scholarly journals In Silico Study of Curcumin and Folic Acid as Potent Inhibitors of Human Transmembrane Protease Serine 2 in the Treatment of COVID-19

2020 ◽  
pp. 3-9
Author(s):  
Noboru Motohashi ◽  
Anuradha Vanam ◽  
Rao Gollapudi
Keyword(s):  
Folic Acid ◽  
In Silico ◽  
3D Structure ◽  
Bond Energies ◽  
Protein Activity ◽  
Drug Candidates ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
In Silico Simulation ◽  
Surface Receptor

Background. Human transmembrane protease 2 (TMPRSS2) protein is essential for priming spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in association with human angiotensin-converting enzyme 2 (ACE2) surface receptor to facilitate viral invasion into host human cell through ACE2 receptor. Impeding TMPRSS2 protein activity is currently a preferred choice of the treatment of coronavirus disease 2019 (COVID-19) which is caused by SARS-CoV-2. Curcumin and folic acid are potential candidates for inhibiting TMPRSS2. Objective. The present study aimed to demonstrate the inhibitory activities of curcumin and folic acid, along with known human serine protease inhibitors such as nafamostat and camostat, on TMPRSS2. Methods. Curcumin and folic acid, along with nafamostat and camostat, were docked on a modeled human TMPRSS2 protein 3D structure. Nafamostat and curcumin interactions with targeted TMPRSS2 protein were identical whereas camostat and folic acid displayed similar interactions. Results. The hydrogen bond (H-bond) energies of docked curcumin, folic acid, nafamostat, and camostat were −19.86 kJ/mol, −17.63 kJ/mol, −10.53 kJ/mol, and −14.41 kJ/mol, respectively. Higher H-bond energies could strengthen protein-ligand interactions. Our results showed binding site similarities between curcumin and nafamostat as well as folic acid and camostat. Conclusion. The current in silico simulation suggested that curcumin and folic acid displayed binding poses with TMPRSS2 which are similar to nafamostat and camostat. Therefore, curcumin and folic acid could emerge as potential drug candidates to control COVID-19.

scholarly journals Novel Small-Molecule Scaffolds as Candidates against the SARS Coronavirus 2 Main Protease: A Fragment-Guided in Silico Approach

Molecules ◽  
2020 ◽  
Vol 25 (23) ◽  
pp. 5501
Author(s):  
Teresa L. Augustin ◽  
Roxanna Hajbabaie ◽  
Matthew T. Harper ◽  
Taufiq Rahman
Keyword(s):  
In Silico ◽  
3D Structure ◽  
The Novel ◽  
Health Crisis ◽  
Protein Ligand Interactions ◽  
Main Protease ◽  
Ligand Interactions ◽  
Spanish Flu ◽  
Active Fragments ◽  
Novel Coronavirus

The ongoing pandemic caused by the novel coronavirus has been the greatest global health crisis since the Spanish flu pandemic of 1918. Thus far, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in over 1 million deaths, and there is no cure or vaccine to date. The recently solved crystal structure of the SARS-CoV-2 main protease has been a major focus for drug-discovery efforts. Here, we present a fragment-guided approach using ZINCPharmer, where 17 active fragments known to bind to the catalytic centre of the SARS-CoV-2 main protease (SARS-CoV-2 Mpro) were used as pharmacophore queries to search the ZINC databases of natural compounds and natural derivatives. This search yielded 134 hits that were then subjected to multiple rounds of in silico analyses, including blind and focused docking against the 3D structure of the main protease. We scrutinised the poses, scores, and protein–ligand interactions of 15 hits and selected 7. The scaffolds of the seven hits were structurally distinct from known inhibitor scaffolds, thus indicating scaffold novelty. Our work presents several novel scaffolds as potential candidates for experimental validation against SARS-CoV-2 Mpro.

scholarly journals Elucidating Binding Sites and Affinities of ERα Agonist and Antagonist to Human Alpha-Fetoprotein by in silico Modeling and Point Mutagenesis

2019 ◽  
Author(s):  
Nurbubu T. Moldogazieva ◽  
Daria S. Ostroverkhova ◽  
Nikolai N. Kuzmich ◽  
Vladimir V. Kadochnikov ◽  
Alexander A. Terentiev ◽  
...  
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
Disulfide Bonds ◽  
Electrostatic Interactions ◽  
Molecular Mechanisms ◽  
3D Structure ◽  
Scoring Function ◽  
Alpha Fetoprotein ◽  
Ligand Interaction ◽  
Ligand Interactions

