{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T04:18:02Z","timestamp":1782361082956,"version":"3.54.5"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022ZD0115100"],"award-info":[{"award-number":["2022ZD0115100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China and Project","award":["U21A20427"],"award-info":[{"award-number":["U21A20427"]}]},{"name":"National Natural Science Foundation of China and Project","award":["WU2022A009"],"award-info":[{"award-number":["WU2022A009"]}]},{"name":"Center of Synthetic Biology and Integrated Bioengineering of Westlake University"},{"name":"Research Center for Industries of the Future at Westlake University","award":["WU2022C041"],"award-info":[{"award-number":["WU2022C041"]}]},{"DOI":"10.13039\/100017940","name":"Zhejiang Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["103346582102"],"award-info":[{"award-number":["103346582102"]}],"id":[{"id":"10.13039\/100017940","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52101023"],"award-info":[{"award-number":["52101023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.<\/jats:p>","DOI":"10.1093\/bib\/bbad409","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T13:24:03Z","timestamp":1700227443000},"source":"Crossref","is-referenced-by-count":25,"title":["Efficient prediction of peptide self-assembly through sequential and graphical encoding"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6224-3823","authenticated-orcid":false,"given":"Zihan","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University , Hangzhou 310058 , China"},{"name":"AI Lab , Research Center for Industries of the Future, , Hangzhou 310030 , China"},{"name":"Westlake University , Research Center for Industries of the Future, , Hangzhou 310030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center for the Industries of the Future, Westlake University , Hangzhou 310030 , China"},{"name":"School of Engineering, Westlake University , Hangzhou 310030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University , Hangzhou 310058 , China"},{"name":"School of Engineering, Westlake University , Hangzhou 310030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research Center for the Industries of the Future, Westlake University , Hangzhou 310030 , China"},{"name":"School of Engineering, Westlake University , Hangzhou 310030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center for the Industries of the Future, Westlake University , Hangzhou 310030 , China"},{"name":"School of Engineering, Westlake University , Hangzhou 310030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stan Z","family":"Li","sequence":"additional","affiliation":[{"name":"AI Lab , Research Center for Industries of the Future, , Hangzhou 310030 , China"},{"name":"Westlake University , Research Center for Industries of the Future, , Hangzhou 310030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"2023121109510988700_ref1","doi-asserted-by":"crossref","DOI":"10.1201\/b15106","volume-title":"Introduction to peptides and proteins","author":"Langel","year":"2009"},{"issue":"10","key":"2023121109510988700_ref2","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1021\/bm301141h","article-title":"Amyloid inspired self-assembled peptide nanofibers","volume":"13","author":"Cinar","year":"2012","journal-title":"Biomacromolecules"},{"issue":"1","key":"2023121109510988700_ref3","first-page":"1","article-title":"Near infrared fluorescent peptide nanoparticles for enhancing esophageal cancer therapeutic efficacy. 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