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相关概念视频

Induced-fit Model01:13

Induced-fit Model

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 characteristics of...
Enzyme Kinetics01:19

Enzyme Kinetics

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...
Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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...
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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.
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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 a mild...
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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 a mild...

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The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis
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CatPred:一个全面的深度学习框架 in vitro酶动力学参数.

Veda Sheersh Boorla1,2, Costas D Maranas3,4

  • 1Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.

Nature communications
|February 28, 2025
PubMed
概括

CatPred是一个深度学习工具,可以预测酶动力学参数,如营业额数 (kcat) 和迈凯利斯常数 (Km). 它提供了准确的预测和不确定性估计,改进了传统的实验分析.

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 酶动力学 酶动力学

背景情况:

  • 酶活性估计传统上依赖于昂贵和耗时的实验分析.
  • 预测动力参数,如周转数 (kcat),迈凯利斯常数 (Km) 和抑制常数 (Ki) 对于酶表征至关重要.
  • 现有的计算方法面临着数据标准化,分布外概括和不确定性量化方面的挑战.

研究的目的:

  • 开发CatPred,这是一个深度学习框架,用于准确地在体外预测酶动力学参数 (kcat,Km,Ki).
  • 解决当前预测方法的局限性,包括数据稀缺性,不同酶序列的性能和模型不确定性.
  • 提供可靠的不确定性估计与动力参数预测一起.

主要方法:

  • 利用深度学习架构和多样化的特征表示,包括预训练的蛋白质语言模型和3D结构特征.
  • 开发并整合了kcat (~23k),Km (~41k) 和Ki (~12k) 数据点的广泛基准数据集.
  • 对不同于训练数据的酶序列的评估模型性能,以评估概括能力.

主要成果:

  • CatPred能够准确地预测酶动力学参数,并与此相关的查询特定不确定性估计.
  • 较低的预测方差与更高的预测准确度相关,表明可靠的不确定性量化.
  • 预训练的蛋白质语言模型功能显著提高了分布外酶序列的性能.
  • 该框架显示了与现有方法相比具有竞争力的性能.

结论:

  • CatPred提供了一种强大而有效的深度学习方法,用于预测关键的酶动力学参数.
  • 该框架能够量化预测不确定性的能力提高了其在实际应用中的可靠性.
  • CatPred提供了有价值的基准数据集和酶研究的强大工具,减少了对实验方法的依赖.