Alpha-fetoprotein (AFP) is a major embryo- and tumor-associated protein capable of binding and transporting variety of hydrophobic ligands including estrogens. AFP has been shown to inhibit estrogen receptor (ER)-positive tumor growth and this can be attributed to its estrogen-binding ability. Despite AFP has long been investigated, its three-dimensional (3D) structure has not been experimentally resolved and molecular mechanisms underlying AFP-ligand interaction remain obscure. In our study we constructed homology-based 3D model of human AFP (HAFP) with the purpose to perform docking of ERα ligands, three agonists (17β-estradiol, estrone and diethylstilbestrol) and three antagonists (tamoxifen, afimoxifene and endoxifen) into the obtained structure. Based on ligand docked scoring function, we identified three putative estrogen- and antiestrogen-binding sites with different ligand binding affinities. Two high-affinity sites were located in (i) a tunnel formed within HAFP subdomains IB and IIA and (ii) opposite side of the molecule in a groove originating from cavity formed between domains I and III, while (iii) the third low-affinity site was found at the bottom of the cavity. 100 ns MD simulation allowed studying their geometries and showed that HAFP-estrogen interactions occur due to van der Waals forces, while both hydrophobic and electrostatic interactions were almost equally involved in HAFP-antiestrogen binding. MM/GBSA rescoring method estimated binding free energies (ΔGbind) and showed that antiestrogens have higher affinities to HAFP as compared to estrogens. We performed in silico point substitutions of amino acid residues to confirm their roles in HAFP-ligand interactions and showed that Thr132, Leu138, His170, Phe172, Ser217, Gln221, His266, His316, Lys453, and Asp478 residues along two disulfide bonds, Cys224-Cys270 and Cys269-Cys277 have key roles in both HAFP-estrogen and HAFP-antiestrogen binding. Data obtained in our study contribute to understanding mechanisms underlying protein-ligand interactions and anti-cancer therapy strategies based on ER-binding ligands.

scholarly journals Elucidating Binding Sites and Affinities of ERα Agonists and Antagonists to Human Alpha-Fetoprotein by In Silico Modeling and Point Mutagenesis

2020 ◽  
Vol 21 (3) ◽  
pp. 893 ◽  
Author(s):  
Nurbubu T. Moldogazieva ◽  
Daria S. Ostroverkhova ◽  
Nikolai N. Kuzmich ◽  
Vladimir V. Kadochnikov ◽  
Alexander A. Terentiev ◽  
...  
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
Electrostatic Interactions ◽  
Molecular Mechanisms ◽  
3D Structure ◽  
Alpha Fetoprotein ◽  
Affinity Binding ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Ligand Interactions

Alpha-fetoprotein (AFP) is a major embryo- and tumor-associated protein capable of binding and transporting a variety of hydrophobic ligands, including estrogens. AFP has been shown to inhibit estrogen receptor (ER)-positive tumor growth, which can be attributed to its estrogen-binding ability. Despite AFP having long been investigated, its three-dimensional (3D) structure has not been experimentally resolved and molecular mechanisms underlying AFP–ligand interaction remains obscure. In our study, we constructed a homology-based 3D model of human AFP (HAFP) with the purpose of molecular docking of ERα ligands, three agonists (17β-estradiol, estrone and diethylstilbestrol), and three antagonists (tamoxifen, afimoxifene and endoxifen) into the obtained structure. Based on the ligand-docked scoring functions, we identified three putative estrogen- and antiestrogen-binding sites with different ligand binding affinities. Two high-affinity binding sites were located (i) in a tunnel formed within HAFP subdomains IB and IIA and (ii) on the opposite side of the molecule in a groove originating from a cavity formed between domains I and III, while (iii) the third low-affinity binding site was found at the bottom of the cavity. Here, 100 ns molecular dynamics (MD) simulation allowed us to study their geometries and showed that HAFP–estrogen interactions were caused by van der Waals forces, while both hydrophobic and electrostatic interactions were almost equally involved in HAFP–antiestrogen binding. Molecular mechanics/Generalized Born surface area (MM/GBSA) rescoring method exploited for estimation of binding free energies (ΔGbind) showed that antiestrogens have higher affinities to HAFP as compared to estrogens. We performed in silico point substitutions of amino acid residues to confirm their roles in HAFP–ligand interactions and showed that Thr132, Leu138, His170, Phe172, Ser217, Gln221, His266, His316, Lys453, and Asp478 residues, along with two disulfide bonds (Cys224–Cys270 and Cys269–Cys277), have key roles in both HAFP–estrogen and HAFP–antiestrogen binding. Data obtained in our study contribute to understanding mechanisms underlying protein–ligand interactions and anticancer therapy strategies based on ERα-binding ligands.

scholarly journals The analysis of NQO1 isoforms as an Off-target to anti-cancer drug RH1 using molecular modeling and structure-based drug discovery approaches

