DATABASE ENRICHMENTS OF MAO-B THROUGH ENSEMBLE DOCKING

Objective: The recent growth of highly resoluted crystallographic structures, together with the continuous improvements of the computing power, has established molecular docking as a leading drug design technique. However, the problems concerning the receptor flexibility and the lowered ability of docking software to correctly score the occurred interactions in some receptors are still relevant. Methods: Recently, several research groups have reported an enhancement in enrichment values when ensemble docking has been applied. Therefore, we utilized the latest technique for a dataset of Monoamine Oxidase–B (MAO-B) inhibitors. The docking program GOLD 5.3 was used in our study. Several docking parameters (grid space, scoring functions and ligand flexibility) were altered in order to achieve the optimal docking protocol. Results: The results of 200 000+docking simulations are represented in a modest table. The ensembled simulations demonstrated low ability of the docking software to correctly score the actives seeded in the dataset. However, the superimposed complex-1S3B-1OJA-1OJC, achieved a moderate enrichment value equaled to 9. No significant improvements were noted when five complexed receptors were employed. Conclusion: As a conclusion, it should be noted that in some cases the ensemble docking enhanced the database enrichments, however overall the value is not suitable for future virtual screening. Further investigations in that area should be considered.

Download Full-text

Suitable Docking Protocol for the Design of Novel Coumarin Derivatives with Selective MAO-B Effects

Journal of Molecular Docking ◽

10.33084/jmd.v1i1.2357 ◽

2021 ◽

Vol 1 (1) ◽

pp. 40-47

Author(s):

Emilio Viktorov Mateev ◽

Iva Valkova ◽

Maya Georgieva ◽

Alexander Zlatkov

Keyword(s):

Correlation Coefficient ◽

Scoring Functions ◽

Pearson’S Correlation ◽

Chain Flexibility ◽

Pearson’S Correlation Coefficient ◽

Pairwise Correlation ◽

Mao B ◽

Docking Protocol ◽

Pearson's Correlation ◽

Pearson's Correlation Coefficient

Recently, the application of molecular docking is drastically increasing due to the rapid growth of resolved crystallographic receptors with co-crystallized ligands. However, the inability of docking softwares to correctly score the occurred interactions between ligands and receptors is still a relevant issue. This study examined the Pearson’s correlation coefficient between the experimental monoamine oxidase-B (MAO-B) inhibitory activity of 44 novel coumarins and the obtained GOLD 5.3 docking scores. Subsequently, optimization of the docking protocol was carried out to achieve the best possible pairwise correlation. Numerous modifications in the docking settings such as alteration in the scoring functions, size of the grid space, presence of active waters, and side-chain flexibility were conducted. Furthermore, ensemble docking simulations into two superimposed complexes were performed. The model was validated with a test set. A significant Pearson’s correlation coefficient of 0.8217 was obtained for the latter. In the final stage of our work, we observed the major interactions between the top-scored ligands and the active site of 1S3B.

Download Full-text

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

10.1101/2021.01.21.427315 ◽

2021 ◽

Author(s):

Sarah Hall-Swan ◽

Dinler A. Antunes ◽

Didier Devaurs ◽

Mauricio M. Rigo ◽

Lydia E. Kavraki ◽

...

Keyword(s):

Supplementary Information ◽

Scoring Functions ◽

Binding Modes ◽

Computational Tool ◽

Receptor Flexibility ◽

Ensemble Docking ◽

Main Protease ◽

Docking Tool ◽

Supplementary Material ◽

User Friendly

AbstractMotivationRecent efforts to computationally identify inhibitors for SARS-CoV-2 proteins have largely ignored the issue of receptor flexibility. We have implemented a computational tool for ensemble docking with the SARS-CoV-2 proteins, including the main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp).ResultsEnsembles of other SARS-CoV-2 proteins are being prepared and made available through a user-friendly docking interface. Plausible binding modes between conformations of a selected ensemble and an uploaded ligand are generated by DINC, our parallelized meta-docking tool. Binding modes are scored with three scoring functions, and account for the flexibility of both the ligand and receptor. Additional details on our methods are provided in the supplementary material.Availabilitydinc-covid.kavrakilab.orgSupplementary informationDetails on methods for ensemble generation and docking are provided as supplementary data [email protected], [email protected]

Download Full-text

Inhibition of Butyrylcholinesterase and Human Monoamine Oxidase-B by the Coumarin Glycyrol and Liquiritigenin Isolated from Glycyrrhiza uralensis

Molecules ◽

10.3390/molecules25173896 ◽

2020 ◽

Vol 25 (17) ◽

pp. 3896

Author(s):

Geum Seok Jeong ◽

Myung-Gyun Kang ◽

Joon Yeop Lee ◽

Sang Ryong Lee ◽

Daeui Park ◽

...

