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

Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

50.8K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
50.8K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
Mechanical Protein Functions01:58

Mechanical Protein Functions

4.9K
Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force. 
4.9K
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
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56

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

Intermolecular Non-Bonded Interactions from Machine Learning Datasets.

Molecules (Basel, Switzerland)·2023
Same author

A Minimum Quantum Chemistry CCSD(T)/CBS Data Set of Dimeric Interaction Energies for Small Organic Functional Groups: Heterodimers.

ACS omega·2022
Same author

Coarse-Grained Simulations Using a Multipolar Force Field Model.

Materials (Basel, Switzerland)·2018
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 5, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.3K

基于量子化学计算的相互作用能量数据集的生物宏分子建模的机器学习力场.

Zhen-Xuan Fan1, Sheng D Chao1,2

  • 1Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.

Bioengineering (Basel, Switzerland)
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

生物分子模拟的精确力场需要精确的非对应相互作用能量. 这项研究验证了SAPT2 (对称调整扰动理论) 理论水平,并使用机器学习 (CLIFF方案) 来开发高效的力场.

关键词:
一开始的能源数据集.人工智能的人工智能是人工智能.机器学习的力量场是机器学习的力量场.非对应相互作用的非对应相互作用.适应对称性的扰动理论.

更多相关视频

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K

相关实验视频

Last Updated: Jul 5, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.3K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K

科学领域:

  • 计算化学是一种计算化学.
  • 生物分子建模模型
  • 机器学习 机器学习

背景情况:

  • 准确的力场对于生物大分子的分子动力学模拟至关重要.
  • 确定适当的量子化学方法和连续能量函数是力场开发中的关键挑战.
  • 之前的工作利用对称性适应扰动理论 (SAPT0) 进行相互作用能量,创建了SOFG-31数据集.

研究的目的:

  • 确定最佳的对称性调整扰乱理论 (SAPT) 理论水平,用于计算相互作用能量.
  • 通过单,双和扰动三次激发/完整基础集 (CCSD) /CBS) 计算,将SAPT相互作用能量与合集进行比较.
  • 利用机器学习开发一种通用力场,用于生物分子动力学模拟.

主要方法:

  • 重新计算的分子间相互作用能量使用先进的SAPT2水平的理论与扩展的基础集.
  • 采用了CLIFF (连续,低维,交互,力场) 方案,一种机器学习技术,用于力场构造.
  • 利用SOFG-31和SOFG-31-异构体数据集进行训练和测试机器学习模型.

主要成果:

  • 在SAPT2层次的理论,与适当的基础集,提供与CCSD(T) /CBS基准一致的相互作用能量.
  • 在CLIFF计划中,成功地使用一个小的训练数据集复制了各种各样的二维相互作用能量模式.
  • 在SAPT能量组件和总SAPT能量中的错误明显低于目标化学精度约1kcal/mol.

结论:

  • SAPT2是一个合适的理论水平,用于准确计算生物分子系统中的非对应相互作用能量.
  • 从量子化学数据中开发准确和高效的力场,CLIFF方案是有效的.
  • 这种方法平衡了生物分子模拟的化学精度和计算效率.