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 degree such that abundant sequences to construct an MSA of a target protein are readily obtainable. Nevertheless, many cases present different ends of the number...
Paper Details
Title
DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment
Published Date
Jan 9, 2020
Journal
Volume
21
Issue
1
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