Falcipain 2 and 3 as malarial drug targets: deciphering the effects of missense mutations and identification of allosteric modulators via computational approaches
- Authors: Okeke, Chiamaka Jessica
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/432170 , vital:72848 , DOI 10.21504/10962/432170
- Description: Malaria, caused by an obligate unicellular protozoan parasite of the genus Plasmodium, is a disease of global health importance that remains a major cause of morbidity and mortality in developing countries. The World Health Organization (WHO) reported nearly 247 million malaria cases in 2021, causing 619,000 deaths, the vast majority ascribed to pregnant women and young children in sub-Saharan Africa. A critical component of malaria mitigation and elimination efforts worldwide is antimalarial drugs. However, resistance to available antimalarial drugs jeopardizes the treatment, prevention, and eradication of the disease. The recent emergence and spread of resistance to artemisinin (ART), the currently recommended first-line antimalarial drug, emphasizes the need to understand the resistance mechanism and apply this knowledge in developing new drugs that are effective against malaria. An insight into ART's mechanism of action indicates that ferrous iron (Fe2+) or heme, released when hemoglobin is degraded, cleaves the endoperoxide bridge. As a result, free radicals are formed, which alkylate many intracellular targets and result in plasmodial proteopathy. Aside from the existing evidence that mutations in the Kelch 13 protein propeller domain affect ART sensitivity and clearance rate by Plasmodium falciparum (Pf) parasites, recent investigations raise the possibility that additional target loci may be involved, and these include a nonsense (S69stop) and four missense variants (K255R, N257E, T343P, and D345G) in falcipain 2 (FP-2) protein. FP-2 and falcipain 3 (FP-3) are cysteine proteases responsible for hydrolyzing hemoglobin in the host erythrocytic cycle, a key virulence factor for malaria parasite growth and metabolism. Due to the obligatory nature of the hemoglobin degradation process, both proteases have become potential antimalarial drug targets attracting attention in recent years for the development of blood-stage antimalarial drugs. The alteration of the expression profile of FP-2 and FP-3 through gene manipulation approaches (knockout) or compound inhibition assays, respectively, induced parasites with swollen food vacuoles due to the accumulation of undegraded hemoglobin. Furthermore, missense mutations in FP-2 confer parasites with decreased ART sensitivity, probably due to altered enzyme efficiency and momentary decreased hemoglobin degradation. Hence, understanding how these mutations affect FP-2 (including those implicated in ART resistance) and FP-3 is imperative to finding potentially effective inhibitors. The first aim of this thesis is to characterize the effects of missense mutations on the partial zymogen complex and the catalytic domain of FP-2 and FP-3 using a range of computational approaches and tools such as homology modeling, molecular dynamics (MD) simulations, comparative essential dynamics, dynamic residue network (DRN) analysis, weighted residue contact map analysis, amongst others. The Pf genomic resource database (PlasmoDB) identified 41 missense mutations located in the partial zymogen and catalytic domains of FP-2 and FP-3. Using structure-based tools, six putative allosteric pockets were identified in FP-2 and FP-3. The effect of mutations on the whole protein, the central core, binding pocket residues and allosteric pockets was evaluated. The accurate 3D homology models of the WT and mutants were calculated. MD simulations were performed on the various systems as a quick starting point. MD simulations have provided a cornerstone for establishing numerous computational tools for describing changes arising from mutations, ligand binding, and environmental changes such as pH and temperature. Post-MD analysis was performed in two stages viz global and local analysis. Global analysis via radius of gyration (Rg) and comparative essential dynamic analysis revealed the conformational variability associated with all mutations. In the catalytic domain of FP-2, the presence of M245I mutation triggered the formation of a cryptic pocket via an exclusive mechanism involving the fusion of pockets 2 and 6. This striking observation was also detected in the partial zymogen complex of FP-2 and induced by A159V, M245I and E249A mutations. A similar observation was uncovered in the presence of A422T mutation in