Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Seit Februar 2021 bietet Wolfgang Brandt, ehemaliger Leiter der Arbeitsgruppe Computerchemie am IPB, sein Citizen Science-Projekt zur Pilzbestimmung an. Dafür hat er in regelmäßigen Abständen öffentliche Vorträge zur Vielfalt…
Münch, J.; Dietz, N.; Barber-Zucker, S.; Seifert, F.; Matschi, S.; Püllmann, P.; Fleishman, S. J.; Weissenborn, M. J.;Functionally diverse peroxygenases by AlphaFold2, design, and signal peptide shufflingACS Catal.144738-4748(2024)DOI: 10.1021/acscatal.4c00883
Unspecific peroxygenases (UPOs) are fungal enzymes that attract significant attention for their ability to perform versatile oxyfunctionalization reactions using H2O2. Unlike other oxygenases, UPOs do not require additional reductive equivalents or electron transfer chains that complicate basic and applied research. Nevertheless, UPOs generally exhibit low to no heterologous production levels and only four UPO structures have been determined to date by crystallography limiting their usefulness and obstructing research. To overcome this bottleneck, we implemented a workflow that applies PROSS stability design to AlphaFold2 model structures of 10 unique and diverse UPOs followed by a signal peptide shuffling to enable heterologous production. Nine UPOs were functionally produced in Pichia pastoris, including the recalcitrant CciUPO and three UPOs derived from oomycetes the first nonfungal UPOs to be experimentally characterized. We conclude that the high accuracy and reliability of new modeling and design workflows dramatically expand the pool of enzymes for basic and applied research.
Publikation
Münch, J.; Soler, J.; Hünecke, N.; Homann, D.; Garcia-Borràs, M.; Weissenborn, M. J.;Computational-aided engineering of a selective unspecific peroxygenase toward enantiodivergent β-ionone hydroxylationACS Catal.138963-8972(2023)DOI: 10.1021/acscatal.3c00702
Unspecific peroxygenases (UPOs) perform oxy-functionalizations for a wide range of substrates utilizing H2O2 without the need for further reductive equivalents or electron transfer chains. Tailoring these promising enzymes toward industrial application was intensely pursued in the last decade with engineering campaigns addressing the heterologous expression, activity, stability, and improvements in chemo- and regioselectivity. One hitherto missing integral part was the targeted engineering of enantioselectivity for specific substrates with poor starting enantioselectivity. In this work, we present the engineering of the short-type MthUPO toward the enantiodivergent hydroxylation of the terpene model substrate, β-ionone. Guided by computational modeling, we designed a small smart library and screened it with a GC−MS setup. After two rounds of iterative protein evolution, the activity increased up to 17-fold and reached a regioselectivity of up to 99.6% for the 4-hydroxy-β-ionone. Enantiodivergent variants were identified with enantiomeric ratios of 96.6:3.4 (R) and 0.3:99.7 (S), respectively.
Publikation
Li, Z.; Meng, S.; Nie, K.; Schwaneberg, U.; Davari, M. D.; Xu, H.; Ji, Y.; Liu, L.;Flexibility regulation of loops surrounding the tunnel entrance in cytochrome P450 enhanced substrate access substantiallyACS Catal.1212800-12808(2022)DOI: 10.1021/acscatal.2c02258
In recent years, the engineering of flexible loops to improve enzyme properties has gained attention in biocatalysis. Herein, we report a loop engineering strategy to improve the stability of the substrate access tunnels, which reveals the molecular mechanism between loops and tunnels. Based on the dynamic tunnel analysis of CYP116B3, five positions (A86, T91, M108, A109, T111) in loops B-B′ and B′-C potentially affecting tunnel frequent occurrence were selected and subjected to simultaneous saturation mutagenesis. The best variant 8G8 (A86T/T91L/M108N/A109M/T111A) for the dealkylation of 7-ethoxycoumarin and the hydroxylation of naphthalene was identified with considerably increased activity (134-fold and 9-fold) through screening. Molecular dynamics simulations showed that the reduced flexibility of loops B-B′ and B′-C was responsible for increasing the stability of the studied tunnel. The redesign of loops B-B′ and B′-C surrounding the tunnel entrance provides loop engineering with a powerful and likely general method to kick on/off the substrate/product transportation.
Publikation
Wittmund, M.; Cadet, F.; Davari, M. D.;Learning epistasis and residue coevolution patterns: Current trends and future perspectives for advancing enzyme engineeringACS Catal.1214243-14263(2022)DOI: 10.1021/acscatal.2c01426
Engineering proteins and enzymes with the desired
functionality has broad applications in molecular biology,
biotechnology, biomedical sciences, health, and medicine. The
vastness of protein sequence space and all the possible proteins it
represents can pose a considerable barrier for enzyme engineering
campaigns through directed evolution and rational design. The
nonlinear effects of coevolution between amino acids in protein
sequences complicate this further. Data-driven models increasingly
provide scientists with the computational tools to navigate through
the largely undiscovered forest of protein variants and catch a
glimpse of the rules and effects underlying the topology of sequence
space. In this review, we outline a complete theoretical journey
through the processes of protein engineering methods such as
directed evolution and rational design and reflect on these strategies and data-driven hybrid strategies in the context of sequence
space. We discuss crucial phenomena of residue coevolution, such as epistasis, and review the history of models created over the past
decade, aiming to infer rules of protein evolution from data and use this knowledge to improve the prediction of the structure−
function relationship of proteins. Data-driven models based on deep learning algorithms are among the most promising methods
that can account for the nonlinear phenomena of sequence space to some degree. We also critically discuss the available models to
predict evolutionary coupling and epistatic effects (classical and deep learning) in terms of their capabilities and limitations. Finally,
we present our perspective on possible future directions for developing data-driven approaches and provide key orientation points
and necessities for the future of the fast-evolving field of enzyme engineering.