eSOL dataset

Jianwen Chen; Shuangjia Zheng; Huiying Zhao; Yuedong Yang

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eSOL dataset

JC Jianwen Chen

SZ Shuangjia Zheng

HZ Huiying Zhao

YY Yuedong Yang

This method is extracted from research article: J Cheminform, Feb 2021

Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map

DOI: 10.1186/s13321-021-00488-1

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To train our model, we employed the eSOL dataset from the previous study [34]. For completeness, we briefly describe the procedure to produce the dataset. The whole solubility database of ensemble E.coli proteins was downloaded from the eSOL website [33], where the solubility was defined as the ratio of the supernatant fraction to the total fraction in the physiochemical experiments named PURE [35]. The 4132 proteins were firstly mapped to the NCBI database by gene names, and 3,144 samples were returned. We further pruned out all sequences using a strict standard that had a sequence identity $\geq 25 %$ or E value $\leq 1 e - 6$ according to previous observation [36], and the final data included a total of 2737 protein sequences. From the dataset, 75% (2052 proteins) were randomly selected as the training set, and the remaining 25% (685 proteins) were used as the independent test.

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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