Structure based virtual screening of natural product molecules as glycosidase inhibitors

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
N. S. Hari Narayana Moorthy ◽  
Natércia F. Brás ◽  
Maria J. Ramos ◽  
Pedro A. Fernandes
Keyword(s):  
Virtual Screening ◽  
Natural Product ◽  
Glycosidase Inhibitors

A primer on natural product-based virtual screening

2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Eleni Koulouridi ◽  
Marilia Valli ◽  
Fidele Ntie-Kang ◽  
Vanderlan da Silva Bolzani
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Molecular Modeling ◽  
Virtual Screening ◽  
Natural Product ◽  
General Purpose ◽  
Target Type ◽  
Computational Techniques ◽  
Specific Subset ◽  
Virtual Libraries

Abstract Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

scholarly journals Identification of a natural product-like STAT3 dimerization inhibitor by structure-based virtual screening

2014 ◽  
Vol 5 (6) ◽  
pp. e1293-e1293 ◽  
Author(s):  
L-J Liu ◽  
K-H Leung ◽  
D S-H Chan ◽  
Y-T Wang ◽  
D-L Ma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Natural Product ◽  
Dimerization Inhibitor

scholarly journals Natural Product Compounds in Alpinia officinarum and Ginger are Potent SARS-CoV-2 Papain-like Protease Inhibitors

2020 ◽  
Author(s):  
Dibakar Goswami ◽  
Mukesh Kumar ◽  
Sunil K. Ghosh ◽  
Amit Das
Keyword(s):  
Virtual Screening ◽  
Natural Product ◽  
Protease Inhibitors ◽  
In Silico ◽  
Drug Target ◽  
Homology Modelling ◽  
Protease Domain ◽  
Lead Compounds ◽  
Alpinia Officinarum ◽  
Natural Product Compounds

SARS-CoV-2 or COVID-19 has caused more than 10,00,000 infections and ~55,000 deaths worldwide spanning over 203 countries, and the numbers are exponentially increasing. Due to urgent need of treating the SARS infection, many approved, pre-clinical, anti-viral, anti-malarial and anti-SARS drugs are being administered to patients. SARS-CoV-2 papain-like protease (PLpro) has a protease domain which cleaves the viral polyproteins a/b, necessary for its survival and replication, and is one of the drug target against SARS-CoV-2. 3D structures of SARS-CoV-2 PLpro were built by homology modelling. Two models having partially open and closed conformations were used in our study. Virtual screening of natural product compounds was performed. We prepared an in house library of compounds found in rhizomes, Alpinia officinarum, ginger and curcuma, and docked them into the solvent accessible S3-S4 pocket of PLpro. Eight compounds from Alpinia officinarum and ginger bind with high in silico affinity to closed PLpro conformer, and hence are potential SARS-CoV-2 PLpro inhibitors. Our study reveal new lead compounds targeting SARS-CoV-2. Further structure based modifications or extract formulations of these compounds can lead to highly potent inhibitors to treat SARS-CoV-2 infections.<br>

scholarly journals Natural Product Compounds in Alpinia officinarum and Ginger are Potent SARS-CoV-2 Papain-like Protease Inhibitors

2020 ◽  
Author(s):  
Dibakar Goswami ◽  
Mukesh Kumar ◽  
Sunil K. Ghosh ◽  
Amit Das
Keyword(s):  
Virtual Screening ◽  
Natural Product ◽  
Protease Inhibitors ◽  
In Silico ◽  
Drug Target ◽  
Homology Modelling ◽  
Protease Domain ◽  
Lead Compounds ◽  
Alpinia Officinarum ◽  
Natural Product Compounds

SARS-CoV-2 or COVID-19 has caused more than 10,00,000 infections and ~55,000 deaths worldwide spanning over 203 countries, and the numbers are exponentially increasing. Due to urgent need of treating the SARS infection, many approved, pre-clinical, anti-viral, anti-malarial and anti-SARS drugs are being administered to patients. SARS-CoV-2 papain-like protease (PLpro) has a protease domain which cleaves the viral polyproteins a/b, necessary for its survival and replication, and is one of the drug target against SARS-CoV-2. 3D structures of SARS-CoV-2 PLpro were built by homology modelling. Two models having partially open and closed conformations were used in our study. Virtual screening of natural product compounds was performed. We prepared an in house library of compounds found in rhizomes, Alpinia officinarum, ginger and curcuma, and docked them into the solvent accessible S3-S4 pocket of PLpro. Eight compounds from Alpinia officinarum and ginger bind with high in silico affinity to closed PLpro conformer, and hence are potential SARS-CoV-2 PLpro inhibitors. Our study reveal new lead compounds targeting SARS-CoV-2. Further structure based modifications or extract formulations of these compounds can lead to highly potent inhibitors to treat SARS-CoV-2 infections.<br>

scholarly journals Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

2021 ◽  
Author(s):  
Janosch Menke ◽  
Joana Massa ◽  
Oliver Koch
Keyword(s):  
Neural Networks ◽  
Natural Products ◽  
Artificial Neural Networks ◽  
Virtual Screening ◽  
Natural Product ◽  
Recent Work ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Depth Analysis

<div>Due to its desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more appropriate for the use with natural products. A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. An in-depth analysis showed that the extracted natural product specific neural fingerprints outperforms traditional as well as natural product specific fingerprints on three datasets. Further, we explore how the activation from the output layer of a network can work as a novel natural product likeness score. Overall two natural product specific datasets were generated, which are publicly available together with the code to create the fingerprints and the novel natural product likeness score.<br></div>

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