Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.

ABSTRACT: High-quality and high-throughput prediction of enzyme commission (EC) numbers is essential for accurate understanding of enzyme functions, which have many implications in pathologies and industrial biotechnology. Several EC number prediction tools are currently available, but their prediction performance needs to be further improved to precisely and efficiently process an ever-increasing volume of protein sequence data. Here, we report DeepEC, a deep learning-based computational framework that predicts EC numbers for protein sequences with high precision and in a high-throughput manner. DeepEC takes a protein sequence as input and predicts EC numbers as output. DeepEC uses 3 convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homolog

SUBMITTER: Ryu JY 

PROVIDER: S-EPMC6628820 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature