Patent application

Koch, O., Bering, L. Mycobacterium tuberculosis Thioredoxin Reductase Inhibitor As Antituberculosis Drug,
EP 17 179 568.5 filed 04th July 2017 & PCT/EP2018/066768 filed 22th June 2018

Selected presentations

•  Off-target prediction based on binding site similarity, 23rd EuroQSAR, Heidelberg, Germany, 09/2022, invited.
•  Neural Fingerprints: Generating Novel Domain-Specific Molecular Fingerprints Using Neural Networks, Discovery Chemistry US, 11/2021, invited.
• The use of data-driven decisions for rational molecular design, MedChemCASES: GDCh online-seminar of the subdivision Medicinal chemistry, 10/2020, invited.
•  Selectivity and Promiscuity of Ligand Binding from a Binding Site Perspective, German Conference on Bioinformatics, Frankfurt, Germany, 09/2020.
•  Computational Medicinal Chemistry: Towards new BRD4 and TrxR Inhibitors. Talk in the context of the DPhG lecture series, Münster, Germany, 01/2019.
•  An Exhaustive Assessment of Computer-Based Drug Discovery Methods by High-Throughput Screening Data. 11th International Conference on Chemical Structures. Noordwijkerhout, The Netherlands, 05/2018.
•  M. tuberculosis thioredoxin reductase inhibitors with antimycobacterial activity in infected macrophages. Frontiers in Medicinal Chemistry, Jena, 03/2018.
•   In silico molecular design: Towards new BRD4 inhibitors and M.tuberculosis TrxR inhibitors showing growth inhibition in infected macrophages. The Institute of Cancer Research, London, UK, 01/2018, invited.
•   M. tuberculosis thioredoxin reductase inhibitors with antimycobacterial activity in infected macrophages, Helmholtz Institute for Pharmaceutical Research Saarland, Saarbrücken, Germany, 12/2017, invited.
•  A medicinal chemistry guide to binding site comparison. Global Pharma R&D Informatics Congress, Lisbon, Portugal, 12/2017, invited.
•  A medicinal chemistry guide to binding site comparison. BIGCHEM second Autumn School Computer-Aided Drug Discovery, Modena, Italy, 10/2017, invited.
•  M. tuberculosis Thioredoxin Reductase Inhibitors with Activity on Mycobacterial Growth in Infected Macrophages Based on In-silico Molecular Design, GDCh Scientific Forum Chemistry 2017, Berlin, Germany, 09/2017.

Authored and edited books, book chapters

6. Kriege, N., Humbeck, L., Koch, O.* Chemical similarity and sub structure search. In: Ranganathan, S., Gribskov, M., Nakai, K. and Schönbach, C. (eds.), Encyclopedia of Bioinformatics and Computational Biology, 2019, Vol. 2, 640–649. Oxford: Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20195-7
5. Brinkjost, T., Ehrt, C., Koch, O.* Binding Site Comparison – Software and Applications. In: Ranganathan, S., Nakai, K., Schönbach C. and Gribskov, M. (eds.), Encyclopedia of Bioinformatics and Computational Biology, 2019, Vol. 2, pp. 650–660. Oxford: Elsevier.  https://doi.org/10.1016/B978-0-12-809633-8.20196-9
4. Selzer, P.M., Marhöfer, R.J., Koch, O. Angewandte Bioinformatik: Eine Einführung, 2. Auflage, Springer Spektrum, Berlin, Germany, 2018.
http://dx.doi.org/10.1007/978-3-662-54135-7
3. Selzer, P.M., Marhöfer, R.J., Koch, O. Applied Bioinformatics: An Introduction, 2. Edition, Springer Nature, Cham, Switzerland, 2018. https://link.springer.com/book/10.1007/978-3-319-68301-0
2. Koch, O.*, Jäger, T., Flohé, L., Selzer, P.M. Inhibition of trypanothione synthetase as therapeutic concept. In Jäger, T., Koch, O., Flohé, L. (Eds) "Trypanosomatid Diseases: Molecular Routes to Drug Discovery", Wiley-VCH, Weinheim, Germany, 2013; 429-443.
http://dx.doi.org/10.1002/9783527670383.ch23
1. Jäger, T., Koch, O., Flohé, L.(Eds) Trypanosomatid Diseases: Molecular Routes to Drug Discovery, Wiley-VCH, Weinheim, Germany, 2013.
http://dx.doi.org/10.1002/9783527670383

Submitted/Preprint Manuscripts

•  Garzinsky, D., Meyer zu Vilsendorf, I., Borecki, D., Hanekamp,W. Lehr, M., Koch,O.* Virtual screening based identification of new scaffolds for the development of cytosolic phospholipase A2α inhibitors.

