New version of TRAPP released
Version 4 uses machine learning and molecular simulation to predict druggability
Researchers from the Molecular and Cellular Modeling (MCM) group at HITS released a new version of the TRAnsient Pockets in Proteins (TRAPP) webserver. Version 4 enables the exploration of changes in the shape of a given binding pocket due to protein motion and of how these changes affect the physicochemical properties of the pocket and ability to bind to molecules like drugs. 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 main new feature in TRAPP version 4 is the option to score the druggability, i.e. the ability to bind to a drug-like compound, of each conformation (or shape) of the protein binding site studied. 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, e.g. by docking molecules into this pocket conformation.
Machine learning was used to develop two statistical models for predicting druggability in TRAPP. The models, which were trained on publicly available and self-augmented datasets, show equivalent or superior performance to existing methods on test sets of protein crystal structures and have sufficient sensitivity to identify potentially druggable protein conformations in trajectories from molecular dynamics simulations. TRAPP includes visualization tools that enable the user to identify the factors affecting the predicted druggability of protein binding pockets.
The TRAPP webserver is freely available for use by the scientific community.
References:
TRAPP webserver: https://trapp.h-its.org/
Jui-Hung Yuan, Sungho Bosco Han, Stefan Richter, Rebecca C Wade, Daria B. Kokh
Druggability Assessment in TRAPP using Machine Learning Approaches
J. Chem. Inf. Model. (2020) 60(3):1685-1699, DOI: 10.1021/acs.jcim.9b01185
Preprint on BioRxiv: doi: 10.1101/2019.12.19.882340
About HITS
The Heidelberg Institute for Theoretical Studies (HITS) was established in 2010 by the physicist and SAP co-founder Klaus Tschira (1940-2015) and the Klaus Tschira Foundation as a private, non-profit research institute. HITS conducts basic research in the natural sciences, mathematics and computer science, with a focus on the processing, structuring, and analyzing of large amounts of complex data and the development of computational methods and software. The research fields range from molecular biology to astrophysics. The shareholders of HITS are the HITS-Stiftung, which is a subsidiary of the Klaus Tschira Foundation, Heidelberg University and the Karlsruhe Institute of Technology (KIT). HITS also cooperates with other universities and research institutes and with industrial partners. The base funding of HITS is provided by the HITS Stiftung with funds received from the Klaus Tschira Foundation. The primary external funding agencies are the Federal Ministry of Education and Research (BMBF), the German Research Foundation (DFG), and the European Union.
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