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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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DEEPre: sequence-based enzyme EC number prediction by deep learning.

Yu Li¹, Sheng Wang¹, Ramzan Umarov¹

¹Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), Computer, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Bioinformatics (Oxford, England)

|October 26, 2017

Summary

This summary is machine-generated.

We developed DEEPre, a computational approach for enzyme function prediction. This method accurately predicts Enzyme Commission numbers from raw enzyme sequences, improving upon existing state-of-the-art techniques.

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Area of Science:

Bioinformatics
Computational Biology
Enzymology

Background:

Experimental enzyme function annotation is time-consuming and expensive.
Accurate computational enzyme function prediction is crucial for various applications.
Existing methods often rely on manually crafted features.

Purpose of the Study:

To develop an advanced computational approach for predicting enzyme function.
To improve the accuracy and efficiency of enzyme function prediction.
To provide a robust tool for classifying enzymes based on their sequences.

Main Methods:

Proposed DEEPre, an end-to-end feature selection and classification model.
Utilized raw enzyme sequence encoding as input, extracting convolutional and sequential features.
Implemented an automatic feature dimensionality uniformization method.

Main Results:

DEEPre demonstrated improved prediction performance over state-of-the-art methods on large-scale datasets.
The DEEPre server outperformed five other servers in predicting the main class of enzymes on a low-homology dataset.
Case studies confirmed DEEPre's capability to distinguish functional differences in enzyme isoforms.

Conclusions:

DEEPre offers a powerful and accurate method for computational enzyme function prediction.
The approach effectively utilizes raw sequence data for improved performance.
DEEPre provides a valuable resource for researchers in enzymology and related fields.