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Therefore, a computational model is highly desired to accurately predict protein solubility from the amino acid sequence. Many methods have been developed, but they are mostly based on the one-dimensional embedding of amino acids that is limited to catch spatially structural information. In this study, we have developed a new structure-aware method\n                    <jats:italic>GraphSol<\/jats:italic>\n                    to predict protein solubility by attentive graph convolutional network (GCN), where the protein topology attribute graph was constructed through predicted contact maps only from the sequence.\n                    <jats:italic>GraphSol<\/jats:italic>\n                    was shown to substantially outperform other sequence-based methods. The model was proven to be stable by consistent\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$${\\text{R}}^{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mrow>\n                              <mml:mtext>R<\/mml:mtext>\n                            <\/mml:mrow>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    of 0.48 in both the cross-validation and independent test of the\n                    <jats:italic>eSOL<\/jats:italic>\n                    dataset. To our best knowledge, this is the first study to utilize the GCN for sequence-based protein solubility predictions. More importantly, this architecture could be easily extended to other protein prediction tasks requiring a raw protein sequence.\n                  <\/jats:p>","DOI":"10.1186\/s13321-021-00488-1","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T17:48:23Z","timestamp":1612806503000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7999-2070","authenticated-orcid":false,"given":"Jianwen","family":"Chen","sequence":"first","affiliation":[]},{"given":"Shuangjia","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Huiying","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"issue":"1","key":"488_CR1","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1186\/1471-2105-15-134","volume":"15","author":"N Habibi","year":"2014","unstructured":"Habibi N, Hashim SZM, Norouzi A, Samian MR (2014) A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli. 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