Identification of novel JMJD2A inhibitor scaffold using shape and electrostatic similarity search combined with docking method and MM-GBSA approach

RSC Advances ◽  
2015 ◽  
Vol 5 (101) ◽  
pp. 82936-82946 ◽  
Author(s):  
Taotao Feng ◽  
Weilin Chen ◽  
Dongdong Li ◽  
Hongzhi Lin ◽  
Fang Liu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Similarity Search ◽  
Docking Method ◽  
Electrostatic Similarity

We present a hierarchical workflow combining shape- and electrostatic-based virtual screening for the identification of novel Jumonji domain-containing protein 2A (JMJD2A) inhibitors.

In silico discovery of novel flavonoids as poly ADP ribose polymerase (PARP) inhibitors

2020 ◽  
Vol 16 ◽  
Author(s):  
Ashish Shah ◽  
Ghanshyam Parmar ◽  
Avinash Kumar Seth
Keyword(s):  
Virtual Screening ◽  
In Silico ◽  
Synthetic Lethality ◽  
Parp Inhibitor ◽  
Parp Inhibitors ◽  
Docking Studies ◽  
Docking Score ◽  
Docking Method ◽  
Anti Cancer ◽  
In Silico Toxicity

Background: The concept of synthetic lethality is emerging field in the treatment of cancer and can be applied for new drug development of cancer as it has been already represented by Poly (ADP-ribose) polymerase (PARPs) inhibitors. Objectives: In this study we performed virtual screening of 329 flavonoids obtained from Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target (NPACT) database to identify novel PARP inhibitors. Materials and methods: Virtual screening carried out using different In Silico methods which includes molecular docking studies, prediction of druglikeness and In Silico toxicity studies. Results: Fifteen out of 329 flavonoids achieved better docking score as compared to rucaparib which is an FDA approved PARP inhibitor. These 15 hits were again rescored using accurate docking mode and drug-likeliness properties were evaluated. Accuracy of docking method was checked using re-docking. Finally NPACT00183 and NPACT00280 were identified as potential PARP inhibitors with docking score of -139.237 and -129.36 respectively. These two flavonoids were also showed no AMES toxicity and no carcinogenicity which was predicted using admetSAR. Conclusion: Our finding suggests that NPACT00183 and NPACT00280 have promising potential to be further explored as PARP inhibitors.

Peptide Similarity Search Based and Virtual Screening Based Strategies to Identify Small Molecules to Inhibit CarD–RNAP Interaction in M. tuberculosis

2018 ◽  
Vol 25 (2) ◽  
pp. 697-709 ◽  
Author(s):  
V. G. Shanmuga Priya ◽  
Priya Swaminathan ◽  
Uday M. Muddapur ◽  
Prayagraj M. Fandilolu ◽  
Rishikesh S. Parulekar ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecules ◽  
Similarity Search

scholarly journals Combination of 2D/3D Ligand-Based Similarity Search in Rapid Virtual Screening from Multimillion Compound Repositories. Selection and Biological Evaluation of Potential PDE4 and PDE5 Inhibitors

Molecules ◽  
2014 ◽  
Vol 19 (6) ◽  
pp. 7008-7039 ◽  
Author(s):  
Krisztina Dobi ◽  
István Hajdú ◽  
Beáta Flachner ◽  
Gabriella Fabó ◽  
Mária Szaszkó ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Similarity Search ◽  
Biological Evaluation ◽  
Pde5 Inhibitors

scholarly journals Multiscale virtual screening optimization for shotgun drug repurposing using the CANDO platform

2020 ◽  
Author(s):  
Matthew L. Hudson ◽  
Ram Samudrala
Keyword(s):  
Virtual Screening ◽  
Decision Tree ◽  
Protein Interactions ◽  
Large Scale ◽  
Therapeutic Potential ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Clinical Indication ◽  
Docking Method ◽  
Useful Signal

AbstractDrug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multidisease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines via large scale modelling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is then compared to all other signatures that are then sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions in the platform used to create the drug-proteome signatures may be determined by any screening or docking method but the primary approach used thus far has been an in house similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and cheminformatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the corresponding two docking-based pipelines it was synthesized from, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking based signature generation methods can capture unique and useful signal for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

Similarity Search Profiling Reveals Effects of Fingerprint Scaling in Virtual Screening.

