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

Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.8K
Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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,...
3.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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...
4.2K

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

Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.

bioRxiv : the preprint server for biology·2026
Same author

DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.

bioRxiv : the preprint server for biology·2026
Same author

Terazosin drives sex-dependent adrenergic-bioenergetic reprogramming to restore network function in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same author

Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology.

Nature machine intelligence·2026
Same author

Cross-level Cross-Scale Inference and Imputation of Single-cell Spatial Proteomics.

Research square·2025
Same author

Brain-penetrant histone deacetylase inhibitor RG2833 improves spatial memory in females of an Alzheimer's disease rat model.

Journal of Alzheimer's disease : JAD·2025

相关实验视频

Updated: Jun 13, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K

半监督的超级学习阐明了研究不足的分子相互作用.

You Wu1, Li Xie2, Yang Liu2

  • 1Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.

Communications biology
|September 9, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了Meta Model Agnostic Pseudo Label Learning (MMAPLE),这是一个深度学习框架,可以克服科学发现的数据限制. MMAPLE有效地利用未标记的数据,即使有分布转移,以加速生物研究.

更多相关视频

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

6.8K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.1K

相关实验视频

Last Updated: Jun 13, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K
Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

6.8K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.1K

科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 生物研究往往受到实验约束和人类偏见的限制.
  • 深度学习与稀缺的标记数据和分布转移作斗争,阻碍了科学发现.
  • 现有的转移学习方法不足以应对分布外 (OOD) 数据挑战.

研究的目的:

  • 开发一个新的深度学习框架,MMAPLE,以应对研究不足的生物问题的挑战.
  • 在传统方法失败的情况下,有效地探索非分销 (OOD) 未标记的数据.
  • 整合元学习,转移学习和半监督学习,以加强生物数据分析.

主要方法:

  • 开发了元模型不可知伪标签学习 (MMAPLE) 框架.
  • 整合超级学习,转移学习和半监督学习成为一个统一的方法.
  • 应用MMAPLE来预测药物标相互作用,人类代谢物-酶相互作用,以及微生物组代谢物-人类受体相互作用.

主要成果:

  • 在多个OOD基准中,MMAPLE在预测-召回方面表现出显著的改善 (11%至242%).
  • 在具有挑战性的OOD数据集上,在各种基准模型上取得了卓越的性能.
  • 确定了新的物种间代谢物-蛋白相互作用,通过活性试验验证.

结论:

  • MMAPLE是一个强大而可通用的框架,用于探索以前未知的生物领域.
  • 该框架有效地克服了稀缺数据和生物数据分析中的分布转移的局限性.
  • MMAPLE促进了关键生物相互作用的发现,包括微生物组与人类相互作用中的生物相互作用.