169 research outputs found

    Molecular modeling studies on HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90 as a potential anti-HIV-1 target.

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    Masters Degree. University of KwaZulu-Natal, Durban.Human immunodeficiency virus (HIV) infection is the leading cause of death globally. This dissertation addresses two HIV-1 target proteins namely, HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90. More specifically for HIV-1 RT, a case study for the identification of potential inhibitors as anti-HIV agents was carried out. A more refined virtual screening (VS) approach was implemented, which was an improvement on work previously published by our group- “target-bound pharmacophore modeling approach”. This study generated a pharmacophore library based only on highly contributing amino acid residues (HCAAR), instead of arbitrary pharmacophores, most commonly used in the conventional approaches in literature. HCAAR were distinguished based on free binding energy (FBE) contributions, obtained using calculation from molecular dynamics (MD) simulations. Previous approaches have relied on the docking score (DS) to generate energy-based pharmacophore models. However, DS are reportedly unreliable. Thus we present a model for a per-residue energy decomposition (PRED), constructed from MD simulation ensembles generating a more trustworthy pharmacophore model which can be applied in drug discovery workflow. This approach was employed in screening for potential HIV-1 RT inhibitors using the pharmacophoric features of the compound GSK952. The complex was subjected to docking and thereafter MD simulations confirmed the stability of the system. Experimentally determined inhibitors with known HIV-RT inhibitory activity were used to validate the proposed protocol. Two potential hits ZINC46849657 and ZINC54359621 showed a significant potential with regards to FBE. Reported results obtained from this work confirm that this new approach is favourable to the future of drug design process. Hsp90 was recently discovered to play a vital role in HIV-1 replication. Thus has emerged, as a promising target for anti-HIV-1 drugs. The molecular mechanism of Hsp90 is poorly understood, thus the second study was aimed to address this issue and provide a clear insight to the inhibition mechanism of Hsp90. Reasonable continuous MD simulations were employed for both unbound and bound Hsp90 conformations, to understand the dimerization and inhibition mechanisms. Results demonstrated that coumermycin A1 (C-A1), a newly discovered Hsp90 inhibitor, binds at the CTD dimer of Hsp90 and lead to a significant separation between orthogonally opposed residues, such as Arg591.B, Lys594.A, Ser663.A, Thr653.B, Ala665.A, Thr649.B, Leu646.B and Asn669A. A Large difference in magnitudes was observed in the radius of gyration (Rg), per-residue fluctuation, root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) confirming a completely more flexible state for the unbound conformation associated with dimerization. Whereas, a less globally correlated motion in the case of the bound conformer of Hsp90 approved a reduction of the dimeric process. This undoubtedly underlines the inhibition process due to ligand binding. The detailed dynamic analyses of Hsp90 presented herein are believed to give a greater insight and understanding to the function and mechanisms of inhibition of Hsp90. The report on the inhibitor-binding mode would also be of great assistance in the design of prospective inhibitors against Hsp90 as potential HIV target

    Design of Phenyl Keto Butanoic Acid Derivatives as Inhibitors against Malate Synthase of M.Tuberculosis based on Docking & MD Simulation Studies

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    The emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb) has intensified efforts to discover novel drugs for tuberculosis (TB) treatment. Targeting the persistent state of Mtb, a condition in which Mtb is resistant to conventional drug therapies, is of particular interest. Persistent bacterial population relies on metabolic pathways that become active in low nutrient environment like glyoxylate shunt. Since the glyoxylate shunt enzymes are not present in mammals, they make attractive drug targets. This study is focused on malate synthase (MS), one of the enzymes in the glyoxylate shunt. Computational approach was used to identify potential inhibitors of MS. Crystal structure of MS (PDB ID-1N8I) in complex with inhibitor was used to rationally design better MS inhibitors. PKBA is identified to be potent inhibitor of malate synthase enzyme. 30 molecules were designed based on malate (product of Malate Synthase) and Phenyl keto butanoic acid (PKBA) backbone. All molecules were screened based on the lowest energy with repeated conformation of ligands, and passed through ADME/tox filters to sort out the toxic compounds. Molecular docking of all designed molecules with the receptor protein (malate synthase) was performed. The binding energy and inhibitory concentration was observed. On the basis of this study, the best molecule having the lowest binding energy and inhibitory concentration was identified. The best molecule identified was further evaluated by molecular dynamics simulation of protein-ligand complex in water solvent model. The rmsd close to 2 A◦ shows the stability of the complex. Inhibitors against MS have been identified and characterized for further development into potential novel anti-tubercular drugs