2020 ◽  
Vol 11 (3) ◽  
pp. 3780-3792
Author(s):  
Pramodkumar P Gupta ◽  
Shanker L Kothari ◽  
Mindaugas Valius ◽  
Jonas Cicenas ◽  
Virupaksha A Bastikar
Keyword(s):  
Drug Target ◽  
Conformational Stability ◽  
3D Structure ◽  
Protein Isoforms ◽  
Cancer Drug ◽  
Normal Tissues ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Approach ◽  
High Level

NQO1 is already evaluated for its high-level expression in numerous human cancers as compared with normal tissues. RH1 acts as an indigenous prodrug to NQO1. In our preceding work Off-targeting of (RH1) drug to protein kinases is well reported. Numerous protein isoforms have reported a potential drug target and biomarkers in cancer and related diseases. In the present study, the 3D structure of all the three NQO1 isoforms is modelled using the homologybased concept, evaluated for their conformational stability and energy forms by MD simulation. MSA and related method used for binding site prediction. Protein-ligand interactions were studied using molecular docking approach. The 3D modelled structure of NQO1 isoform 2 and 3 exhibited a conformational change in the protein FAD and RH1 binding region due to the absence of a few key amino acids. Docking results revealed a good degree of binding energy and interaction between the selected NQO1 isoforms, FAD and RH1. As FAD acts as a floor surface to RH1, a similar trend observed in the NQO1 isoform 2 and 3. Hence the NQO1 isoforms 2 and 3 could be a drug target to anticancer prodrug RH1 and can be further investigated in the lab.

scholarly journals ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 214 ◽  
Author(s):  
Praveen Anand ◽  
Deepesh Nagarajan ◽  
Sumanta Mukherjee ◽  
Nagasuma Chandra
Keyword(s):  
Binding Site ◽  
In Silico ◽  
Ligand Recognition ◽  
Loss Of Function ◽  
Ligand Complex ◽  
Alanine Scanning Mutagenesis ◽  
Ligand Interaction ◽  
Alanine Scanning ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are computed by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide clues to researchers about residues to obtain loss-of-function mutations and to understand drug resistant mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

scholarly journals Geranii Herba as a Potential Inhibitor of SARS-CoV-2 Main 3CLpro, Spike RBD, and Regulation of Unfolded Protein Response: An In Silico Approach

Antibiotics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 863
Author(s):  
Selvaraj Arokiyaraj ◽  
Antony Stalin ◽  
Balakrishnan Senthamarai Kannan ◽  
Hakdong Shin
Keyword(s):  
Unfolded Protein Response ◽  
In Silico ◽  
Viral Infections ◽  
Polyphenolic Compounds ◽  
Cell Surface Receptor ◽  
Target Proteins ◽  
Drug Candidates ◽  
Unfolded Protein ◽  
Surface Receptor ◽  
Protein Response

Background: Since the first patient identified with SARS-CoV-2 symptoms in December 2019, the trend of a spreading coronavirus disease 2019 (COVID-19) infection has remained to date. As for now, there is an urgent need to develop novel drugs or vaccines for the SARS-CoV-2 virus. Methods: Polyphenolic compounds have potential as drug candidates for various diseases, including viral infections. In this study, polyphenolic compounds contained in Geranii Herba were chosen for an in silico approach. The SARS-CoV-2 receptor-binding domain (RBD), 3CLpro (Replicase polyprotein 1ab), and the cell surface receptor glucose-regulated protein 78 (GRP78) were chosen as target proteins. Results: Based on the molecular docking analysis, ellagic acid, gallic acid, geraniin, kaempferitrin, kaempferol, and quercetin showed significant binding interactions with the target proteins. Besides, the molecular dynamic simulation studies support Geranii Herba’s inhibition efficiency on the SARS-CoV-2 RBD. We assume that the active compounds in Geranii Herba might inhibit SARS-CoV-2 cell entry through the ACE2 receptor and inhibit the proteolytic process. Besides, these compounds may help to regulate the cell signaling under the unfolded protein response in endoplasmic reticulum stress through the binding with GRP78 and avoid the SARS-CoV-2 interaction. Conclusions: Hence, the compounds present in Geranii Herba could be used as possible drug candidates for the prevention/treatment of SARS-CoV-2 infection.

scholarly journals Establishing an Analogue Based In Silico Pipeline in the Pursuit of Novel Inhibitory Scaffolds against the SARS Coronavirus 2 Papain-Like Protease

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1134
Author(s):  
Roxanna Hajbabaie ◽  
Matthew T. Harper ◽  
Taufiq Rahman
Keyword(s):  
Molecular Docking ◽  
In Silico ◽  
Drug Targets ◽  
Chemical Space ◽  
Cysteine Proteases ◽  
Lessons Learned ◽  
Scaffold Hopping ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

