Dem IPB wird erneut ein beispielhaftes Handeln im Sinne einer chancengleichheitsorientierten Personal- und Organisationspolitik bescheinigt. Das Institut erhält zum 6. Mal in Folge das TOTAL E-QUALITY…
Die Plant Science Student Conference (PSSC) wird seit 20 Jahren im jährlichen Wechsel von Studierenden der beiden Leibniz-Institute IPK und IPB organisiert. Im Interview erläutern Christina Wäsch…
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.