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Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https:\/\/github.com\/LiDlab\/HyperGraphComplex.<\/jats:p>","DOI":"10.1093\/bib\/bbae274","type":"journal-article","created":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T01:49:37Z","timestamp":1717897777000},"source":"Crossref","is-referenced-by-count":8,"title":["Integration of protein sequence and protein\u2013protein interaction data by hypergraph learning to identify novel protein complexes"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7626-7399","authenticated-orcid":false,"given":"Simin","family":"Xia","sequence":"first","affiliation":[{"name":"School of Basic Medical Sciences, Anhui Medical University , 81 Meishan Road, Shushan District, Hefei 230032 , China"},{"name":"State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics , 38 Life Science Park, Changping District, Beijing 102206 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9580-3292","authenticated-orcid":false,"given":"Dianke","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics , 38 Life Science Park, Changping District, Beijing 102206 , China"},{"name":"State Key Laboratory of Farm Animal Biotech Breeding , College of Biological Sciences, , 2 Yuanmingyuan West Road, Haidian District, Beijing 100193 , China"},{"name":"China Agricultural University , College of Biological Sciences, , 2 Yuanmingyuan West Road, Haidian District, Beijing 100193 , China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8415-4627","authenticated-orcid":false,"given":"Xinru","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics , 38 Life Science Park, Changping District, Beijing 102206 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3371-2392","authenticated-orcid":false,"given":"Zhongyang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics , 38 Life Science Park, Changping District, Beijing 102206 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5013-2269","authenticated-orcid":false,"given":"Huaqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Anhui Medical University , 81 Meishan Road, Shushan District, Hefei 230032 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2866-4904","authenticated-orcid":false,"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics , 38 Life Science Park, Changping District, Beijing 102206 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8680-0468","authenticated-orcid":false,"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Anhui Medical University , 81 Meishan Road, Shushan District, Hefei 230032 , China"},{"name":"State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics , 38 Life Science Park, Changping District, Beijing 102206 , China"}]}],"member":"286","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"2024060901492802300_ref1","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1038\/s41392-020-00315-3","article-title":"Recent advances in the development of protein\u2013protein interactions modulators: mechanisms and clinical 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