國家衛生研究院 NHRI:Item 3990099045/15650
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 12145/12927 (94%)
造訪人次 : 910476      線上人數 : 825
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於NHRI ‧ 管理 到手機版


    請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/15650


    題名: Decrypting orphan GPCR drug discovery via multitask learning
    作者: Huang, WC;Lin, WT;Hung, MS;Lee, JC;Tung, CW
    貢獻者: Institute of Biotechnology and Pharmaceutical Research
    摘要: The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.
    日期: 2024-01-23
    關聯: Journal of Cheminformatics. 2024 Jan 23;16:Article number 10.
    Link to: http://dx.doi.org/10.1186/s13321-024-00806-3
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1758-2946&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:001152085200001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85182806045
    顯示於類別:[童俊維] 期刊論文
    [李靜琪] 期刊論文
    [洪明秀] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    SCP85182806045.pdf2586KbAdobe PDF80檢視/開啟


    在NHRI中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