Computational Simulations Identified Marine-Derived Natural Bioactive Compounds as Replication Inhibitors of SARS-CoV-2

The rapid spread of COVID-19, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide health emergency. Unfortunately, to date, a very small number of remedies have been to be found effective against SARS-CoV-2 infection. Therefore, further research is required to achieve a lasting solution against this deadly disease. Repurposing available drugs and evaluating natural product inhibitors against target proteins of SARS-CoV-2 could be an effective approach to accelerate drug discovery and development. With this strategy in mind, we derived Marine Natural Products (MNP)-based drug-like small molecules and evaluated them against three major target proteins of the SARS-CoV-2 virus replication cycle. A drug-like database from MNP library was generated using Lipinski’s rule of five and ADMET descriptors. A total of 2,033 compounds were obtained and were subsequently subjected to molecular docking with 3CLpro, PLpro, and RdRp. The docking analyses revealed that a total of 14 compounds displayed better docking scores than the reference compounds and have significant molecular interactions with the active site residues of SARS-CoV-2 virus targeted proteins. Furthermore, the stability of docking-derived complexes was analyzed using molecular dynamics simulations and binding free energy calculations. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, binding affinity, and molecular interactions. Our investigation identified two hit compounds against each targeted proteins displaying stable behavior, higher binding affinity and key residual molecular interactions, with good in silico pharmacokinetic properties, therefore can be considered for further in vitro studies.

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Computational Exploration for Lead Compounds That Can Reverse the Nuclear Morphology in Progeria

BioMed Research International ◽

10.1155/2017/5270940 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 7

Author(s):

Shailima Rampogu ◽

Ayoung Baek ◽

Minky Son ◽

Amir Zeb ◽

Chanin Park ◽

...

Keyword(s):

Binding Free Energy ◽

Genetic Disorder ◽

Md Simulations ◽

Free Energy Calculations ◽

Premature Aging ◽

Farnesyltransferase Inhibitors ◽

Docking Simulations ◽

Lipinski’S Rule ◽

Computational Exploration ◽

The Stability

Progeria is a rare genetic disorder characterized by premature aging that eventually leads to death and is noticed globally. Despite alarming conditions, this disease lacks effective medications; however, the farnesyltransferase inhibitors (FTIs) are a hope in the dark. Therefore, the objective of the present article is to identify new compounds from the databases employing pharmacophore based virtual screening. Utilizing nine training set compounds along with lonafarnib, a common feature pharmacophore was constructed consisting of four features. The validated Hypo1 was subsequently allowed to screen Maybridge, Chembridge, and Asinex databases to retrieve the novel lead candidates, which were then subjected to Lipinski’s rule of 5 and ADMET for drug-like assessment. The obtained 3,372 compounds were forwarded to docking simulations and were manually examined for the key interactions with the crucial residues. Two compounds that have demonstrated a higher dock score than the reference compounds and showed interactions with the crucial residues were subjected to MD simulations and binding free energy calculations to assess the stability of docked conformation and to investigate the binding interactions in detail. Furthermore, this study suggests that the Hits may be more effective against progeria and further the DFT studies were executed to understand their orbital energies.

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Exploration of Angiotensin II Type 1 Receptor Modulators by Molecular Fingerprints and Docking Simulations

Letters in Drug Design & Discovery ◽

10.2174/1570180814666170605121418 ◽

2018 ◽

Vol 15 (1) ◽

pp. 95-102 ◽

Cited By ~ 1

Author(s):

Sarfaraj Niazi ◽

Vishwamohana M. Hebbar ◽

Pooja Mathew ◽

Raj K.D. Kumar ◽

Madhusudan Purohit

Keyword(s):

Angiotensin Ii ◽

Binding Affinity ◽

Binding Free Energy ◽

At1 Receptor ◽

Free Energy Calculations ◽

Binding Modes ◽

Docking Simulations ◽

Receptor Modulators

Background: Overexpression of Angiotensin Type 1 (AT1) receptor along with other deregulated oncogenic factors, for e.g. Vascular Endothelial Growth Factor (VEGF) triggers angiogenesis, which is one of the hallmarks of cancer. AT1 receptor, being a key component of the renin - angiotensin system (RAS), also helps in the regulation of blood pressure. In the present study, we report on the identification of novel Angiotensin II Type 1 receptor modulators from among the 265,242 molecules deposited in the NCI compound database (Release 4, 2012) by several rounds of In silico screening steps. Methods: The screening steps involved Lipinski's filter to eliminate the non-drug like candidates followed by fingerprint based similarity filtering using 950 known reference set of AT1 receptor binders. Further screening exercises such as, iterative GOLD based docking and binding free energy calculations were performed to arrive at potential AT1 receptor binding hit candidates. The 2D structural and bioactivity data of the known AT1 receptor modulators retrieved from BindingDB database were also critically reviewed. Results: Docking based virtual screening followed by high flexible docking resulted 10 best fit candidates, which had binding modes roughly similar to the cocrystallized ligand, ZD7155. Of the ten shortlisted candidates, NSC407757 had the binding affinity, on-par, as of the known reference ligand, ZD7155 towards AT1 receptor. An interesting observation of this study is that, the high binding affinity of the hit candidates may be attributed to the similar binding orientation and interactions as of the reference molecule independent of structural similarity with the known AT1 receptor modulators. Conclusion: The hit candidates reported in the present study hold promise as the potential new AT1 receptor modulators. Further study involving structural optimization, synthesis and In vitro binding experiments is warranted for the accelerated discovery of high efficacy novel AT1 receptor modulators.

