scholarly journals A Hybrid DELGA Algorithm for Protein Ligand Docking

2020 ◽  
Vol 9 (4) ◽  
pp. 2836-2840
Keyword(s):  
Genetic Algorithm ◽  
Molecular Modeling ◽  
Binding Energy ◽  
Research Work ◽  
Ligand Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Lamarckian Genetic Algorithm ◽  
Computational Molecular Modeling ◽  
Docking Energy

Protein-ligand docking is a computational molecular modeling method that is used in drug design to predict the optimal binding pose between the ligand and receptor. AutoDock is an open-source freeware program used to predict docking poses. It uses LGA) Lamarckian genetic algorithm to enumerate the binding energy. In this research work, we proposed an approach of hybrid Differential evolution base Lamarckian genetic (DELGA) algorithm to calculate the lowest binding energy. The experiment conducted to compute the 65 molecular instances, the results exposed that our approach predicts lowest docking energy with minimum root mean square deviation (RMSD) in comparison to the LGA, SA and PSO algorithms.

scholarly journals Improvement in a phenomenological formula for ground state binding energies

2016 ◽  
Vol 25 (08) ◽  
pp. 1650046
Author(s):  
G. Gangopadhyay
Keyword(s):  
Binding Energy ◽  
Ground State ◽  
Magic Number ◽  
Substantial Improvement ◽  
Binding Energies ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Neutron Magic Number ◽  
Experimental Values ◽  
New Formula

The phenomenological formula for ground state binding energy derived earlier [G. Gangopadhyay, Int. J. Mod. Phys. E 20 (2011) 179] has been modified. The parameters have been obtained by fitting the latest available tabulation of experimental values. The major modifications include a new term for pairing and introduction of a new neutron magic number at N = 160. The new formula reduced the root mean square deviation to 363[Formula: see text]keV, a substantial improvement over the previous version of the formula.

scholarly journals The Treatment of Alzheimer’s Disease through Molecular Docking Studies using Phyltetralin against Kallikrein_6: A Bioinformatic Approach

2021 ◽  
Vol 9 (VI) ◽  
pp. 3992-3999
Author(s):  
Sarita Negi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Binding Energy ◽  
In Silico ◽  
Receptor Protein ◽  
Mean Square ◽  
Mean Square Deviation ◽  
In Silico Study ◽  
Kallikrein 6

Alzheimer's disease (AD) is a neurodegenerative disease that generally begins leisurely and gets worse with time. Alzheimer’s disease (AD) dementia is the specific beginning of age-related declination of cognitive abilities and function, which eventually leads to death. Alzheimer’s disease (AD) is one of the neurodeteriorating disorders which is one of the mostcritical complications that our current health care system faces. The phenomenon of molecular docking has progressively become a strong tool in the field of pharmaceutical research including drug discovery. The aim of the presentin silico study was to inhibit the expression of KLK-6 (kallikrein-6) which is a target or receptor protein by its interaction with three distinct secondary metabolites for treating Alzheimer's disease (AD) through molecular docking. Methods: The in-silico study was based on molecular docking. Docking was executed amidst ligands- Quercetin (CID: 5280343), Ricinoleic Acid (CID: 643684), Phyltetralin (CID: 11223782), and the target or receptor protein Kallikrein-6 (PDB ID: 1LO6). The protein and the ligands were downloaded in the required format. Through PyRx, the ligands were virtually screened after importing them in the PyRx window. The results of PyRx and SwissADME were analyzed and the best ligand was finalized. Among the three, Phyltetralin was the best ligand contrary to KLK-6 having minimum binding energy and it was following Lipinski’s five rules along with 0 violations. Results: The final docking was carried out between Phyltetralin and KLK-6 through AutoDock Vina. The outcome showed 9 poses with distinct binding energy, RSMD LB (root mean square deviation lower bound) and RSMD UB (root mean square deviation upper bound). With the help of PyMOL which is an open-access tool for molecular visualization, the interaction amidst Phyltetralin and KLK-6 can be visualized. Conclusion: Based on this in silico study it can be concluded that KLK-6 (kallikrein-6) which is responsible for causing AD can be inhibited by ligand Phyltetralin and for the treatment of AD, phyltetralin might act as a potential drug. Thus, in future studies, Phyltetralin from natural sources can prevent Alzheimer's disease and can be proved as a promising and efficient drug for treating Alzheimer's disease.

scholarly journals Desain Turunan Senyawa Leonurine Sebagai Kandidat Obat Anti Inflamasi

2020 ◽  
Vol 6 (1) ◽  
pp. 181-191
Author(s):  
Ruslin Beny ◽  
Nindy Rachma Az Yana ◽  
Mesi Leorita
Keyword(s):  
Binding Energy ◽  
Protein Data Bank ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Inhibitory Activity ◽  
Data Bank ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Cox 2 ◽  
Autodock 4.2

