Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1
- 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
Computer aided approaches against Human African Trypanosomiasis
- Authors: Kimuda, Magambo Phillip
- Date: 2020
- Subjects: African trypanosomiasis , African trypanosomiasis -- Chemotherapy , Genomics , Macrophage migration inhibitory factor , Trypanosoma brucei , Pteridines , Tetrahydrofolate dehydrogenase , Adenylic acid , Molecular dynamics , Principal components analysis , Bioinformatics , Single nucleotide polymorphisms , Single Nucleotide Variants , Candidate Gene Association Study (CGAS)
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/142542 , vital:38089
- Description: The thesis presented here is divided into two parts under a common theme that is the use of computer based tools, genomics, and in vitro experiments to develop innovative ways of tackling Human African Trypanosomiasis (HAT). Part I of this thesis focused on the human host genetic determinants while Part II focused on the discovery of novel chemotherapeutics against the parasite. Part I is further sub-divided into two parts: The first involves a Candidate Gene Association Study (CGAS) on an African population to identify genetic determinants associated with disease and/or susceptibility to HAT. The second involves studying the effects of missense Single Nucleotide Variants (SNVs) on protein structure, dynamics, and function using Macrophage Migration Inhibitory Factor (MIF) as a case study. Part II is also sub-divided into two parts: The first involves a computer based rational drug discovery of potential inhibitors against the Trypanosoma the folate pathway; particularly by targeting Trypanosoma brucei Pteridine Reductase (TbPTR1) which is an enzyme used by trypanosomes to overcome T. brucei Dihydrofolate Reductase (TbDHFR) inhibition. Lastly the derivation of CHARMM force-field parameters that can be used to accurately model the geometry and dynamics of the T. brucei Phosphodiesterase B1 enzyme (TbrPDEB1) bimetallic active site center. The derived parameters were then used in MD simulations to characterise protein-ligand residue interactions that are important in TbrPDEB1 inhibition with the goal of targeting the cyclic Adenosine Monophosphate (cAMP) signalling pathway. In the CGAS we were unable to detect any genetic associations in the Ugandan cohort analysed that passed correction for multiple testing in spite of the study being sufficiently powered. Additionally, our study found no association of the Apo lipoprotein 1 (APOL1) G2 allele association with protection against acute HAT that has been previously reported. Future investigations for example, Genome Wide Association Studies using larger samples sizes (>3000 cases and controls) are required. Macrophage migration inhibitory factor (MIF) is a cytokine that is important in both innate and adaptive immunity that has been shown to play a role in T. brucei pathogenicity using murine models. A total of 27 missense SNVs were modelled using homology modelling to create MIF protein mutants that were investigated using in silico effect prediction tools, molecular dynamics (MD), Principal Component Analysis (PCA), and Dynamic Residue Network (DRN) analysis. Our results demonstrate that mutations P2Q, I5M, P16Q, L23F, T24S, T31I, Y37H, H41P, M48V, P44L, G52C, S54R, I65M, I68T, S75F, N106S, and T113S caused significant conformational changes. Further, DRN analysis showed that residues P2, T31, Y37, G52, I65, I68, S75, N106, and T113S are part of a similar local residue interaction network with functional significance. These results show how polymorphisms such as missense SNVs can affect protein conformation, dynamics, and function. Trypanosomes are auxotrophic for folates and pterins but require them for survival. They scavenge them from their hosts. PTR1 is a multifunctional enzyme that is unique to trypanosomatids that reduces both pterins and folates. In the presence of DHFR inhibitors, PTR1 is over-expressed thus providing an escape from the effects of DHFR inhibition. Both TbPTR1 and TbDHFR are pharmacologically and genetically validated drug targets. In this study 5742 compounds were screened using molecular docking, and 13 promising binding modes were further analysed using MD simulations. The trajectories were analysed using RMSD, Rg, RMSF, PCA, Essential Dynamics Analysis (EDA), Molecular Mechanics Poisson–Boltzmann surface area (MM-PBSA) binding free energy calculations, and DRN analysis. The computational screening approach allowed us to identify five of the compounds, named RUBi004, RUBi007, RUBi014, RUBi016 and RUBi018 that exhibited antitrypanosomal growth activities against trypanosomes in culture with IC50 values of 12.5 ± 4.8 μM, 32.4 ± 4.2 μM, 5.9 ± 1.4 μM, 28.2 ± 3.3 μM, and 9.7 ± 2.1 μM, respectively. Further when used in combination with WR99210 a known TbDHFR inhibitor RUBi004, RUBi007, RUBi014 and RUBi018 showed antagonism while