NPEX:永远不要放弃通过深度强化学习的蛋白质探索
Yuta Shimono1, Masataka Hakamada1, Mamoru Mabuchi1
1Graduate School of Energy Science, Kyoto University, Yoshidahonmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
一种新的深度强化学习方法,永远不要放弃蛋白质探索 (NPEX),加速蛋白质结构的确定. 在不需要先前的结构知识的情况下,NPEX提高了采样效率,彻底改变了药物设计和蛋白质研究.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 人工智能的人工智能
背景情况:
- 确定未知的蛋白质结构,包括转移稳定状态,对于治疗剂设计至关重要.
- 目前的计算方法,如分子动力学和马尔科夫链蒙特卡洛模拟,耗时且需要先前的结构信息.
研究的目的:
- 开发一种创新,高效的蛋白质结构测定方法.
- 在速度和数据要求方面克服现有的计算方法的局限性.
主要方法:
- 开发了使用深度强化学习的永不放弃蛋白质探索 (NPEX) 方法.
- 采用软演员-批评算法和内在奖励系统,在没有事先知识的情况下引入偏见潜力.
- 将NPEX应用于基准模型:双井,三井,阿兰二和酸.
主要成果:
- 与马尔科夫链蒙特卡洛模拟相比,NPEX的采样效率明显更高.
- 该方法有效地确定了蛋白质结构,而不需要先前的领域知识.
- 在蛋白质结构探索中大大提高了计算效率.
结论:
- NPEX方法为蛋白质结构的确定提供了一种革命性的方法.
- 它的增强的计算效率和与先前知识的独立性将加速药物发现和基础蛋白质研究.
更多相关视频
05:41A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
Published on: February 6, 2020
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
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...
Conservation of Protein Domains
Mechanical Protein Function
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,...
Mechanical Protein Functions
Protein Dynamics in Living Cells
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
