scholarly journals Novel Design Strategy for Checkpoint Kinase 2 Inhibitors Using Pharmacophore Modeling, Combinatorial Fusion, and Virtual Screening

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Chun-Yuan Lin ◽  
Yen-Ling Wang
Keyword(s):  
Virtual Screening ◽  
Design Strategy ◽  
Pharmacophore Modeling ◽  
Dna Double Strand Breaks ◽  
Checkpoint Kinase ◽  
Screening Techniques ◽  
Checkpoint Kinase 2 ◽  
Combinatorial Fusion ◽  
Potential Inhibitors ◽  
Novel Design

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (rtest) with the value 0.816 by combining theBesttrainBesttestandFasttrainFasttestprediction results. The potential inhibitors were selected from NCI database by screening according toBesttrainBesttest+FasttrainFasttestprediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study.

Design checkpoint kinase 2 inhibitors by pharmacophore modeling and virtual screening techniques

2013 ◽  
Vol 23 (23) ◽  
pp. 6286-6291 ◽  
Author(s):  
Yen-Ling Wang ◽  
Chun-Yuan Lin ◽  
Kuei-Chung Shih ◽  
Jui-Wen Huang ◽  
Chuan-Yi Tang
Keyword(s):  
Virtual Screening ◽  
Pharmacophore Modeling ◽  
Checkpoint Kinase ◽  
Screening Techniques ◽  
Checkpoint Kinase 2

Discovery of Novel NAMPT Inhibitors Based on Pharmacophore Modeling and Virtual Screening Techniques

2015 ◽  
Vol 17 (10) ◽  
pp. 868-878 ◽  
Author(s):  
Qianying Yi ◽  
Lu Zhou ◽  
Xin Shao ◽  
Taijin Wang ◽  
Guangkai Bao ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Pharmacophore Modeling ◽  
Screening Techniques

scholarly journals Pharmacophore Modeling and Virtual Screening Studies of Checkpoint Kinase 1 Inhibitors

2009 ◽  
Vol 57 (7) ◽  
pp. 704-709 ◽  
Author(s):  
Jin-Juan Chen ◽  
Ting-Lin Liu ◽  
Li-Jun Yang ◽  
Lin-Li Li ◽  
Yu-Quan Wei ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Pharmacophore Modeling ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1

Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods

2012 ◽  
Vol 90 (8) ◽  
pp. 675-692 ◽  
Author(s):  
Premlata K. Ambre ◽  
Raghuvir R. S. Pissurlenkar ◽  
Evans C. Coutinho ◽  
Radhakrishnan P. Iyer
Keyword(s):  
Hydrogen Bond ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Hydrogen Bond Donor ◽  
Hydrogen Bond Acceptor ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1 ◽  
Qsar Models ◽  
Potential Inhibitors

Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.

Discovery of dual binding site acetylcholinesterase inhibitors identified by pharmacophore modeling and sequential virtual screening techniques

2011 ◽  
Vol 21 (4) ◽  
pp. 1105-1112 ◽  
Author(s):  
Shikhar Gupta ◽  
Adyary Fallarero ◽  
Päivi Järvinen ◽  
Daniela Karlsson ◽  
Mark S. Johnson ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Binding Site ◽  
Acetylcholinesterase Inhibitors ◽  
Pharmacophore Modeling ◽  
Screening Techniques

scholarly journals ABBV-744 and Onalespid as Potential Inhibitors of SARS-CoV-2 main Protease Enzyme: A Promising Therapeutics in COVID-19?

2020 ◽  
Author(s):  
Zeynab Fakhar ◽  
Shama Khan ◽  
Aijaz Ahmad
Keyword(s):  
Virtual Screening ◽  
Drug Repurposing ◽  
Pharmacophore Modeling ◽  
Binding Studies ◽  
Active Inhibitor ◽  
Lead Compounds ◽  
Protease Enzyme ◽  
Main Protease ◽  
Dynamics Simulations ◽  
Potential Inhibitors

Abstract A new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening work-flow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the best hit compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered compounds. Binding studies revealed that compound ABBV-744 binds to the Mpro with strong affinity (Gbind -45.43 kcal/mol), and the complex is more stable in comparison with other protein-ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

scholarly journals ABBV-744 as a potential inhibitor of SARS-CoV-2 main protease enzyme against COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zeynab Fakhar ◽  
Shama Khan ◽  
Suliman Y. AlOmar ◽  
Afrah Alkhuriji ◽  
Aijaz Ahmad
Keyword(s):  
Virtual Screening ◽  
Drug Repurposing ◽  
Pharmacophore Modeling ◽  
Binding Energies ◽  
Active Inhibitor ◽  
Lead Compounds ◽  
Protease Enzyme ◽  
Main Protease ◽  
Dynamics Simulations ◽  
Potential Inhibitors

AbstractA new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening workflow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the selected lead compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered lead compounds. Binding energies revealed that compound ABBV-744 binds to the Mpro with strong affinity (ΔGbind −45.43 kcal/mol), and the complex is more stable in comparison with other protein–ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

scholarly journals Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach

Computation ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 77
Author(s):  
Giulia Culletta ◽  
Maria Rita Gulotta ◽  
Ugo Perricone ◽  
Maria Zappalà ◽  
Anna Maria Almerico ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Repurposing ◽  
Pharmacophore Modeling ◽  
World Health ◽  
Quality Model ◽  
Experimental Drugs ◽  
Docking Approach ◽  
Health Organization ◽  
Potential Inhibitors ◽  
Pharmacophore Models

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.

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