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Introduction to Mechanisms of Enzyme Catalysis

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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 presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.

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Related Experiment Video

Updated: Jul 11, 2026

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

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Predicting and annotating catalytic residues: an information theoretic approach.

Beckett Sterner¹, Rohit Singh, Bonnie Berger

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|September 25, 2007

Summary

We developed a new computational method to identify and describe protein catalytic residues using only their amino acid sequences. This approach accurately predicts residue locations and their biochemical functions, aiding enzyme research.

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Modeling an Enzyme Active Site using Molecular Visualization Freeware

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Published on: December 25, 2021

Area of Science:

Computational biology
Enzymology
Bioinformatics

Background:

Identifying a protein's catalytic residues is crucial for understanding enzyme function.
Predicting and annotating these residues is challenging, especially without structural information.

Purpose of the Study:

To introduce a novel computational method for predicting and annotating protein catalytic residues based solely on sequence information.
To enable the determination of both residue location and biochemical role in enzymatic reactions.

Main Methods:

Developed a sequence-based approach utilizing conservation patterns and amino acid composition near known catalytic residues.
Created sequence profiles for catalytic residues and employed Kullback-Leibler (KL) distance to measure biochemical variations.
Applied the method to the glycohydrolase enzyme class.

Main Results:

Achieved 80% sensitivity and 99.4% specificity in predicting catalytic residue locations via cross-validation.
Successfully annotated the biochemical role of 80% of catalytic residues in a separate cross-validation.
Demonstrated favorable comparison with existing methods and highlighted broader applicability due to sequence-based nature.

Conclusions:

The sequence-based method effectively predicts and annotates catalytic residues, offering a valuable tool for enzyme research.
This approach overcomes limitations of structure-dependent methods and can be integrated with existing techniques.