scholarly journals Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

2019 ◽  
Vol 9 (21) ◽  
pp. 4538 ◽  
Author(s):  
Tatiana F. Vieira ◽  
Sérgio F. Sousa
Keyword(s):  
Virtual Screening ◽  
Open Source ◽  
Ligand Docking ◽  
Protein Targets ◽  
Starting Point ◽  
First Choice ◽  
Docking Program ◽  
Binding Pockets ◽  
Overall Performance ◽  
Flexible Ligands

AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.

scholarly journals A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0

2020 ◽  
Vol 21 (14) ◽  
pp. 5152 ◽  
Author(s):  
Silvia Gervasoni ◽  
Giulio Vistoli ◽  
Carmine Talarico ◽  
Candida Manelfi ◽  
Andrea R. Beccari ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Repurposing ◽  
Protein Targets ◽  
In Silico Studies ◽  
Allosteric Sites ◽  
Binding Pockets ◽  
Novel Strategy ◽  
Better Than ◽  
Comprehensive Mapping

(1) Background: Virtual screening studies on the therapeutically relevant proteins of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) require a detailed characterization of their druggable binding sites, and, more generally, a convenient pocket mapping represents a key step for structure-based in silico studies; (2) Methods: Along with a careful literature search on SARS-CoV-2 protein targets, the study presents a novel strategy for pocket mapping based on the combination of pocket (as performed by the well-known FPocket tool) and docking searches (as performed by PLANTS or AutoDock/Vina engines); such an approach is implemented by the Pockets 2.0 plug-in for the VEGA ZZ suite of programs; (3) Results: The literature analysis allowed the identification of 16 promising binding cavities within the SARS-CoV-2 proteins and the here proposed approach was able to recognize them showing performances clearly better than those reached by the sole pocket detection; and (4) Conclusions: Even though the presented strategy should require more extended validations, this proved successful in precisely characterizing a set of SARS-CoV-2 druggable binding pockets including both orthosteric and allosteric sites, which are clearly amenable for virtual screening campaigns and drug repurposing studies. All results generated by the study and the Pockets 2.0 plug-in are available for download.

scholarly journals Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2610 ◽  
Author(s):  
Célien Jacquemard ◽  
Viet-Khoa Tran-Nguyen ◽  
Malgorzata N. Drwal ◽  
Didier Rognan ◽  
Esther Kellenberger
Keyword(s):  
Virtual Screening ◽  
Protein Complexes ◽  
Local Interaction ◽  
Ligand Docking ◽  
Pose Prediction ◽  
Binding Modes ◽  
Individual Comparison ◽  
Docking Program ◽  
The Individual ◽  
Interaction Density

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

scholarly journals Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-Based Virtual Screening

2021 ◽  
Author(s):  
Wei Ma ◽  
Qin Xie ◽  
Jianhang Zhang ◽  
Shiliang Li ◽  
Xiaobing Deng ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Specific Protein ◽  
Classification Model ◽  
Protein Targets ◽  
Computational Docking ◽  
Compound Libraries ◽  
Good Ability ◽  
Docking Protocol ◽  
Docking Program ◽  
Computational Resources

Abstract Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly could reduce the number of compounds for experimental screening, shorten the research period and save the research cost. Howerver, a majority of compouds with low docking scores could waste most of the computational resources. Herein, we report a novel and practical docking-based machine learning method called MLDDM (Machince Learning Docking-by-Docking Models). It is composed of a regression model and a classification model that simulates a classical docking by docking protocol ususally applied in many virtual screening projects. MLDDM could quickly eliminate compounds with low docking scores and the retained compounds with potential high docking scores would be examined for further real docking program. We demonstrated that MLDDM has a good ability to identify active compounds in the case studies for 10 specific protein targets. Compared to pure docking by docking based VS protocol, the VS process with MLDDM can achieve an over 120 times speed increment on average and the consistency rate with corresponding docking by docking VS protocol is above 0.8. Therefore, it would be promising to be used for examing ultra-large compound libraries in the current big data era.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Discovery of Antibacterial Agents Inhibiting the Energy-Coupling Factor (ECF) Transporters by Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Eleonora Diamanti ◽  
Inda Setyawati ◽  
Spyridon Bousis ◽  
leticia mojas ◽  
lotteke Swier ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Antibacterial Agents ◽  
Antimicrobial Agents ◽  
Energy Coupling ◽  
Coupling Factor ◽  
Design Synthesis ◽  
Screening Design ◽  
Starting Point ◽  
Wide Range ◽  
Vitamin Uptake