Peer-Reviewed Publications

47. Marchetti, G.M., Füsser, F., Singh, R.K., Brummel, M., Koch, O., Kümmel, D.*, Hippler, M.*,  Structural analysis revealed a novel conformation of the NTRC reductase domain from Chlamydomonas reinhardtii. J. Struct. Biol., 2021, 214, 107829. https://doi.org/10.1016/j.jsb.2021.107829

46. Patberg, M., Isaak, A., Füsser, F., Ortiz Zacarías, N.V.,  Vinnenberg, L. Schulte, J., Michettia, L., Grey, L., Van der Horst, C:, Hundehege, P., Koch, O., Heitman, L.H., Budde, T., Junker, A. Piperazine squaric acid diamides, a novel class of allosteric P2X7 receptor antagonists. Eur. J. Med. Chem., 2021;  226, 113838. https://doi.org/10.1016/j.ejmech.2021.113838

45. Menke, J., Massa, J., Koch, O.* Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks. Comput. Struct. Biotechnol. J., 2021; 19, 4593-4602. https://doi.org/10.1016/j.csbj.2021.07.032

44. Humbeck, L., Pretzel, J., Spitzer, S., Koch, O.* Discovery of an Unexpected Similarity in Ligand Binding Between BRD4 and PPARγ. ACS Chem. Bio. 2021; 16(7),  1255-1265. https://doi.org/10.1021/acschembio.1c00323

43. Todesca, L.M., Maskri, S., Brömmel, K., Thale, I., Wünsch, B., Koch, O., Schwab, A.* Targeting KCa3.1 channels in cancer. Cell Physiol. Biochem. 2021; 55 (S3), 131-144. https://doi.org/10.33594/000000374

42.  Ilari, D., Maskri, S., Schepmann, D., Köhler, J., Daniliuc, C.G., Koch, O., Wünsch, B.* Diastereoselective synthesis of 2-azabicyclo[3.2.1]octane derivatives as conformationally restricted KOR agonists. Org. Biomol. Chem. 2021; 19, 4082-4099. https://doi.org/10.1039/D1OB00398D

41. Menke, J., Maskri, S., Koch, O.* Computational ion channel research: From the application of artificial intelligence to molecular dynamics simulations. Cell Physiol. Biochem. 2021; 55(S3), 14-45. https://doi.org/10.33594/000000336

40.  Menke, J., Koch, O.* Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-based Virtual Screening. J. Chem. Inf. Model. 2021; 61, 664–675. https://doi.org/10.1021/acs.jcim.0c01208 

39. Brömmel, K., Maskri, S. Bulk, E. Pethő, Z., Rieke, M., Budde, T. Koch, O. Schwab, A., Wünsch, B. Co-staining of KCa3.1 channels in NSCLC cells with a small-molecule fluorescent probe and antibody-based indirect immunofluorescence. ChemMedChem. 2020; 15(24), 2462-2469. https://doi.org/10.1002/cmdc.202000652

38. Brömmel, K., Maskri, S., Maisuls, I., Konken, C.P., Rieke, M., Pethő, Z., Strassert, C.A., Koch, O., Schwab, A., Wünsch, B. Synthesis of Small-Molecule Fluorescent Probes for the In Vitro Imaging of Calcium-Activated Potassium Channel KCa 3.1. Angew. Chem. Int. Ed. Engl. 2020; 59(21): 8277-8284
https://doi.org/10.1002/anie.202001201 

37. Brinkjost, T., Ehrt, C., Koch, O., Mutzel, P. SCOT: Rethinking the Classification of Secondary Structure Elements. Bioinformatics. 2019; 36(8): 2417-2428. https://doi.org/10.1093/bioinformatics/btz826 

