structure, providing flexibility to allow for conformational switching during their substrate-transport cycle, which is typical for proteins whose secondary structures are composed of all α-helices. The scope of this study lied in the preliminary stages of the computer-aided drug design process, and provided insights that can be used to guide the development of strategies aimed at regulating or inhibiting the function of the MATE transporters, offering a possible future solution to the growing challenge of MDR. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2024
- Full Text:
- Date Issued: 2024-04-04
- Authors: Damji, Amira Mahamood
- Date: 2024-04-04
- Subjects: Multidrug resistance , Multidrug and toxic compound extrusion family, eukaryotic , Docking , Molecular dynamics , Drug development , Transmembrane protein
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/435009 , vital:73123
- Description: The rise of multidrug resistance (MDR) has become a pressing global issue, hindering the treatment of cancers and infectious diseases, and imposing a burden on healthcare systems and the economy. The Multidrug and Toxic compound Extrusion (MATE) superfamily of membrane efflux transporters is one of the key players contributing to MDR due to their ability to export a wide range of cationic and hydrophilic xenobiotics, including treatment drugs, from cells, diminishing their efficacy. Targeting MATE transporters holds great promise in achieving some cellular control over MDR, but first, a deeper understanding of their structure-function-dynamics link is required. This study aimed to explore the MATE transporters as potential antimicrobial drug targets using a two-fold in silico approach. First, virtual screening of compounds from the South African Natural Compounds Database (SANCDB) was performed to identify prospective lead inhibitory compounds against the MATE transporters using molecular docking, and top hits were selected based on their binding energy and interaction with the active site on the N-lobe of the protein. Second, to investigate the molecular-level dynamics of their extrusion mechanism, the MATE transporter structures were embedded in a POPC membrane bilayer using the CHARMM-GUI online tool and then subjected to MD simulations for 100 ns with the CHARMM 36m force field using GROMACS. The resulting trajectories were evaluated using three standard metrics – RMSD, RMSF, and Rg; significant global structural changes were observed and key functional regions in both membrane- and non-membrane transmembrane (TM) segments were identified, containing more dynamic and flexible residues than other regions. Furthermore, the MATE transporters showed more of a loosely-packed structure, providing flexibility to allow for conformational switching during their substrate-transport cycle, which is typical for proteins whose secondary structures are composed of all α-helices. The scope of this study lied in the preliminary stages of the computer-aided drug design process, and provided insights that can be used to guide the development of strategies aimed at regulating or inhibiting the function of the MATE transporters, offering a possible future solution to the growing challenge of MDR. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2024
- Full Text:
- Date Issued: 2024-04-04
In silico identification of selective novel hits against the active site of wild type mycobacterium tuberculosis pyrazinamidase and its mutants
- Authors: Gowo, Prudence
- Date: 2021-04
- Subjects: Mycobacterium tuberculosis , Pyrazinamide , Multidrug resistance , Antitubercular agents , Molecular dynamics , Hydrogen bonding , Ligand binding (Biochemistry) , Dynamic Residue Network
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/178007 , vital:42898
- Description: The World Health Organization declared Tuberculosis a global health emergency and has set a goal to eradicate it by 2035. However, effective treatment and control of the disease is being hindered by the emerging Multi-Drug Resistant and Extensively Drug Resistant strains on the most effective first line prodrug, Pyrazinamide (PZA). Studies have shown that the main cause of PZA resistance is due to mutations in the pncA gene that codes for the target protein Pyrazinamidase (PZase). Therefore, this study aimed to identify novel drug compounds that bind to the active site of wild type PZase and study the dynamics of these potential anti-TB drugs in the mutant systems of PZase. This approach will aid in identifying drugs that may be repurposed for TB therapy and/or designed to counteract PZA resistance. This was achieved by screening 2089 DrugBank compounds against the whole wild type (WT) PZase protein in molecular docking using AutoDOCK4.2. Compound screening based on docking binding energy, hydrogen bonds, molecular weight and active site proximity identified 47 compounds meeting all the set selection criteria. The stability of these compounds were analysed in Molecular Dynamic (MD) simulations and were further studied in PZase mutant systems of A3P, A134V, A146V, D8G, D49A, D49G, D63G, H51P, H137R, L85R, L116R, Q10P, R140S, T61P, V139M and Y103S. Generally, mutant-ligand systems displayed little deviation from the WT systems. The compound systems remained compact, with less fluctuations and more hydrogen bond interactions throughout the simulation (DB00255, DB00655, DB00672, DB00782, DB00977, DB01196, DB04573, DB06414, DB08981, DB11181, DB11760, DB13867, DB13952). From this research study, potential drugs that may be repurposed for TB therapy were identified. Majority of these drugs are currently used in the treatment of hypertension, menopause disorders and inflammation. To further understand the mutant-ligand dynamic systems, calculations such as Dynamic Residue Network (DRN) may be done. Also, the bioactivity of these drugs on Mycobacterium tuberculosis may be studied in wet laboratory, to understand their clinical impart in vivo experiments. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2021
- Full Text:
- Date Issued: 2021-04
- Authors: Gowo, Prudence
- Date: 2021-04
- Subjects: Mycobacterium tuberculosis , Pyrazinamide , Multidrug resistance , Antitubercular agents , Molecular dynamics , Hydrogen bonding , Ligand binding (Biochemistry) , Dynamic Residue Network
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/178007 , vital:42898
- Description: The World Health Organization declared Tuberculosis a global health emergency and has set a goal to eradicate it by 2035. However, effective treatment and control of the disease is being hindered by the emerging Multi-Drug Resistant and Extensively Drug Resistant strains on the most effective first line prodrug, Pyrazinamide (PZA). Studies have shown that the main cause of PZA resistance is due to mutations in the pncA gene that codes for the target protein Pyrazinamidase (PZase). Therefore, this study aimed to identify novel drug compounds that bind to the active site of wild type PZase and study the dynamics of these potential anti-TB drugs in the mutant systems of PZase. This approach will aid in identifying drugs that may be repurposed for TB therapy and/or designed to counteract PZA resistance. This was achieved by screening 2089 DrugBank compounds against the whole wild type (WT) PZase protein in molecular docking using AutoDOCK4.2. Compound screening based on docking binding energy, hydrogen bonds, molecular weight and active site proximity identified 47 compounds meeting all the set selection criteria. The stability of these compounds were analysed in Molecular Dynamic (MD) simulations and were further studied in PZase mutant systems of A3P, A134V, A146V, D8G, D49A, D49G, D63G, H51P, H137R, L85R, L116R, Q10P, R140S, T61P, V139M and Y103S. Generally, mutant-ligand systems displayed little deviation from the WT systems. The compound systems remained compact, with less fluctuations and more hydrogen bond interactions throughout the simulation (DB00255, DB00655, DB00672, DB00782, DB00977, DB01196, DB04573, DB06414, DB08981, DB11181, DB11760, DB13867, DB13952). From this research study, potential drugs that may be repurposed for TB therapy were identified. Majority of these drugs are currently used in the treatment of hypertension, menopause disorders and inflammation. To further understand the mutant-ligand dynamic systems, calculations such as Dynamic Residue Network (DRN) may be done. Also, the bioactivity of these drugs on Mycobacterium tuberculosis may be studied in wet laboratory, to understand their clinical impart in vivo experiments. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2021
- Full Text:
- Date Issued: 2021-04
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
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