    Structure- and Ligand-Based Design of Novel Antimicrobial Agents

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    The use of computer based techniques in the design of novel therapeutic agents is a rapidly emerging field. Although the drug-design techniques utilized by Computational Medicinal Chemists vary greatly, they can roughly be classified into structure-based and ligand-based approaches. Structure-based methods utilize a solved structure of the design target, protein or DNA, usually obtained by X-ray or NMR methods to design or improve compounds with activity against the target. Ligand-based methods use active compounds with known affinity for a target that may yet be unresolved. These methods include Pharmacophore-based searching for novel active compounds or Quantitative Structure-Activity Relationship (QSAR) studies. The research presented here utilized both structure and ligand-based methods against two bacterial targets: Bacillus anthracis and Mycobacterium tuberculosis. The first part of this thesis details our efforts to design novel inhibitors of the enzyme dihydropteroate synthase from B. anthracis using crystal structures with known inhibitors bound. The second part describes a QSAR study that was performed using a series of novel nitrofuranyl compounds with known, whole-cell, inhibitory activity against M. tuberculosis. Dihydropteroate synthase (DHPS) catalyzes the addition of p-amino benzoic acid (pABA) to dihydropterin pyrophosphate (DHPP) to form pteroic acid as a key step in bacterial folate biosynthesis. It is the traditional target of the sulfonamide class of antibiotics. Unfortunately, bacterial resistance and adverse effects have limited the clinical utility of the sulfonamide antibiotics. Although six bacterial crystal structures are available, the flexible loop regions that enclose pABA during binding and contain key sulfonamide resistance sites have yet to be visualized in their functional conformation. To gain a new understanding of the structural basis of sulfonamide resistance, the molecular mechanism of DHPS action, and to generate a screening structure for high-throughput virtual screening, molecular dynamics simulations were applied to model the conformations of the unresolved loops in the active site. Several series of molecular dynamics simulations were designed and performed utilizing enzyme substrates and inhibitors, a transition state analog, and a pterin-sulfamethoxazole adduct. The positions of key mutation sites conserved across several bacterial species were closely monitored during these analyses. These residues were shown to interact closely with the sulfonamide binding site. The simulations helped us gain new understanding of the positions of the flexible loops during inhibitor binding that has allowed the development of a DHPS structural model that could be used for high-through put virtual screening (HTVS). Additionally, insights gained on the location and possible function of key mutation sites on the flexible loops will facilitate the design of new, potent inhibitors of DHPS that can bypass resistance mutations that render sulfonamides inactive. Prior to performing high-throughput virtual screening, the docking and scoring functions to be used were validated using established techniques against the B. anthracis DHPS target. In this validation study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, as well as nine scoring functions, were evaluated for their utility in virtual screening against the novel pterin binding site. Their performance in ligand docking and virtual screening against this target was examined by their ability to reproduce a known inhibitor conformation and to correctly detect known active compounds seeded into three separate decoy sets. Enrichment was demonstrated by calculated enrichment factors at 1% and Receiver Operating Characteristic (ROC) curves. The effectiveness of post-docking relaxation prior to rescoring and consensus scoring were also evaluated. Of the docking and scoring functions evaluated, Surflex with SurflexScore and Glide with GlideScore performed best overall for virtual screening against the DHPS target. The next phase of the DHPS structure-based drug design project involved high-throughput virtual screening against the DHPS structural model previously developed and docking methodology validated against this target. Two general virtual screening methods were employed. First, large, virtual libraries were pre-filtered by 3D pharmacophore and modified Rule-of-Three fragment constraints. Nearly 5 million compounds from the ZINC databases were screened generating 3,104 unique, fragment-like hits that were subsequently docked and ranked by score. Second, fragment docking without pharmacophore filtering was performed on almost 285,000 fragment-like compounds obtained from databases of commercial vendors. Hits from both virtual screens with high predicted affinity for the pterin binding pocket, as determined by docking score, were selected for in vitro testing. Activity and structure-activity relationship of the active fragment compounds have been developed. Several compounds with micromolar activity were identified and taken to crystallographic trials. Finally, in our ligand-based research into M. tuberculosis active agents, a series of nitrofuranylamide and related aromatic compounds displaying potent activity was investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation and high internal validity (cross validated r2 \u3e .5) were developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents

