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

Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

3.9K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
3.9K
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

6.8K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
6.8K
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
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
Proteins: From Genes to Degradation02:11

Proteins: From Genes to Degradation

12.1K
Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
Transcription is the synthesis of RNA...
12.1K
Gene Families01:57

Gene Families

8.8K
Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
8.8K

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

Amidase-Catalyzed Desorption of CO<sub>2</sub> Captured in Aqueous Monoethanolamine (MEA) Solutions.

Angewandte Chemie (International ed. in English)·2026
Same author

Engineering Biosensors to Enhance Monoterpene Indole Alkaloid Production in Yeast.

bioRxiv : the preprint server for biology·2026
Same author

EnzymeMiner 2.0: advancing automated enzyme discovery with expansive sequence mining and smart property analysis.

Nucleic acids research·2026
Same author

Taurine inhibits apolipoprotein E4 aggregation.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026
Same author

Tryptophanase Mining and Characterization toward the Biological Production of Indole Derivatives.

ACS omega·2026
Same author

Enzymatic Glucosylation Enhances the Solubility of Niclosamide but Abrogates Its Therapeutic Efficacy.

ACS omega·2026

相关实验视频

Updated: Jun 11, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

17.5K

蛋白质表示:在生物催化剂中为机器学习编码生物信息.

David Harding-Larsen1, Jonathan Funk1, Niklas Gesmar Madsen1

  • 1The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark.

Biotechnology advances
|October 4, 2024
PubMed
概括

机器学习模型可以预测和设计工业用途的酶. 本综述详细介绍了如何将复杂的蛋白质信息转换为数字格式 (蛋白质表示) 以实现准确的机器学习.

关键词:
生物催化剂是一种生物催化剂.酶工程是什么?酶工程是什么?机器学习是机器学习.预测模型的预测模型.蛋白质动力学 蛋白质动力学蛋白质表示的表现 蛋白质表示的表现代表性的学习学习.

更多相关视频

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.7K
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

3.3K

相关实验视频

Last Updated: Jun 11, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

17.5K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.7K
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

3.3K

科学领域:

  • 生物催化和酶工程 生物催化和酶工程
  • 计算生物学和机器学习

背景情况:

  • 酶为传统化学提供了可持续的替代品,但需要工业应用的工程.
  • 机器学习 (ML) 可以通过创建预测模型来加速酶工程.
  • 准确的ML模型取决于有效地将生物数据转换为数字蛋白质表示.

研究的目的:

  • 审查为ML编码蛋白质信息成数值表示的关键方法.
  • 探索初级序列,3D结构和动态表示的要求和诱导偏差.
  • 为了指导选择最佳的蛋白质表示的ML在生物催化剂.

主要方法:

  • 检查已建立和新兴的蛋白质表示策略.
  • 将表示分为固定类型 (基于规则) 和学习类型 (由神经网络衍生) 的分类.
  • 介绍生物催化剂的组合蛋白质基质表示.

主要成果:

  • 对初级序列,3D结构和动态的编码方法的分析.
  • 关于固定和学习表示类别的建议.
  • 确定模型设置 (数据集大小,架构) 和目标 (属性,突变预测,可解释性) 作为关键选择因素.

结论:

  • 在酶工程中,有效的蛋白质表示对于准确的ML模型至关重要.
  • 代表的选择取决于特定的ML模型参数和研究目标.
  • 本综述为在生物催化剂研究中选择适当的蛋白质表示提供了一个框架.