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The bioinformatical branch of our group uses computational methods to discover new domain types or to detect new instances of known domains in poorly characterized proteins. The analysis of a domain's conservation pattern or its phyletic distribution often allows a precise prediction of its function, which is amenable to experimental validation. By combining information on multiple domains in a protein - and by integrating other data sources obtained from a wide variety of model organisms - it is possible to make useful predictions on the function and properties of novel proteins, even if they have never been studied experimentally.
The experimental branch of our group will generate some of the data sources necessary for a successful protein functional prediction, one example being a domain-domain interaction map of major model organisms. In addition, we will complement the bioinformatical screens for protein functions by suitable experimental screens and validate some of the bioinformatical findings. The feasibility of this approach has been documented in several case studies that can be looked up in our publication list.
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论文共 285 篇作者统计合作学者相似作者
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Qiaochu Shen, Keiichi Hasegawa, Nicole Oelerich, Anna Prakken, Lea Weiler Tersch, Junli Wang, Frowin Reichhardt, Alexandra Tersch, Je Cuan Choo, Ton Timmers,Kay Hofmann, Jane E. Parker,
Cell Host & Microbe (2024)
Matteo Veronese,Sebastian Kallabis, Alexander Kaczmarek, Lennart Robers, Simon Schumacher, Anushka Das, Alessia Lofrano,Susanne Brodesser,Stefan Müller,Kay Hofmann,Marcus Krüger,Elena I Rugarli
biorxiv(2024)
Life science allianceno. 2 (2023)
Maria Körner,Susanne Meyer,Gabriella Marincola,Maximilian J Kern,Clemens Grimm, Christina Schülein-Völk,Utz Fischer,Kay Hofmann,Alexander Buchberger
bioRxiv (Cold Spring Harbor Laboratory) (2023)
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Vanessa Boll,Thomas Hermanns,Matthias Uthoff,Ilka Erven, Eva-Maria Hörner,Vera Kozjak-Pavlovic,Ulrich Baumann,Kay Hofmann
Research Square (Research Square)no. 1 (2023): 1-15
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