36. Ehrt, C., Brinkjost, T., Koch, O.* Binding site characterization - similarity, promiscuity, and druggability. Medchemcomm. 2019; 10(7): 1145-1159. Invited, part of the themed issue “new talents”. https://doi.org/10.1039/c9md00102f

35. Géraldy, M., Morgen, M., Sehr, P., Steimbach, R.R., Moi, D., Ridinger, J., Oehme, I., Witt, O., Malz, M., Nogueira, M.S., Koch, O., Gunkel, N., Miller, A.K. Selective Inhibition of Histone Deacetylase 10: Hydrogen Bonding to the Gatekeeper Residue is Implicated. J. Med. Chem. 2019; 62(9): 4426-4443. https://doi.org/10.1021/acs.jmedchem.8b01936 

34. Nogueira, M.S., Koch, O.* The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J. Chem. Inf. Model. 2019; 59(3): 1238 - 1252. Part of the special issue “machine learning in drug discovery”. https://doi.org/10.1021/acs.jcim.8b00773

33. Ehrt, C., Brinkjost, T., Koch, O.* A Benchmark Driven Guide to Binding Site Comparison: An Exhaustive Evaluation Using Tailor-Made Datasets, PLOS Comp. Bio. 2018; 14(11), e1006483. https://doi.org/10.1371/journal.pcbi.1006483

32. Maurer, S., Buchmuller, B., Ehrt, C., Jasper, J., Koch, O., Summerer, D. Overcoming conservation in TALE-DNA interactions: a minimal repeat scaffold enables selective recognition of an oxidized 5-methylcytosine. Chem Sci. 2018; 9(36): 7247-7252. https://dx.doi.org/10.1039/c8sc01958d

31. Gieß, M., Witte, A., Jasper, J., Koch, O., Summerer, D. Complete, Programmable Decoding of Oxidized 5-Methylcytosine Nucleobases in DNA by Chemoselective Blockage of Universal Transcription-Activator-Like Effector Repeats. J. Am. Chem. Soc. 2018; 140(18), 5904-5908. ttps://dx.doi.org/10.1021/jacs.8b02909

30. Jasper, J., Humbeck, L., Brinkjost, T., Koch, O.* A novel interaction fingerprint derived from per atom score contributions: Exhaustive evaluation of interaction fingerprint performance in docking based virtual screening, J. Cheminf. 2018; 10:15. https://dx.doi.org/10.1186/s13321-018-0264-0

29. Humbeck, L., Schäfer, T., Mutzel, P., Koch, O.* CHIPMUNK: A virtual synthesizable small molecule library for medicinal chemistry exploitable for protein-protein interaction modulators. ChemMedChem. 2018; 13, 532 –539, very important paper and cover feature. Part of the special issue “cheminformatics in drug discovery”. http://dx.doi.org/10.1002/cmdc.201700689

28. Krüger, D. M., Glas, A., Bier, D., Pospiech, N., Wallraven, K., Dietrich, L., Ottmann, C., Koch, O.*, Hennig, S.*, Grossmann, T. N.* Structure-Based Design of Non-natural Macrocyclic Peptides That Inhibit Protein–Protein Interactions, J. Med. Chem. 2017; 60(21): 8982-8988.  http://dx.doi.org/10.1021/acs.jmedchem.7b01221

27. Kaitsiotou, H., Keul, M., Hardick, J., Mühlenberg, T., Ketzer, J., Ehrt, C., Krüll, J., Medda, F., Koch, O., Giordanetto, F., Bauer, S.*, Rauh, D.* Inhibitors to overcome secondary mutations in the stem cell factor receptor KIT. J. Med. Chem. 2017; 60(21): 8801-8815.  ttp://dx.doi.org/10.1021/acs.jmedchem.7b00841

26. Flade,S., Jasper, J., Juhasz, M., Dankers, A., Kubik, G., Koch, O.*, Weinhold,E.*, Summerer, D.* The N6-Position of Adenine is a blind spot for TAL-effectors that enables effective binding of methylated and fluorophore-labeled DNA. ACS Chem. Biol. 2017; 12(7): 1719-1725. http://dx.doi.org/10.1021/acschembio.7b00324

