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相关概念视频

End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Propagation of Action Potentials01:23

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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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相关实验视频

Updated: Apr 12, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
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基于图形卷积网络的可变长度ACE抑制的预测.

Yating Rong1, Baolong Feng2, Xiaoshuang Cai3

  • 1Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; Food College, Northeast Agricultural University, Harbin 150030, China.

International journal of biological macromolecules
|October 31, 2024
PubMed
概括
此摘要是机器生成的。

一个新的图形卷积网络 (GCN) 模型有效地使用分子图形预测血管酶I转化酶 (ACE) 抑制,优于传统方法. 这一进展有助于识别功能性食品的潜在抗高血压化合物.

关键词:
作为ACE抑制剂的类.图表 卷积网络 卷积网络分子图表的分子图表.可变长度的变量.

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科学领域:

  • 生物化学 生物化学
  • 计算化学计算化学
  • 生物信息学是一种生物信息学.

背景情况:

  • 传统的分子描述器在完全描述复杂的结构以预测血管酶I转化酶 (ACE) 抑制活性方面存在局限性.
  • 准确预测ACE抑制对于开发具有抗高血压特性的功能性食品至关重要.

研究的目的:

  • 引入和评估分子图形和图形卷积网络 (GCN) 模型,以增强特征和预测ACE抑制活性.
  • 将GCN模型的性能与使用分子描述器的传统机器学习模型进行比较.

主要方法:

  • (2-10种氨基酸) 作为分子图表表示.
  • 一个图形卷积网络 (GCN) 模型被开发用于预测可变长度活性.
  • 将GCN模型的性能与随机森林 (RF),支持矢量机 (SVM) 和k-近邻 (kNN) 模型进行了基准测试.

主要成果:

  • 在预测ACE抑制潜力的过程中,GCN模型达到0.78的准确度,超过了基于分子描述器的ML模型.
  • 在一个独立的测试组中,GCN模型显示了超过98%的准确性,明显超过现有方法.
  • 合成的酸VAPE和AQQKEP表现出强大的ACE抑制活性,IC50值较低 (分别为2.25±0.11和3.75±0.17μM).

结论:

  • 与传统方法相比,分子图和GCN模型提供了一种优越的方法来表征和预测ACE抑制活性.
  • 开发的GCN模型是快速查和识别ACE抑制的强大工具.
  • 这项研究有望在功能性食品中开发新的抗高血压成分.