{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T12:15:03Z","timestamp":1756901703600,"version":"3.44.0"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":56,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Medical Engineering Cross Fund of Shanghai Jiao Tong University","award":["YG2023ZD21"],"award-info":[{"award-number":["YG2023ZD21"]}]},{"DOI":"10.13039\/501100003399","name":"Shanghai Science and Technology Commission","doi-asserted-by":"crossref","award":["YG2023LC03","23DZ2290600","23XD1401900","21ZR1436300","24JS2810200"],"award-info":[{"award-number":["YG2023LC03","23DZ2290600","23XD1401900","21ZR1436300","24JS2810200"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32171242","12171318"],"award-info":[{"award-number":["32171242","12171318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF1205102","2025YFA0921001"],"award-info":[{"award-number":["2023YFF1205102","2025YFA0921001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SJTU Kunpeng & Ascend Center of Excellence, the Center for HPC at Shanghai Jiao Tong University"},{"name":"Shanghai Municipal Science and Technology Major Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Proteins typically interact with multiple partners to regulate biological processes, and peptide drugs targeting multiple receptors have shown strong therapeutic potential, emphasizing the need for multi-target strategies in protein design. However, most current protein sequence design methods focus on interactions with a single receptor, often neglecting the complexity of designing proteins that can bind to two distinct receptors. We introduced Protein Dual-Target Design Network (ProDualNet), a structure-based sequence design method that integrates sequence-structure information from two receptors to design dual-target protein sequences. ProDualNet used a heterogeneous graph network for pretraining and combines noise-augmented single-target data with real dual-target data for fine-tuning. This approach addressed the challenge of limited dual-target protein experimental structures. The efficacy of ProDualNet has been validated across multiple test sets, demonstrating better recovery and success rates compared to other multi-state design methods. In silico evaluation of cases like dual-target\u00a0allosteric binding and non-overlapping interface binding highlights its potential for designing dual-target binding proteins. Data and code are available at https:\/\/github.com\/chengliu97\/ProDualNet.<\/jats:p>","DOI":"10.1093\/bib\/bbaf391","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T12:07:14Z","timestamp":1756210034000},"source":"Crossref","is-referenced-by-count":0,"title":["ProDualNet: dual-target protein sequence design method based on protein language model and structure model"],"prefix":"10.1093","volume":"26","author":[{"given":"Liu","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai ,","place":["China"]}]},{"given":"Ting","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai ,","place":["China"]}]},{"given":"Xiaochen","family":"Cui","sequence":"additional","affiliation":[{"name":"Intelligent Medicine Original (Shanghai) Co., Ltd. , Shanghai ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7496-4182","authenticated-orcid":false,"given":"Hai-Feng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8189-5330","authenticated-orcid":false,"given":"Zhangsheng","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai ,","place":["China"]},{"name":"SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University , Shanghai ,","place":["China"]},{"name":"Center for Biomedical Data 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