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Synthesis, Structural Characterization, and In Vitro and In Silico Antifungal Evaluation of Azo-Azomethine Pyrazoles (PhN2(PhOH)CHN(C3N2(CH3)3)PhR, R = H or NO2)

Molecules ◽

10.3390/molecules26247435 ◽

2021 ◽

Vol 26 (24) ◽

pp. 7435

Author(s):

Dorancelly Fernandez ◽

Andrés Restrepo-Acevedo ◽

Cristian Rocha-Roa ◽

Ronan Le Lagadec ◽

Rodrigo Abonia ◽

...

Keyword(s):

Molecular Docking ◽

Antifungal Activity ◽

Active Sites ◽

Antitumor Agents ◽

Binding Free Energy ◽

Building Blocks ◽

Docking Studies ◽

Free Energy Calculations ◽

Dynamics Simulations

The azo-azomethine imines, R1-N=N-R2-CH=N-R3, are a class of active pharmacological ligands that have been prominent antifungal, antibacterial, and antitumor agents. In this study, four new azo-azomethines, R1 = Ph, R2 = phenol, and R3 = pyrazol-Ph-R’ (R = H or NO2), have been synthesized, structurally characterized using X-ray, IR, NMR and UV–Vis techniques, and their antifungal activity evaluated against certified strains of Candida albicans and Cryptococcus neoformans. The antifungal tests revealed a high to moderate inhibitory activity towards both strains, which is regulated as a function of both the presence and the location of the nitro group in the aromatic ring of the series. These biological assays were further complemented with molecular docking studies against three different molecular targets from each fungus strain. Molecular dynamics simulations and binding free energy calculations were performed on the two best molecular docking results for each fungus strain. Better affinity for active sites for nitro compounds at the “meta” and “para” positions was found, making them promising building blocks for the development of new Schiff bases with high antifungal activity.

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In Vitro Potentiation of Carbapenems with Tannic Acid Against Carbapenemase Producing Enterobacteriaceae: Exploring Natural Products as Potential Carbapenemase Inhibitors

10.1101/366104 ◽

2018 ◽

Author(s):

Anou M. Somboro ◽

John Osei Sekyere ◽

Daniel G. Amoako ◽

Hezekiel M. Kumalo ◽

René Khan ◽

...

Keyword(s):

Tannic Acid ◽

Efflux Pump ◽

Binding Free Energy ◽

Computational Simulation ◽

Free Energy Calculations ◽

Computational Studies ◽

Active Site Residues ◽

Class A ◽

Catalytic Active Site

AbstractResistance to antibiotics is increasing worldwide, necessitating urgent action to sustain the efficacy of existing antibiotics in clinical use. We show that tannic acid (TA) in combination with carbapenems can reduce and/or reverse the minimum inhibitory concentrations (MICs) of carbapenems to susceptible values in Enterobacteriaceae that express class A and B carbapenemases. MICs of carbapenems in the presence and absence of TA and other efflux pump inhibitors, TA-carbapenemases inhibition assays and computational studies were undertaken to determine the effect of TA on carbapenem susceptibility in Enterobacteriaceae. TA had the greatest effect on metallo-β-lactamases (MBLs) followed by class A serine-β-lactamases (SBLs). Antibiotic susceptibility testing showed that TA reversed the MICs of MBLs to susceptible values whilst substantially reducing the MICs of SBLs (class A). Tolerable cytotoxicity effect was observed for the concentrations tested. TA inhibited enzymes with a marked difference between ≈50% inhibition (IC50) for NDM-1 and KPC-2. Computational studies including molecular docking, molecular dynamics simulations and binding free energy calculations showed that TA interact with both MBLs and SBLs hydrophobic sites. Moreover, TA had a stronger binding affinity for MBLs than SBLs as the MBLs, specifically VIM-1 and NDM-1, interact with a larger number of their catalytic active-site residues than that of OXA- 48 and KPC-2. These in vitro evaluations together with computational simulation explain the potentiating effect of TA toward carbapenems against carbapenem-resistance enterobacteriaceae. This study proposes TA as a promising adjuvant for MBLs and SBLs.

