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 Folding01:22

Protein Folding

118.0K
Overview
118.0K
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
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
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

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

ADMET-vault: an interactive framework for real-time ADMET prediction and molecular optimization.

Journal of computer-aided molecular design·2026
Same author

Ophthalmic manifestations at high altitude: A comprehensive study of anterior segment conditions.

Romanian journal of ophthalmology·2026
Same author

Comparative Metabolite Profiling and Antiproliferative Characterization of Lab-Acclimatized and Wild Green Seaweed <i>Acrosiphonia orientalis</i> to Reveal Its Nutraceutical Potential.

Foods (Basel, Switzerland)·2026
Same author

A comparative anti-proliferative and immunomodulatory analysis in wild and lab-acclimatized seaweed extracts unravel the functional biopotentials of Acrosiphonia orientalis.

Scientific reports·2026
Same author

DecoyFinderNetAna: Application of Graph Convolution Neural Networks for Accurate Classification of True Small Molecule Binders from their Decoys.

Current computer-aided drug design·2026
Same author

Intraocular Tuberculosis: Current Insights and Emerging Therapeutic Paradigms.

Seminars in ophthalmology·2025

相关实验视频

Updated: Jun 27, 2025

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
07:33

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

14.3K

使用卷积神经网络和分子动力学模拟来区分稳定和不稳定的蛋白质.

Shreyansh Suyash1, Akshat Jha1, Priyasha Maitra1

  • 1Growdea Technologies Pvt. Ltd., Gurugram, Haryana 122004, India.

Computational biology and chemistry
|April 27, 2024
PubMed
概括

本研究引入了一种机器学习模型,仅使用氨基酸序列来预测蛋白质稳定性. 该模型准确地估计了关键稳定性参数,为传统方法提供了更快的替代方案,并帮助蛋白质工程.

关键词:
卷积神经网络是一种卷积神经网络.双硫化物债券是一种二硫化物债券.展开的吉布斯自由能量热量容量 热量容量 热量容量机器学习 机器学习化的温度 化的温度蛋白质折叠 蛋白质的折叠蛋白质的稳定性 蛋白质的稳定性蛋白质的热力学

更多相关视频

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

2.1K
How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project
07:22

How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project

Published on: February 11, 2019

28.2K

相关实验视频

Last Updated: Jun 27, 2025

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
07:33

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

14.3K
Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

2.1K
How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project
07:22

How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project

Published on: February 11, 2019

28.2K

科学领域:

  • 分子生物学分子生物学
  • 生物化学 生物化学
  • 计算生物学 计算生物学

背景情况:

  • 蛋白质的稳定性对于功能至关重要,受热力学和结构因素的影响.
  • 基于多个参数预测蛋白质稳定性的现有工具是有限的.
  • 关键参数包括展开的自由能量变化 (ΔG),热容量变化 (ΔCp),化温度 (Tm) 和二硫化物键.

研究的目的:

  • 开发一种机器学习模型,从主要序列中预测蛋白质稳定性参数.
  • 为了解决当前蛋白质稳定性预测工具的局限性.
  • 为计算密集的基于物理的方法提供更快,基于序列的替代方案.

主要方法:

  • 开发一个多层卷积神经网络 (CNN) 模型.
  • 单个模型被训练来预测 ΔG, ΔCp, Tm 和二硫化物键.
  • 使用in silico分析进行验证,包括对同类蛋白质进行分子对接和动态模拟.

主要成果:

  • 所有预测模型都实现了高精度,R2值为0.79 (ΔG),0.78 (ΔCp),0.92 (Tm) 和0.92 (二硫化物键).
  • 验证证实了模型在预测稳定性相关性质方面的准确性.
  • 该研究证明了该模型在分析蛋白质稳定性和突变影响方面的实用性.

结论:

  • 开发的ML模型提供了一个快速而准确的方法,可以直接从氨基酸序列中预测蛋白质稳定性参数.
  • 这种方法补充了传统的基于物理的方法,加速了蛋白质工程和药物设计方面的研究.
  • 这些发现提供了对突变对蛋白质稳定性的影响有价值的见解.