The main new feature in TRAPP version 4 is the use of machine learning and molecular simulation to predict druggability, i.e. the ability to bind to a drug-like compound, of the protein binding site studied. In such a way, TRAPP facilitates the discovery of cryptic binding pockets that may not be apparent in the experimentally determined structure of a protein but that can be revealed when the protein motions are simulated.
The druggability score can help the user to easily identify the most druggable conformation of a given binding pocket from a set of conformations obtained from experiments or a molecular dynamics simulation. This predicted druggable conformation can then be used for drug design. TRAPP also includes visualization tools that enable the user to identify the factors affecting the predicted druggability of protein binding pockets.
Jui-Hung Yuan, Sungho Bosco Han, Stefan Richter, Rebecca C Wade, Daria B. Kokh
Druggability Assessment in TRAPP using Machine Learning Approaches
JYuan J, Han SB, Richter S, Wade RC, Kokh DB (2020). Druggability Assessment in TRAPP Using Machine Learning Approaches, J. Chem. Inf. Model. 60(3):1685-1699 1
Stank A, Kokh DB, Horn M, Sizikova E, Neil R, Panecka J, Richter S, Wade RC (2017). TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets., Nucleic Acids Research 45(W1):W325-W330
Kokh DB, Richter S, Henrich S, Czodrowski P, Rippmann F, Wade RC (2013). TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins, J. Chem. Inf. Model. 53(5):1235-1252
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