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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.9K
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...
10.9K
Protein and Protein Structure02:15

Protein and Protein Structure

79.7K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
79.7K
Protein Organization01:24

Protein Organization

6.5K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.5K
Protein and Protein Structures02:15

Protein and Protein Structures

10.5K
10.5K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.1K
3.1K
From DNA to Protein03:06

From DNA to Protein

18.5K
The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
18.5K

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

Antibiotic exposure and indication-specific corticosteroid use differentially modulate outcomes of immune checkpoint inhibitor therapy in hepatobiliary malignancies.

Frontiers in immunology·2026
Same author

One-month early time-restricted eating enhances long-term memory by modulating brain fluid dynamics in males with metabolic syndrome: Evidence from perivascular diffusion and global blood-oxygen-level-dependent-cerebrospinal fluid coupling.

Journal of Alzheimer's disease : JAD·2026
Same author

Sympathetic Overactivation Drives Neurogenic Alveolar Epithelial Pyroptosis via the PIEZO2-ER Stress Pathway in Acute Lung Injury Following Intracerebral Hemorrhage.

CNS neuroscience & therapeutics·2026
Same author

The gut microbial metabolite 3-indolepropionic acid as a functional neuroprotective agent against intracerebral hemorrhage: integrating epidemiological screening with in vivo validation.

Metabolic brain disease·2026
Same author

A New Approach for Quantifying the Total Amount of Organosulfur Compounds in Atmospheric Aerosols.

Analytical chemistry·2026
Same author

Context-dependent short-chain fatty acids in inflammatory skin diseases: Immunometabolic mechanisms, evidence boundaries, and translational perspectives.

Pharmacological research·2026

相关实验视频

Updated: Jul 12, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

16

蛋白序列建模的生成模型:最近的进展和未来的方向

Mehrsa Mardikoraem1, Zirui Wang2, Nathaniel Pascual3

  • 1Michigan State University (MSU)'s Department of Chemical Engineering and Materials Science.

Briefings in bioinformatics
|October 21, 2023
PubMed
概括

机器学习 (ML) 模型,包括生成AI,对于蛋白质工程至关重要,因为大量的未标记的序列数据. 这项研究指导了ML模型的应用,用于预测蛋白质适应性和生成高适应性序列.

关键词:
扩散模型的扩散模型生成性对抗性神经网络 (GANs) 是一种神经网络.生成式机器学习 (ML) 模型自然语言处理 (NLP)蛋白质工程工程 蛋白质工程变化自动编码器 (VAE) 是一种变化自动编码器.

更多相关视频

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.4K
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

7.3K

相关实验视频

Last Updated: Jul 12, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

16
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.4K
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

7.3K

科学领域:

  • 计算生物学 计算生物学
  • 生物技术是生物技术.
  • 人工智能的人工智能

背景情况:

  • 高通量欧米克技术产生了大量与疾病途径相关的蛋白质序列数据.
  • 有限的实验性适应性注释需要先进的机器学习 (ML) 方法.
  • 自主监督和无监督的ML利用未标记的序列用于蛋白质工程.

研究的目的:

  • 为顺序数据分析提供成功的ML模型的概述.
  • 引导ML模型的实施,用于蛋白质适应性预测和生成.
  • 突出ML在蛋白质工程中的成功应用.

主要方法:

  • 审查ML架构:变异自编码器,自回归模型,生成对抗网络和扩散模型.
  • 关于将ML模型应用于蛋白质序列数据的指导,用于适应性预测和生成.
  • 汇编了在蛋白质工程任务中展示ML的案例研究.

主要成果:

  • 详细解释ML模型架构和数学基础.
  • 在蛋白质序列数据上实施ML模型的实际策略.
  • 机器学习应用的例子包括帕拉托普预测,亚细胞定位和de novo蛋白质设计.

结论:

  • 机器学习,特别是生成性AI,为导航蛋白质健身景观提供了强大的工具.
  • 为ML驱动的蛋白质工程提供了结构化的指导和强大的框架.
  • 这项工作为ML在推动蛋白质工程中的未来提供了前性观点.