scholarly journals A pharmacophore model for SARS-CoV-2 3CLpro small molecule inhibitors and in vitro experimental validation of computationally screened inhibitors

2021 ◽  
Author(s):  
Enrico Glaab ◽  
Ganesh Babu Manoharan ◽  
Daniel Abankwa
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Virtual Screening ◽  
Small Molecule ◽  
Drug Target ◽  
Experimental Validation ◽  
Small Molecule Inhibitors ◽  
Pharmacophore Model ◽  
Computational Screening

AbstractAmong the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic and steric features that characterize small molecule inhibitors binding stably to 3CLpro, as well as by an extended collection of known binders.Here, we present combined virtual screening, molecular dynamics simulation, machine learning and in vitro experimental validation analyses which have led to the identification of small molecule inhibitors of 3CLpro with micromolar activity, and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM), and synthetic compounds previously not characterized (e.g. compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts, to identify 3CLpro ligands with improved potency and selectivity.Overall, this study suggests that the integration of virtual screening, molecular dynamics simulations and machine learning can facilitate 3CLpro-targeted small molecule screening investigations. Different receptor-, ligand- and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.

scholarly journals PD-1-Targeted Discovery of Peptide Inhibitors by Virtual Screening, Molecular Dynamics Simulation, and Surface Plasmon Resonance

Molecules ◽  
2019 ◽  
Vol 24 (20) ◽  
pp. 3784 ◽  
Author(s):  
Yuanqiang Wang ◽  
Haiqiong Guo ◽  
Zhiwei Feng ◽  
Siyi Wang ◽  
Yuxuan Wang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Cell Death ◽  
Surface Plasmon Resonance ◽  
Virtual Screening ◽  
Programmed Cell Death ◽  
Surface Plasmon ◽  
Small Molecule ◽  
Plasmon Resonance ◽  
Small Molecule Inhibitors ◽  
Dynamics Simulation

The blockade of the programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) pathway plays a critical role in cancer immunotherapy by reducing the immune escape. Five monoclonal antibodies that antagonized PD-1/PD-L1 interaction have been approved by the Food and Drug Administration (FDA) and marketed as immunotherapy for cancer treatment. However, some weaknesses of antibodies, such as high cost, low stability, poor amenability for oral administration, and immunogenicity, should not be overlooked. To overcome these disadvantages, small-molecule inhibitors targeting PD-L1 were developed. In the present work, we applied in silico and in vitro approaches to develop short peptides targeting PD-1 as chemical probes for the inhibition of PD-1–PD-L1 interaction. We first predicted the potential binding pocket on PD-1/PD-L1 protein–protein interface (PPI). Sequentially, we carried out virtual screening against our in-house peptide library to identify potential ligands. WANG-003, WANG-004, and WANG-005, three of our in-house peptides, were predicted to bind to PD-1 with promising docking scores. Next, we conducted molecular docking and molecular dynamics (MD) simulation for the further analysis of interactions between our peptides and PD-1. Finally, we evaluated the affinity between peptides and PD-1 by surface plasmon resonance (SPR) binding technology. The present study provides a new perspective for the development of PD-1 inhibitors that disrupt PD-1–PD-L1 interactions. These promising peptides have the potential to be utilized as a novel chemical probe for further studies, as well as providing a foundation for further designs of potent small-molecule inhibitors targeting PD-1.

First identification of small-molecule inhibitors of Pontin by combining virtual screening and enzymatic assay

2012 ◽  
Vol 443 (2) ◽  
pp. 549-559 ◽  
Author(s):  
Judith Elkaim ◽  
Michel Castroviejo ◽  
Driss Bennani ◽  
Said Taouji ◽  
Nathalie Allain ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecule ◽  
Tumour Cell ◽  
Small Molecule Inhibitors ◽  
Colorimetric Assay ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Atp Binding Site ◽  
Set Up

