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Related Concept Videos

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: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.

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
Summary
This summary is machine-generated.

CatPred is a deep learning tool that predicts enzyme kinetic parameters like turnover numbers (kcat) and Michaelis constants (Km). It offers accurate predictions with uncertainty estimates, improving upon traditional experimental assays.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Enzyme Kinetics

Background:

  • Enzyme activity estimation traditionally relies on costly and time-consuming experimental assays.
  • Predicting kinetic parameters such as turnover numbers (kcat), Michaelis constants (Km), and inhibition constants (Ki) is crucial for enzyme characterization.
  • Existing computational methods face challenges with data standardization, out-of-distribution generalization, and uncertainty quantification.

Purpose of the Study:

  • To develop CatPred, a deep learning framework for accurate in vitro prediction of enzyme kinetic parameters (kcat, Km, Ki).
  • To address limitations in current prediction methods, including data scarcity, performance on dissimilar enzyme sequences, and model uncertainty.
  • To provide reliable uncertainty estimates alongside kinetic parameter predictions.

Main Methods:

  • Utilized deep learning architectures and diverse feature representations, including pretrained protein language models and 3D structural features.
  • Developed and incorporated extensive benchmark datasets for kcat (~23k), Km (~41k), and Ki (~12k) data points.
  • Evaluated model performance on enzyme sequences dissimilar to training data to assess generalization capabilities.

Main Results:

  • CatPred achieves accurate predictions of enzyme kinetic parameters with associated query-specific uncertainty estimates.
  • Lower predicted variances correlate with higher prediction accuracy, indicating reliable uncertainty quantification.
  • Pretrained protein language model features significantly improve performance on out-of-distribution enzyme sequences.
  • The framework demonstrates competitive performance compared to existing methods.

Conclusions:

  • CatPred offers a robust and efficient deep learning approach for predicting crucial enzyme kinetic parameters.
  • The framework's ability to quantify prediction uncertainty enhances its reliability for practical applications.
  • CatPred provides valuable benchmark datasets and a powerful tool for enzyme research, reducing reliance on experimental methods.