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

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-9937-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-C4F4-B
Genre: Journal Article

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 Creators:
Linker, Stephanie M.1, 2, Author              
Magarkar, Aniket1, Author
Köfinger, Jürgen2, Author              
Hummer, Gerhard2, 3, Author              
Seeliger, Daniel1, Author              
Affiliations:
1Department of Medicinal Chemistry , Boehringer Ingelheim Pharma, Biberach an der Riß, Germany, ou_persistent22              
2Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              
3Institute for Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2019-01-252019-08-112019-09-10
 Publication Status: Published in print
 Pages: 8
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Degree: -

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Title: Journal of Chemical Theory and Computation
  Other : J. Chem. Theory Comput.
Source Genre: Journal
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Publ. Info: Washington, D.C. : American Chemical Society
Pages: - Volume / Issue: 15 (9) Sequence Number: - Start / End Page: 4974 - 4981 Identifier: ISSN: 1549-9618
CoNE: https://pure.mpg.de/cone/journals/resource/111088195283832