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Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

10.26434/chemrxiv.12781310.v1 ◽

2020 ◽

Author(s):

E. Prabhu Raman ◽

Thomas J. Paul ◽

Ryan L. Hayes ◽

Charles L. Brooks III

Keyword(s):

Free Energy ◽

Ligand Binding ◽

Binding Affinity ◽

Binding Free Energy ◽

Computational Cost ◽

Combinatorial Libraries ◽

Free Energy Calculations ◽

Lead Optimization ◽

Efficient Estimation ◽

Lead Compound

Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.

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In Silico Exploration of Phytoconstituents From Phyllanthus emblica and Aegle marmelos as Potential Therapeutics Against SARS-CoV-2 RdRp

Bioinformatics and Biology Insights ◽

10.1177/11779322211027403 ◽

2021 ◽

Vol 15 ◽

pp. 117793222110274

Author(s):

Khushboo Pandey ◽

Kiran Bharat Lokhande ◽

K Venkateswara Swamy ◽

Shuchi Nagar ◽

Manjusha Dake

Keyword(s):

Molecular Docking ◽

Binding Affinity ◽

Simulation Analysis ◽

Binding Free Energy ◽

Free Energy Calculations ◽

Molecular Field ◽

Hydroxyl Groups ◽

Phyllanthus Emblica ◽

Inhibitory Potential ◽

Novel Drug

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide has increased the importance of computational tools to design a drug or vaccine in reduced time with minimum risk. Earlier studies have emphasized the important role of RNA-dependent RNA polymerase (RdRp) in SARS-CoV-2 replication as a potential drug target. In our study, comprehensive computational approaches were applied to identify potential compounds targeting RdRp of SARS-CoV-2. To study the binding affinity and stability of the phytocompounds from Phyllanthus emblica and Aegel marmelos within the defined binding site of SARS-CoV-2 RdRp, they were subjected to molecular docking, 100 ns molecular dynamics (MD) simulation followed by post-simulation analysis. Furthermore, to assess the importance of features involved in the strong binding affinity, molecular field-based similarity analysis was performed. Based on comparative molecular docking and simulation studies of the selected phytocompounds with SARS-CoV-2 RdRp revealed that EBDGp possesses a stronger binding affinity (−23.32 kcal/mol) and stability than other phytocompounds and reference compound, Remdesivir (−19.36 kcal/mol). Molecular field-based similarity profiling has supported our study in the validation of the importance of the presence of hydroxyl groups in EBDGp, involved in increasing its binding affinity toward SARS-CoV-2 RdRp. Molecular docking and dynamic simulation results confirmed that EBDGp has better inhibitory potential than Remdesivir and can be an effective novel drug for SARS-CoV-2 RdRp. Furthermore, binding free energy calculations confirmed the higher stability of the SARS-CoV-2 RdRp-EBDGp complex. These results suggest that the EBDGp compound may emerge as a promising drug against SARS-CoV-2 and hence requires further experimental validation.

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Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Current Medicinal Chemistry ◽

10.2174/0929867325666180514110824 ◽

2020 ◽

Vol 26 (42) ◽

pp. 7598-7622 ◽

Cited By ~ 2

Author(s):

Xiao Hu ◽

Irene Maffucci ◽

Alessandro Contini

Keyword(s):

Drug Discovery ◽

Binding Free Energy ◽

Docking Studies ◽

Free Energy Calculations ◽

Binding Energies ◽

New Drugs ◽

Water Molecules ◽

Open Questions ◽

Dynamics Simulations ◽

Computational Drug Discovery

Background: The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions. Objective: In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered. Results: Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules. Conclusions: Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

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High Throughput in Silico Identification of Novel Phytochemical Inhibitors for the Master Regulator of Inflammation (TNFα)

10.26434/chemrxiv.14178212.v2 ◽

2021 ◽

Author(s):

Pratap Kumar Parida ◽

Dipak Paul ◽

Debamitra Chakravorty

Keyword(s):

