Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis

Ralbovsky, Nicole M.; Smith, Joseph P.

doi:10.1007/s11095-022-03457-x

Cited by 5 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example, the set up developed allowed the identification of the carrier to which the enzyme was bound. The authors suggested that the work could be further expanded to identify and gain insight into how other carriers interact with the enzyme [233].…”

Section: Biocatalyst Formulationmentioning

confidence: 99%

Machine Learning: A Suitable Method for Biocatalysis

Sampaio

Fernandes

2023

Catalysts

View full text Add to dashboard Cite

Biocatalysis is currently a workhorse used to produce a wide array of compounds, from bulk to fine chemicals, in a green and sustainable manner. The success of biocatalysis is largely thanks to an enlargement of the feasible chemical reaction toolbox. This materialized due to major advances in enzyme screening tools and methods, together with high-throughput laboratory techniques for biocatalyst optimization through enzyme engineering. Therefore, enzyme-related knowledge has significantly increased. To handle the large number of data now available, computational approaches have been gaining relevance in biocatalysis, among them machine learning methods (MLMs). MLMs use data and algorithms to learn and improve from experience automatically. This review intends to briefly highlight the contribution of biocatalysis within biochemical engineering and bioprocesses and to present the key aspects of MLMs currently used within the scope of biocatalysis and related fields, mostly with readers non-skilled in MLMs in mind. Accordingly, a brief overview and the basic concepts underlying MLMs are presented. This is complemented with the basic steps to build a machine learning model and followed by insights into the types of algorithms used to intelligently analyse data, identify patterns and develop realistic applications in biochemical engineering and bioprocesses. Notwithstanding, and given the scope of this review, some recent illustrative examples of MLMs in protein engineering, enzyme production, biocatalyst formulation and enzyme screening are provided, and future developments are suggested. Overall, it is envisaged that the present review will provide insights into MLMs and how these are major assets for more efficient biocatalysis.

show abstract

Section: Biocatalyst Formulationmentioning

confidence: 99%