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13 results on '"van Bergeijk, Doris A."'

2. Artificial intelligence for natural product drug discovery

Author

Mullowney, Michael W., Duncan, Katherine R., Elsayed, Somayah S., Garg, Neha, van der Hooft, Justin J. J., Martin, Nathaniel I., Meijer, David, Terlouw, Barbara R., Biermann, Friederike, Blin, Kai, Durairaj, Janani, Gorostiola González, Marina, Helfrich, Eric J. N., Huber, Florian, Leopold-Messer, Stefan, Rajan, Kohulan, de Rond, Tristan, van Santen, Jeffrey A., Sorokina, Maria, Balunas, Marcy J., Beniddir, Mehdi A., van Bergeijk, Doris A., Carroll, Laura M., Clark, Chase M., Clevert, Djork-Arné, Dejong, Chris A., Du, Chao, Ferrinho, Scarlet, Grisoni, Francesca, Hofstetter, Albert, Jespers, Willem, Kalinina, Olga V., Kautsar, Satria A., Kim, Hyunwoo, Leao, Tiago F., Masschelein, Joleen, Rees, Evan R., Reher, Raphael, Reker, Daniel, Schwaller, Philippe, Segler, Marwin, Skinnider, Michael A., Walker, Allison S., Willighagen, Egon L., Zdrazil, Barbara, Ziemert, Nadine, Goss, Rebecca J. M., Guyomard, Pierre, Volkamer, Andrea, Gerwick, William H., Kim, Hyun Uk, Müller, Rolf, van Wezel, Gilles P., van Westen, Gerard J. P., Hirsch, Anna K. H., Linington, Roger G., Robinson, Serina L., and Medema, Marnix H.

Published
2023
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4. Taxonomic and metabolic diversity of Actinomycetota isolated from faeces of a 28,000‐year‐old mammoth

Author

van Bergeijk, Doris A., Augustijn, Hannah E., Elsayed, Somayah S., Willemse, Joost, Carrión, Victor J., Du, Chao, Urem, Mia, Grigoreva, Lena V., Cheprasov, Maksim Y., Grigoriev, Semyon, Jansen, Hans, Wintermans, Bas, Budding, Andries E., Spaink, Herman P., Medema, Marnix H., van Wezel, Gilles P., van Bergeijk, Doris A., Augustijn, Hannah E., Elsayed, Somayah S., Willemse, Joost, Carrión, Victor J., Du, Chao, Urem, Mia, Grigoreva, Lena V., Cheprasov, Maksim Y., Grigoriev, Semyon, Jansen, Hans, Wintermans, Bas, Budding, Andries E., Spaink, Herman P., Medema, Marnix H., and van Wezel, Gilles P.

Abstract

Ancient environmental samples, including permafrost soils and frozen animal remains, represent an archive with microbial communities that have barely been explored. This yet unexplored microbial world is a genetic resource that may provide us with new evolutionary insights into recent genomic changes, as well as novel metabolic pathways and chemistry. Here, we describe Actinomycetota Micromonospora, Oerskovia, Saccharopolyspora, Sanguibacter and Streptomyces species were successfully revived and their genome sequences resolved. Surprisingly, the genomes of these bacteria from an ancient source show a large phylogenetic distance to known strains and harbour many novel biosynthetic gene clusters that may well represent uncharacterised biosynthetic potential. Metabolic profiles of the strains display the production of known molecules like antimycin, conglobatin and macrotetrolides, but the majority of the mass features could not be dereplicated. Our work provides insights into Actinomycetota isolated from an ancient source, yielding unexplored genomic information that is not yet present in current databases.

