{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:16:20Z","timestamp":1770743780622,"version":"3.49.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BK20230626"],"award-info":[{"award-number":["BK20230626"]}]},{"name":"State Key Laboratory of Plant Environmental Resilience","award":["SKLPERKF2401"],"award-info":[{"award-number":["SKLPERKF2401"]}]},{"name":"Open project of State Key Laboratory of Animal Biotech Breeding","award":["2024SKLAB6-1"],"award-info":[{"award-number":["2024SKLAB6-1"]}]},{"name":"Fourth Batch of Leading Innovative Talents Introduction and Training Projects under the Longcheng Talent Plan in Changzhou City","award":["CQ20230086"],"award-info":[{"award-number":["CQ20230086"]}]},{"name":"Changzhou Sci&Tech Program","award":["CJ20241083"],"award-info":[{"award-number":["CJ20241083"]}]},{"name":"Development Project of Jilin Province of China","award":["20220508125RC"],"award-info":[{"award-number":["20220508125RC"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-01034-z","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T12:01:56Z","timestamp":1748865716000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores"],"prefix":"10.1186","volume":"17","author":[{"given":"Lun","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Qingchao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Sen","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"1034_CR1","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/nrm2513","volume":"9","author":"A Heinrichs","year":"2008","unstructured":"Heinrichs A (2008) Different sorting strategies. Nat Rev Mol Cell Biol 9:743\u2013743. https:\/\/doi.org\/10.1038\/nrm2513","journal-title":"Nat Rev Mol Cell Biol"},{"key":"1034_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.trsl.2019.02.001","volume":"209","author":"SK Mathai","year":"2019","unstructured":"Mathai SK, Schwartz DA (2019) Translational research in pulmonary fibrosis. Transl Res 209:1\u201313. https:\/\/doi.org\/10.1016\/j.trsl.2019.02.001","journal-title":"Transl Res"},{"key":"1034_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1186\/s12859-020-3342-z","volume":"21","author":"N Thapa","year":"2020","unstructured":"Thapa N, Chaudhari M, McManus S et al (2020) DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinf 21:63. https:\/\/doi.org\/10.1186\/s12859-020-3342-z","journal-title":"BMC Bioinf"},{"key":"1034_CR4","doi-asserted-by":"publisher","first-page":"16933","DOI":"10.1038\/s41598-022-21366-2","volume":"12","author":"S Pokharel","year":"2022","unstructured":"Pokharel S, Pratyush P, Heinzinger M et al (2022) Improving protein succinylation sites prediction using embeddings from protein language model. Sci Rep 12:16933. https:\/\/doi.org\/10.1038\/s41598-022-21366-2","journal-title":"Sci Rep"},{"key":"1034_CR5","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.1002\/bit.28091","volume":"119","author":"H Wang","year":"2022","unstructured":"Wang H, Zhao H, Zhang J et al (2022) A parallel model of DenseCNN and ordered-neuron LSTM for generic and species-specific succinylation site prediction. Biotech Bioeng 119:1755\u20131767. https:\/\/doi.org\/10.1002\/bit.28091","journal-title":"Biotech Bioeng"},{"key":"1034_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/9923112","volume":"2021","author":"G Huang","year":"2021","unstructured":"Huang G, Shen Q, Zhang G et al (2021) LSTMCNNsucc: a bidirectional LSTM and CNN-based deep learning method for predicting lysine succinylation sites. Biomed Res Int 2021:1\u201310. https:\/\/doi.org\/10.1155\/2021\/9923112","journal-title":"Biomed Res Int"},{"key":"1034_CR7","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1186\/s12859-022-05001-5","volume":"23","author":"J Jia","year":"2022","unstructured":"Jia J, Wu G, Li M, Qiu W (2022) pSuc-EDBAM: predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module. BMC Bioinf 23:450. https:\/\/doi.org\/10.1186\/s12859-022-05001-5","journal-title":"BMC Bioinf"},{"key":"1034_CR8","doi-asserted-by":"publisher","first-page":"1007618","DOI":"10.3389\/fgene.2022.1007618","volume":"13","author":"X Liu","year":"2022","unstructured":"Liu X, Xu LL, Lu YP et al (2022) Deep_KsuccSite: a novel deep learning method for the identification of lysine succinylation sites. Front Genet 13:1007618. https:\/\/doi.org\/10.3389\/fgene.2022.1007618","journal-title":"Front Genet"},{"key":"1034_CR9","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1089\/cmb.2022.0109","volume":"29","author":"Y Xia","year":"2022","unstructured":"Xia