scholarly journals Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

2020 ◽  
Vol 21 (19) ◽  
pp. 7102
Author(s):  
Fabian Mayr ◽  
Gabriele Möller ◽  
Ulrike Garscha ◽  
Jana Fischer ◽  
Patricia Rodríguez Castaño ◽  
...  
Keyword(s):  
Natural Products ◽  
In Silico ◽  
Large Scale ◽  
Target Prediction ◽  
Innovative Drug ◽  
Protein Targets ◽  
Modern Computer ◽  
Cyclooxygenase 1 ◽  
Pharmacological Targets ◽  
Drug Leads

Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature’s treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)—a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.

scholarly journals Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

2020 ◽  
Author(s):  
Fabian Mayr ◽  
Gabriele Möller ◽  
Ulrike Garscha ◽  
Jana Fischer ◽  
Patricia Rodríguez Castaño ◽  
...  
Keyword(s):  
Natural Products ◽  
In Silico ◽  
Large Scale ◽  
Target Prediction ◽  
Innovative Drug ◽  
Protein Targets ◽  
Modern Computer ◽  
Cyclooxygenase 1 ◽  
Pharmacological Targets ◽  
Drug Leads

ABSTRACTNatural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature’s treasures, however, could be used more effectively. Yet, reliable pipelines for large scale target prediction of natural products are still rare. We have developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs) – a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target prediction and guidance on using the respective tools.

scholarly journals Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

2020 ◽  
Author(s):  
Fabian Mayr ◽  
Gabriele Möller ◽  
Ulrike Garscha ◽  
Jana Fischer ◽  
Patricia Rodríguez Castano ◽  
...  
Keyword(s):  
Natural Products ◽  
In Silico ◽  
Large Scale ◽  
Target Prediction ◽  
Innovative Drug ◽  
Protein Targets ◽  
Modern Computer ◽  
Cyclooxygenase 1 ◽  
Pharmacological Targets ◽  
Drug Leads

Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature’s treasures, however, could be used more effectively. Yet, reliable pipelines for large scale target prediction of natural products are still rare. We have developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs) – a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β- hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target prediction and guidance on using the respective tools.

scholarly journals Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1570
Author(s):  
Ghita Ghislat ◽  
Taufiq Rahman ◽  
Pedro J. Ballester
Keyword(s):  
Carbonic Anhydrase ◽  
In Silico ◽  
Large Scale ◽  
Target Prediction ◽  
Cost Effective ◽  
Orphan Drugs ◽  
Orphan Receptor ◽  
Carbonic Anhydrase Ii ◽  
Fundamental Aspect

Background and purpose: Identifying the macromolecular targets of drug molecules is a fundamental aspect of drug discovery and pharmacology. Several drugs remain without known targets (orphan) despite large-scale in silico and in vitro target prediction efforts. Ligand-centric chemical-similarity-based methods for in silico target prediction have been found to be particularly powerful, but the question remains of whether they are able to discover targets for target-orphan drugs. Experimental Approach: We used one of these in silico methods to carry out a target prediction analysis for two orphan drugs: actarit and malotilate. The top target predicted for each drug was carbonic anhydrase II (CAII). Each drug was therefore quantitatively evaluated for CAII inhibition to validate these two prospective predictions. Key Results: Actarit showed in vitro concentration-dependent inhibition of CAII activity with submicromolar potency (IC50 = 422 nM) whilst no consistent inhibition was observed for malotilate. Among the other 25 targets predicted for actarit, RORγ (RAR-related orphan receptor-gamma) is promising in that it is strongly related to actarit’s indication, rheumatoid arthritis (RA). Conclusion and Implications: This study is a proof-of-concept of the utility of MolTarPred for the fast and cost-effective identification of targets of orphan drugs. Furthermore, the mechanism of action of actarit as an anti-RA agent can now be re-examined from a CAII-inhibitor perspective, given existing relationships between this target and RA. Moreover, the confirmed CAII-actarit association supports investigating the repositioning of actarit on other CAII-linked indications (e.g., hypertension, epilepsy, migraine, anemia and bone, eye and cardiac disorders).

scholarly journals In silico study of natural compounds from sesame against COVID-19 by targeting Mpro, PLpro and RdRp

RSC Advances ◽  
2021 ◽  
Vol 11 (36) ◽  
pp. 22398-22408
Author(s):  
Ahmed E. Allam ◽  
Yhiya Amen ◽  
Ahmed Ashour ◽  
Hamdy K. Assaf ◽  
Heba Ali Hassan ◽  
...  
Keyword(s):  
Natural Products ◽  
Traditional Medicine ◽  
In Silico ◽  
Natural Compounds ◽  
New Drug ◽  
In Silico Study ◽  
Drug Leads ◽  
Promising Source

Natural products and traditional medicine products with known safety profiles are a promising source for the discovery of new drug leads.

