scholarly journals Identification of 6-(piperazin-1-yl)-1,3,5-triazine as a chemical scaffold with broad anti-schistosomal activities

2020 ◽  
Vol 5 ◽  
pp. 169
Author(s):  
Gilda Padalino ◽  
Iain W. Chalmers ◽  
Andrea Brancale ◽  
Karl F. Hoffmann
Keyword(s):  
Virtual Screening ◽  
Small Molecules ◽  
Adult Worm ◽  
In Silico ◽  
Egg Production ◽  
Homology Modelling ◽  
Homology Model ◽  
Biological Data ◽  
Significant Activity ◽  
Starting Point

Background: Schistosomiasis, caused by infection with blood fluke schistosomes, is a neglected tropical disease of considerable importance in resource-poor communities throughout the developing world. In the absence of an immunoprophylactic vaccine and due to over-reliance on a single chemotherapy (praziquantel), schistosomiasis control is at risk should drug insensitive schistosomes develop. In this context, application of in silico virtual screening on validated schistosome targets has proven successful in the identification of novel small molecules with anti-schistosomal activity.   Methods: Focusing on the Schistosoma mansoni histone methylation machinery, we herein have used RNA interference (RNAi), ELISA-mediated detection of H3K4 methylation, homology modelling and in silico virtual screening to identify a small collection of small molecules for anti-schistosomal testing. A combination of low to high-throughput whole organism assays were subsequently used to assess these compounds’ activities on miracidia to sporocyst transformation, schistosomula phenotype/motility metrics and adult worm motility/oviposition readouts. Results: RNAi-mediated knockdown of smp_138030/smmll-1 (encoding a histone methyltransferase, HMT) in adult worms (~60%) reduced parasite motility and egg production. Moreover, in silico docking of compounds into Smp_138030/SmMLL-1’s homology model highlighted competitive substrate pocket inhibitors, some of which demonstrated significant activity on miracidia, schistosomula and adult worm lifecycle stages together with variable effects on HepG2 cells. Particularly, the effect of compounds containing a 6-(piperazin-1-yl)-1,3,5-triazine core on adult schistosomes recapitulated the results of the smp_138030/smmll-1 RNAi screens. Conclusions: The biological data and the structure-activity relationship presented in this study define the 6-(piperazin-1-yl)-1,3,5-triazine core as a promising starting point in ongoing efforts to develop new urgently needed schistosomicides.

scholarly journals Identification of 6-(piperazin-1-yl)-1,3,5-triazine as a chemical scaffold with broad anti-schistosomal activities

2020 ◽  
Vol 5 ◽  
pp. 169
Author(s):  
Gilda Padalino ◽  
Iain W. Chalmers ◽  
Andrea Brancale ◽  
Karl F. Hoffmann
Keyword(s):  
Virtual Screening ◽  
Small Molecules ◽  
Adult Worm ◽  
In Silico ◽  
Egg Production ◽  
Homology Modelling ◽  
Homology Model ◽  
Biological Data ◽  
Significant Activity ◽  
Starting Point

Background: Schistosomiasis, caused by infection with blood fluke schistosomes, is a neglected tropical disease of considerable importance in resource-poor communities throughout the developing world. In the absence of an immunoprophylactic vaccine and due to over-reliance on a single chemotherapy (praziquantel), schistosomiasis control is at risk should drug insensitive schistosomes develop. In this context, application of in silico virtual screening on validated schistosome targets has proven successful in the identification of novel small molecules with anti-schistosomal activity.   Methods: Focusing on the Schistosoma mansoni histone methylation machinery, we herein have used RNA interference (RNAi), ELISA-mediated detection of H3K4 methylation, homology modelling and in silico virtual screening to identify a small collection of small molecules for anti-schistosomal testing. A combination of low to high-throughput whole organism assays were subsequently used to assess these compounds’ activities on miracidia to sporocyst transformation, schistosomula phenotype/motility metrics and adult worm motility/oviposition readouts. Results: RNAi-mediated knockdown of smp_138030/smmll-1 (encoding a histone methyltransferase, HMT) in adult worms (~60%) reduced parasite motility and egg production. Moreover, in silico docking of compounds into Smp_138030/SmMLL-1’s homology model highlighted competitive substrate pocket inhibitors, some of which demonstrated significant activity on miracidia, schistosomula and adult worm lifecycle stages together with variable effects on HepG2 cells. Particularly, the effect of compounds containing a 6-(piperazin-1-yl)-1,3,5-triazine core on adult schistosomes recapitulated the results of the smp_138030/smmll-1 RNAi screens. Conclusions: The biological data and the structure-activity relationship presented in this study define the 6-(piperazin-1-yl)-1,3,5-triazine core as a promising starting point in ongoing efforts to develop new urgently needed schistosomicides.

