283 research outputs found

    Structural and functional characterization of the egress and invasion machinery of the Malaria parasite: proposing a new way forward in Malaria therapeutics from an atomistic perspective.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.ABSTRACT The past decade has witnessed numerous efforts to control the invasive tactics of the malarial parasite, including focused research towards selective malarial inhibitors of Plasmodium falciparum, the most lethal strain of the Plasmodium species. The recent discovery of the key mediators of egress and erythrocyte invasion of the malaria parasite has opened a new avenue that may be harnessed for the development of effective therapeutics that may permanently eradicate the malaria virus. These new parasitic targets of P. falciparum are PIX and PX and have gained considerable attention in drug discovery pipelines however, the absence of crystal structures of these enzymes evidenced a lack in structural information, as there is currently little known regarding the structural dynamics, active site domains and the mechanism of inhibition of these enzymes. This has therefore led to the modeling of the 3D protein structure of each enzyme to gain a fundamental understanding regarding the structural and functional characteristics that may be visualized from an atomistic perspective. The emergence of new drug targets has led to the integral use of computational techniques including molecular modeling, molecular docking, virtual screening protocols and molecular dynamic simulations which allow chemists to evaluate and assess millions of compounds and thus funnel out potential lead drugs. These in silico techniques further justify the current use of Computer-Aided Drug Design as a cost-effective approach to fast track the drug discovery process. The above-mentioned techniques, amongst a vast range of other computational tools were integrated in this study to provide insight into conformational changes that elucidate potential inhibitory mechanisms, identification of the active site cleft, characterization and pharmacophoric features leading to novel small molecule inhibitors. This study focused on analysing the flap dynamics specific to the aspartic protease family of enzymes using a defined set of parameters to map out the binding domain for the design of potential antimalarial drugs. To gain a molecular perspective of the conformational binding of two proposed experimental drugs which showed substantial inhibitory activity against PIX and PX molecular dynamic simulations were performed and further evaluated employing in silico thermodynamic analysis to provide insight into the proposed binding of mode of each inhibitor, highlighting the key moieties required for binding. A pharmacophoric model was also generated using in silico tools to screen for tailored inhibitors specific to PIX. The aim of this study was to generate fundamental insight into the structural and functional characterization of two prominent targets that play an indispensable role in survival of the malaria virus. The implementation of the information extracted from this study, may provide a structural outline for molecular biologists, and pharmaceutical scientists to aid in the design of novel antimalarial therapeutics

    Discovery of Alternative Artemisinin Binding Sites in Plasmodium falciparum ATPase-6

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    Malaria is a fatal yet preventable and treatable disease. It is commonly spread through the bite of an infected Anopheles mosquito. Malaria parasites belong to the Plasmodium genus and can be caused by the falciparum, malariae, ovale, vivax, and knowlesi species. Artemisinin is an endoperoxide lactone extracted from qinghaosu (Artemisia annua L. or sweet wormwood). It and its derivatives possess uncharacteristically rapid action against Plasmodium falciparum. Artemisinin is unique in its effectiveness in deadly cerebral malaria. The endoperoxide bridge is crucial for its antimalarial activity; however, how it aids in killing the parasite is unknown. While there are multiple suggested modes of action (MOA) for artemisinin, none to date have a well-characterized protein target except for PfATP6, proposed by Krishna, et al. His work suggested that artemisinin binds to the homologous thapsigargin binding site of PfATP6. Herein, knowledge of the previously proposed mechanisms was utilized along with numerous computational techniques to determine a more detailed and plausible MOA for artemisinin against PfATP6. This led to the discovery of two new, putative PfATP6 binding sites for artemisinin. None of the previous work has explained the co-dependence of antimalarial efficacy on the concentration of Fe(II). In our work, we searched for putative sites containing a cysteine residue, Fe(II) and artemisinin in a conformation allowing for the well-accepted ring-opened C4 primary artemisinin carbon radical to form a covalent bond with a cysteine thiol rather than undergoing an intramolecular self-emolative diradical ring closure. Clearly there are geometric constraints for a transition state that side-steps the latter reaction and instead allows for interception by a cysteine thiol. We have suggested mechanistic possibilities for this capture by the artemisinin C4 radical and propose that PfATP6 is deactivated by blocking the Ca(II) channel by the modification of a cysteine at either C1031 or C92. It is alternatively possible that these modifications lead to alterations in the function of the protein, rendering it dysfunctional. Structure-based virtual screening was then used to screen a commercial database of compounds to find novel inhibitors of PfATP6. Biological testing will be done to determine if targeting these new sites can produce potent antimalarials with less structural complexity than artemisinin itself