Y, Jiang M, Luo Y et al (2022) SuccSPred2.0: a two-step model to predict succinylation sites based on multifeature fusion and selection algorithm. J Comput Biol 29:1085\u20131094. https:\/\/doi.org\/10.1089\/cmb.2022.0109","journal-title":"J Comput Biol"},{"key":"1034_CR10","doi-asserted-by":"publisher","first-page":"894874","DOI":"10.3389\/fcell.2022.894874","volume":"10","author":"J Jia","year":"2022","unstructured":"Jia J, Wu G, Qiu W (2022) pSuc-FFSEA: predicting lysine succinylation sites in proteins based on feature fusion and stacking ensemble algorithm. Front Cell Dev Biol 10:894874. https:\/\/doi.org\/10.3389\/fcell.2022.894874","journal-title":"Front Cell Dev Biol"},{"key":"1034_CR11","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.gpb.2018.10.010","volume":"18","author":"HJ Kao","year":"2020","unstructured":"Kao HJ, Nguyen VN, Huang KY et al (2020) SuccSite: incorporating amino acid composition and informative k-spaced amino acid pairs to identify protein succinylation sites. Genom Proteom Bioinf 18:208\u2013219. https:\/\/doi.org\/10.1016\/j.gpb.2018.10.010","journal-title":"Genom Proteom Bioinf"},{"key":"1034_CR12","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1109\/TCBB.2020.3006144","volume":"19","author":"Q Ning","year":"2022","unstructured":"Ning Q, Ma Z, Zhao X, Yin M (2022) SSKM_Succ: a novel succinylation sites prediction method incorporating K-means clustering with a new semi-supervised learning algorithm. IEEE\/ACM Trans Comput Biol and Bioinf 19:643\u2013652. https:\/\/doi.org\/10.1109\/TCBB.2020.3006144","journal-title":"IEEE\/ACM Trans Comput Biol and Bioinf"},{"key":"1034_CR13","doi-asserted-by":"publisher","first-page":"872","DOI":"10.3390\/biom11060872","volume":"11","author":"H Wang","year":"2021","unstructured":"Wang H, Zhao H, Yan Z et al (2021) MDCAN-Lys: a model for predicting succinylation sites based on multilane dense convolutional attention network. Biomolecules 11:872. https:\/\/doi.org\/10.3390\/biom11060872","journal-title":"Biomolecules"},{"key":"1034_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s13040-022-00290-1","volume":"15","author":"Y Zeng","year":"2022","unstructured":"Zeng Y, Chen Y, Yuan Z (2022) iSuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier. BioData Mining 15:3. https:\/\/doi.org\/10.1186\/s13040-022-00290-1","journal-title":"BioData Mining"},{"key":"1034_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8858489","volume":"2020","author":"L Zhang","year":"2020","unstructured":"Zhang L, Liu M, Qin X, Liu G (2020) Succinylation site prediction based on protein sequences using the IFS-LightGBM (BO) model. Comput Math Methods Med 2020:1\u201315. https:\/\/doi.org\/10.1155\/2020\/8858489","journal-title":"Comput Math Methods Med"},{"key":"1034_CR16","doi-asserted-by":"publisher","first-page":"113592","DOI":"10.1016\/j.ab.2020.113592","volume":"593","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Jia C, Li F, Song J (2020) Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling. Anal Biochem 593:113592. https:\/\/doi.org\/10.1016\/j.ab.2020.113592","journal-title":"Anal Biochem"},{"key":"1034_CR17","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1093\/bioinformatics\/bty157","volume":"34","author":"I Turner","year":"2018","unstructured":"Turner I, Garimella KV, Iqbal Z, McVean G (2018) Integrating long-range connectivity information into de Bruijn graphs. Bioinformatics 34:2556\u20132565. https:\/\/doi.org\/10.1093\/bioinformatics\/bty157","journal-title":"Bioinformatics"},{"key":"1034_CR18","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/j.patcog.2019.05.017","volume":"93","author":"W Xu","year":"2019","unstructured":"Xu W, Shawn K, Wang G (2019) Toward learning a unified many-to-many mapping for diverse image translation. Pattern Recogn 93:570\u2013580. https:\/\/doi.org\/10.1016\/j.patcog.2019.05.017","journal-title":"Pattern Recogn"},{"key":"1034_CR19","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","volume":"559","author":"KT Butler","year":"2018","unstructured":"Butler KT, Davies DW, Cartwright H et al (2018) Machine learning for molecular and materials science. Nature 559:547\u2013555. https:\/\/doi.org\/10.1038\/s41586-018-0337-2","journal-title":"Nature"},{"key":"1034_CR20","doi-asserted-by":"publisher","first-page":"D506","DOI":"10.1093\/nar\/gky1049","volume":"47","author":"The UniProt Consortium","year":"2019","unstructured":"The UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506\u2013D515. https:\/\/doi.org\/10.1093\/nar\/gky1049","journal-title":"Nucleic Acids Res"},{"key":"1034_CR21","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1093\/bioinformatics\/btz955","volume":"36","author":"PD Rohde","year":"2020","unstructured":"Rohde PD, FourieS\u00f8rensen I, S\u00f8rensen P (2020) qgg: an R package for large-scale quantitative genetic analyses. Bioinformatics 36:2614\u20132615. https:\/\/doi.org\/10.1093\/bioinformatics\/btz955","journal-title":"Bioinformatics"},{"key":"1034_CR22","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1093\/bioinformatics\/btab015","volume":"37","author":"S Posada-C\u00e9spedes","year":"2021","unstructured":"Posada-C\u00e9spedes S, Seifert D, Topolsky I et al (2021) V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data. Bioinformatics 37:1673\u20131680. https:\/\/doi.org\/10.1093\/bioinformatics\/btab015","journal-title":"Bioinformatics"},{"key":"1034_CR23","doi-asserted-by":"publisher","first-page":"D451","DOI":"10.1093\/nar\/gkab849","volume":"50","author":"W Zhang","year":"2022","unstructured":"Zhang W, Tan X, Lin S et al (2022) CPLM 4.0: an updated database with rich annotations for protein lysine modifications. Nucleic Acids Res 50:D451\u2013D459. https:\/\/doi.org\/10.1093\/nar\/gkab849","journal-title":"Nucleic Acids Res"},{"key":"1034_CR24","doi-asserted-by":"publisher","first-page":"D377","DOI":"10.1093\/nar\/gkae1005","volume":"53","author":"CR Chung","year":"2025","unstructured":"Chung CR, Tang Y, Chiu YP et al (2025) dbPTM 2025 update: comprehensive integration of PTMs and proteomic data for advanced insights into cancer research. Nucleic Acids Res 53:D377\u2013D386. https:\/\/doi.org\/10.1093\/nar\/gkae1005","journal-title":"Nucleic Acids Res"},{"key":"1034_CR25","doi-asserted-by":"publisher","first-page":"D609","DOI":"10.1093\/nar\/gkae1010","volume":"53","author":"A Bateman","year":"2025","unstructured":"The UniProt Consortium, Bateman A, Martin MJ et al (2025) UniProt: the universal protein knowledgebase in 2025. Nucleic Acids Res 53:D609\u2013D617. https:\/\/doi.org\/10.1093\/nar\/gkae1010","journal-title":"Nucleic Acids Res"},{"key":"1034_CR26","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkz1016","author":"B Fromm","year":"2019","unstructured":"Fromm B, Domanska D, H\u00f8ye E et al (2019) MirGeneDB 20: the metazoan microRNA complement. Nucleic Acids Res. https:\/\/doi.org\/10.1093\/nar\/gkz1016","journal-title":"Nucleic Acids Res"},{"key":"1034_CR27","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1080\/02763869.2020.1826228","volume":"39","author":"J White","year":"2020","unstructured":"White J (2020) PubMed 2.0. Med Ref Serv Q 39:382\u2013387. https:\/\/doi.org\/10.1080\/02763869.2020.1826228","journal-title":"Med Ref Serv Q"},{"key":"1034_CR28","doi-asserted-by":"publisher","first-page":"D1528","DOI":"10.1093\/nar\/gkab848","volume":"50","author":"K Harini","year":"2022","unstructured":"Harini K, Srivastava A, Kulandaisamy A, Gromiha MM (2022) ProNAB: database for binding affinities of protein\u2013nucleic acid complexes and their mutants. Nucleic Acids Res 50:D1528\u2013D1534. https:\/\/doi.org\/10.1093\/nar\/gkab848","journal-title":"Nucleic Acids Res"},{"key":"1034_CR29","doi-asserted-by":"publisher","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","volume":"49","author":"A Bateman","year":"2021","unstructured":"The UniProt Consortium, Bateman A, Martin MJ et al (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480\u2013D489. https:\/\/doi.org\/10.1093\/nar\/gkaa1100","journal-title":"Nucleic Acids Res"},{"key":"1034_CR30","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1093\/bioinformatics\/btaa1086","volume":"37","author":"A Abramova","year":"2021","unstructured":"Abramova A, Osi\u0144ska A, Kunche H et al (2021) CAFE: a software suite for analysis of paired-sample transposon insertion sequencing data. Bioinformatics 37:121\u2013122. