DockoMatic 2.0: High Throughput Inverse Virtual Screening and Homology Modeling

Bullock, Casey W.; Cornia, Nicolas; Jacob, Reed B.; Remm, Andrew; Peavey, Thomas; Weekes, Ken; Mallory, Chris; Oxford, Julia Thom; McDougal, Owen M.; Andersen, Tim

doi:10.1021/ci400047w

Cited by 29 publications

(40 citation statements)

References 20 publications

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“…The heteropentameric Tm- nAChR, characterized using electron microscopy at a resolution of around 4 Å (PDB ID: 2BG9), [20c] served as the template for the creation of the first homology model. The homology models of the pentameric nAChR were created using DockoMatic [36] and MODELLER v9.9 [34] packages. The template search and target-template sequence alignment was performed with DockoMatic, while the sequence alignment was viewed using UCSF Chimera.…”

Section: Methodsmentioning

confidence: 99%

“…The template search and target-template sequence alignment was performed with DockoMatic, while the sequence alignment was viewed using UCSF Chimera. [22, 36a] To allow proper sequence alignment to the target structure, the cytoplasmic portions of the α 3 and β 2 rat sequences were removed. MODELLER was then used to generate 100 models, which were ranked based on DOPE scores [34] and GA341 scores [37] .…”

Section: Methodsmentioning

confidence: 99%

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Acetylcholine Promotes Binding of α‐Conotoxin MII at α₃β₂ Nicotinic Acetylcholine Receptors

Sambasivarao¹,

Roberts²,

Bharadwaj³

et al. 2014

ChemBioChem

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α-Conotoxin MII (α-CTxMII) is a 16 amino acid peptide with the sequence GCCSNPVCHLEHSNLC containing disulfide bonds between Cys2-Cys8 and Cys3-Cys16. This peptide, isolated from the venom of the marine cone snail Conus magus, is a potent and selective antagonist of neuronal nicotinic acetylcholine receptors (nAChRs). To evaluate the impact of channel-ligand interactions on ligand binding affinity, homology models of the heteropentameric α3β2-nAChR were constructed. The models were created in MODELLER using crystal structures of the Torpedo marmorata-nAChR (Tm-nAChR, PDB ID: 2BG9) and the Aplysia californica-acetylcholine binding protein (Ac-AChBP, PDB ID: 2BR8) as templates for the α3 and β2 subunit isoforms derived from rat neuronal nAChR primary amino acid sequences. Molecular docking calculations were performed with AutoDock to evaluate interactions of the heteropentameric nAChR homology models with the ligands acetylcholine (ACh) and α-CTxMII. The nAChR homology models described here bind ACh with commensurate binding energies to previously reported systems, and identify critical interactions that facilitate both ACh and α-CTxMII ligand binding. The docking calculations revealed an increased binding affinity of the α3β2-nAChR for α-CTxMII with ACh bound to the receptor, which was confirmed through two-electrode voltage clamp experiments on oocytes from Xenopus laevis. These findings provide insights into the inhibition and mechanism of electrostatically driven antagonist properties of the α-CTxMIIs on nAChRs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Acetylcholine Promotes Binding of α‐Conotoxin MII at α₃β₂ Nicotinic Acetylcholine Receptors

Sambasivarao¹,

Roberts²,

Bharadwaj³

et al. 2014

ChemBioChem

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“…DockoMatic [163] provides a graphical user interface for setting up and analyzing AutoDock and AutoDock Vina docking jobs, including when run on a cluster. It also includes the ability to run inverse virtual screens (find proteins that bind a given ligand) and support for homology model construction.…”

Section: Virtual Screening and Ligand Designmentioning

confidence: 99%

Open source molecular modeling

Pirhadi

Sunseri

2016

Journal of Molecular Graphics and Modelling

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The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry.

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“…DockoMatic is a free and open source graphical user interface (GUI) that streamlines the use of an array of programs applicable to molecular docking studies and for the generation of protein homology models [3–6]. Built into the interface is the capability to construct ligand or receptor models using MODELLER [7] without needing to be familiar with python script.…”

Section: Background and Introductionmentioning

confidence: 99%

“…Built into the interface is the capability to construct ligand or receptor models using MODELLER [7] without needing to be familiar with python script. Timely integrated modeler (TIM), a GUI wizard, easily guides users in the step-by-step creation of both ligand and receptor models [6]. The DockoMatic GUI facilitates the submission of the desired receptor-ligand system to a molecular docking engine, either AutoDock 4.2 [8] or AutoDock Vina [9].…”

Section: Background and Introductionmentioning

confidence: 99%

Computational exploration of a protein receptor binding space with student proposed peptide ligands

King¹,

Phillips²,

Turner

et al. 2015

Biochem Molecular Bio Educ

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Computational molecular docking is a fast and effective in silico method for the analysis of binding between a protein receptor model and a ligand. The visualization and manipulation of protein to ligand binding in three-dimensional space represents a powerful tool in the biochemistry curriculum to enhance student learning. The DockoMatic tutorial described herein provides a framework by which instructors can guide students through a drug screening exercise. Using receptor models derived from readily available protein crystal structures, docking programs have the ability to predict ligand binding properties, such as preferential binding orientations and binding affinities. The use of computational studies can significantly enhance complimentary wet chemical experimentation by providing insight into the important molecular interactions within the system of interest, as well as guide the design of new candidate ligands based on observed binding motifs and energetics. In this laboratory tutorial, the graphical user interface, DockoMatic, facilitates docking job submissions to the docking engine, AutoDock 4.2. The purpose of this exercise is to successfully dock a 17-amino acid peptide, α-conotoxin TxIA, to the acetylcholine binding protein from Aplysia californica-AChBP to determine the most stable binding configuration. Each student will then propose two specific amino acid substitutions of α-conotoxin TxIA to enhance peptide binding affinity, create the mutant in DockoMatic, and perform docking calculations to compare their results with the class. Students will also compare intermolecular forces, binding energy, and geometric orientation of their prepared analog to their initial α-conotoxin TxIA docking results.

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DockoMatic 2.0: High Throughput Inverse Virtual Screening and Homology Modeling

Cited by 29 publications

References 20 publications

Acetylcholine Promotes Binding of α‐Conotoxin MII at α₃β₂ Nicotinic Acetylcholine Receptors

Acetylcholine Promotes Binding of α‐Conotoxin MII at α₃β₂ Nicotinic Acetylcholine Receptors

Open source molecular modeling

Computational exploration of a protein receptor binding space with student proposed peptide ligands

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DockoMatic 2.0: High Throughput Inverse Virtual Screening and Homology Modeling

Cited by 29 publications

References 20 publications

Acetylcholine Promotes Binding of α‐Conotoxin MII at α3β2 Nicotinic Acetylcholine Receptors

Acetylcholine Promotes Binding of α‐Conotoxin MII at α3β2 Nicotinic Acetylcholine Receptors

Open source molecular modeling

Computational exploration of a protein receptor binding space with student proposed peptide ligands

Contact Info

Product

Resources

About

Acetylcholine Promotes Binding of α‐Conotoxin MII at α₃β₂ Nicotinic Acetylcholine Receptors

Acetylcholine Promotes Binding of α‐Conotoxin MII at α₃β₂ Nicotinic Acetylcholine Receptors