Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis DOI
Jürgen Pleiss

Biochemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 26, 2024

Biocatalysis is becoming a data science. High-throughput experimentation generates rapidly increasing stream of biocatalytic data, which the raw material for mechanistic and novel data-driven modeling approaches predictive design improved biocatalysts bioprocesses. The holistic molecular understanding enzymatic reaction systems will enable us to identify overcome kinetic bottlenecks shift thermodynamics reaction. full characterization community effort; therefore, published methods results should be findable, accessible, interoperable, reusable (FAIR), achieved by developing standardized exchange formats, complete reproducible documentation experimentation, collaborative platforms sustainable software analyzing repositories publishing together with data. FAIRification biocatalysis prerequisite highly automated laboratory infrastructures that improve reproducibility scientific reduce time costs required develop synthesis routes.

Language: Английский

The Development and Opportunities of Predictive Biotechnology DOI Creative Commons
Bettina M. Nestl, Bernd A. Nebel, Verena Resch

et al.

ChemBioChem, Journal Year: 2024, Volume and Issue: 25(13)

Published: May 7, 2024

Recent advances in bioeconomy allow a holistic view of existing and new process chains enable novel production routines continuously advanced by academia industry. All this progress benefits from growing number prediction tools that have found their way into the field. For example, automated genome annotations, for building model structures proteins, structural protein methods such as AlphaFold2

Language: Английский

Citations

6
Digitalization of biocatalysis: Best practices to research data management DOI

Torsten Giess,

Jürgen Pleiss

Methods in enzymology on CD-ROM/Methods in enzymology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Parallel and High Throughput Reaction Monitoring with Computer Vision DOI Creative Commons

Henry Barrington,

Timothy J. McCabe,

Kristin Donnachie

et al.

Angewandte Chemie International Edition, Journal Year: 2024, Volume and Issue: 64(1)

Published: Aug. 22, 2024

Abstract We report the development and applications of a computer vision based reaction monitoring method for parallel high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics one from video, this new approach enables video capture multiple reactions running in parallel. Case studies, beyond well‐plate settings, are described. Analysis dye‐quenching hydroxylations, DMAP‐catalysed esterification, solid‐liquid sedimentation dynamics, metal catalyst degradation, biologically‐relevant sugar‐mediated nitro reduction have each provided insight into scope limitations camera‐enabled as means widening known analytical bottlenecks HTE discovery, mechanistic understanding, optimisation. It is envisaged that nature multi‐reaction time‐resolved datasets made available by will later serve broad range downstream machine learning approaches exploring chemical space.

Language: Английский

Citations

1
Parallel and High Throughput Reaction Monitoring with Computer Vision DOI Creative Commons

Henry Barrington,

Timothy J. McCabe,

Kristin Donnachie

et al.

Angewandte Chemie, Journal Year: 2024, Volume and Issue: 137(1)

Published: Aug. 22, 2024

Abstract We report the development and applications of a computer vision based reaction monitoring method for parallel high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics one from video, this new approach enables video capture multiple reactions running in parallel. Case studies, beyond well‐plate settings, are described. Analysis dye‐quenching hydroxylations, DMAP‐catalysed esterification, solid‐liquid sedimentation dynamics, metal catalyst degradation, biologically‐relevant sugar‐mediated nitro reduction have each provided insight into scope limitations camera‐enabled as means widening known analytical bottlenecks HTE discovery, mechanistic understanding, optimisation. It is envisaged that nature multi‐reaction time‐resolved datasets made available by will later serve broad range downstream machine learning approaches exploring chemical space.

Language: Английский

Citations

0
Sustainable development of anticancer and antidiabetic derivatives by solvent-free heterocyclization DOI Creative Commons

Kajal Patil,

Sachin Mane, Suhas Mohite

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 20, 2024

Abstract Herein we have developed a heterogeneous catalyst for synthesizing various anticancer and antidiabetic derivatives via heterocyclic synthesis under solvent-free conditions at mild temperatures. This approach eliminates the need complex cleanup or column chromatography, thus minimizing waste production. Moreover, can be recovered reused up to multiple times without compromising product yields, demonstrating its sustainability environmental friendliness. Additionally, evaluated each synthetic derivative activities. Initial assays revealed that certain exhibit promising inhibition against human breast cancer cells (MCF7), suggesting their potential as lead structures future agents. Furthermore, synthesized were assessed activity, showing superior efficacy. Notably, containing –H, –CH3, –OCH3 substituents demonstrated excellent activity compared 5-fluorouracil (5-FU), while –H –Br showed notable activities over acarbose, highlighting therapeutic potential. Thus, our study presents highly effective sustainable polyhydroquinoline derivatives, emphasizing catalyst's dual benefits in organic medicinal chemistry applications.

Language: Английский

Citations

0
Development of anticancer and antidiabetic polyhydroquinoline derivatives by solvent-free heterocyclization DOI

K. S. Patil,

S. T. Mane,

Suhas Mohite

et al.

Journal of Nanoparticle Research, Journal Year: 2024, Volume and Issue: 26(9)

Published: Sept. 1, 2024

Language: Английский

Citations

0
Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis DOI
Jürgen Pleiss

Biochemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 26, 2024

Biocatalysis is becoming a data science. High-throughput experimentation generates rapidly increasing stream of biocatalytic data, which the raw material for mechanistic and novel data-driven modeling approaches predictive design improved biocatalysts bioprocesses. The holistic molecular understanding enzymatic reaction systems will enable us to identify overcome kinetic bottlenecks shift thermodynamics reaction. full characterization community effort; therefore, published methods results should be findable, accessible, interoperable, reusable (FAIR), achieved by developing standardized exchange formats, complete reproducible documentation experimentation, collaborative platforms sustainable software analyzing repositories publishing together with data. FAIRification biocatalysis prerequisite highly automated laboratory infrastructures that improve reproducibility scientific reduce time costs required develop synthesis routes.

Language: Английский

Citations

0