Dr. Jan A. Hiss
Areas of interest
- Immunomodulatory Peptides and their de novo design (Antimicrobial Peptides, MHC-I binder)
- Nature Inspired Algorithms (Ant Colony Optimization)
- Targeting Signals in Pathogens (Long Signal Peptides)
- Mixed Reality Applications for Teaching and Research
Book contributions
- Hiss J. A. and Schneider G. (2013) external pagePeptide design by nature-inspired computing. In: De Novo Molecular Design (Schneider, G. , Ed.),Wiley-VCH, Weinheim, pp. 437-461.
- Hiss J. A. and Schneider G. (2011) external pageSorting potential therapeutic targets in Apicomplexa. In: Apicomplexan Parasites (Becker, K., Ed.), Drug Discovery in Infectious Deseases Vol. 2, Wiley-VCH, Weinheim, pp. 35-51.
Highlighted Publications
Allenspach, S., Hiss, J. A. & Schneider, G. (2024) Neural multi-task learning in drug design. external pageNat. Mach. Intell. 6, 124-137.
Fjell C.D. & Hiss J.A., Hancock R.E., Schneider G. external pageDesigning antimicrobial peptides: form follows function. Nat Rev Drug Discov. 2012,11, 37-51.
Publications
Allenspach, S., Hiss, J. A. & Schneider, G. (2024) Neural multi-task learning in drug design. external pageNat. Mach. Intell. 6, 124-137.
Atz, K., Cotos Muñoz, L., Isert, C., Håkansson, M., Focht, D., Nippa, D. F., Hilleke, M., Iff, M., Ledergerber, J., Schiebroek, C. C. G., Hiss, J. A., Merk, D., Schneider, P., Kuhn, B., Grether, U. & Schneider, G. (2023) Deep interactome learning for de novo drug design. Preprint: external pageChemRxiv-2023-cbq9k.
Wyss, C.; Bührer, W.; Furrer, F.; Degonda, A.; Hiss, J.A. Innovative Teacher Education with the Augmented Reality Device Microsoft HoloLens—Results of an Exploratory Study and Pedagogical Considerations. external pageMultimodal Technol. Interact. 2021, 5, 45.
Müller, A., Posselt, G., Gabernet, G., Neuhaus, C., Bachler, S., Blatter, M., Pfeiffer, B., Hiss, J. A., Dittrich, P., Altmann, K.-H., Wessler, S. and Schneider, G. (2020) Morphing of amphipathic helices to explore the activity and selectivity of membranolytic antimicrobial peptides. external pageBiochemistry 59, 3772–3781.
Button, A., Merk, D., Hiss, J. A. and Schneider, G. (2019) Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. external pageNature Mach. Intell. 1, 307-315.
Neuhaus, C. S., Gabernet, G., Steuer, C., Root, K., Hiss, J. A., Zenobi, R. and Schneider, G. (2019) Simulated molecular evolution for anticancer peptides. external pageAngew. Chem. Int. Ed. 58, 1674-1678.
Fuchs, J. A., Brunner, C., Schineis, P., Hiss, J. A. and Schneider, G. (2019) Identification of chemokine ligands by biochemical fragmentation and simulated peptide evolution. external pageAngew. Chem. Int. Ed. 58, 7138-7142.
Gabernet, G., Gautschi, D., Müller, A. T., Neuhaus, C. S., Armbrecht, L., Dittrich, P. S., Hiss, J. A. and Schneider, G. (2019) In silico design and optimization of selective membranolytic anticancer peptides. external pageSci. Rep. 9, 11282.
Grisoni, F., Neuhaus, C. S., Hishinuma, M., Gabernet, G., Hiss, J. A., Kotera, M. and Schneider, G. (2019) De novo design of anticancer peptides by ensemble artificial neural networks. external pageJ. Mol. Model. 25, 112.
Neuhaus, C. S., Gabernet, G., Steuer, C., Root, K., Hiss, J. A., Zenobi, R. and Schneider, G. (2019) Simulated molecular evolution for anticancer peptides. external pageAngew. Chem. Int. Ed. 58, 1674-1678.
Fuchs, J. A., Brunner, C., Schineis, P., Hiss, J. A. and Schneider, G. (2019) Identification of chemokine ligands by biochemical fragmentation and simulated peptide evolution. external pageAngew. Chem. Int. Ed. 58, accepted.
Grisoni, F., Merk, D., Consonni, V., Hiss, J. A., Tagliabue, S. G., Todeschini, R. and Schneider, G. (2018) Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity. Communications Chemistry, external pageCommunications Chemistry, 1, 44.
Allenspach, M. D., Fuchs, J. A., Doriot, N., Hiss, J. A., Schneider, G. and Steuer, C. (2018) Quantification of hydrolyzed peptides and proteins by amino acid fluorescence. J. Pept. Sci., accepted.
Grisoni, F., Neuhaus, C. S., Gabernet, G., Müller, A. T., Hiss, J. A. and Schneider, G. (2018) Designing anticancer peptides by constructive machine learning. external pageChemMedChem 13, 1300–1302.
