人工智能和大数据时代的蛋白质功能预测。

IF 2.9 3区生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Biochemistry Biochemistry Pub Date : 2025-05-17 DOI:10.1021/acs.biochem.5c00186

Riccardo Percudani, Carlo De Rito

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引用次数: 0

摘要

对于致力于将蛋白质与其功能联系起来的研究人员来说，这是一个激动人心的时刻。大多数从基因组序列中提取功能信息的技术都是几年前开发的，主要进展是由大数据的可用性推动的。现在，深度学习和基于人工智能的方法取得了突破性进展，丰富了蛋白质数据库的三维信息，并提供了预测生化特性和生物分子相互作用的潜力，提供了关键的功能见解。这一进展有望增加数据库中功能亮蛋白的比例，并加深我们在分子水平上对生命的理解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predicting Protein Function in the AI and Big Data Era.

It is an exciting time for researchers working to link proteins to their functions. Most techniques for extracting functional information from genomic sequences were developed several years ago, with major progress driven by the availability of big data. Now, groundbreaking advances in deep-learning and AI-based methods have enriched protein databases with three-dimensional information and offer the potential to predict biochemical properties and biomolecular interactions, providing key functional insights. This progress is expected to increase the proportion of functionally bright proteins in databases and deepen our understanding of life at the molecular level.

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来源期刊

Biochemistry Biochemistry 生物-生化与分子生物学

CiteScore

5.50

自引率

3.40%

发文量

336

审稿时长

1-2 weeks

期刊介绍： Biochemistry provides an international forum for publishing exceptional, rigorous, high-impact research across all of biological chemistry. This broad scope includes studies on the chemical, physical, mechanistic, and/or structural basis of biological or cell function, and encompasses the fields of chemical biology, synthetic biology, disease biology, cell biology, nucleic acid biology, neuroscience, structural biology, and biophysics. In addition to traditional Research Articles, Biochemistry also publishes Communications, Viewpoints, and Perspectives, as well as From the Bench articles that report new methods of particular interest to the biological chemistry community.