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

14.9K
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...
14.9K
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

15.9K
Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
15.9K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

10.0K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
10.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.0K
VSEPR Theory for Determination of Electron Pair Geometries
45.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Molecular Models02:00

Molecular Models

43.5K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
43.5K

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

De novo design and experimental characterization of bitter peptides.

NPJ science of food·2026
Same author

Artificial intelligence applied to post-resuscitation ECGs for early prognostication after out-of-hospital cardiac arrest.

Frontiers in cardiovascular medicine·2026
Same author

Human bitter taste receptors undergo internalization in an agonist-selective fashion.

International journal of biological macromolecules·2026
Same author

Architecture-encoded mechanics and communication in microtubules: a multiscale computational study.

Journal of the Royal Society, Interface·2026
Same author

Steroid Hormones Are Potent and Putatively Endogenous Activators of Human Bitter Taste Receptors.

Annals of the New York Academy of Sciences·2026
Same author

Molecular Insights into IAHSP: Influence of the R1611W Mutation on the VPS9 Domain of Alsin.

ACS omega·2025
Same journal

Efficacy of Tinospora cordifolia bioactives as agonists of Smoothened (Smo) receptor to promote oligodendroglial lineage induction for remyelination-based therapy.

Journal of molecular graphics & modelling·2026
Same journal

Dynamic remodeling of USP28 by the selective inhibitor CAS-010: Insights from DFT and molecular dynamics simulations.

Journal of molecular graphics & modelling·2026
Same journal

Beyond the catalytic site: Voxilaprevir and Pasireotide as repurposed therapeutics for conformational inhibition of ADAR1.

Journal of molecular graphics & modelling·2026
Same journal

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

Journal of molecular graphics & modelling·2026
Same journal

A multi-Level Study of 20S proteasome inhibitors: an integrated approach combining chemistry and Modelling.

Journal of molecular graphics & modelling·2026
Same journal

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Journal of molecular graphics & modelling·2026
查看所有相关文章

相关实验视频

Updated: Jan 15, 2026

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

2.5K

可解释的机器学习和深度学习模型用于预测TAS2R-bitter分子相互作用.

Francesco Ferri1, Marco Cannariato2, Lorenzo Pallante2

  • 1Politecnico di Torino, Polito(BIO)MedLab, Department of Mechanical and Aerospace Engineering, Torino, 10129, Italy; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354, Freising, Germany.

Journal of molecular graphics & modelling
|October 15, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了机器学习和深度学习模型,以预测苦味分子与味道受体2型 (TAS2R) 受体的相互作用. 这些可解释的AI模型有助于理解苦味,并为药物发现设计有针对性的苦味化合物.

更多相关视频

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

827
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.6K

相关实验视频

Last Updated: Jan 15, 2026

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

2.5K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

827
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.6K

科学领域:

  • 计算化学是一种计算化学.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 苦味的感知是由2型味觉受体 (TAS2R) 的G蛋白合受体介导的.
  • TAS2Rs的作用超越了味道,影响各种生理功能和疾病.
  • 预测配体-TAS2R相互作用对于口味感知和药物设计至关重要.

研究的目的:

  • 开发可解释的机器学习 (ML) 和深度学习 (DL) 模型,用于预测苦分子-TAS2R相互作用.
  • 为模型培训利用经过实验验证的数据.
  • 提高对苦味化合物特性和TAS2R向的理解.

主要方法:

  • 使用传统的ML和DL方法.
  • 在经过实验验证的数据上训练模型.
  • 综合模型,以提高可解释性和解释性.

主要成果:

  • 开发了高性能和适用的ML和DL模型.
  • 证明了模型的协同集成,以提高可解释性.
  • 关于苦味化合物与受体相互作用的结果的简化解释.

结论:

  • 开发的模型提供了一个强大的工具,用于预测苦药-TAS2R相互作用.
  • 这些模型可以指导设计针对特定TAS2Rs的新苦化合物.
  • 这项研究促进了对味觉感知和药物设计的理解.