Discovery of New AKT1 Inhibitors by Combination of In silico Structure Based Virtual Screening Approaches and Biological Evaluations

2019 ◽  
Author(s):  
Filip Fratev ◽  
Denisse A. Gutierrez ◽  
Renato J. Aguilera ◽  
suman sirimulla
Keyword(s):  
Virtual Screening ◽  
Success Rate ◽  
Protein Interactions ◽  
In Silico ◽  
Biological Data ◽  
In Vitro Binding ◽  
Vitro Binding ◽  
Screening Protocol ◽  
Screening Approaches

AKT1 is emerging as a useful target for treating cancer. Herein, we discovered a new set of ligands that inhibit the AKT1, as shown by in vitro binding and cell line studies, using a newly designed virtual screening protocol that combines structure-based pharmacophore and docking screens. Taking together with the biological data, the combination of structure based pharamcophore and docking methods demonstrated reasonable success rate in identifying new inhibitors (60-70%) proving the success of aforementioned approach. A detail analysis of the ligand-protein interactions was performed explaining observed activities.<br>

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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 In Silico Characterization and Virtual Screening of GntR/HutC Family Transcriptional Regulator MoyR: A Potential Monooxygenase Regulator in Mycobacterium tuberculosis

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1241
Author(s):  
Thanusha Dhananji Abeywickrama ◽  
Inoka Chinthana Perera
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
In Silico ◽  
Streptomyces Coelicolor ◽  
Homology Modelling ◽  
Transcriptional Regulators ◽  
Physicochemical Analysis ◽  
Structure Stability ◽  
In Silico Characterization ◽  
Drug Leads

Mycobacterium tuberculosis is a well-known pathogen due to the emergence of drug resistance associated with it, where transcriptional regulators play a key role in infection, colonization and persistence. The genome of M. tuberculosis encodes many transcriptional regulators, and here we report an in-depth in silico characterization of a GntR regulator: MoyR, a possible monooxygenase regulator. Homology modelling provided a reliable structure for MoyR, showing homology with a HutC regulator DasR from Streptomyces coelicolor. In silico physicochemical analysis revealed that MoyR is a cytoplasmic protein with higher thermal stability and higher pI. Four highly probable binding pockets were determined in MoyR and the druggability was higher in the orthosteric binding site consisting of three conserved critical residues: TYR179, ARG223 and GLU234. Two highly conserved leucine residues were identified in the effector-binding region of MoyR and other HutC homologues, suggesting that these two residues can be crucial for structure stability and oligomerization. Virtual screening of drug leads resulted in four drug-like compounds with greater affinity to MoyR with potential inhibitory effects for MoyR. Our findings support that this regulator protein can be valuable as a therapeutic target that can be used for developing drug leads.

scholarly journals In silico homology modelling and identification of Tousled-like kinase 1 inhibitors for glioblastoma therapy via high throughput virtual screening protein-ligand docking

2015 ◽  
Author(s):  
Kamariah Ibrahim ◽  
Abubakar Danjuma ◽  
Chyan Leong Ng ◽  
Nor Azian Abdul Murad ◽  
Roslan Harun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Energy Minimization ◽  
In Silico ◽  
Structural Information ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Nmr Studies ◽  
Homology Models ◽  
High Throughput Virtual Screening

Background: Glioblastoma multiforme (GBM) is a grade IV brain tumor that arises from star-shaped glial cells supporting neural cells called astrocytes. The survival of GBM patients remains poor despite many specific molecular targets have been developed. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancer such as GBM. TLK1 plays an important role in controlling chromosomal aggregation, cell survival and proliferation. In vitro studies suggested that TLK1 is a potential target for some cancers. Hence, identification of suitable molecular inhibitors for TLK1 is warranted as new therapeutic agents in GBM. To date, there is no direct structural information available from X-ray crystallography and NMR studies for TLK1. In this study, we aimed to create a homology model of TLK1 and to identify suitable molecular inhibitors or compounds that are likely to bind and inhibit TLK1 activity via in silico high-throughput virtual screening (HTVS) protein-ligand docking. Methods: 3D homology models of TLK1 were derived from various servers including HOmology ModellER, i-Tasser, Psipred and Swiss Model. All models were evaluated using Swiss-Model Q-Mean server. Only one model was selected for further analysis. Further validation was performed using PDBsum, 3d2go, ProSA, Procheck analysis and ERRAT. Energy minimization was performed using YASARA energy minimization server. Subsequently, HTVS was performed using Molegro Virtual Docker 6.0 and candidate ligands from ligand.info database. Ligand-docking procedures were analyzed at the catalytic site of TLK1. Drug-like molecules were filtered using FAFDrugs3 ADME-Tox filter. Results and conclusion: High quality homology models were obtained from the 4B8M Aurora B kinase derived from Xenopus levias structure that share 33% sequence identity to TLK1. From the HTVS ligand-docking, two compounds were identified to be the potential inhibitors as it did not violate the Lipinski rule of five and CNS-based filter as a potential drug-like molecule for GBM.

