{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T19:03:27Z","timestamp":1763665407743,"version":"3.41.2"},"reference-count":25,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":91,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326\u2009175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches.<\/jats:p>\n               <jats:p>Database URL: \u00a0https:\/\/proj.cse.cuhk.edu.hk\/aihlab\/pronet\/.<\/jats:p>","DOI":"10.1093\/database\/baae012","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T18:12:36Z","timestamp":1711995156000},"source":"Crossref","is-referenced-by-count":2,"title":["ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles"],"prefix":"10.1093","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5666-2140","authenticated-orcid":false,"given":"Junkang","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK) , Chung Chi Rd, Ma Liu Shui, Hong Kong SAR 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK) , Chung Chi Rd, Ma Liu Shui, Hong Kong SAR 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2382-2248","authenticated-orcid":false,"given":"Siyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Kingdom of Saudi Arabia"},{"name":"KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology , Thuwal 23955, Kingdom of Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5418-005X","authenticated-orcid":false,"given":"Licheng","family":"Zong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK) , Chung Chi Rd, Ma Liu Shui, Hong Kong SAR 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-3574","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Kingdom of Saudi Arabia"},{"name":"KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology , Thuwal 23955, Kingdom of Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3664-6722","authenticated-orcid":false,"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK) , Chung Chi Rd, Ma Liu Shui, Hong Kong SAR 999077, China"},{"name":"The CUHK Shenzhen Research Institute , 4 Gaoxin Ave Nanshan, Shenzhen 518057, China"},{"name":"Institute for Medical Engineering and Science, Massachusetts Institute of Technology , 45 Carleton Street, Cambridge, MA 02142, USA"},{"name":"Wyss Institute for Biologically Inspired Engineering, Harvard University , 201 Brookline Avenue, Boston, MA 02215, USA"},{"name":"Broad Institute of MIT and Harvard, Merkin Building , 415 Main Street, Cambridge, MA 02142, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"2024062912201814000_R1","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. 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