RUBi016 showed an additive effect. These results indicate that the four compounds might be competing with TbDHFR while RUBi016 might be more specific for TbPTR1. These compounds provide scaffolds that can be further optimised to improve their potency and specificity. Lastly, using a systematic approach we derived CHARMM force-field parameters to accurately describe the TbrPDEB1 bi-metal catalytic center. For dynamics, we employed mixed bonded and non-bonded approach. We optimised the structure using a two-layer QM/MM ONIOM (B3LYP/6-31(g): UFF). The TbrPDEB1 bi-metallic center bonds, angles, and dihedrals were parameterized by fitting the energy profiles from Potential Energy Surface (PES) scans to the CHARMM potential energy function. The parameters were validated by means of MD simulations and analysed using RMSD, Rg, RMSF, hydrogen bonding, bond/angle/dihedral evaluations, EDA, PCA, and DRN analysis. The force-field parameters were able to accurately reproduce the geometry and dynamics of the TbrPDEB1 bi-metal catalytic center during MD simulations. Molecular docking was used to identify 6 potential hits, that inhibited trypanosome growth in vitro. The derived force-field parameters were used to simulate the 6 protein-ligand complexes with the aim of elucidating crucial protein-ligand residue interactions. Using the most potent ligand RUBi022 that had an IC50 of 14.96 μM we were able to identify key residue interactions that can be of use in in silico prediction of potential TbrPDEB1 inhibitors. Overall we demonstrate how bioinformatics tools can complement current disease eradication strategies. Future work will focus on identifying variants identified in Genome Wide Association Studies and partnering with wet labs to carry out further enzyme-ligand activity relationship studies, structure determination or characterisation of appropriate protein-ligand complexes by crystallography, and site specific mutation studies
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- 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.
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- Date Issued: 2020
Mechanism of action of non-synonymous single nucleotide variations associated with α-carbonic anhydrases II, IV and VIII
- Authors: Sanyanga, T. Allan
- Date: 2020
- Subjects: Carbonic anhydrase , Carbonic anhydrase -- Therapeutic use , Nucleotides
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/167346 , vital:41470
- Description: The carbonic anhydrase (CA) group of enzymes are Zinc (Zn2+) metalloproteins responsible for the reversible hydration of CO2 to bicarbonate (BCT or HCO− 3 ) and protons (H+) for the facilitation of acid-base balance and homeostasis within the body. Across all organisms, a minimum of six CA families exist, including, α (alpha), β (beta), γ (gamma), δ (delta), η (eta) and ζ (zeta). Some organisms can have more than one family, with exception to humans that contain the α family solely. The α-CA family comprises of 16 isoforms (CA-I to CA-XV) including the CA-VIII, CA-X and CA-XI acatalytic isoforms. Of the catalytic isoforms, CA-II and CA-IV possess one of the fastest rates of reaction, and any disturbances to the function of these enzymes results in CA deficiencies and undesirable phenotypes. CA-II deficiencies result in osteopetrosis with renal tubular acidosis and cerebral calcification, whereas CA-IV deficiencies result in retinitis pigmentosa 17 (RP17). Phenotypic effects generally manifest as a result of poor protein folding and function due to the presence of non-synonymous single nucleotide variations (nsSNVs). Even within the acatalytic isoforms such as CA-VIII that llosterically regulates the affinity of inositol triphosphate (IP3) for the IP3 receptor type 1 (ITPR1) and regulates calcium (Ca2+) signalling, the presence of SNVs also causes phenotypes cerebellar ataxia, mental retardation, and dysequilibrium syndrome 3 (CAMRQ3). Currently the majority of research into the CAs is focused on the inhibition of these proteins to achieve therapeutic effects in patients via the control of HCO− production or reabsorption as observed in glaucoma and diuretic medications. Little research has therefore been devoted into the identification of stabilising or activating compound that could rescue protein function in the case of deficiencies. The main aim of this research was to identify and characterise the effects of nsSNVs on the structure and function of CA-II, CA-IV and CA-VIII to set a foundation for rare disease studies into the CA group of proteins. Combined bioinformatics approaches divided into four main objectives were implemented. These included variant identification, sequence analysis and protein characterisation, force field (FF) parameter generation, molecular dynamics (MD) simulation and dynamic residue network analysis (DRN). Six variants for each of the CA-II, CA-IV and CA-VIII proteins with pathogenic annotations were identified from the HUMA and