In this research, there was docking process of leonurine compounds and their derivatives to Cyclooxygenase-2 (COX-2) enzyme as an anti-inflammatory. The receptor code used is 6COX which is downloaded from the protein data bank site (PDB). The aims of this study was to investigate the interaction of leonurine compounds and their derivatives against COX-2 receptors. All compounds are docked using AutoDock 4.2 software. The docking validation is performed by tethering the ligand-receptor with the parameter of the Root Mean Square Deviation (RMSD) value <2 Å. From the docking validation results, have been obtained RMSD value = 0.31 Å. Analysis of docking results indicates that leonurine and its derivatives are predicted to have good interaction with COX-2 receptors. From the results of this research, it can be concluded that leonurine compounds and their derivatives have inhibitory activity against COX-2 receptors. The docking results showed the lowest binding energy value ligand-receptor (ΔG) best matched in the 11th derived compound which is -7.95 kcal/mol.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Effect of Terminal Groups on Thermomechanical and Dielectric Properties of Silica–Epoxy Composite Modified by Hyperbranched Polyester

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2451
Author(s):  
Jianwen Zhang ◽  
Dongwei Wang ◽  
Lujia Wang ◽  
Wanwan Zuo ◽  
Lijun Zhou ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Dielectric Properties ◽  
Binding Energy ◽  
Epoxy Resin ◽  
Volume Fraction ◽  
Epoxy Composite ◽  
Mean Square Displacement ◽  
Mean Square ◽  
Hyperbranched Polyester ◽  
Free Volume Fraction

To study the effect of hyperbranched polyester with different kinds of terminal groups on the thermomechanical and dielectric properties of silica–epoxy resin composite, a molecular dynamics simulation method was utilized. Pure epoxy resin and four groups of silica–epoxy resin composites were established, where the silica surface was hydrogenated, grafted with silane coupling agents, and grafted with hyperbranched polyester with terminal carboxyl and terminal hydroxyl, respectively. Then the thermal conductivity, glass transition temperature, elastic modulus, dielectric constant, free volume fraction, mean square displacement, hydrogen bonds, and binding energy of the five models were calculated. The results showed that the hyperbranched polyester significantly improved the thermomechanical and dielectric properties of the silica–epoxy composites compared with other surface treatments, and the terminal groups had an obvious effect on the enhancement effect. Among them, epoxy composite modified by the hyperbranched polyester with terminal carboxy exhibited the best thermomechanical properties and lowest dielectric constant. Our analysis of the microstructure found that the two systems grafted with hyperbranched polyester had a smaller free volume fraction (FFV) and mean square displacement (MSD), and the larger number of hydrogen bonds and greater binding energy, indicating that weaker strength of molecular segments motion and stronger interfacial bonding between silica and epoxy resin matrix were the reasons for the enhancement of the thermomechanical and dielectric properties.

scholarly journals Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

1998 ◽  
Vol 19 (14) ◽  
pp. 1639-1662 ◽  
Author(s):  
Garrett M. Morris ◽  
David S. Goodsell ◽  
Robert S. Halliday ◽  
Ruth Huey ◽  
William E. Hart ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Free Energy ◽  
Energy Function ◽  
Binding Free Energy ◽  
Free Energy Function ◽  
Lamarckian Genetic Algorithm ◽  
Automated Docking

Introduction and Preliminary Investigation of Buoyancy Assisted Robots That Are Cheap, Safe, and Will Never Fall Down

2021 ◽  
Author(s):  
Matthew David Williams ◽  
Dennis Hong
Keyword(s):  
Genetic Algorithm ◽  
Preliminary Analysis ◽  
Preliminary Investigation ◽  
Research Work ◽  
Legged Robots ◽  
Dynamical Analysis ◽  
Future Research ◽  
The Novel ◽  
Drag Forces ◽  
New Family

Abstract We introduce and define a new family of mobile robots called BAR (Buoyancy Assisted Robots) that are cheap, safe, and will never fall down. BARs utilize buoyancy from lighter-than-air gases as a way to support the weight of the robot for locomotion. A new BAR robot named BLAIR (Buoyant Legged Actuated Inverted Robot) whose buoyancy is greater than its weight is also presented in this paper. BLAIRs can walk “upside-down” on the ceiling, providing unique advantages that no other robot platforms can. Unlike other legged robots, the mechanics of how BARs walk is fundamentally different. We also perform a preliminary investigation for BARs. This includes comparing safety, cost, and energy consumption with other commercially available robots. Additionally, the preliminary investigation also includes analyzing previous works relating to BARs. A dynamical analysis is performed on the novel robot BLAIR. This is presented to show the impacts of buoyant and drag forces on BLAIRs. Preliminary analysis with the prevalence of drag is presented with simulations using a genetic algorithm and simulations. Results show that BARs with different mechanisms prefer different styles of walking gaits such as prancing or skipping. This work lays the foundation for future research work on the gaits for BARs.

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