25. Patel, H., Brinkjost, T., Koch, O.* PyGOLD: A python based API for docking based virtual screening workflow generation, Bioinformatics. 2017; 33(16): 2589-2590. http://dx.doi.org/10.1093/bioinformatics/btx197

24 Mejuch, T., Garivet, G., Hofer, W., Kaiser, N., Fansa, E.K., Ehrt, C., Koch, O., Baumann, M., Ziegler, S., Wittinghofer, A., Waldmann, H. Small-Molecule Inhibition of the UNC119-Cargo Interaction. Angew. Chem. Int. Ed. Engl. 2017; 56(22): 6181-6186. http://dx.doi.org/10.1002/anie.201701905

23. Schäfer, T.§, Kriege, N.§, Humbeck, L.§, Klein, K., Koch, O.*, Mutzel, P.* Scaffold Hunter: An evolving visual analytics framework for drug discovery, J. Cheminf. 2017; 9: 28. http://dx.doi.org/10.1186/s13321-017-0213-3

22. Walter, A., Helmer, R., Loaëc, N., Preu, L., Meijer, L., Kunick, C., Koch, O.* Identification of CLK1 inhibitors by a fragment-linking based virtual screening, Mol. Inf. 2017; 36(4): 1600123. http://dx.doi.org/10.1002/minf.201600123

21. Voss, S., Krüger, D.M., Koch, O., Wu, Y.W.* Spatiotemporal imaging of small GTPases activity in live cells. Proc. Natl. Acad. Sci. USA. 2016; 113(50): 14348-14353. http://dx.doi.org/10.1073/pnas.1613999113

20. Maurer, S., Giess, M., Koch, O., Summerer, D.* Interrogating Key Positions of Size-Reduced TALE Repeats Reveals a Programmable Sensor of 5-Carboxylcytosin. ACS Chem. Biol. 2016; 11(12): 3294-3299. http://dx.doi.org/10.1021/acschembio.6b00627

19 Humbeck, L., Koch, O* What can we learn from bioactivity data? Cheminformatics tools and applications in chemical biology research, ACS Chem. Biol. 2017; 12(1): 23-35, Review. http://dx.doi.org/10.1021/acschembio.6b00706 

18. Zhao, L., Ehrt, C., Koch, O., Wu, Y.W.* One-Pot N2C/C2C/N2N Ligation to Trap Weak Protein-Protein Interactions. Angew. Chem. Int. Ed. Engl. 2016; 55(28): 8129-8133. http://dx.doi.org/10.1002/anie.201601299

17. Ehrt, C., Brinkjost, T., Koch, O.* The Impact of Binding Site Comparisons on Rational Drug Design. J. Med. Chem. 2016; 59: 4121-4151, Perspective. Part of the special issue “computational methods for medicinal chemistry”. http://dx.doi.org/10.1021/acs.jmedchem.6b00078

16. Orban, O.C., Korn, R.S., Benítez, D., Medeiros, A., Preu, L., Loaëc, N., Meijer, L., Koch, O., Comini, M.A.*, Kunick, C.* 5-Substituted 3-chlorokenpaullone derivatives are potent inhibitors of Trypanosoma brucei bloodstream forms. Bioorg. Med. Chem. 2016; 24(16): 3790-800. http://dx.doi.org/10.1016/j.bmc.2016.06.023

15. Terwege, T., Hanekamp, W., Garzinsky, D., König, S., Koch, O. & Lehr, M. ω-Imidazolyl- and ω-Tetrazolylalkylcarbamates as Inhibitors of Fatty Acid Amide Hydrolase: Biological Activity and in vitro Metabolic Stability. ChemMedChem. 2016; 11: 429-443. http://dx.doi.org/10.1002/cmdc.201500445

14. Pelay-Gimeno, M., Glas, A., Koch, O. & Grossmann, T.N.* Structure-based design of inhibitors of protein‒protein interactions: Mimicking peptide-binding epitope. Angew. Chem. Int. Ed. 2015; 54: 8896-8927, Review. http://dx.doi.org/10.1002/anie.201412070