Müller, A. T., Hiss, J. A. and Schneider, G. (2018) Recurrent neural network model for constructive peptide design. external pageJ. Chem. Inf. Model. 58, 472-479.
Gawehn, E., Hiss, J. A., Brown, J. B. and Schneider, G. (2018) Advancing drug discovery via GPU-based deep learning. external pageExpert Opin. Drug Discov. 13, 579–582.
Fuchs, J. A., Grisoni, F., Kossenjans, M., Hiss, J. A. and Schneider, G. (2018) Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning. external pageMed. Chem. Commun. 9, 1538-1546.
Stutz, K., Müller, A. T., Hiss, J. A., Schneider, P., Blatter, M., Pfeiffer, B., Posselt, G., Kanfer, G., Kornmann, B., Wrede, P., Altmann, K.-H., Wessler, S. and Schneider, G. (2017) Peptide-membrane interaction between targeting and lysis. external pageACS Chem. Biol. 15, 2245–2249.
Pillong, M., Hiss, J. A., Schneider, P., Lin, Y.-C., Posselt, G., Pfeiffer, B., Blatter, M., Müller, A. T., Bachler, S., Neuhaus, C., Dittrich, P., Altmann, K.-H., Wessler, S. and Schneider, G. (2017) Rational design of membrane-pore-forming peptides.
external pageSmall 13, 1701316.
Müller A. T., Gabernet G., Hiss J. A., Schneider G. (2017) modlAMP: Python for antimicrobial peptides. Bioinformatics 2017.
Schneider, P., Müller, A. T., Gabernet, G., Button, A. L., Posselt, G., Wessler, S., Hiss, J. A. and Schneider, G. (2017) Hybrid network model for "deep learning" of chemical data: Application to antimicrobial peptides. external pageMol. Inf. 36, 1600011.
Button, A. L., Hiss, J. A., Schneider, P. and Schneider, G. (2017) Scoring of de novo designed chemical entities by macromolecular target prediction. external pageMol. Inf. 36, 1600110.
Armbrecht, L., Gabernet, G., Kurth, F., Hiss, J. A., Schneider, G. and Dittrich, P. S. (2017) Characterisation of anticancer peptides at the single-cell level. external pageLab Chip, accepted.
Gawehn, E., Hiss, J. A. and Schneider, G. (2016) Deep learning in drug discovery. external pageMol. Inf, 35, 3-14.
Schneider, P., Müller, A. T., Gabernet, G., Button, A. L., Posselt, G., Wessler, S., Hiss, J. A. and Schneider, G. (2016) Hybrid network model for "deep learning" of chemical data: Application to antimicrobial peptides. external pageMol. Inf. 36, 1600011.
Lin, Y.-C., Lim, Y. F., Russo, E., Schneider, P., Bolliger, L., Edenharter, A., Altmann, K.-H., Halin, C., Hiss, J. A. and Schneider, G. (2015) Multidimensional design of anticancer peptides. external pageAngew. Chem. Int. Ed. 54, 10370–10374.
Hiss, J. A., Stutz, K., Posselt, G., Wessler, S. and Schneider, G. (2015) Attractors in sequence space: Peptide morphing by directed simulated evolution. external pageMol. Inf., in press.
Lin, Y.-C., Hiss, J. A., Schneider, P., Thelesklaf, P., Lim, Y. F., Pillong, M., Koehler, F. M., Dittrich, P., Halin, C., Wessler, S. and Schneider, G. (2014) Piloting the membrane-lytic activities of peptides by a self-organizing map. external pageChemBioChem 15, 2225-2231.
Hiss, J. A., Reutlinger, M., Koch, C. P., Perna, A. M., Schneider, P., Rodrigues, T., Haller, S., Folkers, G., Weber, L., Baleeiro, R. B., Walden, P., Wrede, P. and Schneider, G. external pageCombinatorial chemistry by ant colony optimization. Future Med. Chem., 2014, 6, 267-280.
Schneider, G., Lin, Y.-C., Koch, C. P., Pillong, M., Perna, A. M., Reutlinger, M. and Hiss, J. A. external pageAdaptive peptide design. Chimia 2013, 67, 859-863.
Heiber A., Kruse F., Pick C., Grüring C., Flemming S., Oberli A., Schoeler H., Retzlaff S., Mesén-Ramírez P., Hiss J.A., Kadekoppala M., Hecht L., Holder A.A., Gilberger T.W., Spielmann T. external pageIdentification of New PNEPs Indicates a Substantial Non-PEXEL Exportome and Underpins Common Features in Plasmodium falciparum Protein Export. PLoS Pathog. 2013, 9, e1003546.
Koch, C. P., Perna, A. M., Weissmüller, S., Bauer, S., Pillong, M., Baleeiro, R. B., Reutlinger, M., Folkers, G., Walden, P., Wrede, P., Hiss, J. A., Waibler, Z. and Schneider, G. external pageExhaustive proteome mining for functional MHC-I ligands. ACS Chem. Biol. 2013, 8, 1876-1881.