the catalytic domain of FP-3. Local DRN and contact map analyses identified conserved inter-residue interaction changes on important communication networks. This study brings a novel understanding of the effects of missense mutations in FP-2 and FP-3 and provides important insight which may help discover new anti-hemoglobinase drugs. The second aim is the identification of potential allosteric ligands against the WT and mutant systems of FP-2 and FP-3 using various computational tools. Of the six potential allosteric pockets identified in FP-2 and FP-3, pocket 1 was evaluated by SiteMap as the most druggable in both proteins. This pipeline was implemented to screen pocket 1 of FP-2 and FP-3 against 2089 repositionable compounds obtained from the DrugBank database. In order to ensure selectivity and specificity to the Plasmodium protein, the human homologs (Cat K and Cat L) were screened, and compounds binding to these proteins were exempted from further analysis. Subsequently, eight compounds (DB00128, DB00312, DB00766, DB00951, DB02893, DB03754, DB13972, and DB14159) were identified as potential allosteric hits for FP-2 and five (DB00853, DB00951, DB01613, DB04173 and DB09419) for FP-3. These compounds were subjected to MD simulation and post-MD trajectory analysis to ascertain their stability in their respective protein structures. The effects of the stable compounds on the WT and mutant systems of FP-2 and FP-3 were then evaluated using DRN analysis. Attention has recently been drawn towards identifying novel allosteric compounds targeting FP-2 and FP-3; hence this study explores the potential allosteric inhibitory mechanisms in the presence and absence of mutations in FP-2 and FP-3. Overall, the results presented in this thesis provide (i) an understanding of the role mutations in the partial zymogen complex play in the activation of the active enzyme, (ii) an insight into the possible allosteric mechanisms induced by mutations on the active enzymes, and (iii) a computational pipeline for the development of novel allosteric modulators for malaria inhibition studies. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2023
- Full Text:
- Authors: Okeke, Chiamaka Jessica
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/432170 , vital:72848 , DOI 10.21504/10962/432170
- Description: Malaria, caused by an obligate unicellular protozoan parasite of the genus Plasmodium, is a disease of global health importance that remains a major cause of morbidity and mortality in developing countries. The World Health Organization (WHO) reported nearly 247 million malaria cases in 2021, causing 619,000 deaths, the vast majority ascribed to pregnant women and young children in sub-Saharan Africa. A critical component of malaria mitigation and elimination efforts worldwide is antimalarial drugs. However, resistance to available antimalarial drugs jeopardizes the treatment, prevention, and eradication of the disease. The recent emergence and spread of resistance to artemisinin (ART), the currently recommended first-line antimalarial drug, emphasizes the need to understand the resistance mechanism and apply this knowledge in developing new drugs that are effective against malaria. An insight into ART's mechanism of action indicates that ferrous iron (Fe2+) or heme, released when hemoglobin is degraded, cleaves the endoperoxide bridge. As a result, free radicals are formed, which alkylate many intracellular targets and result in plasmodial proteopathy. Aside from the existing evidence that mutations in the Kelch 13 protein propeller domain affect ART sensitivity and clearance rate by Plasmodium falciparum (Pf) parasites, recent investigations raise the possibility that additional target loci may be involved, and these include a nonsense (S69stop) and four missense variants (K255R, N257E, T343P, and D345G) in falcipain 2 (FP-2) protein. FP-2 and falcipain 3 (FP-3) are cysteine proteases responsible for hydrolyzing hemoglobin in the host erythrocytic cycle, a key virulence factor for malaria parasite growth and metabolism. Due to the obligatory nature of the hemoglobin degradation process, both proteases have become potential antimalarial drug targets attracting attention in recent years for the development of blood-stage antimalarial drugs. The alteration of the expression profile of FP-2 and FP-3 through gene manipulation approaches (knockout) or compound inhibition assays, respectively, induced parasites with swollen food vacuoles due to the accumulation of undegraded hemoglobin. Furthermore, missense mutations in FP-2 confer parasites with decreased