Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Protein Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

13.5K
Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...
13.5K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.4K
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...
13.4K
Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

8.9K
When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
8.9K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.5K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.5K
PI3K/mTOR/AKT Signaling Pathway01:22

PI3K/mTOR/AKT Signaling Pathway

4.0K
The mammalian target of rapamycin  (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1  (mTORC1) and mTOR complex 2  (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast,  mTORC2 consists of a...
4.0K
MAPK Signaling Cascades01:07

MAPK Signaling Cascades

6.1K
Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
6.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor Interactions with Structure-Aware Validation.

Journal of chemical information and modeling·2026
Same author

Enhancing aqueous solubility prediction with residual gated graph convolutions and sequential modeling.

Computational biology and chemistry·2026
Same author

PeptideNet: An Integrative Deep Learning Framework for Predicting Diverse Bioactive Peptides Using Protein Language Model Embeddings.

Journal of chemical information and modeling·2026
Same author

Harnessing the Therapeutic Potential of Pomegranate Peel-Derived Bioactive Compounds in Pancreatic Cancer: A Computational Approach.

Pharmaceuticals (Basel, Switzerland)·2025
Same author

The impact of inflammatory markers on clinical outcomes in acute ischemic stroke patients following mechanical thrombectomy: A multicentre study.

Journal of the neurological sciences·2025
Same author

Sex-based differences in inflammatory predictors of outcomes in patients undergoing mechanical thrombectomy: an inverse probability weighting analysis.

Therapeutic advances in neurological disorders·2025

相关实验视频

Updated: Sep 16, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
13:22

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

8.0K

整合图形卷积和注意力机制用于激酶抑制预测.

Hamza Zahid1, Kil To Chong1,2, Hilal Tayara3

  • 1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Jeollabuk-do, Republic of Korea.

Molecules (Basel, Switzerland)
|July 12, 2025
PubMed
概括

本研究引入了一个图形神经网络 (GNN) 模型来预测酶抑制活动. 结合的图形卷积和图形注意网络 (GCN-GAT) 在识别潜在的药物分子治疗酶相关疾病方面取得了卓越的准确性.

关键词:
发现药物的发现.图表注意力网络 图表注意力网络图形卷积网络的图形卷积网络.图表神经网络的神经网络抑制预测抑制的预测.酶抑制预测的预测

更多相关视频

Identification of Kinase-substrate Pairs Using High Throughput Screening
11:13

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

8.3K
Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein
11:23

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Published on: June 30, 2019

6.3K

相关实验视频

Last Updated: Sep 16, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
13:22

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

8.0K
Identification of Kinase-substrate Pairs Using High Throughput Screening
11:13

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

8.3K
Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein
11:23

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Published on: June 30, 2019

6.3K

科学领域:

  • 生物化学和分子生物学
  • 计算生物学和化学信息学
  • 药理学和药物发现

背景情况:

  • 激酶是细胞信号传递中的关键酶;它们的失调与各种人类癌症和疾病有关.
  • 用小药物分子向异常激酶是癌症治疗的关键策略.
  • 以前的努力利用机器学习和深度学习来预测酶抑制,但进展仍在进行中.

研究的目的:

  • 开发和评估图形神经网络 (GNN) 模型,用于预测酶抑制活动.
  • 为了比较一个独立的图形卷积网络 (GCN) 与一个结合的GCN和图形注意网络 (GCN-GAT) 的性能.
  • 在独立的酶数据集上评估开发的模型的预测准确性.

主要方法:

  • 开发两个GNN模型:一个GCN和一个GCN-GAT.
  • 在两个大型激酶数据集上进行培训和10倍交叉验证,其中包括小药物分子和向激酶.
  • 在独立数据集上评估模型性能,使用准确度,MCC,灵敏度,特异性和精度等指标.

主要成果:

  • 结合的GCN-GAT模型在两个独立的激酶数据集上表现出优越的性能,与以前的方法相比.
  • 在独立的Kinase数据集1中,GCN-GAT模型实现了0.96的精度,0.89的MCC,0.90的灵敏度,0.98的特异性和0.91.98的精度.
  • 在独立的激酶数据集2中,GCN-GAT模型的准确度为0.97,MCC为0.90,灵敏度为0.91,特异性为0.99,精度为0.92.

结论:

  • GCN-GAT模型在预测酶抑制活动方面非常有效.
  • 这种方法提供了一个有前途的计算工具,可以加速发现新的酶向药物.
  • 这些发现突显了高级图形神经网络在药物研发中的潜力.