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Related Concept Videos

Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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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...
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Enzyme Kinetics01:19

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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...
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Turnover Number and Catalytic Efficiency01:19

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
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Catalytically Perfect Enzymes01:07

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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...
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Induced-fit Model01:13

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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.
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Determination of Michaelis Constant and Maximum Elimination Rate01:20

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The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
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KinForm: kinetics-informed feature optimised representation models for enzyme kcat and KM prediction.

Saleh Alwer1,2,3, Ronan M T Fleming4,5,6

  • 1Digital Metabolic Twin Centre, University of Galway, Galway, Ireland.

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Summary

KinForm enhances enzyme kinetic predictions by integrating diverse protein embeddings and advanced machine learning. This improves accuracy, especially for enzymes with low sequence similarity, advancing biochemical modeling.

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Area of Science:

  • Biochemistry and Bioinformatics
  • Computational Biology
  • Machine Learning in Biosciences

Background:

  • Enzyme kinetics (turnover number [kcat] and Michaelis constant [KM]) are crucial for biochemical modeling.
  • Existing methods for predicting enzyme kinetics rely on limited protein representations from single protein language models.
  • Experimental kinetic data is scarce and lacks diversity, hindering comprehensive modeling.

Purpose of the Study:

  • To develop KinForm, a machine learning framework for improved prediction and generalization of enzyme kinetic parameters.
  • To optimize protein feature representations beyond standard mean-pooled embeddings.
  • To enhance the accuracy and applicability of computational enzyme kinetics.

Main Methods:

  • KinForm integrates multiple residue-level embeddings (ESM-C, ESM2, ProtT5-XL-UniRef50) from selected transformer layers.
  • Weighted pooling based on per-residue binding-site probability is applied.
  • Principal Component Analysis (PCA) reduces dimensionality, and similarity-based oversampling addresses data imbalance.
  • A rigorous evaluation methodology excluding sequence overlap between protein folds is employed.

Main Results:

  • KinForm significantly outperforms baseline methods on benchmark datasets for enzyme kinetic parameter prediction.
  • Performance gains are most substantial in datasets with low sequence similarity between enzymes.
  • Key contributions include binding-site probability pooling, intermediate-layer selection, PCA, and oversampling strategies.
  • Evaluation excluding sequence overlap provides a more robust measure of generalization.

Conclusions:

  • KinForm offers a superior approach to predicting enzyme kinetic parameters, enhancing computational biochemistry.
  • The framework's ability to generalize, particularly for novel or divergent enzymes, is a significant advancement.
  • Standardizing evaluation by removing sequence overlap is recommended for future kinetic prediction model benchmarking.