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. 2018 Nov 1;8:16189. doi: 10.1038/s41598-018-34533-1

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Separation by structural properties in the latent space when 2 latent dimensions are used in the model. The axes are the 2 latent dimensions and each point is the encoded representation in the 2 dimensions of one input sequence. Clusters generally correspond to the homologues collected for each sequence. (A) Each sequence is coloured by CATH class³² according to the colours shown. (B) Sequences for one CATH architecture, ‘mainly beta single sheet’ (CATH ID 2.20), are highlighted in red.