Deep learning in template-free de novo biosynthetic pathway design of natural products.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae495
Xueying Xie, Lin Gui, Baixue Qiao, Guohua Wang, Shan Huang, Yuming Zhao, Shanwen Sun
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Abstract

Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.

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天然产品无模板从头生物合成途径设计中的深度学习。
天然产物(NPs)在药物开发中不可或缺,尤其是在抗击感染、癌症和神经退行性疾病方面。然而,天然产物的有限可用性带来了巨大挑战。无模板的从头生物合成途径设计为 NP 生产提供了一种战略性解决方案,而深度学习则是这一领域的有力工具。本综述深入探讨了最先进的 NP 生物合成途径设计深度学习算法。它深入讨论了对模型训练至关重要的《京都基因组百科全书》(KEGG)、Reactome 和 UniProt 等数据库,以及用于迁移学习的 Reaxys、SciFinder 和 PubChem 等化学数据库,以扩展模型对更广阔化学空间的理解。报告评估了序列到序列和图到图转换模型在单步准确预测方面的潜力和挑战。此外,它还讨论了用于多步预测的搜索算法和用于预测酶功能的深度学习算法。综述还强调了深度学习在通过酶工程提高催化效率方面的关键作用,这对提高 NP 产量至关重要。此外,它还探讨了大型语言模型在途径设计、酶发现和酶工程中的应用。最后,它探讨了与无模板方法相关的挑战和前景,为潜在的 NP 生物合成途径设计进展提供了见解。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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