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J Chem Theory Comput. 2019 Sep 10;15(9):4974-4981. doi: 10.1021/acs.jctc.9b00069. Epub 2019 Aug 22.

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

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Department of Medicinal Chemistry , Boehringer Ingelheim Pharma , Birkendorfer Straße 65 , 88397 Biberach an der Riß , Germany.
Department of Theoretical Biophysics , Max Planck Institute of Biophysics , Max-von-Laue Straße 3 , 60438 Frankfurt am Main , Germany.
Institute for Biophysics , Goethe University Frankfurt , 60438 Frankfurt am Main , Germany.


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.

[Indexed for MEDLINE]

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