Macromolecular Targets of Antiparasitic Germacranolide Sesquiterpenoids: An In Silico Investigation

2020 ◽  
Vol 23 (6) ◽  
pp. 477-503
Author(s):  
Phillip M. Arnston ◽  
William N. Setzer
Keyword(s):  
Natural Products ◽  
In Silico ◽  
Density Functional ◽  
Molecular Mechanisms ◽  
Neglected Tropical Diseases ◽  
Treatment Options ◽  
Protein Structures ◽  
Current Treatment ◽  
In Silico Screening ◽  
Protein Targets

Background: The parasitic protozoal infections leishmaniasis, human African trypanosomiasis, and Chagas disease are neglected tropical diseases that pose serious health risks for much of the world’s population. Current treatment options suffer from limitations, but plantderived natural products may provide economically advantageous therapeutic alternatives. Several germacranolide sesquiterpenoids have shown promising antiparasitic activities, but the mechanisms of activity have not been clearly established. Objective: The objective is to use in silico screening of known antiparasitic germacranolides against recognized protozoal protein targets in order to provide insight into the molecular mechanisms of activity of these natural products. Methods: Conformational analyses of the germacranolides were carried out using density functional theory, followed by molecular docking. A total of 88 Leishmania protein structures, 86 T. brucei protein structures, and 50 T. cruzi protein structures were screened against 27 antiparasitic germacranolides. Results: The in-silico screening has revealed which of the protein targets of Leishmania spp., Trypanosoma brucei, and Trypanosoma cruzi are preferred by the sesquiterpenoid ligands.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

In-silico Analyses of Natural Products on Leishmania Enzyme Targets

2015 ◽  
Vol 15 (3) ◽  
pp. 253-269 ◽  
Author(s):  
L. Scotti ◽  
H. Ishiki ◽  
F.J.B. Mendonca ◽  
M.S. Silva ◽  
M.T. Scotti
Keyword(s):  
Natural Products ◽  
In Silico ◽  
In Silico Analyses

Drug repurposing using similarity-based target prediction, docking studies and scaffold hopping of lefamulin

2020 ◽  
Vol 17 ◽  
Author(s):  
Shikha Sharma ◽  
Shweta Sharma ◽  
Vaishali Pathak ◽  
Parwinder Kaur ◽  
Rajesh Kumar Singh
Keyword(s):  
In Silico ◽  
Target Prediction ◽  
Drug Repurposing ◽  
Docking Studies ◽  
Target Protein ◽  
Future Perspective ◽  
Antibacterial Drug ◽  
Scaffold Hopping ◽  
Target Proteins ◽  
Renin Inhibitors

Aim: To investigate and validate the potential target proteins for drug repurposing of newly FDA approved antibacterial drug. Background: Drug repurposing is the process of assigning indications for drugs other than the one(s) that they were initially developed for. Discovery of entirely new indications from already approved drugs is highly lucrative as it minimizes the pipeline of the drug development process by reducing time and cost. In silico driven technologies made it possible to analyze molecules for different target proteins which are not yet explored. Objective: To analyze possible targets proteins for drug repurposing of lefamulin and their validation. Also, in silico prediction of novel scaffolds from lefamulin has been performed for assisting medicinal chemists in future drug design. Methods: A similarity-based prediction tool was employed for predicting target protein and further investigated using docking studies on PDB ID: 2V16. Besides, various in silico tools were employed for prediction of novel scaffolds from lefamulin using scaffold hopping technique followed by evaluation with various in silico parameters viz., ADME, synthetic accessibility and PAINS. Results: Based on the similarity and target prediction studies, renin is found as the most probable target protein for lefamulin. Further, validation studies using docking of lefamulin revealed the significant interactions of lefamulin with the binding pocket of the target protein. Also, three novel scaffolds were predicted using scaffold hopping technique and found to be in the limit to reduce the chances of drug failure in the physiological system during the last stage approval process. Conclusion: To encapsulate the future perspective, lefamulin may assist in the development of the renin inhibitors and, also three possible novel scaffolds with good pharmacokinetic profile can be developed into both as renin inhibitors and for bacterial infections.

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