{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T16:51:26Z","timestamp":1771606286344,"version":"3.50.1"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371423 and 61572444"],"award-info":[{"award-number":["62371423 and 61572444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Henan Province of China","doi-asserted-by":"crossref","award":["232300421117"],"award-info":[{"award-number":["232300421117"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Scientific and Technological Project of Henan Province","award":["252102211042"],"award-info":[{"award-number":["252102211042"]}]},{"name":"Major Science and Technology Projects in Henan Province","award":["201400210400"],"award-info":[{"award-number":["201400210400"]}]},{"name":"Key Research and Development Projects in Henan Province","award":["241111210500"],"award-info":[{"award-number":["241111210500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein\u2013protein interactions play a fundamental role in biological systems. Accurate detection of protein\u2013protein interaction sites (PPIs) remains a challenge. And, the methods of PPIs prediction based on biological experiments are expensive. Recently, a lot of computation-based methods have been developed and made great progress. However, current computational methods only focus on one form of protein, using only protein spatial conformation or primary sequence. And, the protein\u2019s natural hierarchical structure is ignored.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we propose a novel network architecture, HSSPPI, through hierarchical and spatial-sequential modeling of protein for PPIs prediction. In this network, we represent protein as a hierarchical graph, in which a node in the protein is a residue (residue-level graph) and a node in the residue is an atom (atom-level graph). Moreover, we design a spatial-sequential block for capturing complex interaction relationships from spatial and sequential forms of protein. We evaluate HSSPPI on public benchmark datasets and the predicting results outperform the comparative models. This indicates the effectiveness of hierarchical protein modeling and also illustrates that HSSPPI has a strong feature extraction ability by considering spatial and sequential information simultaneously.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code of HSSPPI is available at https:\/\/github.com\/biolushuai\/Hierarchical-Spatial-Sequential-Modeling-of-Protein.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbaf079","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T01:14:31Z","timestamp":1741137271000},"source":"Crossref","is-referenced-by-count":2,"title":["HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction"],"prefix":"10.1093","volume":"26","author":[{"given":"Yuguang","family":"Li","sequence":"first","affiliation":[{"name":"Zhengzhou University School of Computer and Artificial 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