scholarly journals Molecular Dynamic Simulation and Inhibitor Prediction of Cysteine Synthase Structured Model as a Potential Drug Target for Trichomoniasis

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Satendra Singh ◽  
Gaurav Sablok ◽  
Rohit Farmer ◽  
Atul Kumar Singh ◽  
Budhayash Gautam ◽  
...  
Keyword(s):  
Drug Target ◽  
Minimum Energy ◽  
Dynamics Simulation ◽  
Radius Of Gyration ◽  
Energy Calculation ◽  
Cysteine Synthase ◽  
Structured Model ◽  
Proposed Model ◽  
Modeled Structure ◽  
Inhibitory Molecule

In our presented research, we made an attempt to predict the 3D model for cysteine synthase (A2GMG5_TRIVA) using homology-modeling approaches. To investigate deeper into the predicted structure, we further performed a molecular dynamics simulation for 10 ns and calculated several supporting analysis for structural properties such as RMSF, radius of gyration, and the total energy calculation to support the predicted structured model of cysteine synthase. The present findings led us to conclude that the proposed model is stereochemically stable. The overall PROCHECK G factor for the homology-modeled structure was −0.04. On the basis of the virtual screening for cysteine synthase against the NCI subset II molecule, we present the molecule 1-N, 4-N-bis [3-(1H-benzimidazol-2-yl) phenyl] benzene-1,4-dicarboxamide (ZINC01690699) having the minimum energy score (−13.0 Kcal/Mol) and a log Pvalue of 6 as a potential inhibitory molecule used to inhibit the growth ofT. vaginalisinfection.

scholarly journals Exploring the Binding Mechanism and Accessible Angle of SARS-CoV-2 Spike and ACE2 by Molecular Dynamics Simulation and Free Energy Calculation

2020 ◽  
Author(s):  
Cheng Peng ◽  
Zhengdan Zhu ◽  
Yulong Shi ◽  
Xiaoyu Wang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Drug Target ◽  
Decomposition Analysis ◽  
Dynamics Simulation ◽  
Energy Decomposition Analysis ◽  
Computational Techniques ◽  
Energy Calculation ◽  
Health Crisis ◽  
S Protein ◽  
Public Health Crisis ◽  
Binding Mechanisms

<p></p><p>The SARS-CoV-2 has caused more than 2,000 deaths as of 20 February 2020 worldwide but there is no approved effective drug. The <a>SARS-CoV-2</a> spike (S) glycoprotein is a key drug target due to its indispensable function for viral infection and fusion with ACE2 as a receptor. To facilitate the drug discovery and development with S protein as drug target, various computational techniques were used in this study to evaluate the binding mechanisms between S protein and its acceptor ACE2. Impressively, SARS-CoV-2 S protein has higher affinity binding to ACE2 at two different “up” angles of RBD than SARS-CoV S protein to ACE2 at the same angles. The energy decomposition analysis showed that more interactions formed between SARS-CoV-2 S protein and ACE2, which may partially account for its higher infectiousness than SARS-CoV. In addition, we found that 52.2° is a starting accessible “up” angle of the BRD of SARS-CoV-2 S protein to bind ACE2, demonstrating that BRD is not necessary to be fully opened in order to bind ACE2. We hope that this work will be helpful for the design of effective SARS-CoV-2 S protein inhibitors to address the ongoing public health crisis.</p><p></p>

scholarly journals A Drug Repurposing Approach to Identify Therapeutics by Screening Medicines for Malaria Ventures Exploiting SARS-CoV-2 Main Protease

2021 ◽  
Author(s):  
Rashmi Tyagi ◽  
Anubrata Paul ◽  
V. Samuel Raj ◽  
Krishna Kumar Ojha ◽  
Manoj Kumar Yadav
Keyword(s):  
Free Energy ◽  
Catalytic Domain ◽  
Binding Free Energy ◽  
Dynamics Simulation ◽  
Radius Of Gyration ◽  
Energy Calculation ◽  
High Morbidity ◽  
Molecular Docking Study ◽  
Virus Propagation ◽  
Main Protease

