Insilico study of stigmasterol extracted from pluchea indica as antiferility in men

Docking is a method to predict the strength of the interaction between the receptor and the ligand based on the binding affinity value. The docking carried out in this study is a specific docking with a grid box imitating the bond between AR and inhibitor control drugs. This study aimed to investigate stigmasterol from beluntas leaves potential molecularly as antifertility in men. This type of research is descriptive and exploratory. The research was carried out from November to December 2019. The research was carried out with the help of Indonesia's Bioinformatics and Biomolecular Analysis Organization (INBIO). Method of analysis with 3 steps: (1) Looking for a collection of metabolites and pass online, (2) molecular docking and MD simulation, and (3) drug-likeness and toxicity. Determination of compound potency based on binding affinity value. The more negative the binding affinity, the stronger the interaction between the receptor and the ligand. The results showed that it was predicted that stigmasterol could attach to the same active site (of Methyltrienolone) as the control to affect AR. Stigmasterol has a binding affinity value close to that of the inhibitor control, which is -5.4 kcal/mol, while the inhibitory control has a binding affinity value of -4.6 kcal/mol. The ideal value for control is -7 kcal/mol. The results of the MD Simulation analysis found that the AR-Stigmasterol complex was more stable than the AR-Cyproterone acetate complex due to its lower RMSD and RMSF values. The finding of this study confirmed that stigmasterol from beluntas leaves has the potential as an antifertility for men

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials \& methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results \& discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility

Autonomous homing and docking tasks for an underwater vehicle

This paper briefly introduces a strategy for autonomous homing and docking tasks using an autonomous underwater vehicle. The control and guidance based path following for those tasks are described in this work. A standard sliding mode for controller design is briefly given. The method provides robust motion control efforts for an underwater vehicle’s decoupled system whilst minimising chattering effects. In a guidance system, the vector field based on a conventional artificial potential field method gives a desired trajectory with a use of existing information from sensors in the network. A well structured Line-of-Sight method is used for an AUV to follow the path. It provides guidance for an AUV to follow the predefined trajectory to a required position with the final desired orientation at the dock. Integration of a control and guidance system provides a complete system for this application. Simulation studies are illustrated in the paper

A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates

A Study of Archiving Strategies in Multi-Objective PSO for Molecular Docking

Molecular docking is a complex optimization problem aimed at predicting the position of a ligand molecule in the active site of a receptor with the lowest binding energy. This problem can be formulated as a bi-objective optimization problem by minimizing the binding energy and the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands. In this context, the SMPSO multi-objective swarm-intelligence algorithm has shown a remarkable performance. SMPSO is characterized by having an external archive used to store the non-dominated solutions and also as the basis of the leader selection strategy. In this paper, we analyze several SMPSO variants based on different archiving strategies in the scope of a benchmark of molecular docking instances. Our study reveals that the SMPSOhv, which uses an hypervolume contribution based archive, shows the overall best performance.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Elucidating Signal Transduction Modulatory Drug Target Network of Colon Cancer: A Network Biology Approach

Latest evaluation and validation of cancer drugs and their targets has demonstrated the lack and inadequate development of new and better drugs, based on available protocols. Even though the specificity of drug targets is a great challenge in the pharmaco-proteomics field of cancer biology, for eradicating such hurdles and paving the way for the drugs of future, a novel step has been envisaged here to study the relation between drug target network and the corresponding drug network using the advanced concepts of proteomics and network biology. The literature mining was done for the collection of receptors and the ligands. About 1000 natural compounds were collected and out of those 300 molecules showed anti-cancer activity against colon cancer. Ligand Vs multiple receptor docking was done using the software Quantum 3.3.0; the results were further used for the designing of a well connected Protein Ligand Interaction (PLI) network of colon cancer. The obtained network is then extrapolated to sort out the receptors expressed in the specific cancer type. The network is then statistically analyzed and represented by the graphical interpretation, in order to ascertain the hub nodes and their locally parsed neighbours. Based on the best docking scores, the graphs obtained from the docking analysis are statistically validated with the help of VisANT. In the network three hub nodes Neutrophil cytosol factor 2, UV excision repair protein RAD23 homolog A, & Receptor-type tyrosine-protein phosphatase eta were identified, which showed the highest interaction with the ligands. Butyrate and Farnesol showed highest interaction as ligands. Multiple Sequence Alignment was done of the binding site sequence of the drug targets to find out the evolutionary closeness of the binding sites. The phylogenetic tree was also constructed to further validate the observation. Further in-vitro and in-vivo studies needs to be done to analyse the receptor specificity and anti tumor activity of these compounds in Colon cancer

Virtual screening for NS5B inhibitors of Hepatitis C virus

Hepatitis C Virus (HCV) infection is a serious cause of chronic liver disease worldwide with more than 170 million infected individuals at a risk of developing significant morbidity and mortality. To date there is no effective drug for the treatment or vaccine to prevent this infection. The present study aims to discover novel inhibitors which target an allosteric binding site of RNA dependent RNA polymerase enzyme of HCV. A structure based virtual screening of Zinc database by computational docking and the post docking analysis of energy calculations and interactions followed by ADMET studies were conducted. Our study revealed 10 compounds which has more potential than the existing inhibitor to be considered as lead compounds.
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Dynamic analysis of Apollo-Salyut/Soyuz docking

The use of a docking-system computer program in analyzing the dynamic environment produced by two impacting spacecraft and the attitude control systems is discussed. Performance studies were conducted to determine the mechanism load and capture sensitivity to parametric changes in the initial impact conditions. As indicated by the studies, capture latching is most sensitive to vehicle angular-alinement errors and is least sensitive to lateral-miss error. As proved by load-sensitivity studies, peak loads acting on the Apollo spacecraft are considerably lower than the Apollo design-limit loads