Published
2024

5. Artificial intelligence for natural product drug discovery

Author

Mullowney, Michael W., Duncan, Katherine R., Elsayed, Somayah S., Garg, Neha, van der Hooft, Justin J. J., Martin, Nathaniel I., Meijer, David, Terlouw, Barbara R., Biermann, Friederike, Blin, Kai, Durairaj, Janani, Gonzalez, Marina Gorostiola, Helfrich, Eric J. N., Huber, Florian, Leopold-Messer, Stefan, Rajan, Kohulan, de Rond, Tristan, van Santen, Jeffrey A., Sorokina, Maria, Balunas, Marcy J., Beniddir, Mehdi A., van Bergeijk, Doris A., Carroll, Laura M., Clark, Chase M., Clevert, Djork-Arne, Dejong, Chris A., Du, Chao, Ferrinho, Scarlet, Grisoni, Francesca, Hofstetter, Albert, Jespers, Willem, Kalinina, Olga V., Kautsar, Satria A., Kim, Hyunwoo, Leao, Tiago F., Masschelein, Joleen, Rees, Evan R., Reher, Raphael, Reker, Daniel, Schwaller, Philippe, Segler, Marwin, Skinnider, Michael A., Walker, Allison S., Willighagen, Egon L., Zdrazil, Barbara, Ziemert, Nadine, Goss, Rebecca J. M., Guyomard, Pierre, Volkamer, Andrea, Gerwick, William H., Kim, Hyun Uk, Mueller, Rolf, van Wezel, Gilles P., van Westen, Gerard J. P., Hirsch, Anna K. H., Linington, Roger G., Robinson, Serina L., Medema, Marnix H., Mullowney, Michael W., Duncan, Katherine R., Elsayed, Somayah S., Garg, Neha, van der Hooft, Justin J. J., Martin, Nathaniel I., Meijer, David, Terlouw, Barbara R., Biermann, Friederike, Blin, Kai, Durairaj, Janani, Gonzalez, Marina Gorostiola, Helfrich, Eric J. N., Huber, Florian, Leopold-Messer, Stefan, Rajan, Kohulan, de Rond, Tristan, van Santen, Jeffrey A., Sorokina, Maria, Balunas, Marcy J., Beniddir, Mehdi A., van Bergeijk, Doris A., Carroll, Laura M., Clark, Chase M., Clevert, Djork-Arne, Dejong, Chris A., Du, Chao, Ferrinho, Scarlet, Grisoni, Francesca, Hofstetter, Albert, Jespers, Willem, Kalinina, Olga V., Kautsar, Satria A., Kim, Hyunwoo, Leao, Tiago F., Masschelein, Joleen, Rees, Evan R., Reher, Raphael, Reker, Daniel, Schwaller, Philippe, Segler, Marwin, Skinnider, Michael A., Walker, Allison S., Willighagen, Egon L., Zdrazil, Barbara, Ziemert, Nadine, Goss, Rebecca J. M., Guyomard, Pierre, Volkamer, Andrea, Gerwick, William H., Kim, Hyun Uk, Mueller, Rolf, van Wezel, Gilles P., van Westen, Gerard J. P., Hirsch, Anna K. H., Linington, Roger G., Robinson, Serina L., and Medema, Marnix H.

Abstract

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.|Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.

Published
2024
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6. Taxonomic and metabolic diversity of Actinomycetota isolated from faeces of a 28,000‐year‐old mammoth

Author

van Bergeijk, Doris A., primary, Augustijn, Hannah E., additional, Elsayed, Somayah S., additional, Willemse, Joost, additional, Carrión, Victor J., additional, Du, Chao, additional, Urem, Mia, additional, Grigoreva, Lena V., additional, Cheprasov, Maksim Y., additional, Grigoriev, Semyon, additional, Jansen, Hans, additional, Wintermans, Bas, additional, Budding, Andries E., additional, Spaink, Herman P., additional, Medema, Marnix H., additional, and van Wezel, Gilles P., additional

Published
2024
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9. Taxonomic and metabolic diversity of Actinobacteria isolated from faeces of a 28,000-year-old mammoth

Author

van Bergeijk, Doris A., primary, Augustijn, Hannah E., additional, Elsayed, Somayah S., additional, Willemse, Joost, additional, Carrión, Victor J., additional, Urem, Mia, additional, Grigoreva, Lena V., additional, Cheprasov, Maksim Y., additional, Grigoriev, Semyon, additional, Wintermans, Bas, additional, Budding, Andries E., additional, Spaink, Herman P., additional, Medema, Marnix H., additional, and van Wezel, Gilles P., additional

Published
2022
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12. Mast cells in neuroinflammation and brain disorders

Author

Afd Pharmacology, Pharmacology, Hendriksen, Erik, van Bergeijk, Doris, Oosting, Ronald S, Redegeld, Frank A, Afd Pharmacology, Pharmacology, Hendriksen, Erik, van Bergeijk, Doris, Oosting, Ronald S, and Redegeld, Frank A

Published
2017

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