scholarly journals In Silico Identification of Novel Aromatic Compounds as Potential HIV-1 Entry Inhibitors Mimicking Cellular Receptor CD4

Viruses ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 746 ◽  
Author(s):  
Alexander M. Andrianov ◽  
Grigory I. Nikolaev ◽  
Yuri V. Kornoushenko ◽  
Wei Xu ◽  
Shibo Jiang ◽  
...  
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Cellular Receptor ◽  
Dynamic Simulations ◽  
Entry Inhibitors ◽  
Fusion Inhibitors ◽  
Gp120 Protein ◽  
Hiv 1

Despite recent progress in the development of novel potent HIV-1 entry/fusion inhibitors, there are currently no licensed antiviral drugs based on inhibiting the critical interactions of the HIV-1 envelope gp120 protein with cellular receptor CD4. In this connection, studies on the design of new small-molecule compounds able to block the gp120-CD4 binding are still of great value. In this work, in silico design of drug-like compounds containing the moieties that make the ligand active towards gp120 was performed within the concept of click chemistry. Complexes of the designed molecules bound to gp120 were then generated by molecular docking and optimized using semiempirical quantum chemical method PM7. Finally, the binding affinity analysis of these ligand/gp120 complexes was performed by molecular dynamic simulations and binding free energy calculations. As a result, five top-ranking compounds that mimic the key interactions of CD4 with gp120 and show the high binding affinity were identified as the most promising CD4-mimemic candidates. Taken together, the data obtained suggest that these compounds may serve as promising scaffolds for the development of novel, highly potent and broad anti-HIV-1 therapeutics.

scholarly journals Insight Derived from Molecular Docking and Molecular Dynamics Simulations into the Binding Interactions Between HIV-1 Protease Inhibitors and SARS-CoV-2 3CLpro

2020 ◽  
Author(s):  
peng sang ◽  
Shuhui Tian ◽  
Zhaohui Meng ◽  
Liquan Yang
Keyword(s):  
Molecular Docking ◽  
Protease Inhibitors ◽  
Binding Affinity ◽  
Proteinase Inhibitors ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Respiratory Illness ◽  
Free Energy Calculations ◽  
Binding Interactions ◽  
Hiv 1

<p>A novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) was identified from respiratory illness patients in Wuhan, Hubei Province, China, which has recently emerged as a serious threat to the world public health. Hower, no approved drugs have been found to effectively inhibit the virus. Since it has been reported that the HIV-1 protease inhibitors can be used as anti-SARS drugs by tegarting SARS-CoV 3CLpro, we choose six approved anti-HIV-1 drugs to investigate their binding interactions between 3CLpro, and to evaluate their potential to become clinical drugs for the new coronavirus pneumonia (COVID19) caused by SARS-CoV-2 infection. The molecular docking results indicate that, the 3CLpro of SARS-CoV-2 has a higher binding affinity for all the studied inhibitors than its SARS homologue. Two docking complexes (indinavir and darunavir) with high docking scores were futher subjected to MM-PBSA binding free energy calculations to detail the molecular interactions between these two proteinase inhibitors and the 3CLpro. Our results show that darunavir has the best binding affinity with SARS-CoV-2 and SARS-CoV 3CLpro among all inhibitors, indicating it has the potential to become an anti-COVID-19 clinical drug. The likely reason behind the increased binding affinity of HIV-1 protease inhibitors toward SARS-CoV2 3CLpro than that of SARS-CoV were investigated by MD simulations. Our study provides insight into the possible role of structural flexibility during interactions between 3CLpro and inhibitors, and sheds light on the structure-based design of anti-COVID-19 drugs targeting the SARS-CoV-2 3CLpro. </p><div><br></div>

scholarly journals Insight Derived from Molecular Docking and Molecular Dynamics Simulations into the Binding Interactions Between HIV-1 Protease Inhibitors and SARS-CoV-2 3CLpro

2020 ◽  
Author(s):  
peng sang ◽  
Shuhui Tian ◽  
Zhaohui Meng ◽  
Liquan Yang
Keyword(s):  
Molecular Docking ◽  
Protease Inhibitors ◽  
Binding Affinity ◽  
Proteinase Inhibitors ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Respiratory Illness ◽  
Free Energy Calculations ◽  
Binding Interactions ◽  
Hiv 1

