浙江大学 | 药学院 | English Version
     
     
IDRB: 数据库
据库构建

本课题组已成功建立多个生物信息学和药物信息学数据库,具体如下:

  TTD: Therapeutic Target Database
    Database URL: https://idrblab.org/ttd/

    Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Therefore, we developed the Therapeutic Target Database (TTD) to provide information about known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs directed at each of these targets. TTD was known to be one of the most popular pharmaceutical databases around the world, and included the links to relevant databases containing information about target function, sequence, 3D structure, ligand binding properties, enzyme nomenclature and drug structure, therapeutic class, and clinical development status.

    Our Publication(s) Describing This Database:

  1. Y. X. Wang, S. Zhang, F. C. Li, Y. Zhou, Y. Zhang, Z. W. Wang, R. Y. Zhang, J. Zhu, Y. X. Ren, Y. Tan, C. Qin, Y. H. Li, X. X. Li, Y. Z. Chen*, F. Zhu*. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res (当年影响因子: 11.501, 生物一区 TOP 期刊). 48(D1): 1031-1041 (2020).  
  2. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.15% in 2021.
    科技媒体及新闻报道:

  3. Y. H. Li, C. Y. Yu, X. X. Li, P. Zhang, J. Tang, Q. X. Yang, T. T. Fu, X. Y. Zhang, X. J. Cui, G. Tu, Y. Zhang, S. Li, F. Y. Yang, Q. Sun, C. Qin, X. Zeng, Z. Chen, Y. Z. Chen*, F. Zhu*. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res (当年影响因子: 11.561, 生物一区 TOP 期刊). 46(D1): 1121-1127 (2018).  
  4. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.15% in 2021.
    • The Percentile in Subject Area shown in InCites™ was 0.14% in 2020.
    • The Percentile in Subject Area shown in InCites™ was 0.16% in 2019.
    领域内专家评论:
    • Introduced by OMICTOOLS as "useful for facilitating patient focused research, discovery and clinical investigations of the targeted therapeutics".

  5. H. Yang, C. Qin, Y. H. Li, L. Tao, J. Zhou, C. Y. Yu, F. Xu, Z. Chen, F. Zhu*, Y. Z. Chen*. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res (当年影响因子: 9.202, 生物一区 TOP 期刊). 44(D1): 1069-1074 (2016).  
  6. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.83% in 2021.
    • The Percentile in Subject Area shown in InCites™ was 0.78% in 2020.
    • The Percentile in Subject Area shown in InCites™ was 0.66% in 2019.
    • The Percentile in Subject Area shown in InCites™ was 0.71% in 2018.
    • The Percentile in Subject Area shown in InCites™ was 0.87% in 2017.

  7. F. Zhu, Z. Shi, C. Qin, L. Tao, X. Liu, F. Xu, L. Zhang, Y. Song, X. H. Liu, J. X. Zhang, B. C. Han, P. Zhang, Y. Z. Chen*. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res (当年影响因子: 8.026, 生物一区 TOP 期刊). 40(D1): 1128-1136 (2012).  
  8. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.69% in 2021.
    • The Percentile in Subject Area shown in InCites™ was 0.66% in 2020.
    • The Percentile in Subject Area shown in InCites™ was 0.60% in 2019.
    • The Percentile in Subject Area shown in InCites™ was 0.62% in 2018.
    • The Percentile in Subject Area shown in InCites™ was 0.31% in 2017.

    领域内专家评论:

    • "FACULTYof1000" as "the top 2% of published articles in biology and medicine" and "a most useful resource for scientists and companies working on drug discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations".
    • Prof. Chris Southan in his blog as "Therapeutic Target Database in PubChem".

  9. F. Zhu, B. C. Han, P. Kumar, X. H. Liu, X. H. Ma, X. N. Wei, L. Huang, Y. F. Guo, L. Y. Han, C. J. Zheng, Y. Z. Chen*. Update of TTD: therapeutic target database. Nucleic Acids Res (当年影响因子: 7.479, 生物一区 TOP 期刊). 38(D1): 787-791 (2010).  
  10. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 2.95% in 2017.
  VARIDT: VARIability of Drug Transporter Database
    Database URL: https://idrblab.org/varidt/

    The absorption, distribution and excretion of drugs are largely determined by their transporters (DTs), the variability of which has thus attracted considerable attention. There are three aspects of variability: epigenetic regulation and genetic polymorphism, species/tissue/disease-specific DT abundances, and exogenous factors modulating DT activity. The variability data of each aspect are essential for clinical study, and a collective consideration among multiple aspects becomes essential in precision medicine. However, no database is constructed to provide the comprehensive data of all aspects of DT variability. Herein, the Variability of Drug Transporter Database (VARIDT) was introduced to provide such data. First, 177 and 146 DTs were confirmed, for the first time, by the transporting drugs approved and in clinical/preclinical, respectively. Second, for the confirmed DTs, VARIDT comprehensively collected all aspects of their variability (23,947 DNA methylations, 7,317 noncoding RNA/histone regulations, 1,278 genetic polymorphisms, differential abundance profiles of 257 DTs in 21,781 patients/healthy individuals, expression of 245 DTs in 67 tissues of human/model organism, 1,225 exogenous factors altering the activity of 148 DTs), which allowed mutual connection between any aspects. Due to huge amount of accumulated data, VARIDT made it possible to generalize characteristics to reveal disease etiology and optimize clinical treatment, and is freely accessible at: https://idrblab.org/varidt/.