Small Molecule ◽

High Throughput ◽

In Silico ◽

Small Molecule Inhibitors ◽

Binding Free Energy ◽

Natural Compounds ◽

Free Energy Calculations ◽

Compound Libraries ◽

Relative Binding

<a>The over expression of Tumor necrosis factor-α (TNFα) has been implicated in a variety of disease and is classified as a therapeutic target for inflammatory diseases (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis).Commercially available therapeutics are biologics which are associated with several risks and limitations. Small molecule inhibitors and natural compounds (saponins) were identified by researchers as lead molecules against TNFα, however, </a>they were often associated with high IC50 values which can lead to their failure in clinical trials. This warrants research related to identification of better small molecule inhibitors by screening of large compound libraries. Recent developments have demonstrated power of natural compounds as safe therapeutics, hence, in this work, we have identified TNFα phytochemical inhibitors using high throughput in silico screening approaches of 6000 phytochemicals followed by 200 ns molecular dynamics simulations and relative binding free energy calculations. The work yielded potent hits that bind to TNFα at its dimer interface. The mechanism targeted was inhibition of oligomerization of TNFα upon phytochemical binding to restrict its interaction with TNF-R1 receptor. MD simulation analysis resulted in identification of two phytochemicals that showed stable protein-ligand conformations over time. The two compounds were triterpenoids: Momordicilin and Nimbolin A with relative binding energy- calculated by MM/PBSA to be -190.5 kJ/Mol and -188.03 kJ/Mol respectively. Therefore, through this work it is being suggested that these phytochemicals can be used for further in vitro analysis to confirm their inhibitory action against TNFα or can be used as scaffolds to arrive at better drug candidates.

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Nature Potential for COVID-19: Targeting SARS-CoV-2 Mpro Inhibitor with Bioactive Compound

10.26434/chemrxiv.14112515 ◽

2021 ◽

Author(s):

Kaushik Kumar Bharadwaj ◽

Tanmay Sarkar ◽

Arabinda Ghosh ◽

Debabrat Baishya ◽

Bijuli Rabha ◽

...

Keyword(s):

Binding Energy ◽

Antiviral Activity ◽

Binding Free Energy ◽

Bioactive Compound ◽

Binding Pocket ◽

Stable Conformation ◽

Lipinski’S Rule ◽

Macrolactin A

Corona viruses were first identified in 1931 and SARS-CoV-2 is the most recent. COVID-19 is a pandemic that put most of the world on lockdown and the search for therapeutic drugs is still on-going. Therefore, this study uses in silico screening to identify natural bioactive compounds from fruits, herbaceous plants and marine invertebrates that are able to inhibit protease activity in SARS-CoV-2(PDB: 6LU7). We have used various screening strategies such as drug likeliness, antiviral activity value prediction, molecular docking, ADME (absorption, distribution, metabolism, and excretion), molecular dynamics (MD) simulation and MM/GBSA (molecular mechanics/generalized born and surface area continuum solvation). 17 compounds were shortlisted using Lipinski’s rule. 5 compounds revealed significantly good predicted antiviral activity values and out of them only 2 compounds, Macrolactin A and Stachyflin, showed good binding energy values of -9.22 and -8.00 kcal/mol within the binding pocket, catalytic residues (HIS 41 and CYS 145) of Mpro. These two compounds were further analyzed for their ADME properties. The ADME evaluation of these 2 compounds suggested that they could be effective as therapeutic agents for developing drugs for clinical trials. MD simulations showed that protein-ligand complexes of Macrolactin A and Stachyflin were stable for 100 nano seconds. The MM/GBSA calculations of Mpro – Macrolactin A complex indicated higher binding free energy (-42.58 ± 6.35 kcal/mol) with Mpro protein target receptor (6LU7). DCCM and PCA analysis on the residual movement in the MD trajectories confirmed the good stability on Macrolactin A bound state of 6LU7. This signify the stable conformation of 6LU7 with high binding energy with Macrolactin A. Thus, this study showed that Macrolactin A could be an effective therapeutical agent for SARS-CoV-2protease (6LU7) inhibition. Additional in vitro and in vivo validations are needed to determine efficacy and dose of Macrolactin A in biological systems.

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Current Trends in Docking Methodologies

Oncology ◽

10.4018/978-1-5225-0549-5.ch032 ◽

2017 ◽

pp. 829-847

Author(s):

Shubhandra Tripathi ◽

Akhil Kumar ◽

Amandeep Kaur Kahlon ◽

Ashok Sharma

Keyword(s):

Free Energy ◽

Molecular Docking ◽

Binding Affinity ◽

Binding Free Energy ◽

Energy Calculation ◽

Molecular Binding ◽

New Horizons ◽

Current Trends ◽

Complex Scheme ◽

Dynamics Simulations

Molecular docking was earlier considered to predict the binding affinity of the receptor and ligand molecules. With the progress in computational power and developing approaches, new horizons are now opening for accurate prediction of molecular binding affinity. In the current book chapter, recent strategies for Computer-Aided Drug Designing (CADD) including virtual screening and molecular docking, encompassing molecular dynamics simulations, and binding free energy calculation methods are discussed. Brief overview of different binding free energy methods MMPBSA, MMGBSA, LIE and TI have also been given along with the recent Relaxed Complex Scheme protocol.

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