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. © 2015 by The International Union of Biochemistry and Molecular Biology, 44:63–67, 2016.

α-Conotoxin MII (α-CTxMII) is a 16-residue peptide with the sequence GCCSNPVCHLEHSNLC, containing Cys2–Cys8 and Cys3–Cys16 disulfide bonds. 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 with the aid of experimentally characterized 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 binding energies commensurate with those of 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, and this 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.