Ensembl databases. These included the pathogenic variants K18E, K18Q, H107Y, P236H, P236R and N252D for CA-II. CA-IV included the pathogenic R69H, R219C and R219S, and benign N86K, N177K and V234I variants. CA-VIII included pathogenic S100A, S100P, G162R and R237Q, and benign S100L and E109D variants. CA-II has been more extensively studied than CA-IV and CA-VIII, therefore residues essential to its function and stability are known. To discover important residues and regions within the CA-IV and CA-VIII proteins sequence and motif analysis was performed across the α-CA family, using CA-II as a reference. Sequence analysis identified multiple conserved residues between the two acatalytic CA-II and CA-IV, and the acatalytic CA-VIII isoforms that were proposed to be essential for protein stability. With exception to the benign N86K CA-IV variant, none of the other pathogenic or benign CA-II, CA-IV and CA-VIII SNVs were located at functionally or structurally important residues. Motif analysis identified 11 conserved and important motifs within the α-CA family. Several of the identified variants were located on these motifs including K18E, K18Q, H107Y and N252D (CA-II); N86K, R219C, R219S and V234I (CA-IV); and E109D, G162R and R237Q (CA-VIII). As there were no x-ray crystal structures of the variant proteins, homology modelling was performed to calculate the protein structures for characterisation. In CA-VIII, the substitution of Ser for Pro at position 100 (variant S100P) resulted in destruction of the β-sheet that the SNV was located on. Little is known about the mechanism of interaction between CA-VIII and ITPR1, and residues involved. SiteMap and CPORT were used to identify binding site amino for CA-VIII and results identified 38 potential residues. Traditional FFs are incapable of performing MD simulations of metalloproteins. The AMBER ff14SB FF was extended and Zn2+ FF parameters calculated to add support for metalloprotein MD simulations. In the protein, Zn2+ was noted to have a charge less than +1. Variant effects on protein structure were then investigated using MD simulations. Root mean square deviation (RMSD) and radius of gyration (Rg) results indicated subtle SNV effects to the variant global structure in CA-II and CA-IV. However, with regards to CA-VIII RMSD analysis highlighted that variant presence was associated with increases to the structural rigidity of the protein. Principal component analysis (PCA) in conjunction with free energy analysis was performed to observe variant effects on protein conformational sampling in 3D space. The binding of BCT to CA-II induced greater protein conformational sampling and was associated with higher free energy. In CA-IV and CA-VIII PCA analysis revealed key differences in the mechanism of action of pathogenic and benign SNVs. In CA-IV, wild-type (WT) and benign variant protein structures clustered into single low energy well hinting at the presence of more stable structures. Pathogenic variants were associated with higher free energy and proteins sampled more conformations without settling into a low energy well. PCA analysis of CA-VIII indicated the opposite to CA-IV. Pathogenic variants were clustered into low energy wells, while the WT and benign variants showed greater conformational sampling. Dynamic cross correlation (DCC) analysis was performed using the MD-TASK suite to determine variant effects on residue movement. CA-II WT protein revealed that BCT and CO2 were associated with anti-correlated and correlated residue movement, highlighting at opposite mechanisms. In CA-IV and CA-VIII variant presence resulted in a change to residue correlation compared to the WT proteins. DRN analysis was performed to investigate SNV effects of residue accessibility and communication. Results demonstrated that SNVs are associated with allosteric effects on the CA protein structures, and effects are located on the stability assisting residues of the aromatic clusters and the active site of the proteins. CA-II studies discovered that Glu117 is the most important residue for communication, and variant presence results in a decrease to the usage of the residue. This effect was greatest in the CA-II H107Y SNV, and suggests that variants could have an effect on Zn2+ dissociation from the active site. Decreases to the usage of Zn2+ coordinating residues were also noted. Where this occurred, compensatory increases to the usage of other primary and secondary coordination residues were observed, that could possibly assist with the maintenance of Zn2+ within the active site. The CA-IV variants R69H and R219C highlighted potentially similar pathogenic mechanisms, whereas N86K and N177K hinted at potentially similar benign mechanisms. Within CA-VIII, variant presence was associated with changes to the accessibility of the N-terminal binding site residues. The benign CA-VIII variants highlighted possible compensatory mechanisms, whereby as one group of N-terminal