<p>COVID-19 pandemic makes the human-kind standstill and results in high morbidity and mortality cases worldwide. Still, there are no approved antiviral drugs with proven efficacy nor any therapeutic vaccines to combat the disease as per the current date. In the present study, SARS-CoV-2 main protease (Mpro) has been taken as a potential drug target considering its crucial role in virus propagation. We have used 400 diverse bioactive inhibitors with proven antibacterial and antiviral properties for screening against Mpro target. Our screening result identifies ten compounds with higher binding affinity than N3 (used as a reference compound to validate the experiment). All the compounds possess desire physicochemical properties. Later on, in-depth docking and superimposition of selected complexes confirm that only three compounds (MMV1782211, MMV1782220 and MMV1578574) are actively interacting with the catalytic domain of Mpro. </p> <p>Furthermore, the selected three molecules complexed with Mpro and N3-Mpro as control are subjected to molecular dynamics simulation study (root means square deviation, root mean square fluctuation, hydrogen bonding, solvent-accessible area and radius of gyration). MMV1782211-Mpro complex shows a strong and stable interaction as compared to others. The MM/PBSA free energy calculation shows the highest binding free energy of –115.8 kJ/mol for MMV1782211 compound also cross-confirms our molecular docking study. Therefore, our <i>in silico</i> findings become very interesting towards developing alternative medicine against SARS-CoV-2 Mpro target. So, we can expect prompt actions in this direction to combat the COVID-19.</p>

scholarly journals A Drug Repurposing Approach to Identify Therapeutics by Screening Medicines for Malaria Ventures Exploiting SARS-CoV-2 Main Protease

2021 ◽  
Author(s):  
Rashmi Tyagi ◽  
Anubrata Paul ◽  
V. Samuel Raj ◽  
Krishna Kumar Ojha ◽  
Manoj Kumar Yadav
Keyword(s):  
Free Energy ◽  
Catalytic Domain ◽  
Binding Free Energy ◽  
Dynamics Simulation ◽  
Radius Of Gyration ◽  
Energy Calculation ◽  
High Morbidity ◽  
Molecular Docking Study ◽  
Virus Propagation ◽  
Main Protease

<p>COVID-19 pandemic makes the human-kind standstill and results in high morbidity and mortality cases worldwide. Still, there are no approved antiviral drugs with proven efficacy nor any therapeutic vaccines to combat the disease as per the current date. In the present study, SARS-CoV-2 main protease (Mpro) has been taken as a potential drug target considering its crucial role in virus propagation. We have used 400 diverse bioactive inhibitors with proven antibacterial and antiviral properties for screening against Mpro target. Our screening result identifies ten compounds with higher binding affinity than N3 (used as a reference compound to validate the experiment). All the compounds possess desire physicochemical properties. Later on, in-depth docking and superimposition of selected complexes confirm that only three compounds (MMV1782211, MMV1782220 and MMV1578574) are actively interacting with the catalytic domain of Mpro. </p> <p>Furthermore, the selected three molecules complexed with Mpro and N3-Mpro as control are subjected to molecular dynamics simulation study (root means square deviation, root mean square fluctuation, hydrogen bonding, solvent-accessible area and radius of gyration). MMV1782211-Mpro complex shows a strong and stable interaction as compared to others. The MM/PBSA free energy calculation shows the highest binding free energy of –115.8 kJ/mol for MMV1782211 compound also cross-confirms our molecular docking study. Therefore, our <i>in silico</i> findings become very interesting towards developing alternative medicine against SARS-CoV-2 Mpro target. So, we can expect prompt actions in this direction to combat the COVID-19.</p>

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Lane Departure Warning Estimation Using Yaw Acceleration

2020 ◽  
Vol 11 (1) ◽  
pp. 102-111
Author(s):  
Em Poh Ping ◽  
J. Hossen ◽  
Wong Eng Kiong
Keyword(s):  
Vehicle Dynamics ◽  
Dynamic Responses ◽  
Dynamics Simulation ◽  
Steering Wheel ◽  
Mathematical Representation ◽  
Vehicle Speed ◽  
Lane Departure Warning ◽  
Model Based ◽  
Lane Departure ◽  
Proposed Model