    Antituberculosis Drug Repurposing: A New Hope for Tackling Multi-Challenging TB in Timely Manner

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    Tuberculosis still stands as the world’s leading infectious disease as 1/4th of the world’s population harbors Latent TB infection (LTBI) > 10 million develops active TB and ~ 1.5 million people die per year. Approximately 4,65,000 people fell ill with multidrug or rifampicin-resistant tuberculosis (MDR/RR-TB)/year. This deadly TB scenario demands new TB drug regimens to tackle global infection reservoir, and worldwide spread of drug resistance and DS TB. Successful entry of single new drug into market is much complicated mission owing to time, cost, efficacy, and safety issues. Therefore, drug repurposing seems one reliable hope to meet the challenges of modern TB drug discovery timely, as it starts with examining market acclaimed drugs against other diseases for their efficacies against tuberculosis avoiding several lengthy and costly steps required for new molecules. Several drugs have been identified, which show potential for TB treatment. There is need for careful consideration of various trial designs to ensure that TB phase III trials are initiated for fruitful development of new TB treatment regimens. TB drug repurposing will not only give fast track novel drugs but will also serve to identify new targets for future development in cost-effective manner

    Drug Repurposing

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    This book focuses on various aspects and applications of drug repurposing, the understanding of which is important for treating diseases. Due to the high costs and time associated with the new drug discovery process, the inclination toward drug repurposing is increasing for common as well as rare diseases. A major focus of this book is understanding the role of drug repurposing to develop drugs for infectious diseases, including antivirals, antibacterial and anticancer drugs, as well as immunotherapeutics

    In Silico Design and Selection of CD44 Antagonists:implementation of computational methodologies in drug discovery and design

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    Drug discovery (DD) is a process that aims to identify drug candidates through a thorough evaluation of the biological activity of small molecules or biomolecules. Computational strategies (CS) are now necessary tools for speeding up DD. Chapter 1 describes the use of CS throughout the DD process, from the early stages of drug design to the use of artificial intelligence for the de novo design of therapeutic molecules. Chapter 2 describes an in-silico workflow for identifying potential high-affinity CD44 antagonists, ranging from structural analysis of the target to the analysis of ligand-protein interactions and molecular dynamics (MD). In Chapter 3, we tested the shape-guided algorithm on a dataset of macrocycles, identifying the characteristics that need to be improved for the development of new tools for macrocycle sampling and design. In Chapter 4, we describe a detailed reverse docking protocol for identifying potential 4-hydroxycoumarin (4-HC) targets. The strategy described in this chapter is easily transferable to other compounds and protein datasets for overcoming bottlenecks in molecular docking protocols, particularly reverse docking approaches. Finally, Chapter 5 shows how computational methods and experimental results can be used to repurpose compounds as potential COVID-19 treatments. According to our findings, the HCV drug boceprevir could be clinically tested or used as a lead molecule to develop compounds that target COVID-19 or other coronaviral infections. These chapters, in summary, demonstrate the importance, application, limitations, and future of computational methods in the state-of-the-art drug design process

    Diverse computational tools towards the understanding of HIV targets and design of potential drug candidates.

