Computer-Aided Discovery in Antimicrobial Research: In Silico Model for Virtual Screening of Potent and Safe Anti-Pseudomonas Agents

2015 ◽  
Vol 18 (3) ◽  
pp. 305-314 ◽  
Author(s):  
Alejandro Speck-Planche ◽  
Maria Cordeiro
Keyword(s):  
Virtual Screening ◽  
In Silico ◽  
In Silico Model ◽  
Computer Aided

Discovery of New AKT1 Inhibitors by Combination of In silico Structure Based Virtual Screening Approaches and Biological Evaluations

2019 ◽  
Author(s):  
Filip Fratev ◽  
Denisse A. Gutierrez ◽  
Renato J. Aguilera ◽  
suman sirimulla
Keyword(s):  
Virtual Screening ◽  
Success Rate ◽  
Protein Interactions ◽  
In Silico ◽  
Biological Data ◽  
In Vitro Binding ◽  
Vitro Binding ◽  
Screening Protocol ◽  
Screening Approaches

AKT1 is emerging as a useful target for treating cancer. Herein, we discovered a new set of ligands that inhibit the AKT1, as shown by in vitro binding and cell line studies, using a newly designed virtual screening protocol that combines structure-based pharmacophore and docking screens. Taking together with the biological data, the combination of structure based pharamcophore and docking methods demonstrated reasonable success rate in identifying new inhibitors (60-70%) proving the success of aforementioned approach. A detail analysis of the ligand-protein interactions was performed explaining observed activities.<br>

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

Promising Anti-stroke Signature of Voglibose: Investigation through In- Silico Molecular Docking and Virtual Screening in In-Vivo Animal Studies

2020 ◽  
Vol 20 (3) ◽  
pp. 223-235
Author(s):  
Pooja Shah ◽  
Vishal Chavda ◽  
Snehal Patel ◽  
Shraddha Bhadada ◽  
Ghulam Md. Ashraf
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
In Silico ◽  
Reductase Activity ◽  
Infarct Volume ◽  
Docking Studies ◽  
Serum Glucose ◽  
In Vivo Studies ◽  
In Silico Molecular Docking

Background: Postprandial hyperglycemia considered to be a major risk factor for cerebrovascular complications. Objective: The current study was designed to elucidate the beneficial role of voglibose via in-silico in vitro to in-vivo studies in improving the postprandial glycaemic state by protection against strokeprone type 2 diabetes. Material and Methods: In-Silico molecular docking and virtual screening were carried out with the help of iGEMDOCK+ Pymol+docking software and Protein Drug Bank database (PDB). Based on the results of docking studies, in-vivo investigation was carried out for possible neuroprotective action. T2DM was induced by a single injection of streptozotocin (90mg/kg, i.v.) to neonates. Six weeks after induction, voglibose was administered at the dose of 10mg/kg p.o. for two weeks. After eight weeks, diabetic rats were subjected to middle cerebral artery occlusion, and after 72 hours of surgery, neurological deficits were determined. The blood was collected for the determination of serum glucose, CK-MB, LDH and lipid levels. Brains were excised for determination of brain infarct volume, brain hemisphere weight difference, Na+-K+ ATPase activity, ROS parameters, NO levels, and aldose reductase activity. Results: In-silico docking studies showed good docking binding score for stroke associated proteins, which possibly hypotheses neuroprotective action of voglibose in stroke. In the present in-vivo study, pre-treatment with voglibose showed a significant decrease (p<0.05) in serum glucose and lipid levels. Voglibose has shown significant (p<0.05) reduction in neurological score, brain infarct volume, the difference in brain hemisphere weight. On biochemical evaluation, treatment with voglibose produced significant (p<0.05) decrease in CK-MB, LDH, and NO levels in blood and reduction in Na+-K+ ATPase, oxidative stress, and aldose reductase activity in brain homogenate. Conclusion: In-silico molecular docking and virtual screening studies and in-vivo studies in MCAo induced stroke, animal model outcomes support the strong anti-stroke signature for possible neuroprotective therapeutics.

Lead Molecule Prediction and Characterization for Designing MERS-CoV 3C-like Protease Inhibitors: An In silico Approach

2018 ◽  
Vol 15 (1) ◽  
pp. 82-88 ◽  
Author(s):  
Md. Mostafijur Rahman ◽  
Md. Bayejid Hosen ◽  
M. Zakir Hossain Howlader ◽  
Yearul Kabir
Keyword(s):  
Binding Energy ◽  
Middle East ◽  
Virtual Screening ◽  
In Silico ◽  
Screening Method ◽  
Middle East Respiratory Syndrome ◽  
Cytochrome P450 Enzymes ◽  
Drug Molecules ◽  
Essential Enzyme ◽  
Reduced Toxicity

