通过深度学习,彻底改变了蛋白质与蛋白质相互作用的预测
Jing Zhang1, Jesse Durham2, Qian Cong2
1Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; HaroldC.Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address: https://twitter.com/jzhang_genome.
深度学习推进了用于预测蛋白质结构和相互作用的计算方法. 这些计算方法,特别是那些使用共同进化分析的方法,对于理解疾病机制和开发新疗法至关重要.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 结构生物学 结构生物学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 是生物过程的基础.
- 个人防护剂的干扰与各种疾病有关.
- 准确的PPI研究对于生物和医学研究至关重要.
研究的目的:
- 审查近期用于3D蛋白质复合体建模的计算方法.
- 突出深度学习应用在预测蛋白质相互作用伙伴.
- 讨论PPI预测中的生物医学相关性和未来挑战.
主要方法:
- 审查最先进的计算技术.
- 强调对基因组数据应用的深度学习算法.
- 专注于共同进化分析,用于预测PPI.
主要成果:
- 深度学习显著提高了蛋白质复杂模型的准确性.
- 计算预测现在在准确度上与实验方法相美.
- 同进化衍生的深度学习模型对PPI预测有前途.
结论:
- 计算方法,特别是深度学习,正在彻底改变PPI研究.
- 准确的PPI预测具有广泛的生物医学应用.
- 应对当前的挑战将进一步推动该领域的发展.
更多相关视频
10:21Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
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-protein Interfaces
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-Protein Interfaces
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...
Protein Complexes with Interchangeable Parts
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...
