DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment

DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment

Abstract Background Recently developed methods of protein contact prediction, a crucially important step for protein structure prediction, depend heavily on deep neural networks (DNNs) and multiple sequence alignments (MSAs) of target proteins. Protein sequences are accumulating to an increasing deg...

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Bibliographic Details
Main Authors: Hiroyuki Fukuda, Kentaro Tomii
Format: Article
Language:English
Published: BMC 2020-01-01
Series:BMC Bioinformatics
Subjects:
Contact prediction
Convolutional neural network
Deep learning
Multiple sequence alignment
Protein
Secondary structure prediction
Online Access:https://doi.org/10.1186/s12859-019-3190-x

Internet

https://doi.org/10.1186/s12859-019-3190-x

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