13. Längle, D., Marquadt, V., Heider, E., Vigante, B., Duburs, G., Luntena, I., Flötgen, D., Golz, C., Strohmann, C., Koch, O., Schade, D.* Design, synthesis and 3D-QSAR studies of novel 1,4-dihydropyridines as TGFβ/Smad inhibitors. Eur. J. Med. Chem. 2015; 95: 249-266. http://dx.doi.org/10.1016/j.ejmech.2015.03.027

12. Maiwald, F., Benítez, D., Charquero, D., Dar, M.A., Erdmann, H., Preu, L., Koch, O., Hölscher, C., Loaëc, N., Meijer, L., Comini, M.A.*, Kunick, C.* 9- and 11-substituted 4-azapaullones are potent and selective inhibitors of African trypanosoma. Eur. J. Med. Chem. 2014; 83: 274-283. http://dx.doi.org/10.1016/j.ejmech.2014.06.020

11. Schade, D.*, Kotthaus, J., Riebling, L., Kotthaus, J., Müller-Fielitz, H., Raasch, W., Koch, O., Seidel, N., Schmidtke, M., Clement, B. Development of novel potent orally bioavailable oseltamivir derivatives active against resistant influenza A. J. Med. Chem. 2014; 57(3): 759-769. http://dx.doi.org/10.1021/jm401492x

10. Klein, K., Koch, O., Kriege, N.*, Mutzel, P., Schäfer, T. Visual Analysis of Biological Activity Data with Scaffold Hunter. Mol. Inf. 2013; 32 (11-12): 964-975, invited, authors in alphabetic order. http://dx.doi.org/10.1002/minf.201300087

9. Koch, O.*, Jäger, T., Heller, K., Khandavalli, P.C., Pretzel, J., Becker, K., Flohé, L., Selzer, P.M.* Identification of M. tuberculosis Thioredoxin Reductase Inhibitors Based on High-Throughput Docking Using Constraints. J. Med. Chem. 2013; 56(12): 4849-4859. http://dx.doi.org/10.1021/jm3015734

8. Koch, O.*, Cappel, D., Nocker, M., Jäger, T., Flohé, L., Sotriffer, C.A., Selzer, P.M.* Molecular dynamics reveal binding mode of glutathionylspermidine by trypanothione synthetase. PLoS One. 2013; 8(2): e56788. http://dx.doi.org/10.1371/journal.pone.0056788

7. Koch, O.* Advances in the prediction of turn structures in peptides and proteins, Molecular Informatics. 2012; 31(9): 624-630, invited. http://dx.doi.org/10.1002/minf.201200021

6. Koch, O.*: The Use of Secondary Structure Element Information in Drug Design: Polypharmacology and Conserved Motifs in Protein-Ligand Binding and Protein-Protein Interfaces. Future Medicinal Chemistry. 2011; 3(6): 699-708, invited. http://dx.doi.org/10.4155/fmc.11.26

5. Koch, O.*, Cole, J.*: An automated method for consistent helix assignment using turn information. Proteins. 2011; 79(5): 1416-1426. http://dx.doi.org/10.1002/prot.22968

4. Koch, O., Cole, J., Block, P., Klebe, G.*: Secbase: database module to retrieve secondary structure elements with ligand binding motifs. J. Chem. Inf. Model. 2009; 49(10): 2388-2402. http://dx.doi.org/10.1021/ci900202d

3. Koch, O., Klebe, G.*: Turns revisited: a uniform and comprehensive classification of normal, open, and reverse turn families minimizing unassigned random chain portions. Proteins. 2009; 74(2): 353-367. http://dx.doi.org/10.1002/prot.22185

2. Meissner, M., Koch, O., Klebe, G., Schneider, G.*: Prediction of turn types in protein structure by machine-learning classifiers. Proteins. 2009; 74(2): 344-352. http://dx.doi.org/10.1002/prot.22164

1. Koch, O., Bocola, M., Klebe, G.*: Cooperative effects in hydrogen-bonding of protein secondary structure elements: a systematic analysis of crystal data using Secbase. Proteins. 2005; 61(2): 310-317. http://dx.doi.org/10.1002/prot.20613