Ulrich Schwaneberg

Prof. Ulrich Schwaneberg


Research Focus & Metrics

Academic Career Length

31Years (1996-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 December 2019 in Chemical Society Reviews
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UMUlrich Markel
Ulrich Markel
KEKhalil D. Essani
Khalil D. Essani
VBVolkan Besirlioglu
Volkan Besirlioglu
JSJohannes Schiffels
Johannes Schiffels
WSWolfgang R. Streit
Wolfgang R. Streit
USUlrich Schwaneberg
Ulrich Schwaneberg
Enzymes are versatile catalysts and their synthetic potential has been recognized for a long time. In order to exploit their full potential, enzymes often need to be re-engineered or optimized for a given application. (Semi-) rational design has emerged as a powerful means to engineer proteins, but requires detailed knowledge about structure function relationships. In turn, directed evolution methodologies, which consist of iterative rounds of diversity generation and screening, can improve an enzyme's properties with virtually no structural knowledge. Current diversity generation methods grant us access to a vast sequence space (libraries of >1012 enzyme variants) that may hide yet unexplored catalytic activities and selectivity. However, the time investment for conventional agar plate or microtiter plate-based screening assays represents a major bottleneck in directed evolution and limits the improvements that are obtainable in reasonable time. Ultrahigh-throughput screening (uHTS) methods dramatically increase the number of screening events per time, which is crucial to speed up biocatalyst design, and to widen our knowledge about sequence function relationships. In this review, we summarize recent advances in uHTS for directed enzyme evolution. We shed light on the importance of compartmentalization to preserve the essential link between genotype and phenotype and discuss how cells and biomimetic compartments can be applied to serve this function. Finally, we discuss how uHTS can inspire novel functional metagenomics approaches to identify natural biocatalysts for novel chemical transformations.

6 December 2018 in Angewandte Chemie International Edition
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Uwe T. BornscheuerUwe T. Bornscheuer
Uwe T. Bornscheuer
BHBernhard Hauer
Bernhard Hauer
KJKarl-Erich Jaeger
Karl-Erich Jaeger
USUlrich Schwaneberg
Ulrich Schwaneberg

2 April 2015 in Chemical Communications
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FCFeng Cheng
Feng Cheng
LZLeilei Zhu
Leilei Zhu
USUlrich Schwaneberg
Ulrich Schwaneberg
A KnowVolution: knowledge gaining directed evolution including four phases is proposed in this feature article, which generates improved enzyme variants and molecular understanding.

31 December 2003 in Biotechnology and Bioengineering
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Tuck Seng WongTuck Seng Wong
Tuck Seng Wong
FAFrances H. Arnold
Frances H. Arnold
USUlrich Schwaneberg
Ulrich Schwaneberg

1 February 2009 in Current Opinion in Chemical Biology
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Journal Article
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ASAmol V Shivange
Amol V Shivange
JMJan Marienhagen
Jan Marienhagen
HMHemanshu Mundhada
Hemanshu Mundhada
ASAlexander Schenk
Alexander Schenk
USUlrich Schwaneberg
Ulrich Schwaneberg

13 February 2004 in Nucleic Acids Research
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Tuck Seng WongTuck Seng Wong
Tuck Seng Wong
KTKang Lan Tee
Kang Lan Tee
BHBerhard Hauer
Berhard Hauer
USUlrich Schwaneberg
Ulrich Schwaneberg
Sequence saturation mutagenesis (SeSaM) is a conceptually novel and practically simple method that truly randomizes a target sequence at every single nucleotide position. A SeSaM experiment can be accomplished within 2–3 days and comprises four steps: generating a pool of DNA fragments with random length, ‘tailing’ the DNA fragments with universal base using terminal transferase at 3′‐termini, elongating DNA fragments in a PCR to the full‐length genes using a single‐stranded template and replacing the universal bases by standard nucleotides. Random mutations are created at universal sites due to the promiscuous base‐pairing property of universal bases. Using enhanced green fluorescence protein as the model system and deoxyinosine as the universal base, we proved by sequencing 100 genes the concept of the SeSaM method and achieved a random distribution of mutations with the mutational bias expected for deoxyinosine.