residues loses accessibility, there was an increase to the accessibility of other binding site residues to possibly balance the effect. Catalytically, the proton shuttle residue His64 in CA-II was found to occupy a novel conformation named the “faux in” that brought the imidazole group even closer to the Zn2+ compared to the “in” conformation. Overall, compared to traditional MD simulations the incorporation of DRN allowed more detailed investigations into the variant mechanisms of action. This highlights the importance of network analysis in the study of the effects of missense mutations on the structure and function of proteins. Investigations of diseases at the molecular level is essential in the identification of disease pathogenesis and assists with the development of specifically tailored and better treatment options especially in the cases of genetically associated rare diseases.
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- Date Issued: 2020
Understanding of the underlying resistance mechanism of the Kat-G protein against isoniazid in Mycobacterium tuberculosis using bioinformatics approaches
- Authors: Barozi, Victor
- Date: 2020
- Subjects: Mycobacterium tuberculosis , Isoniazid , Drug resistance in microorganisms , Proteins -- Microbiology
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/146592 , vital:38540
- Description: Tuberculosis (TB) is a multi-organ infection caused by rod-shaped acid-fast Mycobacterium tuberculosis. The World Health Organization (WHO) ranks TB among the top 10 fatal infections and the leading the cause of death from a single infection. In 2017, TB was responsible for an estimated 1.3 million deaths among both the HIV negative and positive populations worldwide (WHO, 2018). Approximately 23% (roughly 1.7 billion) of the world’s population is estimated to have latent TB with a high risk of reverting to active TB infection. In 2017, an estimated 558,000 people developed drug resistant TB worldwide with 82% of the cases being multi-drug resistant TB (WHO, 2018). South Africa is ranked among the 30 high TB burdened countries with a TB incidence of 322,000 cases in 2017 accounting for 3% of the world’s TB cases. TB is curable and is clinically managed through a combination of intensive and continuation phases of first-line drugs (isoniazid, rifampicin, ethambutol, and pyrazinamide). Second-line drugs which include fluoroquinolones, injectable aminoglycoside and injectable polypeptides are used in cases of first line drug resistance. The third-line drugs include amoxicillin, clofazimine, linezolid and imipenem. These have variable but unproven efficacy to TB and are the last resort in cases of total drug resistance (Jilani et al., 2019). TB drug resistance to first-line drugs especially isoniazid in M. tuberculosis has been attributed to single nucleotide polymorphisms (SNPs) in the catalase peroxidase enzyme (katG), a protein important in the activation of the pro-drug isoniazid. The SNPs especially at position 315 of the katG enzyme are believed to reduce the sensitivity of the M. tuberculosis to isoniazid while still maintaining the enzyme’s catalytic activity - a mechanism not completely understood. KatG protein is important for protecting the bacteria from hydro peroxides and hydroxyl radicals present in an aerobic environment. This study focused on understanding the mechanism of isoniazid drug resistance in M. tuberculosis as a result of high confidence mutations in the katG through modelling the enzyme with its respective variants, performing MD simulations to explore the protein behaviour, calculating the dynamic residue network analysis (DRN) of the variants in respect to the wild type katG and finally performing alanine scanning. From the MD simulations, it was observed that the high confidence mutations i.e. S140R, S140N, G279D, G285D, S315T, S315I, S315R, S315N, G316D, S457I and G593D were not only reducing the backbone flexibility of the protein but also reducing the protein’s conformational variation and space. All the variant protein structures were observed to be more compact compared to the wild type. Residue fluctuation results indicated reduced residue flexibility across all variants in the loop region (position 26-110) responsible for katG dimerization. In addition, mutation S315T is believed to reduce the size of the active site access channel in the protein. From the DRN data, residues in the interface region between the N and C-terminal domains were observed to gain importance in the variants irrespective of the mutation location indicating an allosteric effect of the mutations on the interface region. Alanine scanning results established that residue Leucine at position 48 was not only important in the protein communication but also a destabilizing residue across all the variants. The study not only demonstrated change in the protein behaviour but also showed allosteric effect of the mutations in the katG protein.