ART sensitivity, probably due to altered enzyme efficiency and momentary decreased hemoglobin degradation. Hence, understanding how these mutations affect FP-2 (including those implicated in ART resistance) and FP-3 is imperative to finding potentially effective inhibitors. The first aim of this thesis is to characterize the effects of missense mutations on the partial zymogen complex and the catalytic domain of FP-2 and FP-3 using a range of computational approaches and tools such as homology modeling, molecular dynamics (MD) simulations, comparative essential dynamics, dynamic residue network (DRN) analysis, weighted residue contact map analysis, amongst others. The Pf genomic resource database (PlasmoDB) identified 41 missense mutations located in the partial zymogen and catalytic domains of FP-2 and FP-3. Using structure-based tools, six putative allosteric pockets were identified in FP-2 and FP-3. The effect of mutations on the whole protein, the central core, binding pocket residues and allosteric pockets was evaluated. The accurate 3D homology models of the WT and mutants were calculated. MD simulations were performed on the various systems as a quick starting point. MD simulations have provided a cornerstone for establishing numerous computational tools for describing changes arising from mutations, ligand binding, and environmental changes such as pH and temperature. Post-MD analysis was performed in two stages viz global and local analysis. Global analysis via radius of gyration (Rg) and comparative essential dynamic analysis revealed the conformational variability associated with all mutations. In the catalytic domain of FP-2, the presence of M245I mutation triggered the formation of a cryptic pocket via an exclusive mechanism involving the fusion of pockets 2 and 6. This striking observation was also detected in the partial zymogen complex of FP-2 and induced by A159V, M245I and E249A mutations. A similar observation was uncovered in the presence of A422T mutation in the catalytic domain of FP-3. Local DRN and contact map analyses identified conserved inter-residue interaction changes on important communication networks. This study brings a novel understanding of the effects of missense mutations in FP-2 and FP-3 and provides important insight which may help discover new anti-hemoglobinase drugs. The second aim is the identification of potential allosteric ligands against the WT and mutant systems of FP-2 and FP-3 using various computational tools. Of the six potential allosteric pockets identified in FP-2 and FP-3, pocket 1 was evaluated by SiteMap as the most druggable in both proteins. This pipeline was implemented to screen pocket 1 of FP-2 and FP-3 against 2089 repositionable compounds obtained from the DrugBank database. In order to ensure selectivity and specificity to the Plasmodium protein, the human homologs (Cat K and Cat L) were screened, and compounds binding to these proteins were exempted from further analysis. Subsequently, eight compounds (DB00128, DB00312, DB00766, DB00951, DB02893, DB03754, DB13972, and DB14159) were identified as potential allosteric hits for FP-2 and five (DB00853, DB00951, DB01613, DB04173 and DB09419) for FP-3. These compounds were subjected to MD simulation and post-MD trajectory analysis to ascertain their stability in their respective protein structures. The effects of the stable compounds on the WT and mutant systems of FP-2 and FP-3 were then evaluated using DRN analysis. Attention has recently been drawn towards identifying novel allosteric compounds targeting FP-2 and FP-3; hence this study explores the potential allosteric inhibitory mechanisms in the presence and absence of mutations in FP-2 and FP-3. Overall, the results presented in this thesis provide (i) an understanding of the role mutations in the partial zymogen complex play in the activation of the active enzyme, (ii) an insight into the possible allosteric mechanisms induced by mutations on the active enzymes, and (iii) a computational pipeline for the development of novel allosteric modulators for malaria inhibition studies. , Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 2023
- Full Text:
In silico characterization of missense mutations in infectious diseases: case studies of tuberculosis and COVID-19
- Authors: Barozi, Victor
- Date: 2023-10-13
- Subjects: Uncatalogued
- 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
- Full Text:
- Authors: Barozi, Victor
- Date: 2023-10-13
- Subjects: Uncatalogued
- 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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