• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

dbPepNeo2.0：一个基于质谱和 TCR 识别的人类肿瘤新抗原肽数据库。

dbPepNeo2.0: A Database for Human Tumor Neoantigen Peptides From Mass Spectrometry and TCR Recognition.

机构信息

College of Food Science and Technology, Shanghai Ocean University, Shanghai, China.

Shanghai-Ministry of Science and Technology (MOST) Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China.

出版信息

Front Immunol. 2022 Apr 13;13:855976. doi: 10.3389/fimmu.2022.855976. eCollection 2022.

DOI:10.3389/fimmu.2022.855976
PMID:35493528
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9043652/
Abstract

Neoantigens are widely reported to induce T-cell response and lead to tumor regression, indicating a promising potential to immunotherapy. Previously, we constructed an open-access database, i.e., dbPepNeo, providing a systematic resource for human tumor neoantigens to storage and query. In order to expand data volume and application scope, we updated dbPepNeo to version 2.0 (http://www.biostatistics.online/dbPepNeo2). Here, we provide about 801 high-confidence (HC) neoantigens (increased by 170%) and 842,289 low-confidence (LC) HLA immunopeptidomes (increased by 107%). Notably, 55 class II HC neoantigens and 630 neoantigen-reactive T-cell receptor-β (TCRβ) sequences were firstly included. Besides, two new analytical tools are developed, DeepCNN-Ineo and BLASTdb. DeepCNN-Ineo predicts the immunogenicity of class I neoantigens, and BLASTdb performs local alignments to look for sequence similarities in dbPepNeo2.0. Meanwhile, the web features and interface have been greatly improved and enhanced.

摘要

新抗原被广泛报道能诱导 T 细胞反应，导致肿瘤消退，这表明其在免疫治疗方面有很大的潜力。此前，我们构建了一个开放获取的数据库，即 dbPepNeo，为人类肿瘤新抗原的存储和查询提供了一个系统的资源。为了扩大数据量和应用范围，我们将 dbPepNeo 更新到了 2.0 版本（http://www.biostatistics.online/dbPepNeo2）。在这里，我们提供了大约 801 个高可信度（HC）新抗原（增加了 170%）和 842,289 个低可信度（LC）HLA 免疫肽组（增加了 107%）。值得注意的是，首次纳入了 55 个 II 类 HC 新抗原和 630 个新抗原反应性 T 细胞受体-β（TCRβ）序列。此外，还开发了两个新的分析工具，即 DeepCNN-Ineo 和 BLASTdb。DeepCNN-Ineo 预测了 I 类新抗原的免疫原性，BLASTdb 则在 dbPepNeo2.0 中进行局部比对，以寻找序列相似性。同时，网站的功能和界面得到了极大的改进和增强。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/df91cfe12b65/fimmu-13-855976-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/2fcd5097265a/fimmu-13-855976-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/17e9dffb2b44/fimmu-13-855976-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/83f256386798/fimmu-13-855976-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/c19f574f96df/fimmu-13-855976-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/0ab834e69031/fimmu-13-855976-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/df91cfe12b65/fimmu-13-855976-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/2fcd5097265a/fimmu-13-855976-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/17e9dffb2b44/fimmu-13-855976-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/83f256386798/fimmu-13-855976-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/c19f574f96df/fimmu-13-855976-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/0ab834e69031/fimmu-13-855976-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0fa/9043652/df91cfe12b65/fimmu-13-855976-g006.jpg

相似文献

1
dbPepNeo2.0: A Database for Human Tumor Neoantigen Peptides From Mass Spectrometry and TCR Recognition.dbPepNeo2.0：一个基于质谱和 TCR 识别的人类肿瘤新抗原肽数据库。
Front Immunol. 2022 Apr 13;13:855976. doi: 10.3389/fimmu.2022.855976. eCollection 2022.
2
dbPepNeo: a manually curated database for human tumor neoantigen peptides.dbPepNeo：一个人类肿瘤新生抗原肽的人工 curated 数据库。
Database (Oxford). 2020 Jan 1;2020. doi: 10.1093/database/baaa004.
3
GNIFdb: a neoantigen intrinsic feature database for glioma.GNIFdb：一个用于胶质瘤的新抗原内在特征数据库。
Database (Oxford). 2022 Feb 12;2022. doi: 10.1093/database/baac004.
4
Immunological ignorance is an enabling feature of the oligo-clonal T cell response to melanoma neoantigens.免疫忽视是寡克隆 T 细胞对黑色素瘤新抗原反应的一个促进特征。
Proc Natl Acad Sci U S A. 2019 Nov 19;116(47):23662-23670. doi: 10.1073/pnas.1906026116. Epub 2019 Nov 4.
5
DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.DeepHLApan：一种考虑 HLA-肽结合和免疫原性的新型抗原预测的深度学习方法。
Front Immunol. 2019 Nov 1;10:2559. doi: 10.3389/fimmu.2019.02559. eCollection 2019.
6
Neoantigen-reactive T cells exhibit effective anti-tumor activity against colorectal cancer.针对结直肠癌，具有新抗原反应性的 T 细胞表现出有效的抗肿瘤活性。
Hum Vaccin Immunother. 2022 Dec 31;18(1):1-11. doi: 10.1080/21645515.2021.1891814. Epub 2021 Mar 9.
7
Neodb: a comprehensive neoantigen database and discovery platform for cancer immunotherapy.Neodb：一个全面的新抗原数据库和癌症免疫治疗发现平台。
Database (Oxford). 2023 Jun 13;2023. doi: 10.1093/database/baad041.
8
A proteogenomic approach to target neoantigens in solid tumors.一种针对实体瘤中的新抗原的蛋白质基因组学方法。
Expert Rev Proteomics. 2020 Nov-Dec;17(11-12):797-812. doi: 10.1080/14789450.2020.1881889.
9
Recent advances in immunopeptidomic-based tumor neoantigen discovery.免疫肽组学基础上的肿瘤新生抗原发现的最新进展。
Adv Immunol. 2023;160:1-36. doi: 10.1016/bs.ai.2023.10.001. Epub 2023 Nov 3.
10
COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome.基于 COSMIC 的突变数据库提高了 HLA-I 免疫肽组的鉴定效率。
J Transl Med. 2024 Feb 10;22(1):144. doi: 10.1186/s12967-023-04821-0.