Discovery of New AKT1 Inhibitors by Combination of In silico Structure Based Virtual Screening Approaches and Biological Evaluations

2019 ◽  
Author(s):  
Filip Fratev ◽  
Denisse A. Gutierrez ◽  
Renato J. Aguilera ◽  
suman sirimulla
Keyword(s):  
Virtual Screening ◽  
Success Rate ◽  
Protein Interactions ◽  
In Silico ◽  
Biological Data ◽  
In Vitro Binding ◽  
Vitro Binding ◽  
Screening Protocol ◽  
Screening Approaches

AKT1 is emerging as a useful target for treating cancer. Herein, we discovered a new set of ligands that inhibit the AKT1, as shown by in vitro binding and cell line studies, using a newly designed virtual screening protocol that combines structure-based pharmacophore and docking screens. Taking together with the biological data, the combination of structure based pharamcophore and docking methods demonstrated reasonable success rate in identifying new inhibitors (60-70%) proving the success of aforementioned approach. A detail analysis of the ligand-protein interactions was performed explaining observed activities.<br>

Virtual screening of small molecules databases for discovery of novel PARP-1 inhibitors: combination of in silico and in vitro studies

2016 ◽  
Vol 35 (9) ◽  
pp. 1899-1915 ◽  
Author(s):  
Ramin Ekhteiari Salmas ◽  
Ayhan Unlu ◽  
Muhammet Bektaş ◽  
Mine Yurtsever ◽  
Mert Mestanoglu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecules ◽  
In Silico ◽  
In Vitro Studies ◽  
Parp 1

scholarly journals In silico homology modelling and identification of Tousled-like kinase 1 inhibitors for glioblastoma therapy via high throughput virtual screening protein-ligand docking

2016 ◽  
Author(s):  
Kamariah Ibrahim ◽  
Abubakar Danjuma ◽  
Chyan Leong Ng ◽  
Nor Azian Abdul Murad ◽  
Roslan Harun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Energy Minimization ◽  
In Silico ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Molegro Virtual Docker ◽  
Homology Models ◽  
High Throughput Virtual Screening

Background: Glioblastoma multiforme (GBM) is a grade IV brain tumor that arises from star-shaped glial cells supporting neural cells called astrocytes. The survival of GBM patients remains poor despite many specific molecular targets that have been developed and used for therapy. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancers such as GBM. TLK1 plays an important role in controlling chromosomal aggregation, cell survival and proliferation. In vitro studies suggested that TLK1 is a potential target for some cancers; hence, the identification of suitable molecular inhibitors for TLK1 is warranted as a new therapeutic agents in GBM. To date, there is no structure available for TLK1. In this study, we aimed to create a homology model of TLK1 and to identify suitable molecular inhibitors or compounds that are likely to bind and inhibit TLK1 activity via in silico high-throughput virtual screening (HTVS) protein-ligand docking. Methods: 3D homology models of TLK1 were derived from various servers including HOmology ModellER, i-Tasser, Psipred and Swiss Model. All models were evaluated using Swiss Model Q-Mean server. Only one model was selected for further analysis. Further validation was performed using PDBsum, 3d2go, ProSA, Procheck analysis and ERRAT. Energy minimization was performed using YASARA energy minimization server. Subsequently, HTVS was performed using Molegro Virtual Docker 6.0 and candidate ligands from ligand.info database. Ligand-docking procedures were analyzed at the putative catalytic site of TLK1. Drug-like molecules were filtered using FAF-Drugs3, which is an ADME-Tox filtering program. Results and conclusion: High quality homology models were obtained from the Aurora B kinase (PDB ID:4B8M) derived from Xenopus levias structure that share 33% sequence identity to TLK1. From the HTVS ligand-docking, two compounds were identified to be the potential inhibitors as it did not violate the Lipinski rule of five and the CNS-based filter as a potential drug-like molecule for GBM.

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