Machine Learning: A Suitable Method for Biocatalysis

Author:

Sampaio Pedro Sousa1ORCID,Fernandes Pedro234ORCID

Affiliation:

1. Computação e Cognição Centrada nas Pessoas, Lusofona University, Campo Grande, 376, 1749-024 Lisbon, Portugal

2. BioRG (Biomedical Research Group) and Faculty of Engineering, Lusofona University (ULHT), Campo Grande, 376, 1749-024 Lisbon, Portugal

3. iBB—Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal

4. Associate Laboratory i4HB—Institute for Health and Bioeconomy at Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal

Abstract

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.

Publisher

MDPI AG

Subject

Physical and Theoretical Chemistry,Catalysis,General Environmental Science

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