• What interactions are formed between the protein and the
• Does the ligand bind in a competitive or noncompetitive site?
The analysis can be completed within a class period, but
depending on the complexity of the selected research question, it
may also be extended to a homework assignment followed by a written report on the docking results. Note that due to space limitation,
we provide little background on the computational methods behind
protein–ligand docking, but for interested students we recommend
Kitchen et al. (2004).
We administered a survey following our most recent workshop that
asked whether students (1) found the material useful, (2) enjoyed
the exercises, (3) understood the process of drug discovery, and
(4) became more interested in bioinformatics and computer modeling
in general. The survey was given to 35 students, and 90% responded
positively to all four of these questions. When asked what they appreciated most about the entire event, they responded with positive comments about (1) being able to explore and learn through hands-on
activities; (2) learning what resources exist for studies of drug interactions and discovery, including online databases; (3) being able to visualize structures and simulations of biomolecules; (4) the structure of
the activity (building up from basic background to more complex
concepts); (5) the combination of discussion, analysis, and computational work; and (6) the good pace of the workshop.
We also assessed student understanding of the learning objectives of the workshop in a pre- and post-workshop quiz. The questions on the quiz covered the concepts of proteins folding to a
biologically active native state, methods for obtaining new therapeutic drugs, and types of biomolecules with which therapeutics
interact. Among the 35 attendees, 34% answered all questions correctly on the pre-workshop quiz, while 60% did so on the post-workshop quiz. Given that the workshops are attended by students
from different majors, we conclude that covering at least some
aspects of protein structure and function in the context of drug
design, and in the format of four consecutive hands-on computer
exercises, is highly effective and engaging.
We are proposing a set of easy and versatile computer-based exercises that introduce students to protein structure, function, involvement in human disease, and interactions with therapeutic drugs.
Students become acquainted with concepts behind computer-aided, rational drug design and computer modeling of protein
function. Tools recommended here can be used as an easy and fast
illustration of the concepts behind drug design or they can be used
in more advanced, inquiry-based, dry-lab experiments.
Bikadi, Z. & Hazai, E. (2009). Application of the PM6 semi-empirical method
to modeling proteins enhances docking accuracy of AutoDock. Journal
of Cheminformatics, 1, 15.
DiMasi, J.A., Grabowski, H.G. & Hansen, R.W. (2016). Innovation in the
pharmaceutical industry: new estimates of R&D costs. Journal of Health
Economics, 47, 20–33.
Grosdidier, A., Zoete, V. & Michielin, O. (2011). SwissDock, a protein-small
molecule docking web service based on EADock DSS. Nucleic Acids
Research, 39, W270–W277.
Hanwell, M.D., Curtis, D.E., Lonie, D.C., Vandermeersch, T., Zurek, E. &
Hutchison, G.R. (2012). Avogadro: an advanced semantic chemical
editor, visualization, and analysis platform. Journal of Cheminformatics,
Humphrey, W., Dalke, A. & Schulten, K. (1996). VMD – Visual Molecular
Dynamics. Journal of Molecular Graphics, 14, 33–38.
Irwin, J.J. & Shoichet, B.K. (2005). ZINC – a free database of commercially
available compounds for virtual screening. Journal of Chemical
Information and Modeling, 45, 177–182.
Kaitin, K.I. (2010). Deconstructing the drug development process: the new
face of innovation. Clinical Pharmacology & Therapeutics, 87,
Kapetanovic, I.M. (2008). Computer-aided drug discovery and development
(CADD): in silico-chemico-biological approach. Chemico-Biological
Interactions, 171, 165–176.
Kitchen, D.W., Decornez, H., Furr, J.R. & Bajorath, J. (2004). Docking and
scoring in virtual screening for drug discovery: methods and
applications. Nature Reviews Drug Discovery, 3, 935–949.
Li, Y.H., Yu, C.Y., Li, X.X., Zhang, P., Tang, J., Yang, Q., et al. (2018).
Therapeutic target database update 2018: enriched resource for
facilitating bench-to-clinic research of targeted therapeutics. Nucleic
Acids Research, 46, D1121–D1127.
Lipinski, C.A., Lombardo, F., Dominy, B.W. & Feeney, P.J. (1997).
Experimental and computational approaches to estimate solubility and
permeability in drug discovery and development settings. Advanced
Drug Delivery Reviews, 23, 3–25.
Schmidt, T., Bergner, A. & Schwede, T. (2014). Modelling three-dimensional
protein structures for applications in drug design. Drug Discovery
Today, 19, 890–987.
Schüttelkopf, A.W. & van Aalten, D.M. (2004). PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta
Crystallographica D, 60, 1355–1363.
Sterling, T. & Irwin, J.J. (2015). ZINC 15 – ligand discovery for everyone.
Journal of Chemical Information and Modeling, 55, 2324–2337.
Volkamer, A., Kuhn, D., Rippmann, F. & Rarey, M. (2012). DoGSiteScorer:
a web server for automatic binding site prediction, analysis and
druggability assessment. Bioinformatics, 28, 2074–2075.
AGNIESZKA SZARECKA ( email@example.com) is an Associate Professor in
the Department of Cell and Molecular Biology and CHRISTOPHER DOBSON
( firstname.lastname@example.org) is a Professor in the Department of Biology at Grand
Valley State University, Allendale, MI 49401.