The ongoing coronavirus pandemic has been a burden on the worldwide population, with mass fatalities and devastating socioeconomic consequences. It has particularly drawn attention to the lack of approved small-molecule drugs to inhibit SARS coronaviruses. Importantly, lessons learned from the SARS outbreak of 2002–2004, caused by severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), can be applied to current drug discovery ventures. SARS-CoV-1 and SARS-CoV-2 both possess two cysteine proteases, the main protease (Mpro) and the papain-like protease (PLpro), which play a significant role in facilitating viral replication, and are important drug targets. The non-covalent inhibitor, GRL-0617, which was found to inhibit replication of SARS-CoV-1, and more recently SARS-CoV-2, is the only PLpro inhibitor co-crystallised with the recently solved SARS-CoV-2 PLpro crystal structure. Therefore, the GRL-0617 structural template and pharmacophore features are instrumental in the design and development of more potent PLpro inhibitors. In this work, we conducted scaffold hopping using GRL-0617 as a reference to screen over 339,000 ligands in the chemical space using the ChemDiv, MayBridge, and Enamine screening libraries. Twenty-four distinct scaffolds with structural and electrostatic similarity to GRL-0617 were obtained. These proceeded to molecular docking against PLpro using the AutoDock tools. Of two compounds that showed the most favourable predicted binding affinities to the target site, as well as comparable protein-ligand interactions to GRL-0617, one was chosen for further analogue-based work. Twenty-seven analogues of this compound were further docked against the PLpro, which resulted in two additional hits with promising docking profiles. Our in silico pipeline consisted of an integrative four-step approach: (1) ligand-based virtual screening (scaffold-hopping), (2) molecular docking, (3) an analogue search, and, (4) evaluation of scaffold drug-likeness, to identify promising scaffolds and eliminate those with undesirable properties. Overall, we present four novel, and lipophilic, scaffolds obtained from an exhaustive search of diverse and uncharted regions of chemical space, which may be further explored in vitro through structure-activity relationship (SAR) studies in the search for more potent inhibitors. Furthermore, these scaffolds were predicted to have fewer off-target interactions than GRL-0617. Lastly, to our knowledge, this work contains the largest ligand-based virtual screen performed against GRL-0617.

scholarly journals Docking studies in targeting proteins involved in cardiovascular disorders using phytocompounds from Terminalia arjuna

2020 ◽  
Author(s):  
Vikas Kumar ◽  
Nitin Sharma ◽  
Anuradha Sourirajan ◽  
Prem Kumar Khosla ◽  
Kamal Dev
Keyword(s):  
Molecular Docking ◽  
3D Structure ◽  
Three Dimensional ◽  
Data Bank ◽  
Docking Studies ◽  
Terminalia Arjuna ◽  
Structure Based Drug Design ◽  
Lipinski’S Rule ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

AbstractTerminalia arjuna (Roxb.) Wight and Arnot (T. arjuna) commonly known as Arjuna has been known for its cardiotonic nature in heart failure, ischemic, cardiomyopathy, atherosclerosis, myocardium necrosis and also has been used in the treatment of different human disorders such as blood diseases, anaemia and viral diseases. Our focus has been on phytochemicals which do not exhibit any cytotoxicity and have significant cardioprotective activity. Since Protein-Ligand interactions play a key role in structure-based drug design, therefore with the help of molecular docking, we screened 19 phytochemicals present in T. arjuna and investigated their binding affinity against different cardiovascular target proteins. The three-dimensional (3D) structure of target cardiovascular proteins were retrieved from Protein Data Bank, and docked with 3D Pubchem structures of 19 phytochemicals using Autodock vina. Molecular docking and drug-likeness studies were made using ADMET properties while Lipinski’s rule of five was performed for the phytochemicals to evaluate their cardio protective activity. Among all selected phytocompounds, arjunic acid, arjungenin, and terminic acid were found to fulfill all ADMET rules, drug likeness, and are less toxic in nature. Our studies, therefore revealed that these three phytochemicals from T. arjuna can be used as promising candidates for developing broad spectrum drugs against cardiovascular diseases.

Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

2018 ◽  
Author(s):  
Maciej Wójcikowski ◽  
Michał Kukiełka ◽  
Marta Stepniewska-Dziubinska ◽  
Pawel Siedlecki
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Linear Model ◽  
Binding Affinity ◽  
Small Molecule ◽  
3D Structure ◽  
Protein Ligand Interactions ◽  
Core Set ◽  
Ligand Interactions ◽  
Extended Connectivity

<div>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of fingerprints, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions. Here, we present a Protein-Ligand Extended Connectivity (PLEC) fingerprint that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC fingerprints were used to construct different machine learning (ML) models tailored for predicting protein-ligand affinities (pK<sub>i/d</sub>). Even the simplest linear model built on the PLEC fingerprint achieved R<sub>p</sub>=0.83 on the PDBbind v2016 "core set”, demonstrating its descriptive power. The PLEC fingerprint has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).</div>

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