<p>A novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) was identified from respiratory illness patients in Wuhan, Hubei Province, China, which has recently emerged as a serious threat to the world public health. Hower, no approved drugs have been found to effectively inhibit the virus. Since it has been reported that the HIV-1 protease inhibitors can be used as anti-SARS drugs by tegarting SARS-CoV 3CLpro, we choose six approved anti-HIV-1 drugs to investigate their binding interactions between 3CLpro, and to evaluate their potential to become clinical drugs for the new coronavirus pneumonia (COVID19) caused by SARS-CoV-2 infection. The molecular docking results indicate that, the 3CLpro of SARS-CoV-2 has a higher binding affinity for all the studied inhibitors than its SARS homologue. Two docking complexes (indinavir and darunavir) with high docking scores were futher subjected to MM-PBSA binding free energy calculations to detail the molecular interactions between these two proteinase inhibitors and the 3CLpro. Our results show that darunavir has the best binding affinity with SARS-CoV-2 and SARS-CoV 3CLpro among all inhibitors, indicating it has the potential to become an anti-COVID-19 clinical drug. The likely reason behind the increased binding affinity of HIV-1 protease inhibitors toward SARS-CoV2 3CLpro than that of SARS-CoV were investigated by MD simulations. Our study provides insight into the possible role of structural flexibility during interactions between 3CLpro and inhibitors, and sheds light on the structure-based design of anti-COVID-19 drugs targeting the SARS-CoV-2 3CLpro. </p><div><br></div>

Potential HIV-1 fusion inhibitors mimicking gp41-specific broadly neutralizing antibody 10E8: In silico discovery and prediction of antiviral potency

2018 ◽  
Vol 16 (02) ◽  
pp. 1840007 ◽  
Author(s):  
Alexander M. Andrianov ◽  
Ivan A. Kashyn ◽  
Alexander V. Tuzikov
Keyword(s):  
In Silico ◽  
Binding Free Energy ◽  
Neutralizing Antibody ◽  
Free Energy Calculations ◽  
Fusion Inhibitor ◽  
Broadly Neutralizing Antibody ◽  
Screening Platform ◽  
Dynamics Simulations ◽  
Hiv 1 ◽  
Inhibitor Scaffolds

An integrated computational approach to in silico drug design was used to identify novel HIV-1 fusion inhibitor scaffolds mimicking broadly neutralizing antibody (bNab) 10E8 targeting the membrane proximal external region (MPER) of the HIV-1 gp41 protein. This computer-based approach included (i) generation of pharmacophore models representing 3D-arrangements of chemical functionalities that make bNAb 10E8 active towards the gp41 MPER segment, (ii) shape and pharmacophore-based identification of the 10E8-mimetic candidates by a web-oriented virtual screening platform pepMMsMIMIC, (iii) high-throughput docking of the identified compounds with the gp41 MPER peptide, and (iv) molecular dynamics simulations of the docked structures followed by binding free energy calculations. As a result, eight hits-able to mimic pharmacophore properties of bNAb 10E8 by specific and effective interactions with the MPER region of the HIV-1 protein gp41 were selected as the most probable 10E8-mimetic candidates. Similar to 10E8, the predicted compounds target the critically important residues of a highly conserved hinge region of the MPER peptide that provides a conformational flexibility necessary for its functioning in cell-virus membrane fusion process. In light of the data obtained, the identified small molecules may present promising HIV-1 fusion inhibitor scaffolds for the design of novel potent antiviral drugs.

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

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

<p>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.</p>

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

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.

