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Structure-based protein and small molecule generation using EGNN and diffusion models: a comprehensive review

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1016/j.csbj.2024.06.021
AuthorSearch for: ; Search for: 1ORCID identifier: https://orcid.org/0000-0001-6515-2556; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0002-9198-6439
Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
Subjectdiffusion models; protein-backbone generation; generative models; graph representation; equivariant graph neural network; protein sequence
Abstract
Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.
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PublisherElsevier
Research Network of Computational and Structural Biotechnology
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  • Computational and Structural Biotechnology Journal 23, no. C (26 June 2024): 2779–2797.
LanguageEnglish
Peer reviewedYes
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Record identifier08d30830-5c80-42f0-8da9-e722ed4c83ed
Record created2024-07-03
Record modified2024-07-03
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