Koch C.P., Perna A.M., Pillong M., Todoroff N.K., Wrede P., Folkers G., Hiss J.A., Schneider G. external pageScrutinizing MHC-I Binding Peptides and Their Limits of Variation. PLoS Comput Biol. 2013 Jun;9(6):e1003088.
Koch, C. P., Pillong, M., Hiss, J. A. and Schneider, G. external pageComputational resources for MHC ligand identification. Mol. Inf. 2013, 32, 326-336.
Pasini E.M., Braks J.A., Fonager J., Klop O., Aime E., Spaccapelo R., Otto T.D., Berriman M., Hiss J.A., Thomas A.W., Mann M., Janse C.J., Kocken C.H., Franke-Fayard B. external pageProteomic and genetic analyses demonstrate that Plasmodium berghei blood stages export a large and diverse repertoire of proteins. Mol Cell Proteomics. 2012, 12, 426-448 .
Geppert T., Bauer S., Hiss J.A., Conrad E., Reutlinger M., Schneider P., Weisel M., Pfeiffer B., Altmann K.H., Waibler Z., Schneider G. external pageImmunosuppressive Small Molecule Discovered by Structure-Based Virtual Screening for Inhibitors of Protein-Protein Interactions. Angew Chem Int Ed Engl. 2012, 51, 258-261.
Fjell C.D. & Hiss J.A., Hancock R.E., Schneider G. external pageDesigning antimicrobial peptides: form follows function. Nat Rev Drug Discov. 2012,11, 37-51.
Reutlinger M., Guba W., Martin R.E., Alanine A.I., Hoffmann T., Klenner A., Hiss J.A., Schneider P., Schneider G. external pageNeighborhood-Preserving Visualization of Adaptive Structure-Activity Landscapes: Application to Drug Discovery. Angew Chem Int Ed Engl. 2011, 50, 11633-11636.
Schneider G., Geppert T., Hartenfeller M., Reisen F., Klenner A., Reutlinger M., Hähnke V., Hiss J.A., Zettl H., Keppner S., Spänkuch B., Schneider P. external pageReaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Med Chem. 2011, 3, 415-424.
Resch E. & Hiss J.A., Schreiner A., Schneider G., Starzinski-Powitz A. external pageLong signal peptides of RGMa and DCBLD2 are dissectible into subdomains according to the NtraC model. Mol Biosyst. 2011, 7, 942-951.
Alamuri P., Löwer M., Hiss J.A., Himpsl S.D., Schneider G., Mobley H.L. external pageAdhesion, Invasion, and Agglutination Mediated by Two Trimeric Autotransporters in Human Uropathogen Proteus mirabilis. Infect Immun. 2010, 78, 4882-4894.
Wisniewska J.M., Jäger N., Freier A., Losch F.O., Wiesmüller K-H., Walden P., Wrede P., Schneider G., Hiss J.A., external pageMHC I Stabilizing Potential of Computer-Designed Octapeptides. J. Biomed. Biotechnol., 2010, 396847.
Jäger N. ,Wisniewska J.M., Hiss J.A., Freier A., Losch F.O., Walden P., Wrede P., Schneider G., external pageAttractors in Sequence Space: Agent-Based Exploration of MHC I Binding Peptides. Molecular Informatics. 2010, 29, 65-74.
Hiss J.A., Hartenfeller M., Schneider G. external pageConcepts and applications of “natural computing” techniques in de novo drug and peptide design. Curr. Pharm. Design. 2010, 16, 1656-1665.
Hiss J.A., Schneider G. external pageDomain organization of long autotransporter signal sequences. Bioinform Biol Insights. 2009, 3, 189-204.
Hiss J.A., Schneider G. external pageArchitecture, function and prediction of long signal peptides. Brief Bioinform. 2009, 10, 569-578.
Spork S., Hiss J.A., Mandel K., Sommer M., Kooij T.W.A., Chu T., Schneider G., Maier U.G., Przyborski J.M. external pageAn unusual ERAD-like complex is targeted to the apicoplast of Plasmodium falciparum. Eukaryot Cell. 2009, 8, 1134-1145.
Hiss J.A. & Resch E., Schreiner A., Meissner M., Starzinski-Powitz A., Schneider G., external pageDomain Organization of Long Signal Peptides of Single-Pass Integral Membrane Proteins Reveals Multiple Functional Capacity. PLoS ONE. 2008, 3, e2767.
Hiss J.A., Przyborski J.M., Schwarte F., Lingelbach K., Schneider G., external pageThe Plasmodium Export Element Revisited. 2008, PLoS ONE. 3, e1560.
Hiss J.A., Bredenbeck A., Losch F.O., Wrede P., Walden P., Schneider, G. external pageDesign of MHC I stabilizing peptides by agent-based exploration of sequence space. Protein Eng. Des. Sel. 2007, 20, 99-108.