Keyword(s):

Monoamine Oxidase ◽

Binding Affinity ◽

Competitive Inhibitor ◽

Glycyrrhiza Uralensis ◽

Form Hydrogen Bond ◽

Docking Simulations ◽

Mao B ◽

Mao A ◽

Ic50 Values ◽

Noncompetitive Inhibitor

Eight compounds were isolated from the roots of Glycyrrhiza uralensis and tested for cholinesterase (ChE) and monoamine oxidase (MAO) inhibitory activities. The coumarin glycyrol (GC) effectively inhibited butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) with IC50 values of 7.22 and 14.77 µM, respectively, and also moderately inhibited MAO-B (29.48 µM). Six of the other seven compounds only weakly inhibited AChE and BChE, whereas liquiritin apioside moderately inhibited AChE (IC50 = 36.68 µM). Liquiritigenin (LG) potently inhibited MAO-B (IC50 = 0.098 µM) and MAO-A (IC50 = 0.27 µM), and liquiritin, a glycoside of LG, weakly inhibited MAO-B (>40 µM). GC was a reversible, noncompetitive inhibitor of BChE with a Ki value of 4.47 µM, and LG was a reversible competitive inhibitor of MAO-B with a Ki value of 0.024 µM. Docking simulations showed that the binding affinity of GC for BChE (−7.8 kcal/mol) was greater than its affinity for AChE (−7.1 kcal/mol), and suggested that GC interacted with BChE at Thr284 and Val288 by hydrogen bonds (distances: 2.42 and 1.92 Å, respectively) beyond the ligand binding site of BChE, but that GC did not form hydrogen bond with AChE. The binding affinity of LG for MAO-B (−8.8 kcal/mol) was greater than its affinity for MAO-A (−7.9 kcal/mol). These findings suggest GC and LG should be considered promising compounds for the treatment of Alzheimer’s disease with multi-targeting activities.

Download Full-text

Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus’s butyrylcholinesterase inhibitors

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2021.1994012 ◽

2021 ◽

pp. 1-18

Author(s):

Ankit Ganeshpurkar ◽

Ravi Singh ◽

Devendra Kumar ◽

Gopichand Gutti ◽

Divya Sardana ◽

...

Keyword(s):

Machine Learning ◽

Homology Model ◽

Scoring Functions ◽

Docking Protocol ◽

Model Docking

Download Full-text

Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633619500275 ◽

2019 ◽

Vol 18 (05) ◽

pp. 1950027 ◽

Cited By ~ 1

Author(s):

Qiangna Lu ◽

Lian-Wen Qi ◽

Jinfeng Liu

Keyword(s):

Ligand Binding ◽

Considerable Improvement ◽

Ligand Docking ◽

Water Molecules ◽

Binding Modes ◽

Binding Prediction ◽

Docking Simulations ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Docking Program

Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

Download Full-text

Improved protein docking by predicted interface residues

10.1101/2021.08.25.457642 ◽

2021 ◽

Author(s):

Gabriele Pozzati ◽

Petras Kundrotas ◽

Arne Elofsson

Keyword(s):

State Of The Art ◽

Protein Docking ◽

Prediction Methods ◽

Scoring Functions ◽

Protein Interface ◽

High Importance ◽

Docking Algorithm ◽

Docking Protocol ◽

Template Free ◽

Interface Prediction

ABSTRACTScoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

Download Full-text

Inhibition of Butyrylcholinesterase by Glycyrol, a Coumarin, Isolated from Glycyrrhiza Uralensis

10.20944/preprints202008.0306.v1 ◽

2020 ◽

Author(s):

Geum Seok Jeong ◽

Myung-Gyun Kang ◽

Joon Yeop Lee ◽

Sang Ryong Lee ◽

Daeui Park ◽

...

