从序列到蛋白质结构和形态动力学与人工智能/机器学习
Alexander M Ille1, Emily Anas2, Michael B Mathews
1Rutgers Cancer Institute, Rutgers, The State University of New Jersey, Newark, New Jersey 07103, USA.
像AlphaFold2这样的人工智能/机器学习模型可以从氨基酸序列中预测蛋白质结构. 未来的AI/ML模型可以使用序列和核磁共振数据来预测蛋白质结构动力学.
科学领域:
- 生物化学和结构生物学
- 计算生物学和生物信息学
- 生命科学中的人工智能
背景情况:
- 2024年诺贝尔化学奖承认AI/ML模型对*de novo*蛋白质结构的预测.
- 像AlphaFold2,RoseTTAFold和ESMFold这样的模型利用神经网络和注意力机制.
- 这些AI/ML模型是基于这样的假设:蛋白质结构是由它的氨基酸序列决定的.
研究的目的:
- 为了审查蛋白质中的序列结构关系.
- 提出蛋白质的结构动力学也依赖于序列.
- 概述一个概念AI/ML模型,用于预测蛋白质构成组合.
主要方法:
- 对现有的AI/ML模型进行蛋白质结构预测的概述 (AlphaFold2,RoseTTAFold,ESMFold).
- 讨论基础假设,将氨基酸序列与蛋白质结构联系起来.
- 一个新的AI/ML模型架构的概念化,用于结构动态预测.
主要成果:
- 人工智能/ML模型已经在预测静态蛋白质结构方面取得了成功.
- AlphaFold2可以通过子样本序列对齐来预测多种蛋白质构造.
- 核磁共振 (NMR) 光谱学提供适合AI/ML训练的形状敏感数据.
结论:
- 蛋白质序列和结构之间的决定性关系已经得到了很好的证实.
- 蛋白质结构动力学可能依赖于序列,为AI/ML提供了新的途径.
- 使用AI/ML和NMR数据对蛋白质动态的序列信息预测对生物科学具有变革性的潜力.
更多相关视频
05:08Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
Published on: July 8, 2025
07:08Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
相关概念视频
Protein Folding
Protein Organization
The primary structure of a protein is its amino acid sequence....
Protein and Protein Structure
A protein's shape is critical to its function. For example, an enzyme...
Conservation of Protein Domains Over Different Proteins
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
Conserved Binding Sites
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
Intrinsically Disordered Proteins