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

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

<p><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 <i>in silico </i>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 <i>in vitro</i> analysis to confirm their inhibitory action against TNFα or can be used as scaffolds to arrive at better drug candidates.</p>

scholarly journals The Development of Tetrazole Derivatives as Protein Arginine Methyltransferase I (PRMT I) Inhibitors

2019 ◽  
Vol 20 (15) ◽  
pp. 3840 ◽  
Author(s):  
Sun ◽  
Wang ◽  
Yang ◽  
Zhu ◽  
Wu ◽  
...  
Keyword(s):  
Binding Free Energy ◽  
Arginine Methylation ◽  
Free Energy Calculations ◽  
Protein Arginine Methyltransferase ◽  
Dynamic Simulations ◽  
Arginine Methyltransferase ◽  
Diverse Range ◽  
Protein Arginine Methyltransferase 1 ◽  
Canonical Wnt ◽  
Tetrazole Derivatives

Protein arginine methyltransferase 1 (PRMT1) can catalyze protein arginine methylation by transferring the methyl group from S-adenosyl-L-methionine (SAM) to the guanidyl nitrogen atom of protein arginine, which influences a variety of biological processes. The dysregulation of PRMT1 is involved in a diverse range of diseases, including cancer. Therefore, there is an urgent need to develop novel and potent PRMT1 inhibitors. In the current manuscript, a series of 1-substituted 1H-tetrazole derivatives were designed and synthesized by targeting at the substrate arginine-binding site on PRMT1, and five compounds demonstrated significant inhibitory effects against PRMT1. The most potent PRMT1 inhibitor, compound 9a, displayed non-competitive pattern with respect to either SAM or substrate arginine, and showed the strong selectivity to PRMT1 compared to PRMT5, which belongs to the type II PRMT family. It was observed that the compound 9a inhibited the functions of PRMT1 and relative factors within this pathway, and down-regulated the canonical Wnt/β-catenin signaling pathway. The binding of compound 9a to PRMT1 was carefully analyzed by using molecular dynamic simulations and binding free energy calculations. These studies demonstrate that 9a was a potent PRMT1 inhibitor, which could be used as lead compound for further drug discovery.

In silico search, chemical characterization and immunogenic evaluation of amino-terminated G4-PAMAM-HIV peptide complexes using three-dimensional models of the HIV-1 gp120 protein

2019 ◽  
Vol 177 ◽  
pp. 77-93 ◽  
Author(s):  
Rolando Alberto Rodríguez-Fonseca ◽  
Martiniano Bello ◽  
María Ángeles de los Muñoz-Fernández ◽  
José Luis Jiménez ◽  
Saúl Rojas-Hernández ◽  
...  
Keyword(s):  
In Silico ◽  
Three Dimensional ◽  
Chemical Characterization ◽  
Gp120 Protein ◽  
Peptide Complexes ◽  
Dimensional Models ◽  
Three Dimensional Models ◽  
Hiv 1

scholarly journals The Significance of Halogen Bonding in Ligand–Receptor Interactions: The Lesson Learned from Molecular Dynamic Simulations of the D4 Receptor

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 91 ◽  
Author(s):  
Rafał Kurczab ◽  
Katarzyna Kucwaj-Brysz ◽  
Paweł Śliwa
Keyword(s):  
Molecular Docking ◽  
Hot Spots ◽  
Binding Free Energy ◽  
Halogen Bonding ◽  
Free Energy Calculations ◽  
Dynamic Simulations ◽  
Receptor Complexes ◽  
Molecular Core ◽  
D4 Receptor ◽  
Dynamics Simulations

Recently, a computational approach combining a structure–activity relationship library containing pairs of halogenated ligands and their corresponding unsubstituted ligands (called XSAR) with QM-based molecular docking and binding free energy calculations was developed and used to search for amino acids frequently targeted by halogen bonding, also known as XB hot spots. However, the analysis of ligand–receptor complexes with halogen bonds obtained by molecular docking provides a limited ability to study the role and significance of halogen bonding in biological systems. Thus, a set of molecular dynamics simulations for the dopamine D4 receptor, recently crystallized with the antipsychotic drug nemonapride (5WIU), and the five XSAR sets were performed to verify the identified hot spots for halogen bonding, in other words, primary (V5x40), and secondary (S5x43, S5x461 and H6x55). The simulations confirmed the key role of halogen bonding with V5x40 and H6x55 and supported S5x43 and S5x461. The results showed that steric restrictions and the topology of the molecular core have a crucial impact on the stabilization of the ligand–receptor complex by halogen bonding.

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