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Catalytically Perfect Enzymes

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.

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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.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....

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Updated: May 29, 2025

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and

Aya Fujiwara¹, Sunao Nakanowatari¹, Yohei Cho¹

¹Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan.

Science and Technology of Advanced Materials

|February 5, 2025

Summary

This summary is machine-generated.

This study introduces a new framework combining high-throughput experimentation and machine learning for solid catalyst development. It enables efficient discovery of new catalyst designs and knowledge transfer across different materials.

Keywords:

Catalyst informatics descriptor high-throughput experimentation machine learning oxidative coupling of methane

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Area of Science:

Materials Science
Chemical Engineering
Catalysis

Background:

Traditional solid catalyst development relies on inefficient trial-and-error methods.
Lack of knowledge transfer limits insights across diverse catalyst families.

Purpose of the Study:

To develop an integrated framework for comprehensive catalyst knowledge acquisition.
To overcome fragmentation in catalyst design by combining high-throughput experimentation (HTE) and automatic feature engineering (AFE) with active learning.

Main Methods:

Demonstrated framework for oxidative coupling of methane (OCM) using various supported catalysts (BaO, CaO, La2O3, TiO2, ZrO2).
Employed active learning until machine learning model robustness was achieved, testing 333 new catalysts.
Developed a method for knowledge transfer between catalyst supports.

Main Results:

Extracted catalyst design rules, identifying synergistic combinations in high-performing catalysts.
Demonstrated successful knowledge transfer, where refined features on one support improved predictions on others.
Achieved robust machine learning models for catalyst performance prediction.

Conclusions:

The integrated framework advances catalyst design understanding and promotes reliable machine learning applications.
This approach facilitates efficient discovery and optimization of solid catalysts.
Enables broader application of valuable insights across different catalyst families.