Docking Ligands into Flexible and Solvated Macromolecules. 7. Impact of Protein Flexibility and Water Molecules on Docking-Based Virtual Screening Accuracy

2014 ◽  
Vol 54 (11) ◽  
pp. 3198-3210 ◽  
Author(s):  
Eric Therrien ◽  
Nathanael Weill ◽  
Anna Tomberg ◽  
Christopher R. Corbeil ◽  
Devin Lee ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Flexibility ◽  
Water Molecules ◽  
Screening Accuracy

scholarly journals The role of water and protein flexibility in the structure-based virtual screening of allosteric GPCR modulators: an mGlu5 receptor case study

2019 ◽  
Vol 33 (9) ◽  
pp. 787-797 ◽  
Author(s):  
Zoltán Orgován ◽  
György G. Ferenczy ◽  
György M. Keserű
Keyword(s):  
Virtual Screening ◽  
Ligand Binding ◽  
Conformational Changes ◽  
Metabotropic Glutamate Receptor ◽  
Protein Structures ◽  
Protein Flexibility ◽  
Water Molecules ◽  
Large Set ◽  
Allosteric Modulators

Abstract Stabilizing unique receptor conformations, allosteric modulators of G-protein coupled receptors (GPCRs) might open novel treatment options due to their new pharmacological action, their enhanced specificity and selectivity in both binding and signaling. Ligand binding occurs at intrahelical allosteric sites and involves significant induced fit effects that include conformational changes in the local protein environment and water networks. Based on the analysis of available crystal structures of metabotropic glutamate receptor 5 (mGlu5) we investigated these effects in the binding of mGlu5 receptor negative allosteric modulators. A large set of retrospective virtual screens revealed that the use of multiple protein structures and the inclusion of selected water molecules improves virtual screening performance compared to conventional docking strategies. The role of water molecules and protein flexibility in ligand binding can be taken into account efficiently by the proposed docking protocol that provided reasonable enrichment of true positives. This protocol is expected to be useful also for identifying intrahelical allosteric modulators for other GPCR targets.

scholarly journals PARAMETER OPTIMIZATION AND VIRTUAL SCREENING INDONESIAN HERBAL DATABASE AS HUMAN IMMUNODEFICIENCY VIRUS -1 INTEGRASE INHIBITOR USING AUTODOCK AND VINA

2017 ◽  
Vol 9 ◽  
pp. 90
Author(s):  
Arry Yanuar ◽  
Rezi Riadhi Syahdi ◽  
Widya Dwi Aryati
Keyword(s):  
Human Immunodeficiency Virus ◽  
Virtual Screening ◽  
Integrase Inhibitor ◽  
Water Molecules ◽  
Autodock Vina ◽  
Unit Space ◽  
Immunodeficiency Virus ◽  
Acquired Immunodeficiency ◽  
Immunodeficiency Syndrome ◽  
Hiv 1

Objective: Human immunodeficiency virus (HIV-1) is a virus that causes acquired immunodeficiency syndrome, a disease considered to be one of themost dangerous because of its high mortality, morbidity, and infectivity. The emergence of mutant HIV strains has led treatment to target proteaseas reverse transcriptase and integrase enzyme become less effective. This study aims to provide knowledge about the potential of HIV-1 integraseinhibitors for use as guiding compounds in the development of new anti-HIV drugs.Methods: This study used AutoDock and AutoDock Vina for virtual screening of the Indonesian herbal database for inhibitors of HIV-1 integrase andis validated using a database of the directory of useful decoys. Optimization was accomplished by selecting the grid size, the number of calculations,and the addition of two water molecules and a magnesium atom as cofactor.Results: This study determined that the best grid box size is 21.1725×21.1725×21.1725 in unit space size (1 unit space equals to macromolecules 1Ǻ),using AutoDock Vina with EF and AUC values, 3.93 and 0.693, respectively. Three important water molecules have meaning in molecular dockingaround the binding pocket.Conclusions: This study obtained the top ten ranked compounds using AutoDock Vina. The compounds include: Casuarinin; Myricetin-3-O-(2’’,6’’-di-O-α-rhamnosyl)-β-glucoside; 5,7,2’,4’-tetrahydroxy-6,3’-diprenylisoflavone 5-O-(4’’-rhamnosylrhamnoside); myricetin 3-robinobioside; cyanidin3-[6-(6-ferulylglucosyl)-2-xylosylgalactoside]; mesuein, cyanidin 7-(3-glucosyl-6-malonylglucoside)-4’-glucoside; kaempferol 3-[glucosyl-(1→3)-rhamnosyl-(1→6)-galactoside]; 3-O-galloylepicatechin-(4-β→8)-epicatechin-3-O-gallate; and quercetin 4’-glucuronide.