Keyword(s):

Binding Affinity ◽

Competitive Inhibitor ◽

Glycyrrhiza Uralensis ◽

Form Hydrogen Bond ◽

Docking Simulations ◽

Mao B ◽

Mao A ◽

Ic50 Values ◽

Noncompetitive Inhibitor ◽

Inhibitory Activities

Eight compounds were isolated from the roots of Glycyrrhiza uralensis and tested for cholinesterase (ChE) and monoamine oxidase (MAO) inhibitory activities. Glycyrol (GC) effectively inhibited butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) with IC50 values of 7.22 and 14.77 µM, respectively, and also moderately inhibited MAO-B (29.48 µM). Six of the other seven compounds only weakly inhibited AChE and BChE, whereas liquiritin apioside moderately inhibited AChE (IC50 = 36.68 µM). Liquiritigenin (LG) potently inhibited MAO-B (IC50 = 0.098 µM) and MAO-A (IC50 = 0.27 µM), and liquiritin, a glycoside of LG, weakly inhibited MAO-B (&gt; 40 µM). GC was a reversible, noncompetitive inhibitor of BChE with a Ki value of 4.47 µM, and LG was a reversible competitive inhibitor of MAO-B with a Ki value of 0.024 µM. Docking simulations showed that the binding affinity of GC for BChE (-7.8 kcal/mol) was greater than its affinity for AChE (-7.1 kcal/mol), and suggested that GC interacted with BChE at Thr284 and Val288 by hydrogen bonds (distances: 2.42 and 1.92 Å, respectively) beyond the ligand binding site of BChE, but that GC did not form hydrogen bond with AChE. The binding affinity of LG for MAO-B (-8.8 kcal/mol) was greater than its affinity for MAO-A (-7.9 kcal/mol). These findings suggest GC and LG should be considered promising compounds for the treatment of Alzheimer’s disease with multi-targeting activities.

Download Full-text

Quercetin and Related Chromenone Derivatives as Monoamine Oxidase Inhibitors: Targeting Neurological and Mental Disorders

Molecules ◽

10.3390/molecules24030418 ◽

2019 ◽

Vol 24 (3) ◽

pp. 418 ◽

Cited By ~ 8

Author(s):

Priyanka Dhiman ◽

Neelam Malik ◽

Eduardo Sobarzo-Sánchez ◽

Eugenio Uriarte ◽

Anurag Khatkar

Keyword(s):

Monoamine Oxidase ◽

Neurodegenerative Disorders ◽

Research Groups ◽

Mao B ◽

Chemistry Research ◽

Current Review ◽

Treatment Of Depression ◽

New Findings ◽

Parkinson’S Diseases ◽

Preclinical Trials

Monoamine oxidase inhibitions are considered as important targets for the treatment of depression, anxiety, and neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases. This has encouraged many medicinal chemistry research groups for the development of most promising selective monoamine oxidase (MAO) inhibitors. A large number of plant isolates also reported for significant MAO inhibition potential in recent years. Differently substituted flavonoids have been prepared and investigated as MAO-A and MAO-B inhibitors. Flavonoid scaffold showed notable antidepressant and neuroprotective properties as revealed by various and established preclinical trials. The current review made an attempt to summarizing and critically evaluating the new findings on the quercetin and related flavonoid derivatives functions as potent MAO isoform inhibitors.

Download Full-text

Towards a Computational Ecotoxicity Assay

10.26434/chemrxiv.11944371.v1 ◽

2020 ◽

Author(s):

Natasha Kamerlin ◽

Mickaël G. Delcey ◽

Sergio Manzetti ◽

David van der Spoel

Keyword(s):

Scoring Function ◽

Scoring Functions ◽

Autodock Vina ◽

Future Directions ◽

Delayed Effects ◽

Adverse Health Effects ◽

Docking Protocol ◽

Potential Perturbation ◽

Rapid Generation ◽

Insight Into

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals, to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants and toxins deriving from the chemical industry. The method predicts the binding energy of the pollutants to a set of carefully selected receptors, under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecularlevel information and insight into potential perturbation pathways, aiding in the prioritisation of chemicals for further tests. Two scoring functions are compared, Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, though hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.<br>

Download Full-text