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    Ph. D. University of KwaZulu-Natal, Durban 2014.HIV/AIDS still remains to be a challenging epidemic infecting millions of individuals worldwide. The morbidity and mortality rates of HIV-infected patients has been well documented over the years. Despite on-going HIV/AIDS research and access to antiretroviral therapy, to date still no cure exists for this deliberating disease. In recent years, computational approaches have emerged as close counterparts to experiments in modern drug discovery process and in understanding complex biological phenomena. An array of in-silico computational techniques were implemented ranging from molecular dynamic (MD) simulations, de-novo design, hybrid structure-based and pharmacophore-based virtual screening, quantitative structure-activity relationship (QSAR), homology modeling, principle component analysis (PCA), residue interaction network analysis (RIN), substrate envelope analysis (SEA), to molecular mechanics and quantum mechanics. The first report (Chapter 4), demonstrated a unique strategy for developing dual acting inhibitors against HIV-1 protease (PR) and reverse transcriptase (RT). The designed targets exhibited binding affinities and dual inhibiting activity comparable to, and in some cases better than, known active reference drugs. The second study (Chapter 5), reported the activity of flexible hydroquinone-based compounds as non-nucleoside reverse transcriptase inhibitors (NNRTIs), as proposed by Bruccoleri, where no experimental or computational work supported his proposal. Results concluded that the novel flexible hydroquinone-based compounds showed improved binding affinity as compared to FDA-approved prototype drugs and more specifically potent potential mutant-resistant NNRT inhibitor activity. The third report (Chapter 6), explored the activity of novel CCR5 antagonists as potential HIV- 1 entry inhibitors. Ten scaffolds were identified as novel CCR5 antagonists or potential HIV-1 entry inhibitors. Furthermore, from the generated atom-based 3D-QSAR model, all of the parameters showed certain reliability and feasible predictability to help us design new and high selectivity CCR5 inhibitors. The fourth study (Chapter 7), explored the atomistic basis of why the M184I single mutation renders complete resistance of HIV-1 RT to lamivudine. Multiple molecular dynamics simulations, binding free energy calculations, principle component analysis (PCA) and residue interaction network (RIN) analyses adequately clarified the effect of the M184I mutation on drug resistance to lamvudine. Results presented in this study verified that M184I mutation decreased drug binding affinity, distorted ligand optimum orientation in RT active site and affected the overall protein conformational landscape. The results also provided some potential clues for further design of novel inhibitors that are less susceptible to drug resistance. In the fifth study (Chapter 8), we identified potential HIV-Nef inhibitors by exploiting the structural features of B9 using an integrated computational tools framework. The top identified hit compounds demonstrated comparatively better binding affinities and relatable binding modes compared to the prototype antagonist, B9. Top identified hits were proposed as new potential novel leads targeting HIV-Nef with a detailed analysis of their respective binding modes. The sixth report (Chapter 9), aimed to reveal the dimer packing and unpacking phenomena of HIV-Nef in its apo and inhibitor bound conformations using molecular dynamic simulations. Results verified a more conformational flexible nature of HIV-Nef dimer in the absence of an inhibitor.as compared to B9 bound conformation of HIV-Nef, which was found to be more conformationally rigid with a lesser inter-dimeric association. We believe that the results obtained from these several studies could be of great benefit in the development of more effective therapeutic interventions for the treatment and cure of HIV/AIDS

