Protein representations: Encoding biological information for machine learning in biocatalysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F24%3A00081679" target="_blank" >RIV/00159816:_____/24:00081679 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/24:00138463

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0734975024001538?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0734975024001538?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.biotechadv.2024.108459" target="_blank" >10.1016/j.biotechadv.2024.108459</a>

Result language

angličtina

Original language name

Protein representations: Encoding biological information for machine learning in biocatalysis

Original language description

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30400 - Medical biotechnology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Biotechnology Advances

ISSN

0734-9750

e-ISSN

1873-1899

Volume of the periodical

77

Issue of the periodical within the volume

October

Country of publishing house

GB - UNITED KINGDOM

Number of pages

20

Pages from-to

108459

UT code for WoS article

001332287000001

EID of the result in the Scopus database

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