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa1086","journal-title":"Bioinformatics"},{"key":"1034_CR31","doi-asserted-by":"publisher","first-page":"D1010","DOI":"10.1093\/nar\/gkab971","volume":"50","author":"SA Krause","year":"2022","unstructured":"Krause SA, Overend G, Dow JAT, Leader DP (2022) FlyAtlas 2 in 2022: enhancements to the Drosophila melanogaster expression atlas. Nucleic Acids Res 50:D1010\u2013D1015. https:\/\/doi.org\/10.1093\/nar\/gkab971","journal-title":"Nucleic Acids Res"},{"key":"1034_CR32","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","volume":"28","author":"L Fu","year":"2012","unstructured":"Fu L, Niu B, Zhu Z et al (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150\u20133152. https:\/\/doi.org\/10.1093\/bioinformatics\/bts565","journal-title":"Bioinformatics"},{"key":"1034_CR33","doi-asserted-by":"publisher","first-page":"i468","DOI":"10.1093\/bioinformatics\/btab331","volume":"37","author":"P Li","year":"2021","unstructured":"Li P, Jiang X, Zhang G et al (2021) Utilizing image and caption information for biomedical document classification. Bioinformatics 37:i468\u2013i476. https:\/\/doi.org\/10.1093\/bioinformatics\/btab331","journal-title":"Bioinformatics"},{"key":"1034_CR34","doi-asserted-by":"publisher","first-page":"D1139","DOI":"10.1093\/nar\/gkab784","volume":"50","author":"R Li","year":"2022","unstructured":"Li R, Qu H, Wang S et al (2022) CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer. Nucleic Acids Res 50:D1139\u2013D1146. https:\/\/doi.org\/10.1093\/nar\/gkab784","journal-title":"Nucleic Acids Res"},{"key":"1034_CR35","doi-asserted-by":"publisher","first-page":"D562","DOI":"10.1093\/nar\/gkaa895","volume":"49","author":"GK Kanev","year":"2021","unstructured":"Kanev GK, de Graaf C, Westerman BA et al (2021) KLIFS: an overhaul after the first 5 years of supporting kinase research. Nucleic Acids Res 49:D562\u2013D569. https:\/\/doi.org\/10.1093\/nar\/gkaa895","journal-title":"Nucleic Acids Res"},{"key":"1034_CR36","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1093\/bioinformatics\/btaa003","volume":"36","author":"N Strodthoff","year":"2020","unstructured":"Strodthoff N, Wagner P, Wenzel M, Samek W (2020) UDSMProt: universal deep sequence models for protein classification. Bioinformatics 36:2401\u20132409. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa003","journal-title":"Bioinformatics"},{"key":"1034_CR37","doi-asserted-by":"publisher","first-page":"4764","DOI":"10.1093\/bioinformatics\/btab554","volume":"37","author":"Q Yan","year":"2021","unstructured":"Yan Q, Forno E, Celed\u00f3n JC et al (2021) CHIT: an allele-specific method for testing the association between molecular quantitative traits and phenotype\u2013genotype interaction. Bioinformatics 37:4764\u20134770. https:\/\/doi.org\/10.1093\/bioinformatics\/btab554","journal-title":"Bioinformatics"},{"key":"1034_CR38","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1093\/bioinformatics\/btz759","volume":"36","author":"AW George","year":"2020","unstructured":"George AW, Verbyla A, Bowden J (2020) Eagle: multi-locus association mapping on a genome-wide scale made routine. Bioinformatics 36:1509\u20131516. https:\/\/doi.org\/10.1093\/bioinformatics\/btz759","journal-title":"Bioinformatics"},{"key":"1034_CR39","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1093\/bioinformatics\/btq358","volume":"26","author":"M Arunachalam","year":"2010","unstructured":"Arunachalam M, Jayasurya K, Tomancak P, Ohler U (2010) An alignment-free method to identify candidate orthologous enhancers in multiple Drosophila genomes. Bioinformatics 26:2109\u20132115. https:\/\/doi.org\/10.1093\/bioinformatics\/btq358","journal-title":"Bioinformatics"},{"key":"1034_CR40","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1002\/prot.21743","volume":"71","author":"SO Yesylevskyy","year":"2008","unstructured":"Yesylevskyy SO, Kharkyanen VN, Demchenko AP (2008) The blind search for the closed states of hinge-bending proteins. Proteins 71:831\u2013843. https:\/\/doi.org\/10.1002\/prot.21743","journal-title":"Proteins"},{"key":"1034_CR41","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1002\/prot.20222","volume":"57","author":"J Guo","year":"2004","unstructured":"Guo J, Wetzel R, Xu Y (2004) Molecular modeling of the core of A\u03b2 amyloid fibrils. Proteins 57:357\u2013364. https:\/\/doi.org\/10.1002\/prot.20222","journal-title":"Proteins"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01034-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-01034-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01034-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T12:01:57Z","timestamp":1748865717000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-01034-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,2]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1034"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-01034-z","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,2]]},"assertion":[{"value":"5 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"92"}}