    Computer-aided approaches in drug design: the exigent way forward: dynamic perspectives into the mechanistic activities of small molecule inhibitors toward antiviral, antitubercular and anticancer therapeutic interventions.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.The crucial role of CADD in the drug design process is now indisputable and has proven over the years that it can accelerate the discovery potential drug candidates while reducing the associated cost. Using knowledge and information about biological target or knowledge about a ligand with proven bioactivity, CADD, and its techniques can influence various drug discovery pipeline stages. The ability CADD approaches to elucidate drug-target interactions at the atomistic level allows for investigations of the mechanism of drugs' actions, revealing atomistic insights that influence drug design and improvement. CADD approaches also seek to augment traditional in vitro and in vivo experimental techniques and not replace them since CADD approaches can also allow modeling complex biological processes that hitherto seemed impossible to explore using experimental methods. According to the World Health Organization (WHO), featuring prominently in the top ten causes of death are cancer, lower respiratory tract infection, tuberculosis (TB), and viral infections such as HIV/AIDS. Collectively, these diseases are of global health concerns, considering a large number of associated deaths yearly. Over the years, several therapeutic interventions have been employed to treat, manage, or cure these diseases, including chemotherapy, surgery, and radiotherapy. Of these options, small molecule inhibitors have constituted an integral component in chemotherapy, thereby undoubtedly playing an essential role in patient management. Although significant success has been achieved using existing therapeutic approaches, the emergence of drug resistance and the challenges of associated adverse side effects has prompted the need for the drug design processes against these diseases to remain innovative, including combining existing drugs and establishing improved therapeutic options that could overcome resistance while maintaining minimal side effects to patients. Therefore, an exploration of drug target interactions towards unraveling mechanisms of actions as performed in the reports in this thesis are relevant since the molecular mechanism provided could form the basis for the design and identification of new therapeutic agents, improvement of the therapeutic activity of existing drugs, and also aid in the development of novel therapeutic strategies against these diseases of global health concern. Therefore the studies in this thesis employed CADD approaches to investigates molecular mechanisms of actions of novel therapeutic strategies directed towards some crucial therapeutics implicated in viral infections, tuberculosis, and cancer. Therapeutic targets studied included; SARS-CoV-2 RNA dependent RNA polymerase (SARS-CoV-2 RdRp), Human Rhinovirus B14 (HRV-B14) and human N-myristoyltransferases in viral infections, Dihydrofolate reductase (DHFR) and Flavin-dependent thymidylate synthase (FDTS) in TB, human variants of TCRCD1d, and Protein Tyrosine Phosphatase Receptor Zeta (PTPRZ) in cancer. The studies in this thesis is divided into three domains and begins with a thorough review of the concept of druggability and drug-likeness since the crux of the subsequent reports revolved around therapeutic targets and their inhibitions by small molecule inhibitors. This review highlights the principles of druggability and drug-likeness while detailing the recent advancements in drug discovery. The review concludes by presenting the different computational, highlighting their reliability for predictive analysis. In the first domain of the research, we sought to unravel the inhibitory mechanism of some small molecule inhibitors against some therapeutic targets in viral infections by explicitly focusing on the therapeutic targets; SARS-CoV-2 RdRp, HRV-B14, and N-myristoyltransferase. Therapeutic targeting of SARS-CoV-2 RdRp has been extensively explored as a viable approach in the treatment of COVID-19. By examining the binding mechanism of Remdesivir, which hitherto was unclear, this study sought to unravel the structural and conformational implications on SARS-CoV-2 RdRp and subsequently identify crucial pharmacophoric moieties of Remdesivir required for its inhibitory potency. Computational analysis showed that the modulatory activity of Remdesivir is characterized by an extensive array of high-affinity and consistent molecular interactions with specific active site residues that anchor Remdemsivir within the binding pocket for efficient binding. Results also showed that Remdesivir binding induces minimal individual amino acid perturbations, subtly interferes with deviations of C-α atoms, and restricts the systematic transition of SARS-CoV-2 RdRp from the “buried” hydrophobic region to the “surface exposed” hydrophilic region. Based on observed high-affinity