In silico study for prediction of novel bioactivities of the endophytic fungal alkaloid, mycoleptodiscin B for human targets

Deshapriya, Uthpala S.; Dinuka, D. L. Senal; Ratnaweera, Pamoda B.; Ratnaweera, Chinthaka N.

doi:10.1016/j.jmgm.2020.107767

Cited by 5 publications

(4 citation statements)

References 65 publications

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“…According to the previous studies, the change of conformational entropy (– TΔS ) was usually evaluated by quasi harmonic analysis [47] , [48] . Considering the heavy computational cost and low prediction accuracy for quasi harmonic analysis, – TΔS item is often neglected [49] , [50] when only the relative binding free energies of similar ligands are needed [46] . Since the relative binding free energies between HT and three protein systems are considered in our study, –TΔS term was ignored.…”

Section: Methodsmentioning

confidence: 99%

Molecular insights into the inhibition mechanism of harringtonine against essential proteins associated with SARS-CoV-2 entry

Yang

Zhao

et al. 2023

International Journal of Biological Macromolecules

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Section: Methodsmentioning

confidence: 99%

Molecular insights into the inhibition mechanism of harringtonine against essential proteins associated with SARS-CoV-2 entry

Yang

Zhao

et al. 2023

International Journal of Biological Macromolecules

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“…With the benefits of using vertebrate models for cryptococcosis also come rising concerns about the ethical aspects of research using animal models, the higher maintenance costs, and more elaborate experimental manipulations [48,80]. This presses on the need to adapt novel and alternative (in silico) approaches in order to minimize animal experiments where possible [143][144][145].…”

Section: R's Conceptmentioning

confidence: 99%

Preclinical Models for Cryptococcosis of the CNS and Their Characterization Using In Vivo Imaging Techniques

Roosen,

Maes,

Musetta

et al. 2024

JoF

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Infections caused by Cryptococcus neoformans and Cryptococcus gattii remain a challenge to our healthcare systems as they are still difficult to treat. In order to improve treatment success, in particular for infections that have disseminated to the central nervous system, a better understanding of the disease is needed, addressing questions like how it evolves from a pulmonary to a brain disease and how novel treatment approaches can be developed and validated. This requires not only clinical research and research on the microorganisms in a laboratory environment but also preclinical models in order to study cryptococci in the host. We provide an overview of available preclinical models, with particular emphasis on models of cryptococcosis in rodents. In order to further improve the characterization of rodent models, in particular the dynamic aspects of disease manifestation, development, and ultimate treatment, preclinical in vivo imaging methods are increasingly used, mainly in research for oncological, neurological, and cardiac diseases. In vivo imaging applications for fungal infections are rather sparse. A second aspect of this review is how research on models of cryptococcosis can benefit from in vivo imaging methods that not only provide information on morphology and tissue structure but also on function, metabolism, and cellular properties in a non-invasive way.

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“…Natural products are diverse in terms of structure and biological activities [5][6][7][8] and thus offer superior therapeutic options and safety than the conventional and synthetic compounds used in high throughput screening processes in drug discovery and development pipelines [9]. Thus, natural products, especially phytochemical extracts from medicinal flora have proven to be immensely valuable as a lead compound for drug discovery and development [10,11].…”

Section: Introductionmentioning

confidence: 99%

In-silico investigation of curcumin drug-likeness, gene-targets and prognostic relevance of the targets in panels of human cancer cohorts

Oshevire¹,

Mustapha²,

Alozieuwa³

et al. 2021

GSC Biol. and Pharm. Sci.

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Despite advancements in diagnostic and standard treatment modalities, cancer survival rate remains disappointing globally. It has however, been recognized that exploring the therapeutic properties of secondary metabolite from natural products may alleviate the problems of drug resistance and toxicity that besiege the conventional therapies, and hence improve the overall prognosis of cancer patient. To this end curcumin, a polyphenolic natural compound has been widely studied for it anticancer activities in in vitro and in vivo models. Computational technology has significantly improved the success rate of drug discovery and development, hence, it has become a widely explore tool in drug candidate identification. In this study we used computational approached to identify 12 genes that are potential druggable candidate for curcumin. The genes identified were found to be enriched in cancer and drug resistance associated signaling pathways. Interestingly, the top 3 identified genes; Microtubule-associated protein tau (MAPT), Toll-like receptor 9 (TLR9) and Tyrosyl-DNA phosphodiesterase 1 (TDP1) were observed to be over expressed in multiple cancer cohorts and were associated with poor prognoses of the patients. Curcumin has good physicochemical, bioavailability and ADMET properties. Importantly, it met the Lipinski's Rule of 5 for drug likeness and thus worthy of further in vitro and in vivo confirmation studies.

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In silico study for prediction of novel bioactivities of the endophytic fungal alkaloid, mycoleptodiscin B for human targets

Cited by 5 publications

References 65 publications

Molecular insights into the inhibition mechanism of harringtonine against essential proteins associated with SARS-CoV-2 entry

Molecular insights into the inhibition mechanism of harringtonine against essential proteins associated with SARS-CoV-2 entry

Preclinical Models for Cryptococcosis of the CNS and Their Characterization Using In Vivo Imaging Techniques

In-silico investigation of curcumin drug-likeness, gene-targets and prognostic relevance of the targets in panels of human cancer cohorts

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