Here, we report on the virtual screening, design, synthesis and structure–activity relationships (SARs) of the first class of selective, antibacterial agents against the energy-coupling factor (ECF) transporters. The ECF transporters are a family of transmembrane proteins involved in the uptake of vitamins in a wide range of bacteria. Inhibition of the activity of these proteins could reduce the viability of pathogens that depend on vitamin uptake. Because of their central role in the metabolism of bacteria and their absence in humans, ECF transporters are novel potential antimicrobial targets to tackle infection. The hit compound’s metabolic and plasma stability, the potency (20, MIC Streptococcus pneumoniae = 2 µg/mL), the absence of cytotoxicity and a lack of resistance development under the conditions tested here suggest that this scaffold may represent a promising starting point for the development of novel antimicrobial agents with an unprecedented mechanism of action.<br>

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

scholarly journals Identification of Potential Kinase Inhibitors within the PI3K/AKT Pathway of Leishmania Species

Biomolecules ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1037
Author(s):  
Rodrigo Ochoa ◽  
Amaya Ortega-Pajares ◽  
Florencia A. Castello ◽  
Federico Serral ◽  
Darío Fernández Do Porto ◽  
...  
Keyword(s):  
Kinase Inhibitors ◽  
Screening Method ◽  
Molecular Targets ◽  
Leishmania Species ◽  
Akt Pathway ◽  
Protein Protein Interaction ◽  
Starting Point ◽  
Binding Pockets ◽  
Protein Protein Interaction Networks

Leishmaniasis is a public health disease that requires the development of more effective treatments and the identification of novel molecular targets. Since blocking the PI3K/AKT pathway has been successfully studied as an effective anticancer strategy for decades, we examined whether the same approach would also be feasible in Leishmania due to their high amount and diverse set of annotated proteins. Here, we used a best reciprocal hits protocol to identify potential protein kinase homologues in an annotated human PI3K/AKT pathway. We calculated their ligandibility based on available bioactivity data of the reported homologues and modelled their 3D structures to estimate the druggability of their binding pockets. The models were used to run a virtual screening method with molecular docking. We found and studied five protein kinases in five different Leishmania species, which are AKT, CDK, AMPK, mTOR and GSK3 homologues from the studied pathways. The compounds found for different enzymes and species were analysed and suggested as starting point scaffolds for the design of inhibitors. We studied the kinases’ participation in protein–protein interaction networks, and the potential deleterious effects, if inhibited, were supported with the literature. In the case of Leishmania GSK3, an inhibitor of its human counterpart, prioritized by our method, was validated in vitro to test its anti-Leishmania activity and indirectly infer the presence of the enzyme in the parasite. The analysis contributes to improving the knowledge about the presence of similar signalling pathways in Leishmania, as well as the discovery of compounds acting against any of these kinases as potential molecular targets in the parasite.

scholarly journals Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer’s Disease: Computational Refining and Biochemical Evaluation

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1946
Author(s):  
Nitin Chitranshi ◽  
Ashutosh Kumar ◽  
Samran Sheriff ◽  
Veer Gupta ◽  
Angela Godinez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hydrogen Bond ◽  
Virtual Screening ◽  
Cathepsin B ◽  
Amyloid Β ◽  
Proteolytic Degradation ◽  
Hydrogen Bond Acceptor ◽  
Starting Point ◽  
The Brain

Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.

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