- Full Text:
- Date Issued: 2020
Understanding the underlying resistance mechanism of Mycobacterium tuberculosis against Rifampicin by analyzing mutant DNA - directed RNA polymerase proteins via bioinformatics approaches
- Authors: Monama, Mokgerwa Zacharia
- Date: 2020
- Subjects: Mycobacterium tuberculosis , Rifampin , Drug resistance , Homology (Biology) , Tuberculosis -- Chemotherapy
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/167508 , vital:41487
- Description: Tuberculosis or TB is an airborne disease caused by the non-motile bacilli, Mycobacterium tuberculosis (MTB). There are two main forms of TB, namely, latent TB or LTB, asymptomatic and non-contagious version which according to the World Health Organization (WHO) is estimated to afflict over a third of the world’s population; and active TB or ATB, a symptomatic and contagious version which continues to spread, affecting millions worldwide. With the already high reported prevalence of TB, the emergence of drug-resistant strains has prompted the development of novel approaches to enhance the efficacy of known drugs and a desperate search for novel compounds to combat MTB infections. It was for this very purpose that this study was conducted. A look into the resistance mechanism of Rifampicin (Rifampin or RIF), one of the more potent first-line drugs, might prove beneficial in predicting the consequence of an introduced mutation (which usually occur as single nucleotide polymorphisms or SNPs) and perhaps even overcome it using appropriate therapeutic interventions that improve RIF’s efficacy. To accomplish this task, models of acceptable quality were generated for the WT and clinically relevant, RIF resistance conferring, SNPs occurring at codon positions D516, H526 and S531 (E .coli numbering system) using MODELLER. The models were accordingly ranked using GA341 and z-DOPE score, and subsequently validated with QMEAN, PROCHECK and VERIFY3D. MD simulations spanning 100 ns were run for RIF-bound (complex) and RIF-free (holo) DNA-directed RNA polymerase (DDRP) protein systems for the WT and SNP mutants using GROMACS. The MD frames were analyzed using RMSD, Rg and RMSF. For further analysis, MD-TASK was used to analyze the calculated dynamic residue networks (DRNs) from the generated MD frames, determining both change in average shortest path (ΔL) and betweenness centrality (ΔBC). The RMSD analysis revealed that all of the SNP complex models displayed a level instability higher than that of the WT complex. A majority of the SNP complex models were also observed to have similar compactness to the WT holo when looking at the calculated Rg. The RMSF results also hinted towards possible physiological consequences of the mutations (generally referred to as a fitness cost) highlighted by the increased fluctuations of the zinc-binding domain and the MTB SI α helical coiled coil. For the first time, to the knowledge of the authors, DRN analysis was employed for the DDRP protein for both holo and complex systems, revealing insightful information about the residues that play a key role in the change in distance between residue pairs along with residues that play an essential role in protein communication within the calculated RIN. Overall, the data supported the conclusions drawn by a recent study that only concentrated on RIF-resistance in rpoB models which suggested that the binding pocket for the SNP models may result in the changed coordination of RIF which may be the main contributor to its impaired efficacy.
- Full Text:
- Date Issued: 2020