Welcome to ECDeePred

Prediction by Deep Learning of the Enzymatic Functions of Protein Sequences Based on the EC Nomenclature

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What Do You Need?

Problem Definition

Nomenclature Committee of the International Union of Biochemistry classifies enzymes using Enzyme Commission (EC) numbers according to the reactions they catalyse. EC number is a machine-readable, four digit numerical representation, creating a functional hierarchy.

Prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics:

  • High costs

    Trying to automate wet-lab based procedures causes high costs.

  • Time-consuming nature of wet-lab based procedures

    Determining the EC number using wet-lab procedures often takes a long time.

What We Offer?

Solution

ECDeePred will allow its users to detect the EC numbers of their enzymes using neural networks and SVM. Thanks to ECDeePred, you can both access the most up-to-date protein information and discover the structure of the hierarchical enzyme tree.

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Login and Save Important Sequences

You can keep the sequence and EC number information that you need to use repeatedly in your research by logging into ECDeePred and access them instantly.

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Access Up-to-Date UniProt Data Instantly

Thanks to our reliable database, which is regularly updated depending on the UniProt database, you can easily access proteins with previously determined EC numbers with peace of mind.

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Detect and Classify Unregistered Enzymes

If you come across a protein in your research that is not in the UniProt database, you can get the first idea about the enzymatic characteristic thanks to our accurate and high-performance prediction models.

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Explore and Interact with the Enzyme Tree

You can see the results from the database or prediction model on the enzyme tree, and you may get lost while exploring different branches thanks to the interactive user interface.

Statistics
Level 0 Classification Accuracy85%
Level 1 Classification Accuracy75%
Level 2 Classification Accuracy90%
Level 3 Classification Accuracy90%

Proteins

Users

Poster

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Team

Our Team

Prof. Dr. Mehmet Volkan Atalay

Supervisor

Alperen Dalkıran

Supervisor

Berke Ateş Aytekin

Machine Learning Engineer

Cenk Gurbet

Database Administrator

Mehmet Yağız Gündüz

Front-End Developer

Melisa Nur Kart

Back-End Developer

Uygar Yaşar

Front-End Developer