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Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models

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Linker,  Stephanie M.
Department of Medicinal Chemistry , Boehringer Ingelheim Pharma, Biberach an der Riß, Germany;
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;

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Köfinger,  Jürgen
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;

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Hummer,  Gerhard
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Institute for Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany;

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Linker, S. M., Magarkar, A., Köfinger, J., Hummer, G., & Seeliger, D. (2019). Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models. Journal of Chemical Theory and Computation, 15(9), 4974-4981. doi:10.1021/acs.jctc.9b00069.


Cite as: http://hdl.handle.net/21.11116/0000-0004-9937-2
Abstract
Predicting the costructure of small-molecule ligands and their respective target proteins has been a long-standing problem in drug discovery. For weak binding compounds typically identified in fragment-based screening (FBS) campaigns, determination of the correct binding site and correct binding mode is usually done experimentally via X-ray crystallography. For many targets of pharmaceutical interest, however, establishing an X-ray system which allows for sufficient throughput to support a drug discovery project is not possible. In this case, exploration of fragment hits becomes a very laborious and consequently slow process with the generation of protein/ligand cocrystal structures as the bottleneck of the entire process. In this work, we introduce a computational method which is able to reliably predict binding sites and binding modes of fragment-like small molecules using solely the structure of the apoprotein and the ligand's chemical structure as input information. The method is based on molecular dynamics simulations and Markov-state models and can be run as a fully automated protocol requiring minimal human intervention. We describe the application of the method to a representative subset of different target classes and fragments from historical FBS efforts at Boehringer Ingelheim and discuss its potential integration into the overall fragment-based drug discovery workflow.