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Enzymes

Enzymes

Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Introduction to Enzymes

Introduction to Enzymes

The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that bind the substrates and convert them into products. Many enzymes also...

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Restriction enzymes are bacterial enzymes used to cut DNA in a sequence-specific manner. To cleave DNA, they bind to specific palindromic sequences called restriction sites. Such palindromic DNA sequences or inverted repeats are commonly found in regions of functional significance, such as the origin of replication, gene operator sites, and regions containing transcription termination signals.
The host bacteria protect their own genomic DNA from these enzymes by methylating these sites. Some...

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Related Experiment Video

Updated: Jul 17, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Distinguishing enzyme structures from non-enzymes without alignments.

Paul D Dobson¹, Andrew J Doig

¹Department of Biomolecular Sciences, UMIST, P.O. Box 88, Manchester M60 1QD, UK.

Journal of Molecular Biology

|July 10, 2003

Summary

Predicting protein function from structure is crucial. This study introduces a novel method using structural features and machine learning to classify proteins as enzymes or non-enzymes, achieving 80% accuracy without sequence alignment.

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Last Updated: Jul 17, 2026

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Structural biology
Computational biology
Biochemistry

Background:

Protein function prediction is vital due to the rapid increase in protein structure data.
Current methods often fail for proteins lacking similarity to known functional annotations.
Predicting function directly from structure offers a complementary approach.

Purpose of the Study:

To develop a method for predicting protein function (enzymatic vs. non-enzymatic) directly from structural features.
To assess the accuracy of machine learning models trained on structural descriptors.
To identify key structural features that distinguish between enzyme and non-enzyme classes.

Main Methods:

Utilized a non-redundant set of 1178 high-resolution protein structures from the Protein Data Bank.
Described proteins using simple structural features (secondary structure, amino acid propensities, surface properties, ligands).
Employed the support vector machine (SVM) algorithm for classification and feature selection.

Main Results:

Achieved 77% accuracy in predicting protein function using 52 structural features.
An optimized model with 36 features reached 80% prediction accuracy.
Identified secondary structure content, amino acid frequencies, disulfide bonds, and cleft size as key predictive features.

Conclusions:

Protein function (enzymatic or not) can be predicted from structure alone, independent of sequence or structural similarity.
The developed method provides an accurate and efficient approach for functional annotation of proteins.
This structure-based method complements existing sequence-based approaches and is applicable to novel protein structures.