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Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092
Published on: October 2, 2017
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CAZyme3D: A Database of 3D Structures for Carbohydrate-active Enzymes
N R Siva Shanmugam1, Yanbin Yin1
1Nebraska Food for Health Center, Department of Food Science and Technology, University of Nebraska - Lincoln, Lincoln, NE 68588, USA.
Journal of Molecular Biology
|February 17, 2025
Summary
CAZyme3D is a new database of 3D protein structures for Carbohydrate Active Enzymes (CAZymes). It uses structural similarity to help discover novel CAZymes, improving research in health, nutrition, and bioenergy.
Area of Science:
- Biochemistry and Structural Biology
- Bioinformatics and Computational Biology
- Enzymology
Background:
- Carbohydrate Active Enzymes (CAZymes) are crucial for understanding complex carbohydrate roles in human health, nutrition, gut microbiome, bioenergy, plant disease, and carbon cycling.
- Current CAZyme identification relies on sequence similarity, limiting the discovery of distantly related enzymes.
- A gap exists in dedicated 3D structure databases for CAZymes, hindering structural homology-based analysis.
Purpose of the Study:
- To develop CAZyme3D, a comprehensive database of 3D structures for CAZymes, enabling structural similarity searches.
- To establish a hierarchical classification system for CAZymes based on structural similarity.
- To provide a tool for discovering novel CAZymes through structural comparison.
Main Methods:
- Generated a large dataset of 870,740 predicted 3D CAZyme structures using AlphaFold.
- Performed structural similarity-based clustering on 188,574 nonredundant CAZyme structures.
- Developed a hierarchical classification including existing CAZy levels and new subclass, structural cluster (SC) group, and SC levels.
- Implemented a web-based tool for structural similarity searches using query protein sequences or PDB structures.
Main Results:
- CAZyme3D provides a comprehensive repository of predicted CAZyme 3D structures.
- Structural clustering successfully organized CAZymes into subclasses based on shared structural folds.
- New classifications (SCs and SC groups) were defined, differentiating from sequence-based subfamilies.
- The platform allows users to perform structural similarity searches against the CAZyme3D database.
Conclusions:
- CAZyme3D addresses the need for a dedicated 3D structure database for CAZymes.
- The structural classification provides a novel framework for understanding CAZyme relationships.
- CAZyme3D serves as a valuable tool for the discovery of novel CAZymes and functional annotation through structural analysis.
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