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

Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

31.8K
Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
31.8K
Enzyme Kinetics01:19

Enzyme Kinetics

103.5K
Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
Scientists typically study enzyme kinetics with a fixed amount of enzyme in the controlled environment of a test tube. When more reactant, or substrate, is...
103.5K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.8K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.8K
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

10.4K
For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
10.4K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
223
Induced-fit Model01:13

Induced-fit Model

88.5K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
88.5K

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

A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent <i>k</i><sub>cat</sub>/<i>K</i><sub>m</sub> Prediction in β-Glucosidases.

ACS synthetic biology·2025
Same author

The spatial separation of basic amino acids is similar in RHAMM and hyaluronan binding peptide P15-1 despite different sequences and conformations.

Proteoglycan research·2024
Same author

Hotspot Wizard-informed engineering of a hyperthermophilic β-glucosidase for enhanced enzyme activity at low temperatures.

Biotechnology and bioengineering·2024
Same author

Occurrence and risks of microplastics in the ecosystems of the Middle East and North Africa (MENA).

Environmental science and pollution research international·2023
Same author

Author Correction: CO<sub>2</sub> doping of organic interlayers for perovskite solar cells.

Nature·2021
Same author

CO<sub>2</sub> doping of organic interlayers for perovskite solar cells.

Nature·2021

相关实验视频

Updated: Jan 8, 2026

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.4K

机器学习模型的进步用于预测酶动力学参数.

Ali Malli1, Denys Vasyutyn1, Jin Ryoun Kim1

  • 1Department of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.

Journal of chemical information and modeling
|December 17, 2025
PubMed
概括

机器学习模型现在可以预测酶动力学参数,这对酶工程和合成生物学至关重要. 全球和本地模型的进步提供了强大的工具,尽管存在数据稀缺的挑战.

科学领域:

  • 生物化学 生化学
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 酶动力学参数 (kcat,Km,kcat/Km,Ki) 对酶工程,代谢建模和合成生物学至关重要.
  • 实验性确定是昂贵和耗时的;传统的计算方法是不够的.
  • 机器学习 (ML) 模型为这些参数的in silico预测提供了一个有希望的替代方案.

研究的目的:

  • 审查基于ML的酶动力学参数预测的最新进展.
  • 要突出当前ML模型的应用和局限性.
  • 概述改善ML预测的未来机会.

主要方法:

  • 全球ML模型的审查,这些模型在不同类型的酶上受过训练.
  • 针对特定酶家族量身定制的本地ML模型的审查.
  • 讨论ML模型在突变效应预测,酶挖矿和代谢建模中的应用.

主要成果:

  • 全球和本地ML模型已经在预测酶动力学参数方面取得了成功.
  • 这些模型有助于各种应用,包括蛋白质工程和系统生物学.
  • 数据稀缺是主要的限制,影响模型性能和范围.
关键词:
人工智能的人工智能是人工智能.催化活动的催化活动.酶工程是指酶工程的工程.酵素进化 酶进化的过程酶开采采矿的方法动力参数 动力参数机器学习是机器学习.

更多相关视频

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

11.2K
Defining Substrate Specificities for Lipase and Phospholipase Candidates
08:59

Defining Substrate Specificities for Lipase and Phospholipase Candidates

Published on: November 23, 2016

15.5K

相关实验视频

Last Updated: Jan 8, 2026

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.4K
Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

11.2K
Defining Substrate Specificities for Lipase and Phospholipase Candidates
08:59

Defining Substrate Specificities for Lipase and Phospholipase Candidates

Published on: November 23, 2016

15.5K

结论:

  • 机器学习提供了一种强大的方法来预测酶动力学参数,加速了酶工程和合成生物学方面的研究.
  • 通过高通量数据生成和半监督学习克服数据稀缺性是未来进步的关键.
  • 精确的基于ML的预测可以为所需的功能提供更好的蛋白质序列注释.