dbPTH: A Comprehensive Database for Protein Targets of Herbal Ingredients

The identification of herbal ingredient-target interactions (ITIs) is essential for understanding the molecular basis of herbal pharmacology and facilitating target discovery in drug development. Here, we report a comprehensive database for protein targets of herbal ingredients (dbPTH), containing 165,967 experimentally identified ITIs across 27,981 protein targets and 4856 ingredients across 8 species. The potential orthologs of these protein targets were computationally identified in up to 1138 eukaryotic species, containing 36,594,449 highly potential ITIs for 2,657,336 potential targets. Compared with four reported herbal ingredient-target databases, TCMSP, HERB, HIT 2.0, and BATMAN-TCM 2.0, dbPTH 1.0 achieved 41.81-, 34.47-, 16.55-, and 9.72-fold increases in the number of experimentally verified ITIs covered, respectively. For convenience, we classified the herbal ingredients and corresponding protein targets into 65 chemical subtypes and 9 protein subgroups, respectively. Also, we carefully annotated the data, especially for human targets, using the knowledge from 110 public resources that cover 14 aspects, including genetic variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, post-translational modification, herb-target relationship, protein structural annotation, subcellular localization, biological pathway, protein expression/proteomics, domain annotation, physicochemical property, mRNA expression, and DNA & RNA element. With a data volume of ∼ 21.4 GB, dbPTH 1.0 is not only helpful for better understanding the functional impacts of herbal ingredients but also provides a highly useful resource for further pharmaceutical design. dbPTH 1.0 is freely accessible at http://dbpth.biocuckoo.cn/.

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Journal
Genomics Proteomics & Bioinformatics
Published
2026-06-08
DOI
https://doi.org/10.1093/gpbjnl/qzag040
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

dbPTH: A Comprehensive Database for Protein Targets of Herbal Ingredients

Ke Yang, Na Wang, Jianzhen Peng, Saisai Tian et al.
Genomics Proteomics & Bioinformatics
Computational Drug Discovery Methods
article

dbPTH: A Comprehensive Database for Protein Targets of Herbal Ingredients

Ke Yang, Na Wang, Jianzhen Peng, Saisai Tian, Jianxin Chen, Di Peng, Yu Xue, Xinhe Huang
article en

Abstract

The identification of herbal ingredient-target interactions (ITIs) is essential for understanding the molecular basis of herbal pharmacology and facilitating target discovery in drug development. Here, we report a comprehensive database for protein targets of herbal ingredients (dbPTH), containing 165,967 experimentally identified ITIs across 27,981 protein targets and 4856 ingredients across 8 species. The potential orthologs of these protein targets were computationally identified in up to 1138 eukaryotic species, containing 36,594,449 highly potential ITIs for 2,657,336 potential targets. Compared with four reported herbal ingredient-target databases, TCMSP, HERB, HIT 2.0, and BATMAN-TCM 2.0, dbPTH 1.0 achieved 41.81-, 34.47-, 16.55-, and 9.72-fold increases in the number of experimentally verified ITIs covered, respectively. For convenience, we classified the herbal ingredients and corresponding protein targets into 65 chemical subtypes and 9 protein subgroups, respectively. Also, we carefully annotated the data, especially for human targets, using the knowledge from 110 public resources that cover 14 aspects, including genetic variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, post-translational modification, herb-target relationship, protein structural annotation, subcellular localization, biological pathway, protein expression/proteomics, domain annotation, physicochemical property, mRNA expression, and DNA & RNA element. With a data volume of ∼ 21.4 GB, dbPTH 1.0 is not only helpful for better understanding the functional impacts of herbal ingredients but also provides a highly useful resource for further pharmaceutical design. dbPTH 1.0 is freely accessible at http://dbpth.biocuckoo.cn/.

Genomics Proteomics & Bioinformatics
Beijing University of Chinese Medicine (CN), Second Military Medical University (CN), Beijing Jiaotong University (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
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