    Molecular Docking: Metamorphosis in Drug Discovery

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    Molecular docking is recognized a part of computer-aided drug design that is mostly used in medicinal chemistry. It has proven to be an effective, quick, and low-cost technique in both scientific and corporate contexts. It helps in rationalizing the ligands activity towards a target to perform structure-based drug design (SBDD). Docking assists the revealing of novel compound of therapeutic interest, forecasting ligand-protein interaction at a molecular basis and delineating structure activity relationships (SARs). Molecular docking acts as a boon to identify promising agents in emergence of diseases which endangering the human health. In this chapter, we engrossed on the techniques, types, opportunities, challenges and success stories of molecular docking in drug development

    QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors

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    This study presents a robust and integrated methodology that harnesses a range of computational techniques to facilitate the design and prediction of new inhibitors targeting the JAK3/STAT pathway. This methodology encompasses several strategies, including QSAR analysis, pharmacophore modeling, ADMET prediction, covalent docking, molecular dynamics (MD) simulations, and the calculation of binding free energies (MM/GBSA). An efficacious QSAR model was meticulously crafted through the employment of multiple linear regression (MLR). The initial MLR model underwent further refinement employing an artificial neural network (ANN) methodology aimed at minimizing predictive errors. Notably, both MLR and ANN exhibited commendable performance, showcasing R2 values of 0.89 and 0.95, respectively. The model's precision was assessed via leave-one-out cross-validation (CV) yielding a Q2 value of 0.65, supplemented by rigorous Y-randomization. , The pharmacophore model effectively differentiated between active and inactive drugs, identifying potential JAK3 inhibitors, and demonstrated validity with an ROC value of 0.86. The newly discovered and designed inhibitors exhibited high inhibitory potency, ranging from 6 to 8, as accurately predicted by the QSAR models. Comparative analysis with FDA-approved Tofacitinib revealed that the new compounds exhibited promising ADMET properties and strong covalent docking (CovDock) interactions. The stability of the new discovered and designed inhibitors within the JAK3 binding site was confirmed through 500 ns MD simulations, while MM/GBSA calculations supported their binding affinity. Additionally, a retrosynthetic study was conducted to facilitate the synthesis of these potential JAK3/STAT inhibitors. The overall integrated approach demonstrates the feasibility of designing novel JAK3/STAT inhibitors with robust efficacy and excellent ADMET characteristics that surpass Tofacitinib by a significant margin

    Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.

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    HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2A -R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs

    Has Molecular Docking Ever Brought us a Medicine?

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    Molecular docking has been developed and improving for many years, but its ability to bring a medicine to the drug market effectively is still generally questioned. In this chapter, we introduce several successful cases including drugs for treatment of HIV, cancers, and other prevalent diseases. The technical details such as docking software, protein data bank (PDB) structures, and other computational methods employed are also collected and displayed. In most of the cases, the structures of drugs or drug candidates and the interacting residues on the target proteins are also presented. In addition, a few successful examples of drug repurposing using molecular docking are mentioned in this chapter. It should provide us with confidence that the docking will be extensively employed in the industry and basic research. Moreover, we should actively apply molecular docking and related technology to create new therapies for diseases

    Rational Drug Design for Neglected Diseases: Implementation of Computational Methods to Construct Predictive Devices and Examine Mechanisms

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    Over a billion individuals worldwide suffer from neglected diseases. This equates to approximately one-sixth of the human population. These infections are often endemic in remote tropical regions of impoverished populations where vectors can flourish and infected individuals cannot be effectively treated due to a lack of hospitals, medical equipment, drugs, and trained personnel. The few drugs that have been approved for the treatments of such illnesses are not widely used because they are riddled with inadequate implications of cost, safety, drug availability, administration, and resistance. Hence, there exists an eminent need for the design and development of improved new therapeutics. Influential world-renowned scientists in the Consortium for Parasitic Drug Development (CPDD) have preformed extensive biological testing for compounds active against parasites that cause neglected diseases. These data were acquired through several collaborations and found applicable to computational studies that examine quantitative structure-activity relationships through the development of predictive models and explore structural relationships through docking. Both of these in silico tools can contribute to an understanding of compound structural importance for specific targets. The compilation of manuscripts presented in this dissertation focus on three neglected diseases: trypanosomiasis, Chagas disease, and leishmaniasis. These diseases are caused by kinetoplastid parasites Trypanosoma brucei, Trypanosoma cruzi, and Leishmania spp., respectively. Statistically significant predictive devices were developed for the inhibition of the: (1) T. brucei P2 nucleoside transporter, (2) T. cruzi parasite at two temperatures, and (3) two species of Leishmania. From these studies compound structural importance was assessed for the targeting of each parasitic system. Since these three parasites are all from the Order Kinetoplastida and the kinetoplast DNA has been determined a viable target, compound interactions with DNA were explored to gain insight into binding modes of known and novel compounds

    Machine learning for target discovery in drug development.

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    The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant 807281) and FCT/FEDER (02/SAICT/2017, Grant 28333) for funding. G.J.L.B. is a Royal Society University Research Fellow (URF\R\180019) and a FCT Investigator (IF/00624/2015)
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