{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T09:45:15Z","timestamp":1781516715579,"version":"3.54.1"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972416"],"award-info":[{"award-number":["61972416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022LZH009"],"award-info":[{"award-number":["ZR2022LZH009"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2021YFA1000103-3"],"award-info":[{"award-number":["2021YFA1000103-3"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Protein\u2013protein interactions (PPIs) play a critical role in cellular functions, which are essential for maintaining the proper physiological state of organisms. Therefore, identifying PPI sites with high accuracy is crucial. Recently, graph neural networks (GNNs) have achieved significant progress in predicting PPI sites, but there is still potential for further enhancement. In this study, we introduce GTE-PPIS, an innovative PPI site predictor that utilizes two components: a graph transformer and an equivariant GNN, to collaboratively extract features. These extracted features are subsequently processed through a multilayer perceptron to generate the final predictions. Our experimental results show that GTE-PPIS consistently outperforms existing methods on multiple evaluation metrics across benchmark datasets, strongly supporting the effectiveness of our approach.<\/jats:p>","DOI":"10.1093\/bib\/bbaf290","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T18:07:11Z","timestamp":1750961231000},"source":"Crossref","is-referenced-by-count":3,"title":["GTE-PPIS: a protein\u2013protein interaction site predictor based on graph transformer and equivariant graph neural network"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2741-433X","authenticated-orcid":false,"given":"Xun","family":"Wang","sequence":"first","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7665-2541","authenticated-orcid":false,"given":"Tongyu","family":"Han","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8755-9083","authenticated-orcid":false,"given":"Runqiu","family":"Feng","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4257-5690","authenticated-orcid":false,"given":"Zhijun","family":"Xia","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6610-8962","authenticated-orcid":false,"given":"Hanyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6028-6610","authenticated-orcid":false,"given":"Wenqian","family":"Yu","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanhuan","family":"Dai","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2183-2586","authenticated-orcid":false,"given":"Haonan","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1440-0790","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China) , Qingdao 266580, Shandong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"2025062614070957900_ref1","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1002\/pmic.200700131","article-title":"Methods for the detection and analysis of protein\u2013protein interactions","volume":"7","author":"Bergg\u00e5rd","year":"2007","journal-title":"Proteomics"},{"key":"2025062614070957900_ref2","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1093\/bib\/bbx022","article-title":"Review and comparative assessment of sequence-based predictors of protein-binding residues","volume":"19","author":"Zhang","year":"2018","journal-title":"Brief Bioinform"},{"key":"2025062614070957900_ref3","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1093\/bfgp\/els036","article-title":"Protein\u2013protein interaction networks: unraveling the wiring of molecular machines within the cell","volume":"11","author":"Rivas","year":"2012","journal-title":"Brief Funct Genomics"},{"key":"2025062614070957900_ref4","doi-asserted-by":"crossref","first-page":"e49029","DOI":"10.1371\/journal.pone.0049029","article-title":"Wiki-pi: a web-server of annotated human protein\u2013protein interactions to aid in discovery of protein function","volume":"7","author":"Orii","year":"2012","journal-title":"PloS One"},{"key":"2025062614070957900_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gm441","article-title":"Protein\u2013protein interaction networks: probing disease mechanisms using model systems","volume":"5","author":"Kuzmanov","year":"2013","journal-title":"Genome Med"},{"key":"2025062614070957900_ref6","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1038\/mt.2015.214","article-title":"Modulation of protein\u2013protein interactions for the development of novel therapeutics","volume":"24","author":"Petta","year":"2016","journal-title":"Mol Ther"},{"key":"2025062614070957900_ref7","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1371\/journal.pcbi.0030042","article-title":"Deciphering protein\u2013protein interactions. Part I. experimental techniques and databases","volume":"3","author":"Shoemaker","year":"2007","journal-title":"PLoS Comput Biol"},{"key":"2025062614070957900_ref8","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/TCBB.2019.2953908","article-title":"Imbalance data processing strategy for protein interaction sites prediction","volume":"18","author":"Wang","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025062614070957900_ref9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.neucom.2016.02.022","article-title":"Protein\u2013protein interaction sites prediction by ensembling SVM and sample-weighted random forests","volume":"193","author":"Wei","year":"2016","journal-title":"Neurocomputing"},{"key":"2025062614070957900_ref10","doi-asserted-by":"crossref","first-page":"4794","DOI":"10.1093\/bioinformatics\/btz428","article-title":"SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions","volume":"35","author":"Hou","year":"2019","journal-title":"Bioinformatics"},{"key":"2025062614070957900_ref11","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1093\/bioinformatics\/bty647","article-title":"BIPSPI: a method for the prediction of partner-specific protein\u2013protein interfaces","volume":"35","author":"Sanchez-Garcia","year":"2019","journal-title":"Bioinformatics"},{"key":"2025062614070957900_ref12","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1002\/prot.21248","article-title":"Prediction-based fingerprints of protein\u2013protein interactions","volume":"66","author":"Porollo","year":"2007","journal-title":"Proteins"},{"key":"2025062614070957900_ref13","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1016\/j.jmb.2020.02.026","article-title":"ProNA2020 