引用本文的文献

1
TransHLA: a Hybrid Transformer model for HLA-presented epitope detection.TransHLA：一种用于HLA呈递表位检测的混合Transformer模型。
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf008.
2
[Prediction of MHC II antigen peptide-T cell receptors binding based on foundation model].基于基础模型的MHC II类抗原肽-T细胞受体结合预测
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Dec 25;41(6):1243-1249. doi: 10.7507/1001-5515.202405024.
3
Transformers meets neoantigen detection: a systematic literature review.变压器与新抗原检测：系统文献综述。

本文引用的文献

1
caAtlas: An immunopeptidome atlas of human cancer.癌症图谱：人类癌症免疫肽组图谱
iScience. 2021 Sep 9;24(10):103107. doi: 10.1016/j.isci.2021.103107. eCollection 2021 Oct 22.
2
The Role of Neoantigens in Cancer Immunotherapy.新抗原在癌症免疫治疗中的作用。
Front Oncol. 2021 Aug 27;11:682325. doi: 10.3389/fonc.2021.682325. eCollection 2021.
3
DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.DLpTCR：一种用于预测 T 细胞受体识别的免疫原性肽的集成深度学习框架。
J Integr Bioinform. 2024 Jul 4;21(2). doi: 10.1515/jib-2023-0043. eCollection 2024 Jun 1.
4
Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.人工智能与新抗原：为精准癌症免疫治疗铺平道路。
Front Immunol. 2024 May 29;15:1394003. doi: 10.3389/fimmu.2024.1394003. eCollection 2024.
5
Neoantigen cancer vaccines: a new star on the horizon.新抗原癌症疫苗：崭露头角的新星。
Cancer Biol Med. 2023 Dec 29;21(4):274-311. doi: 10.20892/j.issn.2095-3941.2023.0395.
6
Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges.新抗原靶向的TCR工程化T细胞免疫疗法：当前进展与挑战
Biomark Res. 2023 Dec 1;11(1):104. doi: 10.1186/s40364-023-00534-0.
7
Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling.通过整合 RNA 测序数据进行变异调用，提高新抗原预测。
Front Immunol. 2023 Sep 4;14:1251603. doi: 10.3389/fimmu.2023.1251603. eCollection 2023.
8
The Value of Microbes in Cancer Neoantigen Immunotherapy.微生物在癌症新抗原免疫治疗中的价值。
Pharmaceutics. 2023 Aug 14;15(8):2138. doi: 10.3390/pharmaceutics15082138.
9
Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis.解析肿瘤特异性新抗原免疫原性预测：一项全面分析。
Front Immunol. 2023 Jul 25;14:1094236. doi: 10.3389/fimmu.2023.1094236. eCollection 2023.
10
Advances in vaccine development for cancer prevention and treatment in Lynch Syndrome.林奇综合征的癌症预防和治疗疫苗研发进展。
Mol Aspects Med. 2023 Oct;93:101204. doi: 10.1016/j.mam.2023.101204. Epub 2023 Jul 19.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab335.
4
The Clinical Application of Neoantigens in Esophageal Cancer.新抗原在食管癌中的临床应用
Front Oncol. 2021 Jul 27;11:703517. doi: 10.3389/fonc.2021.703517. eCollection 2021.
5
Generation of neoantigen-specific T cells for adoptive cell transfer for treating head and neck squamous cell carcinoma.为头颈部鳞状细胞癌的过继细胞转移生成新抗原特异性 T 细胞。
Oncoimmunology. 2021 May 25;10(1):1929726. doi: 10.1080/2162402X.2021.1929726.
6
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity.DeepImmuno：基于深度学习的 T 细胞免疫原性肽预测与生成
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab160.
7
Neoantigen: A New Breakthrough in Tumor Immunotherapy.肿瘤免疫治疗新突破：新抗原
Front Immunol. 2021 Apr 16;12:672356. doi: 10.3389/fimmu.2021.672356. eCollection 2021.
8
Gene fusion neoantigens: Emerging targets for cancer immunotherapy.基因融合新抗原：癌症免疫治疗的新兴靶点。
Cancer Lett. 2021 May 28;506:45-54. doi: 10.1016/j.canlet.2021.02.023. Epub 2021 Mar 4.
9
Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma.个体化新抗原疫苗诱导黑色素瘤患者持久的记忆 T 细胞应答和表位扩展。
Nat Med. 2021 Mar;27(3):515-525. doi: 10.1038/s41591-020-01206-4. Epub 2021 Jan 21.
10
Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. anthem：一种用户自定义工具，用于快速准确地预测肽段与 HLA Ⅰ类分子的结合。
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbaa415.