scholarly journals A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2414
Author(s):  
Weixing Dai ◽  
Dianjing Guo
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Learning Algorithm ◽  
Screening Method ◽  
Chemical Characteristic ◽  
High Dimensional ◽  
Learning Approaches ◽  
Generalization Ability ◽  
Model Interpretation ◽  
Screening Accuracy

Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical characteristic of ligands are high-dimensional. We here describe a machine learning algorithm LBS (local beta screening) for ligand-based virtual screening. The unique characteristic of LBS is that it quantifies the generalization ability of screening directly by a refined loss function, and thus can assess the risk of over-fitting accurately and efficiently for imbalanced and high-dimensional data in ligand-based virtual screening without the help of resampling methods such as cross validation. The robustness of LBS was demonstrated by a simulation study and tests on real datasets, in which LBS outperformed conventional algorithms in terms of screening accuracy and model interpretation. LBS was then used for screening potential activators of HIV-1 integrase multimerization in an independent compound library, and the virtual screening result was experimentally validated. Of the 25 compounds tested, six were proved to be active. The most potent compound in experimental validation showed an EC50 value of 0.71 µM.

Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy

2009 ◽  
Vol 49 (6) ◽  
pp. 1455-1474 ◽  
Author(s):  
Jason B. Cross ◽  
David C. Thompson ◽  
Brajesh K. Rai ◽  
J. Christian Baber ◽  
Kristi Yi Fan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Pose Prediction ◽  
Screening Accuracy

Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds

2017 ◽  
Vol 57 (8) ◽  
pp. 2077-2088 ◽  
Author(s):  
Eric J. Martin ◽  
Valery R. Polyakov ◽  
Li Tian ◽  
Rolando C. Perez
Keyword(s):  
Virtual Screening ◽  
Screening Accuracy

Structure based virtual screening of GSK-3β: Importance of protein flexibility and induced fit

2009 ◽  
Vol 19 (19) ◽  
pp. 5582-5585 ◽  
Author(s):  
Nibha Mishra ◽  
Arijit Basu ◽  
Venkatesan Jayaprakash ◽  
Ashoke Sharon ◽  
Mahua Basu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Flexibility ◽  
Induced Fit ◽  
Gsk 3Β

scholarly journals An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

2017 ◽  
Author(s):  
Irene Maffucci ◽  
Xiao Hu ◽  
Valentina Fumagalli ◽  
Alessandro Contini
Keyword(s):  
Binding Energy ◽  
Virtual Screening ◽  
Md Simulation ◽  
Protein Complexes ◽  
Efficient Implementation ◽  
Water Molecules ◽  
Contact Interface ◽  
Experimental Binding Energy ◽  
Selection Of

The Nwat-MMGBSA method, whose theory has been described in Maffucci & Contini, JCTC 2013, 9, 2706, is based on the inclusion as part of the receptor of a given number of water molecules (Nwat) which are the closest to a residue (generally the ligand) or to a selection of residues (the contact interface) in each frame of the MD simulation. The method was shown to improve the correlation between predicted and experimental binding energy in both ligand-receptor and protein-protein complexes (Maffucci & Contini, JCIM 2016, 56, 1692). Here, we report on the optimization of the Nwat-MMGBSA protocol for its use to rescore docking results. We also report an automatic workflow, based on three independent scripts (which can be concatenated in a fully automated procedure) to easily employ Nwat-MMGBSA rescoring in virtual screening application. The protocol has been tuned using three different examples, and then tested in two retrospective virtual screening examples. In each example, the Nwat-MMGBSA method has been compared with the standard MMGBSA approach (Nwat=0). A link to download the scripts, working examples and tutorials is also provided.<br><br>

scholarly journals Identification of novel β‐secretase inhibitors through the inclusion of protein flexibility in virtual screening calculations

2008 ◽  
Vol 22 (S1) ◽  
Author(s):  
Sandro Cosconati ◽  
Ruth Huey ◽  
Luciana Marinelli ◽  
Ettore Novellino ◽  
David S. Goodsell ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Flexibility
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