AbstractLane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

scholarly journals Scaffold Hopping of α-Rubromycin Enables Direct Access to FDA-Approved Cromoglicic Acid as a SARS-CoV-2 MPro Inhibitor

2021 ◽  
Vol 14 (6) ◽  
pp. 541
Author(s):  
Hani A. Alhadrami ◽  
Ahmed M. Sayed ◽  
Heba Al-Khatabi ◽  
Nabil A. Alhakamy ◽  
Mostafa E. Rateb
Keyword(s):  
Binding Free Energy ◽  
Human Fibroblasts ◽  
Critical Time ◽  
Dynamics Simulation ◽  
Direct Access ◽  
Energy Calculation ◽  
Wide Range ◽  
Normal Human ◽  
Novel Scaffold

The COVID-19 pandemic is still active around the globe despite the newly introduced vaccines. Hence, finding effective medications or repurposing available ones could offer great help during this serious situation. During our anti-COVID-19 investigation of microbial natural products (MNPs), we came across α-rubromycin, an antibiotic derived from Streptomyces collinus ATCC19743, which was able to suppress the catalytic activity (IC50 = 5.4 µM and Ki = 3.22 µM) of one of the viral key enzymes (i.e., MPro). However, it showed high cytotoxicity toward normal human fibroblasts (CC50 = 16.7 µM). To reduce the cytotoxicity of this microbial metabolite, we utilized a number of in silico tools (ensemble docking, molecular dynamics simulation, binding free energy calculation) to propose a novel scaffold having the main pharmacophoric features to inhibit MPro with better drug-like properties and reduced/minimal toxicity. Nevertheless, reaching this novel scaffold synthetically is a time-consuming process, particularly at this critical time. Instead, this scaffold was used as a template to explore similar molecules among the FDA-approved medications that share its main pharmacophoric features with the aid of pharmacophore-based virtual screening software. As a result, cromoglicic acid (aka cromolyn) was found to be the best hit, which, upon in vitro MPro testing, was 4.5 times more potent (IC50 = 1.1 µM and Ki = 0.68 µM) than α-rubromycin, with minimal cytotoxicity toward normal human fibroblasts (CC50 > 100 µM). This report highlights the potential of MNPs in providing unprecedented scaffolds with a wide range of therapeutic efficacy. It also revealed the importance of cheminformatics tools in speeding up the drug discovery process, which is extremely important in such a critical situation.

scholarly journals Screening marine algae metabolites as high-affinity inhibitors of SARS-CoV-2 main protease (3CLpro): an in silico analysis to identify novel drug candidates to combat COVID-19 pandemic

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Ghazala Muteeb ◽  
Adil Alshoaibi ◽  
Mohammad Aatif ◽  
Md. Tabish Rehman ◽  
M. Zuhaib Qayyum
Keyword(s):  
Hydrophobic Interactions ◽  
In Silico Analysis ◽  
Salt Bridge ◽  
Competitive Inhibitor ◽  
Binding Energies ◽  
Dynamics Simulation ◽  
In Vivo Studies ◽  
Energy Calculation

AbstractThe recent dissemination of SARS-CoV-2 from Wuhan city to all over the world has created a pandemic. COVID-19 has cost many human lives and created an enormous economic burden. Although many drugs/vaccines are in different stages of clinical trials, still none is clinically available. We have screened a marine seaweed database (1110 compounds) against 3CLpro of SARS-CoV-2 using computational approaches. High throughput virtual screening was performed on compounds, and 86 of them with docking score <  − 5.000 kcal mol−1 were subjected to standard-precision docking. Based on binding energies (< − 6.000 kcal mol−1), 9 compounds were further shortlisted and subjected to extra-precision docking. Free energy calculation by Prime-MM/GBSA suggested RC002, GA004, and GA006 as the most potent inhibitors of 3CLpro. An analysis of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of RC002, GA004, and GA006 indicated that only RC002 (callophysin A, from red alga Callophycus oppositifolius) passed Lipinski’s, Veber’s, PAINS and Brenk’s filters and displayed drug-like and lead-like properties. Analysis of 3CLpro-callophysin A complex revealed the involvement of salt bridge, hydrogen bonds, and hydrophobic interactions. callophysin A interacted with the catalytic residues (His41 and Cys145) of 3CLpro; hence it may act as a mechanism-based competitive inhibitor. Docking energy and docking affinity of callophysin A towards 3CLpro was − 8.776 kcal mol−1 and 2.73 × 106 M−1, respectively. Molecular dynamics simulation confirmed the stability of the 3CLpro-callophysin A complex. The findings of this study may serve as the basis for further validation by in vitro and in vivo studies.

