Investigating Enzyme Biochemistry by Deep Learning: A Computational Tool for a New Era

• Published in: Journal of computational biophysics and chemistry Vol. 23; no. 6; pp. 781 - 799
• Main Authors: Rayka, Milad, Mirzaei, Morteza, Farnoosh, Gholamreza, Latifi, Ali Mohammad
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.08.2024; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Artificial neural networks; Biofuels; Biological activity; Catalytic converters; Chemical reactions; Deep learning; Enzyme kinetics; Enzymes; Machine learning; Parameter identification; Software; Unsupervised learning; deep learning; Enzyme; artificial intelligence; bioinformatics
• DOI: 10.1142/S2737416524400052
• ISSN: 2737-4165

Enzymes are protein molecules that play a crucial role in various biological processes in living organisms. They function as catalysts in biological reactions such as digestion, metabolism, DNA replication and other physiological processes. Furthermore, enzymes are widely used in food production, pharmaceuticals and biofuel production. In these industries, they accelerate desired chemical reactions as biocatalysts. Therefore, applying computational methods and data-driven algorithms to predict enzyme properties is essential. Over the past decade, deep learning has made remarkable advancements in science and technology. Deep learning is a subset of machine learning algorithms that rely on artificial neural networks. These algorithms can be employed for supervised, semi-supervised and unsupervised learning. Here, to provide an update on the current literature, we provide an overview of various deep learning algorithms and recent advancements in their application to enzyme science. These applications can generally be categorized into diverse subjects: function prediction, enzyme kinetic parameters prediction, enzyme-substrate identification, condition optimization, thermophilic property prediction, enzyme catalytic site prediction and enzyme design. In conclusion, we discuss the convergence of enzyme science and deep learning, highlighting the potential opportunities and challenges. Artificial intelligence algorithms have diverse applications in enzyme science. In this review paper, we focus on deep learning algorithms and their implications on various tasks, such as enzyme design, kinetic parameters prediction, etc.