interactions with SARS-CoV-2 RdRp, a pharmacophore model was generated, which showcased the crucial functional moieties of Remdesivir. The pharmacophore was subsequently employed for virtual screening to identify potential inhibitors of SARS-CoV-2 RdRp. The structural insights and the optimized pharmacophoric model provided would augment the design of improved analogs of Remdesivir that could expand treatment options for COVID-19. The next study sought to explore the therapeutic targeting of human rhinoviruses (HRV) amidst challenges associated with the existence of a wide variety of HRV serotypes. By employing advanced computational techniques, the molecular mechanism of inhibition of a novel benzothiophene derivative that reportedly binds HRV-B14 was investigated. An analysis of the residue-residue interaction profile revealed of HRV upon the benzothiophene derivative binding revealed a distortion of the hitherto compacted and extensively networked HRV structure. This was evidenced by the fewer inter-residue hydrogen bonds, reduced van der Waals interactions, and increased residue flexibility. However, a decrease in the north-south wall's flexibility around the canyon region also suggested that the benzothiophene derivative's binding impedes the “breathing motion” of HRV-B14; hence its inhibition. The next study in the first domain of the research investigated the structural and molecular mechanisms of action associated with the dual inhibitory activity of IMP-1088. This novel compound reportedly inhibits human N-myristoyltransferase subtypes 1 and 2 towards common cold therapy. This is because it has emerged that the pharmacological inhibition of Nmyristoyltransferase is an efficient non-cytotoxic strategy to completely thwart the replication process of rhinovirus toward common cold treatment. Using augmentative computational and nanosecond-based analyses, findings of the study revealed that the steady and consistent interactions of IMP-1088 with specific residues; Tyr296, Phe190, Tyr420, Leu453, Gln496, Val181, Leu474, Glu182, and Asn246, shared within the binding pockets of both HNMT subtypes, in addition to peculiar structural changes account for its dual inhibitory potency. Findings thus unveiled atomistic and structural perspectives that could form the basis for designing novel dualacting inhibitors of N-myristoyltransferase towards common cold therapy. In the second domain of the research, the mechanism of action of some small molecule inhibitors against DHFR, FDTS, and Mtb ATP synthase in treating tuberculosis is extensively investigated and reportedly subsequently. To begin with, the dual therapeutic targeting of crucial enzymes in the folate biosynthetic pathway was explored towards developing novel treatment methods for TB. Therefore, the study investigated the molecular mechanisms and structural dynamics associated with dual inhibitory activity of PAS-M against both DHFR and FDTS, which hitherto was unclear. MD simulations revealed that PAS-M binding towards DHFR and FDTS is characterized by a recurrence of strong conventional hydrogen bond interactions between a peculiar site residue the 2-aminov decahydropteridin-4-ol group of PAS-M. Structural dynamics of the bound complexes of both enzymes revealed that, upon binding, PAS-M is anchored at the entrance of hydrophobic pockets by a strong hydrogen bond interaction while the rest of the structure gains access to deeper hydrophobic residues to engage in favorable interactions. Further analysis of atomistic changes of both enzymes showed increased C-α atom deviations and an increase C-α atoms radius of gyration consistent with structural disorientations. These conformational changes possibly interfered with the enzymes' biological functions and hence their inhibition as experimentally reported. Additionally, in this domain, the therapeutic targeting of the ATP machinery of Mtb by Bedaquiline (BDQ) was explored towards unravelling the structures and atomistic perspectives that account for the ability of BDQ to selectively inhibits mycobacterial F1Fo-ATP synthase via its rotor c-ring. BDQ is shown to form strong interaction with Glu65B and Asp32B and, consequently, block these residues' role in proton binding and ion. BDQ binding was also revealed to impede the rotatory motion of the rotor c-ring by inducing a compact conformation on the ring with its bulky structure. Complementary binding of two molecules of BDQ to the rotor c-ring, proving that increasing the number of BDQ molecule enhances inhibitory potency. The last study in this research domain investigated the impact of triple mutations (L59V, E61D, and I66M) on the binding of BDQ to Mtb F1F0 ATP-synthase. The study showed that the mutations significantly impacted the binding affinity of BDQ, evidenced by a decrease in the estimated binding free energy (ΔG). Likewise, the structural integrity and conformational architecture of F1F0 ATP-synthase was distorted due to the mutation, which could have interfered with the binding of BDQ. The third domain of the research in this thesis