    Our Publication(s) Describing This Database:

  1. J. Y. Yin, W. Sun, F. C. Li, J. J. Hong, X. X. Li, Y. Zhou, Y. J. Lu, M. Z. Liu, X. Zhang, N. Chen, X. P. Jin, J. Xue, S. Zeng*, L. S. Yu*, F. Zhu*. VARIDT 1.0: variability of drug transporter database. Nucleic Acids Res (当年影响因子: 11.501, 生物一区 TOP 期刊). 48(D1): 1042-1050 (2020).  
  2. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.33% in 2021.
    科技媒体及新闻报道:

  INTEDE: Interactome of Drug-metabolizing Enzymes
    Database URL: https://idrblab.org/intede/

    Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC), and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1,047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3,359 MICBIOs between 225 microbial species and 185 DMEs; 47,778 XEOTICs between 4,150 xenobiotics and 501 DMEs; 7,849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.

    Our Publication(s) Describing This Database:

  1. J. Y. Yin, F. C. Li, Y. Zhou, M. J. Mou, Y. J. Lu, K. L. Chen, J. Xue, Y. C. Luo, J. B. Fu, X. He, J. Q. Gao, S. Zeng*, L. S. Yu*, F. Zhu*. INTEDE: interactome of drug-metabolizing enzymes. Nucleic Acids Res (当年影响因子: 16.971, 生物一区 TOP 期刊). 49(D1): 1233-1243 (2021).  
  2. 科技媒体及新闻报道:

  GIMICA: Host Genetic and Immune Factors Shaping Human Microbiota
    Database URL: https://idrblab.org/gimica/

    Besides the environmental factors having tremendous impacts on the composition of microbial community, the host factors have recently gained extensive attentions on their roles in shaping human microbiota. There are two major types of host factors: host genetic factors (HGFs) and host immune factors (HIFs). These factors of each type are essential for defining the chemical and physical landscapes inhabited by microbiota, and the collective consideration of both types have great implication to serve comprehensive health management. However, no database was available to provide the comprehensive factors of both types. Herein, a database entitled ‘Host Genetic and Immune Factors Shaping Human Microbiota (GIMICA)’ was constructed. Based on the 4,257 microbes confirmed to inhabit nine sites of human body, 2,851 HGFs (1,368 single nucleotide polymorphisms (SNPs), 186 copy number variations (CNVs), and 1,297 non-coding ribonucleic acids (RNAs)) modulating the expression of 370 microbes were collected, and 549 HIFs (126 lymphocytes and phagocytes, 387 immune proteins, and 36 immune pathways) regulating the abundance of 455 microbes were also provided. All in all, GIMICA enabled the collective consideration not only between different types of host factor but also between the host and environmental ones, which is freely accessible without login requirement at: https://idrblab.org/gimica/.

    Our Publication(s) Describing This Database:

  1. J. Tang, X. L. Wu, M. J. Mou, C. Wang, L. D. Wang, F. C. Li, M. Y. Guo, J. Y. Yin, W. Q. Xie, X. N. Wang, Y. X. Wang, Y. B. Ding*, W. W. Xue*, F. Zhu*. GIMICA: host genetic and immune factors shaping human microbiota. Nucleic Acids Res (当年影响因子: 16.971, 生物一区 TOP 期刊). 49(D1): 715-722 (2021).  
  2. 科技媒体及新闻报道:

  SYNBIP: SYNthetic BInding Proteins for Research, Diagnosis and Therapy
    Database URL: https://idrblab.org/synbip/

    The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP; and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.

    Our Publication(s) Describing This Database:

  1. X. N. Wang, F. C. Li, W. Q. Qiu, B. B. Xu, Y. L. Li, X. C. Lian, H. Y. Yu, Z. Zhang, J. X. Wang, Z. R. Li, W. W. Xue*, F. Zhu*. SYNBIP: synthetic binding proteins for research, diagnosis and therapy. Nucleic Acids Res (当年影响因子: 16.971, 生物一区 TOP 期刊). accepted: NAR-02606-Data-E-2021 (2021).
  NPCDR: Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation
    Database URL: https://idrblab.org/npcdr/

    Natural product (NP) has a long history in promoting modern drug discovery, which has derived or inspired a large number of currently prescribed drugs. Recently, the NPs have emerged as the ideal candidates to combine with other therapeutic strategies to deal with the persistent challenge of conventional therapy, and the molecular regulation mechanism underlying these combinations is crucial for the related communities. Thus, it is urgently demanded to comprehensively provide the disease-specific molecular regulation data for various NP-based drug combinations. However, no database has been developed yet to describe such valuable information. In this study, a newly developed database entitled ‘Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation (NPCDR)’ was thus introduced. This database was unique in (a) providing the comprehensive information of NP-based drug combinations & describing their clinically or experimentally validated therapeutic effect, (b) giving the disease-specific molecular regulation data for a number of NP-based drug combinations, (c) fully referencing all NPs, drugs, regulated molecules/pathways by cross-linking them to the available databases describing their biological or pharmaceutical characteristics. Therefore, NPCDR is expected to have great implications for the future practice of network pharmacology, medical biochemistry, drug design, and medicinal chemistry. This database is now freely accessible without any login requirement at both official (https://idrblab.org/npcdr/) and mirror (http://npcdr.idrblab.net/) sites.

    Our Publication(s) Describing This Database:

  1. X. N. Sun, Y. T. Zhang, Y. Zhou, X. C. Lian, L. L. Yan, T. Pan, T. Jin, H. Xie, Z. M. Liang, W. Q. Qiu, J. X. Wang, Z. R. Li, F. Zhu*, X. B. Sui*. NPCDR: natural product-based drug combination and its disease-specific molecular regulation. Nucleic Acids Res (当年影响因子: 16.971, 生物一区 TOP 期刊). accepted: NAR-02683-Data-E-2021 (2021).
IDRB: Innovative Drug Research and Bioinformatics Group


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