{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:27:49Z","timestamp":1759336069788,"version":"build-2065373602"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022QF111"],"award-info":[{"award-number":["ZR2022QF111"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Foundation","award":["WFU2023BS47"],"award-info":[{"award-number":["WFU2023BS47"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Protein\u2013protein interactions (PPIs) are fundamental to biological processes, yet experimental determination of PPIs remains costly and labor-intensive. While computational methods have emerged as promising alternatives, sequence-based approaches face critical challenges: (1) effectively capturing long-range dependencies and critical biochemical patterns in variable-length sequences, and (2) balancing computational efficiency with sensitivity to subtle residue-level interactions. Here, we present Dual Protein Embedding-based Graph Model (DPEG), which leverages dynamic graph attention networks to enable robust sequence-driven PPI prediction. Unlike structure-dependent methods, DPEG operates solely on sequence data, bypassing the need for structural or domain annotations. Specifically, we employ ESM-2 to transform sequences into residue-level graphs, preserving evolutionary and physicochemical context. To address variable sequence lengths, we design a module that can represent protein sequences of arbitrary lengths as graph networks at the amino acid level. Further, a gated attention mechanism is introduced to adaptively refining residue representations. Finally, a dynamic attention mechanism prioritizes functionally critical motifs within the graph. Evaluated on four diverse PPI datasets spanning different species and interaction types, DPEG achieves state-of-the-art performance and demonstrates strong cross-dataset generalizability. By integrating deep sequence semantics with graph-based interaction modeling, DPEG advances sequence-only PPI prediction, offering a scalable and biologically plausible framework for proteome-wide studies.<\/jats:p>","DOI":"10.1093\/bib\/bbaf517","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T12:20:22Z","timestamp":1759321222000},"source":"Crossref","is-referenced-by-count":0,"title":["Dual-protein embedding-based graph model with dynamic attention for interaction prediction"],"prefix":"10.1093","volume":"26","author":[{"given":"Shunpeng","family":"Pang","sequence":"first","affiliation":[{"name":"School of Computer Engineering , WeiFang University, 5147 East Dongfeng Road, Kuiwen District, Weifang, Shandong 261061,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingjian","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology , 777 East Jialingjiang Road, Huangdao District, Qingdao, Shandong 266525 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shugang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , 1299 Sansha Road, Huangdao District, Qingdao, Shandong 266100 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum , 66 West Changjiang Road, Huangdao District, Qingdao, Shandong 266580 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University , 308 Ningxia Road, Laoshan District, Qingdao, Shandong 266071 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, WeiFang University , 5147 East Dongfeng Road, Kuiwen District, Weifang, Shandong 261061 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology , 777 East Jialingjiang Road, Huangdao District, Qingdao, Shandong 266525 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Guo","sequence":"additional","affiliation":[{"name":"Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang Key Laboratory of Grapevine Improvement and Utilization , 699 Binhu Road, Xiashan District, Weifang, Shandong 261325 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