investigated some small molecule inhibitors' inhibitory mechanism against some therapeutic targets in cancer, specifically PTPRZ and hTCRvi CD1d. Studies in the third domain of the research in the thesis began with the investigation of the investigation of the inhibitory mechanism of NAZ2329, an allosteric inhibitor of PTPRZ, by specifical investigating its binding effect on the atomic flexibility of the WPD-loop. Having been established as crucial determinant of the catalytic activity of PTPRZ an implicated protein in glioblastoma cells, its successfully therapeutic modulation could present a viable treatment option in glioblastoma. Structural insights from an MD simulation revealed that NAZ2329 binding induces an open conformation of the WPD-loop which subsequently prevents the participation of the catalytic aspartate of PTPRZ from participating in catalysis hence inhibiting the activity of PTPRZ. A pharmacophore was also created based of high energy contributing residues which highlighted essential moieties of NAZ2329 and could be used in screening compound libraries for potential inhibitors of PTPRZ. A second study in this domain sought to explore how structural modification could improve a therapeutic agent's potency from an atomistic perspective. This study was based on an earlier report in which the incorporation of a hydrocinnamoyl ester on C6’’ and C4-OH truncation of the sphingoid base of KRN7000 generated a novel compound AH10-7 high therapeutic potency and selectivity in human TCR-CD1d and subsequently results in the activation of invariant natural killer T cells (iNKT). The hydrocinnamoyl ester moiety was shown to engage in high-affinity interactions, possibly accounting for the selectivity and higher potency of AH10-7. Molecular and structural perspectives provided could aid in the design of novel α-GalCer derivatives for cancer immunotherapeutics. Chapter 3 provides theoretical insights into the various molecular modeling tools and techniques employed to investigate the various conformational changes, structural conformations, and the associated mechanism of inhibitions of the studied inhibitors towards viral, tuberculosis, and cancer therapy. Chapter 4 provided sufficient details on druggability and drug-likeness principles and their recent advancements in the drug discovery field. The study also presents the different computational tools and their reliability of predictive analysis in the drug discovery domain. It thus provides a comprehensive guide for computational-oriented drug discovery research. Chapter 5 provides an understanding of the binding mechanism of Remdesivir, providing structural and conformational implications on SARS-CoV-2 RdRp upon its binding and identifying its crucial pharmacophoric moieties. Chapter 6 explains the mechanism of inhibition of a novel benzothiophene derivative, revealing its distortion of the native extensively networked and compact residue profile. Chapter 7 unravels molecular and structural bases behind this dual inhibitory potential of the novel inhibitor IMP-1088 toward common cold therapy using augmentative computational and cheminformatics methods. The study also highlights the pharmacological propensities of IMP- 1088. Chapter 8 unravels the molecular mechanisms and structural dynamics of the dual inhibitory activity of PAS-M towards DHFR and FDTS. Chapter 9 reports the structural dynamics and atomistic perspectives that account for the reported ability of BDQ to halt the ion shuttling ability of mycobacterial c-ring. Chapter 10 presents the structural dynamics and conformational changes that occur on Mtb F1F0 ATP-synthase binding as a result of the triple mutations using molecular dynamics simulations, free energy binding, and residue interaction network (RIN) analyses. Chapter 11 explored the impact of NAZ2329, a recently identified allosteric inhibitor of Protein Tyrosine Phosphatase Receptor Zeta (PTPRZ), on the atomic flexibility of the WPD-loop, an essential loop in the inhibition of PTPRZ. The study also presents the drug-likeness of NAZ2329 using in silico techniques and its general inhibitory mechanism. Chapter 12 provides atomistic insights into the structural dynamics and selective mechanisms of AH10-7 for human TCR-CD1d towards activating iNKT cells. The studies in this thesis collectively present a thorough and comprehensive in silico perspective that characterizes the pharmacological inhibition of some known therapeutic targets in viral infections, tuberculosis, and cancer. The augmentative integration of computational methods to provide structural insights could help design highly selective inhibitors of these therapeutic targets. Therefore, the findings presented are fundamental to the design and development of next generation lead compounds with improved therapeutic activities and minimal toxicities

    Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

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    This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs
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