机器学习技术从序列中推断蛋白质结构和功能:全面的审查
Gopal Srivastava1, Mengmeng Liu2, Xialong Ni1
1Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.
Methods in molecular biology (Clifton, N.J.)
|November 22, 2024
概括
机器学习从序列数据准确地预测蛋白质结构和功能. 本综述涵盖了关键的方法,挑战和进展,突出了机器学习.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 了解蛋白质结构和功能对于生物学见解和药物开发至关重要.
- 越来越多的蛋白质序列数据需要先进的计算方法.
- 机器学习 (ML) 提供了强大的工具,可以直接从序列中预测蛋白质特征.
研究的目的:
- 在蛋白质结构和功能推断中提供ML应用的全面审查.
- 讨论各种ML方法,包括深度学习架构.
- 突出当前的挑战和该领域最近的进展.
主要方法:
- 机器学习技术的审查,重点是卷积神经网络 (CNN) 和自然语言处理 (NLP).
- ML的应用用于预测蛋白质的二级和三级结构,残留-残留接触,蛋白质功能和亚细胞定位.
- 探索用于蛋白质分析的深度学习架构的进步.
主要成果:
- 机器学习技术,特别是深度学习,在预测各种蛋白质属性方面显示出显著的前景.
- 在推断复杂蛋白质结构和功能信息方面,CNN和NLP的成功应用.
- 确定关键挑战,包括数据质量和模型可解释性.
结论:
- 机器学习对于推进对蛋白质结构和功能的理解至关重要.
- 机器学习有可能彻底改变药物发现和个性化医学.
- 深度学习模型的持续开发将进一步提高预测能力.
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