In silico characterization of missense mutations in infectious diseases: case studies of tuberculosis and COVID-19
- Authors: Barozi, Victor
- Date: 2023-10-13
- Subjects: Microbial mutation , COVID-19 (Disease) , Drug resistance in microorganisms , Antitubercular agents , Tuberculosis , Molecular dynamics , Single nucleotide polymorphisms
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/431626 , vital:72791 , DOI 10.21504/10962/431626
- Description: One of the greatest challenges facing modern medicine and the global public health today is antimicrobial drug resistance (AMR). This “silent pandemic,” as coined by the world health organization (WHO), is steadily increasing with an estimated 4.95 million mortalities attributed to AMR in 2019, 1.27 million of which were directly linked to AMR. Some of the contributors to AMR include self-prescription, drug overuse, sub-optimal drug prescriptions by health workers, and inaccessibility to drugs, especially in remote areas, which leads to poor adherence. The situation is aggravated by the upsurge of new zoonotic infections like the coronavirus disease 2019, which present unique challenges and take the bulk of resources hence stunting the fight against AMR. Quite alarming still is our current antimicrobial arsenal, which hasn’t had any novel antimicrobial drug discovery/addition, of a new class, since the 1980s. This puts a burden on the existing broad-spectrum antimicrobial drugs which are already struggling against multi-drug resistant strains like multi-drug resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). Besides the search for new antimicrobial agents, the other avenue for addressing AMR is studying drug resistance mechanisms, especially single nucleotide polymorphisms (SNPs), that change drug target characteristics. With the advancement of computational power and data storage resources, computational approaches can be applied in mutational studies to provide insight into the drug resistance mechanisms with an aim to inform future drug design and development. Therefore, in the first part of this thesis, we employ integrative in silico approaches, including 3D structure modeling, molecular dynamic (MD) simulations, comparative essential dynamics (ED), and protein network analysis approaches i.e., dynamic residue network (DRN) analysis to decipher drug resistance mechanisms in tuberculosis (TB). This involved an investigation of the drug resistance mutations in the catalase-peroxidase (KatG) and pyrazinamidase (MtPncA) enzymes which are responsible for activation of TB first-line drugs; Isoniazid (INH) and Pyrazinamide (PZA), respectively. In the case of KatG, eleven high confidence (HC) KatG mutations associated with a high prevalence of phenotypic INH resistance were identified and their 3D structures modeled before subjecting them to MD simulations. Global analysis showed an unstable KatG structure and active site environment in the mutants compared to the wildtype. Active site dynamics in the mutants compromised cofactor (heme) interactions resulting in less bonds/interactions compared to the wildtype. Given the importance of the heme, reduced interactions affect enzyme function. Trajectory analysis also showed asymmetric protomer behavior both in the wildtype and mutant systems. DRN analysis identified the KatG dimerization domain and C-terminal domain as functionally important and influential in the enzyme function as per betweenness centrality and eigenvector centrality distribution. In the case of the MtPncA enzyme, our main focus was on understanding the MtPncA binding ability of Nicotinamide (an analogue of PZA) in comparison to PZA, especially in the presence of 82 resistance conferring MtPncA mutations. Like in KatG, the mutant structures were modeled and subjected to MD simulations and analysis. Interestingly, more MtPncA mutants favored NAM interactions compared to PZA i.e., 34 MtPncA mutants steadily coordinated NAM compared to 21 in the case of PZA. Trajectory and ligand interaction analysis showed how increased active site lid loop dynamics affect the NAM binding, especially in the systems with the active site mutations i.e., H51Y, W68R, C72R, L82R, K96N, L159N, and L159R. This led to fewer protein-ligand interactions and eventually ligand ejection. Network analysis further identified the protein core, metal binding site (MBS), and substrate binding site as the most important regions of the enzyme. Furthermore, the degree of centrality analysis showed how specific MtPncA mutations i.e., C14H, F17D, and T412P, interrupt intra-protein communication from the MtPncA core to the MBS, affecting enzyme activity. The analysis of KatG and MtPncA enzyme mutations not only identified the effects of mutations on enzyme behaviour and communication, but also established a framework of computational approaches that can be used for mutational studies in any protein. Besides AMR, the continued encroachment of wildlife habitats due to population growth has exposed humans to wildlife pathogens leading to zoonotic diseases, a recent example being coronavirus disease 2019 (COVID-19). In the second part of the thesis, the established computational approaches in Part 1, were employed to investigate the changes in inter-protein interactions and communication patterns between the severe acute respiratory coronavirus 2 (SARS-CoV-2) with the human host receptor protein (ACE2: angiotensin-converting enzyme 2) consequent to mutations in the SARS-CoV-2 receptor binding domain (RBD). Here, the focus was on RBD mutations of the Omicron sub-lineages. We identified four Omicron-sub lineages with RBD mutations i.e., BA.1, BA.2, BA.3 and BA.4. Each sub-lineage mutations were modeled into RBD structure in complex with the hACE2. MD analysis of the RBD-hACE2 complex highlighted how the RBD mutations change the conformational flexibility of both the RBD and hACE2 compared to the wildtype (WT). Furthermore, DRN analysis identified novel allosteric paths composed of residues with high betweenness and eigenvector centralities linking the RBD to the hACE2 in both the wildtype and mutant systems. Interestingly, these paths were modified with the progression of Omicron sub-lineages, highlighting how the virus evolution affects protein interaction. Lastly, the effect of mutations on S RBD and hACE2 interaction was investigated from the hACE2 perspective by focusing on mutations in the hACE2 protein. Here, naturally occurring hACE2 polymorphisms in African populations i.e., S19P, K26R, M82I, K341R, N546D, and D597Q, were identified and their effects on RBD-hACE2 interactions investigated in presence of the Omicron BA.4/5 RBD mutations. The hACE2 polymorphisms subtly affected the complex dynamics; however, RBD-hACE2 interaction analysis showed that hACE2 mutations effect the complex formation and interaction. Here, the K26R mutation favored RBD-hACE2 interactions, whereas S19P resulted in fewer inter-protein interactions than the reference system. The M82I mutation resulted in a higher RBD-hACE2 binding energy compared to the wildtype meaning that the mutation might not favor RBD binding to the hACE2. On the other hand, K341R had the most RBD-hACE2 interactions suggesting that it probably favors RBD binding to the hACE2. N546D and D597Q had diminutive differences to the reference system. Interestingly, the network of high betweenness centrality residues linking the two proteins, as seen in the previous paragraph, were maintained/modified in presence of hACE2 mutations. HACE2 mutations also changed the enzyme network patterns resulting in a concentration of high eigenvector centrality residues around the zinc-binding and active site region, ultimately influencing the enzyme functionality. Altogether, the thesis highlights fundamental structural and network changes consequent to mutations both in TB and COVID-19 proteins of interest using in silico approaches. These approaches not only provide a new context on impact of mutations in TB and COVID target proteins, but also presents a framework that be implemented in other protein mutation studies. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2023
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- Date Issued: 2023-10-13
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.
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- Date Issued: 2020