The human protein Pontin, which belongs to the AAA+ (ATPases associated with various cellular activities) family, is overexpressed in several cancers and its silencing in vitro leads to tumour cell growth arrest and apoptosis, making it a good target for cancer therapy. In particular, high levels of expression were found in hepatic tumours for which the therapeutic arsenal is rather limited. The three-dimensional structure of Pontin has been resolved previously, revealing a hexameric assembly with one ADP molecule co-crystallized in each subunit. Using Vina, DrugScore and Xscore, structure-based virtual screening of 2200 commercial molecules was conducted into the ATP-binding site formed by a dimer of Pontin in order to prioritize the best candidates. Complementary to the in silico screening, a versatile and sensitive colorimetric assay was set up to measure the disruption of the ATPase activity of Pontin. This assay allowed the determination of inhibition curves for more than 20 top-scoring compounds, resulting in the identification of four ligands presenting an inhibition constant in the micromolar concentration range. Three of them inhibited tumour cell proliferation. The association of virtual screening and experimental assay thus proved successful for the discovery of the first small-molecule inhibitors of Pontin.

scholarly journals Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1245 ◽  
Author(s):  
Kelton L. B. dos Santos ◽  
Jorddy N. Cruz ◽  
Luciane B. Silva ◽  
Ryan S. Ramos ◽  
Moysés F. A. Neto ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Screening ◽  
Adenosine Receptor ◽  
Pharmacophore Model ◽  
Qsar Model ◽  
Positive Control ◽  
Receptor Type ◽  
Binding Modes

Adenosine Receptor Type 2A (A2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A2AAR. For the search for A2AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC50 = 7.54407; ZINC04257548 pEC50 = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan®), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to A2AAR.

Virtual Screening with a Structure-Based Pharmacophore Model to Identify Small-Molecule Inhibitors of CARM1

2019 ◽  
Vol 59 (1) ◽  
pp. 522-534 ◽  
Author(s):  
Ting Ran ◽  
Wenjuan Li ◽  
Bingling Peng ◽  
Binglan Xie ◽  
Tao Lu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecule ◽  
Small Molecule Inhibitors ◽  
Pharmacophore Model

scholarly journals Targeting dysregulation signatures of p53-MDM2 interactions to identify newer small molecule inhibitors for cancer therapy: Insight from computational direction

2021 ◽  
Author(s):  
Prosper Obed Chukwuemeka ◽  
Haruna Isiyaku Umar ◽  
Opeyemi Iwaloye ◽  
Oluwaseyi Matthew Oretade ◽  
Christopher Busayo Olowosoke ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Small Molecule ◽  
Small Molecule Inhibitors ◽  
Therapeutic Option ◽  
Pharmacophore Model ◽  
In Vivo Studies ◽  
Hydrogen Bond Acceptor ◽  
Potential Inhibitors

Abstract Dysregulation of the p53-MDM2 interactions has been implicated in majority of human tumors presenting a target for finding small molecule inhibitors. In this study, a training set of 17 experimentally tested inhibitors of MDM2 was used to develop series of pharmacophore models among which a four-featured (AHRR_1) model with one hydrogen bond acceptor, one hydrophobic group and two aromatic ring features and characterized by a survival score of 4.176 was considered significant among the top ranked generated hypothesis. Further, the model was validated by an external set of actives and decoy molecules and was found to exhibit encouraging statistical attributes (such as AUC > 0.7, BEDROC > 0.5 and EF > 1.0 etc). The model was used to screen the ZINC compound database, from the database, the top best 1375 hits satisfying the pharmacophore model was were docked to MDM2 protein to identify the likely interactions of the compounds as well as their binding affinity with MDM2. Further, druglikeness and pharmacokinetic properties screening on top-ranked compounds with higher binding affinity than reference inhibitors revealed four compounds (ZINC02639178, ZINC38933175, ZINC77969611, and ZINC06752762) with suitable pharmacological properties including low ligand toxicity. Investigation of the dynamic behaviour of each candidate inhibitors in complex with MDM2 via molecular dynamic simulation suggested ZINC02639178 and ZINC06752762 as the most potential inhibitors. Thus, these compounds may emerged as therapeutic option for cancer treatment after extensive in vitro and in vivo studies.

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