predicts protein\u2013DNA, protein\u2013RNA, and protein\u2013protein binding proteins and residues from sequence","volume":"432","author":"Qiu","year":"2020","journal-title":"J Mol Biol"},{"key":"2025062614070957900_ref14","doi-asserted-by":"crossref","first-page":"i343","DOI":"10.1093\/bioinformatics\/btz324","article-title":"SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences","volume":"35","author":"Zhang","year":"2019","journal-title":"Bioinformatics"},{"key":"2025062614070957900_ref15","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1093\/bioinformatics\/btq302","article-title":"Applying the Na\u00efve Bayes classifier with kernel density estimation to the prediction of protein\u2013protein interaction sites","volume":"26","author":"Murakami","year":"2010","journal-title":"Bioinformatics"},{"key":"2025062614070957900_ref16","doi-asserted-by":"crossref","first-page":"9848","DOI":"10.1038\/s41598-019-46369-4","article-title":"Predicting protein\u2013protein interactions from matrix-based protein sequence using convolution neural network and feature-selective rotation forest","volume":"9","author":"Wang","year":"2019","journal-title":"Sci Rep"},{"key":"2025062614070957900_ref17","doi-asserted-by":"crossref","first-page":"106533","DOI":"10.1016\/j.compbiomed.2022.106533","article-title":"ATFE-Net: axial transformer and feature enhancement-based CNN for ultrasound breast mass segmentation","volume":"153","author":"Ma","year":"2023","journal-title":"Comput Biol Med"},{"key":"2025062614070957900_ref18","doi-asserted-by":"crossref","first-page":"bbab228","DOI":"10.1093\/bib\/bbab228","article-title":"LSTM-PHV: prediction of human-virus protein\u2013protein interactions by LSTM with word2vec","volume":"22","author":"Tsukiyama","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025062614070957900_ref19","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1016\/j.csbj.2022.04.029","article-title":"DeepMC-iNABP: deep learning for multiclass identification and classification of nucleic acid-binding proteins","volume":"20","author":"Cui","year":"2022","journal-title":"Comput Struct Biotechnol J"},{"key":"2025062614070957900_ref20","doi-asserted-by":"crossref","first-page":"bbac480","DOI":"10.1093\/bib\/bbac480","article-title":"HN-PPISP: a hybrid network based on MLP-mixer for protein\u2013protein interaction site prediction","volume":"24","author":"Kang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025062614070957900_ref21","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","article-title":"Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network","volume":"38","author":"Yuan","year":"2022","journal-title":"Bioinformatics"},{"key":"2025062614070957900_ref22","doi-asserted-by":"crossref","first-page":"bbad122","DOI":"10.1093\/bib\/bbad122","article-title":"AGAT-PPIS: a novel protein\u2013protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping","volume":"24","author":"Zhou","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025062614070957900_ref23","doi-asserted-by":"crossref","DOI":"10.1109\/TCBB.2024.3410350","article-title":"RGCNPPIS: a residual graph convolutional network for protein\u2013protein interaction site prediction","volume":"21","author":"Zhong","year":"2024","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025062614070957900_ref24","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jtbi.2014.01.028","article-title":"Sequence-based prediction of protein\u2013protein interaction sites with l1-logreg classifier","volume":"348","author":"Dhole","year":"2014","journal-title":"J Theor Biol"},{"key":"2025062614070957900_ref25","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J Mol Biol"},{"key":"2025062614070957900_ref26","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2025062614070957900_ref27","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/nmeth.1818","article-title":"HHblits: lightning-fast iterative protein sequence searching by HMM\u2013HMM alignment","volume":"9","author":"Remmert","year":"2012","journal-title":"Nat Methods"},{"key":"2025062614070957900_ref28","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1002\/bip.360221211","article-title":"Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features","volume":"22","author":"Kabsch","year":"1983","journal-title":"Biopolymers"},{"key":"2025062614070957900_ref29","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1038\/s41592-022-01490-7","article-title":"ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction","volume":"19","author":"Tubiana","year":"2022","journal-title":"Nat Methods"},{"key":"2025062614070957900_ref30","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1093\/bioinformatics\/btaa750","article-title":"DELPHI: accurate deep ensemble model for protein interaction sites prediction","volume":"37","author":"Yiwei Li","year":"2021","journal-title":"Bioinformatics"},{"key":"2025062614070957900_ref31","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1093\/bioinformatics\/btz699","article-title":"Protein\u2013protein interaction site prediction through combining local and global features with deep neural networks","volume":"36","author":"Zeng","year":"2020","journal-title":"Bioinformatics"},{"key":"2025062614070957900_ref32","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","article-title":"Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning","volume":"17","author":"Gainza","year":"2020","journal-title":"Nat Methods"},{"key":"2025062614070957900_ref33","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/s42003-023-04462-5","article-title":"Learning the protein language of proteome-wide protein\u2013protein binding sites via explainable ensemble deep learning","volume":"6","author":"Hou","year":"2023","journal-title":"Commun Biol"},{"key":"2025062614070957900_ref34","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2025062614070957900_ref35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J Big Data"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf290\/63502295\/bbaf290.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf290\/63502295\/bbaf290.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T18:07:17Z","timestamp":1750961237000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf290\/8164226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf290","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,5]]},"published":{"date-parts":[[2025,5,1]]},"article-number":"bbaf290"}}