ChemInform ◽  
2005 ◽  
Vol 36 (6) ◽  
Author(s):  
Ling Xue ◽  
Florence L. Stahura ◽  
Juergen Bajorath
Keyword(s):  
Virtual Screening ◽  
Similarity Search

Molecular fingerprint similarity search in virtual screening

Methods ◽  
2015 ◽  
Vol 71 ◽  
pp. 58-63 ◽  
Author(s):  
Adrià Cereto-Massagué ◽  
María José Ojeda ◽  
Cristina Valls ◽  
Miquel Mulero ◽  
Santiago Garcia-Vallvé ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Similarity Search ◽  
Molecular Fingerprint

scholarly journals Lapatinib, Nilotinib and Lomitapide Inhibit Haemozoin Formation in Malaria Parasites

Molecules ◽  
2020 ◽  
Vol 25 (7) ◽  
pp. 1571 ◽  
Author(s):  
Ana Carolina C. de Sousa ◽  
Keletso Maepa ◽  
Jill M. Combrinck ◽  
Timothy J. Egan
Keyword(s):  
Experimental Evaluation ◽  
Antimalarial Activity ◽  
Drug Repositioning ◽  
Hit Rate ◽  
Docking Method ◽  
Electrostatic Similarity ◽  
Approved Drugs ◽  
Novel Structures ◽  
Antimalarial Action

With the continued loss of antimalarials to resistance, drug repositioning may have a role in maximising efficiency and accelerating the discovery of new antimalarial drugs. Bayesian statistics was previously used as a tool to virtually screen USFDA approved drugs for predicted β-haematin (synthetic haemozoin) inhibition and in vitro antimalarial activity. Here, we report the experimental evaluation of nine of the highest ranked drugs, confirming the accuracy of the model by showing an overall 93% hit rate. Lapatinib, nilotinib, and lomitapide showed the best activity for inhibition of β-haematin formation and parasite growth and were found to inhibit haemozoin formation in the parasite, providing mechanistic insights into their mode of antimalarial action. We then screened the USFDA approved drugs for binding to the β-haematin crystal, applying a docking method in order to evaluate its performance. The docking method correctly identified imatinib, lapatinib, nilotinib, and lomitapide. Experimental evaluation of 22 of the highest ranked purchasable drugs showed a 24% hit rate. Lapatinib and nilotinib were chosen as templates for shape and electrostatic similarity screening for lead hopping using the in-stock ChemDiv compound catalogue. The actives were novel structures worthy of future investigation. This study presents a comparison of different in silico methods to identify new haemozoin-inhibiting chemotherapeutic alternatives for malaria that proved to be useful in different ways when taking into consideration their strengths and limitations.

scholarly journals Exploring Different Virtual Screening Strategies for Acetylcholinesterase Inhibitors

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Nibha Mishra ◽  
Arijit Basu
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Similarity Search ◽  
Acetylcholinesterase Inhibitors ◽  
Enrichment Factors ◽  
Pharmacophore Modeling ◽  
Receiver Operating Curve ◽  
Ache Inhibitors ◽  
Docking Protocol ◽  
Screening Strategies

The virtual screening problems associated with acetylcholinesterase (AChE) inhibitors were explored using multiple shape, and structure-based modeling strategies. The employed strategies include molecular docking, similarity search, and pharmacophore modeling. A subset from directory of useful decoys (DUD) related to AChE inhibitors was considered, which consists of 105 known inhibitors and 3732 decoys. Statistical quality of the models was evaluated by enrichment factor (EF) metrics and receiver operating curve (ROC) analysis. The results revealed that electrostatic similarity search protocol using EON (ET_combo) outperformed all other protocols with outstanding enrichment of>95% in top 1% and 2% of the dataset with an AUC of 0.958. Satisfactory performance was also observed for shape-based similarity search protocol using ROCS and PHASE. In contrast, the molecular docking protocol performed poorly with enrichment factors<30% in all cases. The shape- and electrostatic-based similarity search protocol emerged as a plausible solution for virtual screening of AChE inhibitors.

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