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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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相关实验视频

Updated: Jul 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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一个双种群多目标进化算法,由生成对抗网络驱动,用于基准测试和蛋白质对接.

Honglei Cheng1, Gai-Ge Wang1, Liyan Chen2

  • 1School of Computer Science and Technology, Ocean University of China, Qingdao, China.

Computers in biology and medicine
|November 29, 2023
PubMed
概括

本研究介绍了一种双种群进化算法 (DGMOEA),以克服多目标优化问题 (MOP) 中的模型崩. DGMOEA提高了解决方案的质量和多样性,在基准功能和蛋白质-对接方面显著优于现有的方法.

关键词:
深度学习是一种深度学习.双重的人口 - 双重的人口进化算法是一种进化算法.生成性的对抗性网络.多目标优化多目标优化蛋白质 - 基对接

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科学领域:

  • 计算科学与工程 计算科学与工程
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 多目标优化问题 (MOP) 涉及同时优化多个相互冲突的目标.
  • 基于模型的进化算法 (MBEAs) 使用机器学习,但遭受模型崩,导致局部最佳和减少多样性.
  • 解决模型崩对于改善MBEA在复杂的优化任务中的性能至关重要.

研究的目的:

  • 提出一种新的双种群多目标进化算法,由瓦斯斯坦生成对抗网络带梯度惩罚 (DGMOEA) 驱动.
  • 加强高质量的解决方案的产生,并提高MOP的多样性.
  • 评估DGMOEA在基准函数和现实世界蛋白质-接问题上的有效性.

主要方法:

  • 开发了DGMOEA,一个双人群算法,利用Wasserstein生成对抗网络带有梯度惩罚.
  • 协调双人群以协作生成优越的候选解决方案.
  • 与20个多目标基准函数和LEADS-PEP数据集的7个最先进的算法进行了比较.

主要成果:

  • 总干事办公室 (DGMOEA) 展示了对MOP的显著改进,超过了比较算法.
  • 在20个基准指标中分别在15个和18个基准指标上,在反向代际距离 (IGD) 和超量 (HV) 度量方面取得了卓越的表现.
  • 在蛋白质-对接中有效减少根平均平方偏差 (RMSD),在LEADS-PEP数据集上产生具有竞争力的结果.

结论:

  • DGMOEA有效地解决了MBEA中的模型崩问题,提高了解决方案的质量和多样性.
  • 拟议的算法为解决复杂的MOP提供了强大的和高效的方法.
  • 总监局显示,在计算生物学领域有很大的应用潜力,例如蛋白质-接.