Background: 3C-like protease also called the main protease is an essential enzyme for the completion of the life cycle of Middle East Respiratory Syndrome Coronavirus. In our study we predicted compounds which are capable of inhibiting 3C-like protease, and thus inhibit the lifecycle of Middle East Respiratory Syndrome Coronavirus using in silico methods. </P><P> Methods: Lead like compounds and drug molecules which are capable of inhibiting 3C-like protease was identified by structure-based virtual screening and ligand-based virtual screening method. Further, the compounds were validated through absorption, distribution, metabolism and excretion filtering. Results: Based on binding energy, ADME properties, and toxicology analysis, we finally selected 3 compounds from structure-based virtual screening (ZINC ID: 75121653, 41131653, and 67266079) having binding energy -7.12, -7.1 and -7.08 Kcal/mol, respectively and 5 compounds from ligandbased virtual screening (ZINC ID: 05576502, 47654332, 04829153, 86434515 and 25626324) having binding energy -49.8, -54.9, -65.6, -61.1 and -66.7 Kcal/mol respectively. All these compounds have good ADME profile and reduced toxicity. Among eight compounds, one is soluble in water and remaining 7 compounds are highly soluble in water. All compounds have bioavailability 0.55 on the scale of 0 to 1. Among the 5 compounds from structure-based virtual screening, 2 compounds showed leadlikeness. All the compounds showed no inhibition of cytochrome P450 enzymes, no blood-brain barrier permeability and no toxic structure in medicinal chemistry profile. All the compounds are not a substrate of P-glycoprotein. Our predicted compounds may be capable of inhibiting 3C-like protease but need some further validation in wet lab.

In silico discovery of novel flavonoids as poly ADP ribose polymerase (PARP) inhibitors

2020 ◽  
Vol 16 ◽  
Author(s):  
Ashish Shah ◽  
Ghanshyam Parmar ◽  
Avinash Kumar Seth
Keyword(s):  
Virtual Screening ◽  
In Silico ◽  
Synthetic Lethality ◽  
Parp Inhibitor ◽  
Parp Inhibitors ◽  
Docking Studies ◽  
Docking Score ◽  
Docking Method ◽  
Anti Cancer ◽  
In Silico Toxicity

Background: The concept of synthetic lethality is emerging field in the treatment of cancer and can be applied for new drug development of cancer as it has been already represented by Poly (ADP-ribose) polymerase (PARPs) inhibitors. Objectives: In this study we performed virtual screening of 329 flavonoids obtained from Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target (NPACT) database to identify novel PARP inhibitors. Materials and methods: Virtual screening carried out using different In Silico methods which includes molecular docking studies, prediction of druglikeness and In Silico toxicity studies. Results: Fifteen out of 329 flavonoids achieved better docking score as compared to rucaparib which is an FDA approved PARP inhibitor. These 15 hits were again rescored using accurate docking mode and drug-likeliness properties were evaluated. Accuracy of docking method was checked using re-docking. Finally NPACT00183 and NPACT00280 were identified as potential PARP inhibitors with docking score of -139.237 and -129.36 respectively. These two flavonoids were also showed no AMES toxicity and no carcinogenicity which was predicted using admetSAR. Conclusion: Our finding suggests that NPACT00183 and NPACT00280 have promising potential to be further explored as PARP inhibitors.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Design, Synthesis and In-Vitro Biological Activity of Some New 1,3-Thiazolidine-4-One Derivatives as Chemotherapeutic Agents Using Virtual Screening Strategies

2020 ◽  
Vol 16 ◽  
Author(s):  
Pragya Nayak ◽  
Monica Kachroo
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Virtual Screening ◽  
In Silico ◽  
Cancer Cell Line ◽  
Inhibitory Response ◽  
Chemotherapeutic Agents ◽  
Acceptor Site ◽  
Hydrogen Bond Acceptor ◽  
Screening Strategies

: A series of new heteroaryl thiazolidine-4-one derivatives were designed and subjected to in-silico prioritization using various virtual screening strategies. Two series of thiazolidinone derivatives were synthesized and screened for their in-vitro antitubercular, anticancer, antileishmanial and antibacterial (Staphylococcus aureus; Streptococcus pneumonia; Escherichia coli; Pseudomonas aeruginosa) activities. The compounds with electronegative substitutions exhibited positive antitubercular activity, the derivatives possessing a methyl substitution exhibited good inhibitory response against breast cancer cell line MCF-7 while the compounds possessing a hydrogen bond acceptor site like hydroxyl and methoxy substitution in their structures exhibited good in-vitro antileishmanial activity. Some compounds exhibited potent activity against gram positive bacteria Pseudomonas aeruginosa as compared to the standards. Altogether, the designed compounds exhibited good in-vitro anti-infective potential which was in good agreement with the in-silico predictions and they can be developed as important lead molecules for anti-infective and chemotherapeutic drug research.

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