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深度学习时代中的蛋白质工程

Bingxin Zhou^1,2, Yang Tan^2,3,4, Yutong Hu⁵

¹Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China.

|January 2, 2025

概括

深度学习促进了工业,健康和环境的蛋白质工程. 这篇评论详细介绍了蛋白质理解和工程的深度学习方法,指导生物学家和计算机科学家实现新的突破.

科学领域:

生物技术是生物技术.
计算生物学计算生物学
人工智能的人工智能

背景情况:

深度学习 (DL) 已成为蛋白质工程中的一个强大的工具.
蛋白质工程解决了工业生产,医疗保健和环境可持续性的关键挑战.

研究的目的:

通过深度学习的透视来审查蛋白质工程问题.
为生物学家和计算机科学家提供在这个跨学科领域的全面指南.

主要方法:

讨论蛋白质序列和结构的表示方法.
最先进的蛋白质语言模型和几何深度学习的总结.
探索多模式生物数据学习和编码管道.

主要成果:

对蛋白质理解和工程的深度学习方法的详细概述.
确定常见下游任务和基准数据集.
专注于诸如突变部位识别和属性预测等用于虚拟查的应用.

结论:

深度学习为蛋白质工程应用提供先进的工具.
采用标准化的方法和整合的数据资源将加速进展.
未来的生物学和计算机科学社区的整合将释放DL在蛋白质工程方面的全部潜力.

关键词:

人工智能的人工智能是人工智能.几何深度学习的几何深度学习蛋白质工程工程蛋白质工程蛋白质语言模型合成生物学合成生物学

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