THE "FOLDING" BEHAVIORS OF HOMOPOLYMERS WITH ONE END FIXED

2004 ◽  
Vol 18 (15) ◽  
pp. 2123-2139 ◽  
Author(s):  
BIN XUE ◽  
JUN WANG ◽  
WEI WANG
Keyword(s):  
Molecular Dynamics ◽  
Chain Length ◽  
Conformational Changes ◽  
Canonical Ensemble ◽  
Interaction Strength ◽  
Simulation Method ◽  
Dynamics Simulation ◽  
Radius Of Gyration ◽  
Native Conformation ◽  
Collapse Temperature

We study the "folding" behaviors of homopolymers with one end fixed. By using canonical ensemble molecular dynamics simulation method, we observe the conformational changes during folding processes. Long chains collapse to the helical nuclei, then regroup to helix from the free-end to form the compact conformations through the middle stages of helix-like coil and helix-like cone, while short chains do not apparently have the above mentioned middle stages. Through simulated annealing, the native conformation of homopolymer chain in our model is found to be helix. We show the relations between specific heat C v (T) and radius of gyration R g (T) as functions of temperature, chain length and the interaction strength, respectively. We find that these two quantities match well and can be combined to interpret the "folding" process of the homopolymer. It is found that the collapse temperature Tθ and the native-like folding temperature T f do not change with the chain length in our model, however the interaction strength affects the values of Tθ and T f .

Xanthine oxidase inhibitory activity of Euphorbia peplus L. phenolics

2021 ◽  
Vol 24 ◽  
Author(s):  
Emadeldin M. Kamel ◽  
Noha A. Ahmed ◽  
Ashraf A. El-Bassuony ◽  
Omnia E. Hussein ◽  
Barakat Alrashdi ◽  
...  
Keyword(s):  
Xanthine Oxidase ◽  
Active Site ◽  
Inhibitory Activity ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Solvent Accessible Surface Area ◽  
Radius Of Gyration ◽  
Euphorbia Peplus

Background: Various phenolics show inhibitory activity towards xanthine oxidase (XO), an enzyme that generates reactive oxygen species which cause oxidative damage. Objective: This study investigated the XO inhibitory activity of Euphorbia peplus phenolics. Methods: The dried powdered aerial parts of E. peplus were extracted, fractioned and phenolics were isolated and identified. The XO inhibitory activity of E. peplus extract (EPE) and the isolated phenolics was investigated in vitro and in vivo. Results: Three phenolics were isolated from the ethyl acetate fraction of E. peplus. All isolated compounds and the EPE showed inhibitory activity towards XO in vitro. In hyperuricemic rats, EPE and the isolated phenolics decreased uric acid and XO activity. Molecular docking showed the binding modes of isolated phenolics with XO, depicting significant interactions with the active site amino acid residues. Molecular dynamics simulation trajectories confirmed the interaction of isolated phenolics with XO by forming hydrogen bonds with the active site residues. Also, the root mean square (RMS) deviations of XO and phenolics-XO complexes achieved equilibrium and fluctuated during the 10 ns MD simulations. The radius of gyration and solvent accessible surface area investigations showed that different systems were stabilized at ≈ 2500 ps. The RMS fluctuations profile depicted that the drug binding site exhibited a rigidity behavior during the simulation. Conclusion: In vitro, in vivo and computational investigations showed the XO inhibitory activity of E. peplus phenolics. These phenolics might represent promising candidates for the development of XO inhibitors.

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