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

Conserved Binding Sites01:49

Conserved Binding Sites

5.3K
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...
5.3K
Conserved Binding Sites01:49

Conserved Binding Sites

2.0K
2.0K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

5.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
5.8K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.9K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.9K
Ligand Binding Sites02:40

Ligand Binding Sites

15.5K
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...
15.5K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K

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

Correlated clustering and projection for dimensionality reduction.

Machine learning: science and technology·2026
Same author

VARIANT: Web Server for Decoding and Analyzing Viral Mutations at Genome and Protein Levels.

ArXiv·2026
Same author

Manifold topological deep learning for biomedical data.

Nature communications·2026
Same author

A review of recent advances in generative artificial intelligence models for biomolecular sciences.

Acta pharmaceutica Sinica. B·2026
Same author

CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.

Machine learning: science and technology·2026
Same author

Topology-preserving Hodge decomposition in the Eulerian representation.

Beijing journal of pure & applied mathematics·2026
Same journal

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes.

ArXiv·2026
Same journal

A Positron Range Correction with Texture Preservation Framework in PET Imaging.

ArXiv·2026
Same journal

Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations.

ArXiv·2026
Same journal

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets.

ArXiv·2026
Same journal

Ridge-filter crosstalk in conformal proton FLASH planning: dependence on beamlet pitch and iterative mitigation.

ArXiv·2026
Same journal

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding.

ArXiv·2026
查看所有相关文章

相关实验视频

Updated: Mar 8, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.8K

拓机器学习 蛋白质-核酸结合亲和力 突变后的变化

Xiang Liu, Junjie Wee, Guo-Wei Wei

    ArXiv
    |June 10, 2025
    PubMed
    概括
    此摘要是机器生成的。

    一个新的拓机器学习模型 (TopoML) 准确地预测了蛋白质突变如何影响蛋白质-DNA和蛋白质-RNA结合. 这种计算方法增强了对疾病机制和治疗开发的理解.

    更多相关视频

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

    2.7K
    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
    07:15

    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

    Published on: January 16, 2019

    11.5K

    相关实验视频

    Last Updated: Mar 8, 2026

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
    07:08

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

    Published on: July 14, 2015

    7.8K
    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

    2.7K
    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
    07:15

    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

    Published on: January 16, 2019

    11.5K

    科学领域:

    • 计算生物学 计算生物学
    • 生物物理学的生物物理.
    • 机器学习 机器学习

    背景情况:

    • 了解蛋白质-核酸相互作用对于疾病研究和药物开发至关重要.
    • 预测突变对结合的影响现有的实验和计算方法是有限的.
    • 准确预测突变诱导的结合变化是一个重大挑战.

    研究的目的:

    • 开发一种新的计算模型,用于预测蛋白质突变对蛋白质核酸结合亲和力的影响.
    • 整合各种数据类型,包括拓,物理化学和基于序列的特征,以进行强大的相互作用建模.

    主要方法:

    • 开发了一个拓机器学习模型 (TopoML),结合了从拓数据分析中获得的持久拉普拉西亚模型.
    • 使用多视角特征:物理化学性质,拓结构和蛋白质变压器嵌入.
    • 该模型在含有单点氨基酸突变的蛋白质-DNA和蛋白质-RNA数据集上进行了验证.

    主要成果:

    • 与最先进的方法相比,TopoML模型显示出更高的性能.
    • 实现了对蛋白质-DNA和蛋白质-RNA复合体突变诱导的结合亲和力变化的准确预测.
    • 整合框架有效地捕获了复杂的绑定交互表示.

    结论:

    • 拟议的TopoML模型在预测蛋白质与核酸结合的突变效应方面取得了重大进展.
    • 这种方法可以帮助解开疾病机制,加快针对性的治疗方法的开发.
    • TopoML为研究蛋白质与核酸相互作用提供了一个更准确,更有效的计算工具.