※ Related resources & tools:
▼ Public database integrated in dbPTH
(1) Post-translational modification
(2) Genetic variation & mutation
(3) Protein-protein interaction
(4) Protein functional annotation
(7) Protein expression/Proteomics
(12) Protein structural annotation
▼ Public database integrated in dbPTH
(1) Post-translational modification
1. dbPTM: A comprehensive database for experimentally identified PTMs with annotations (Huang, et al., 2016).
2. ProteomeScount: An online database for PTMs in proteins (Matthew, et al., 2014).
3. iPTMnet: An integrated database of protein PTMs in the context of system biology (Huang, et al., 2018).
4. BioGRID: A public database that curated genetic, chemical interactions and proteins with a large number of PTM sites (Oughtred, et al., al.,2019).
5. mUbiSiDa: A resource contains experimentally validated ubiquitinated proteins with sites for several species (Chen, et al., 2014).
6. ActiveDriverDB: An online database developed to visualize and explore mutations affecting PTM sites in human proteins/genes (Krassowski, et al., 2018).
7. CPLM 4.0: CPLM 4.0 is an updated database with rich annotations for protein lysine modifications (Zhang, et, et al., 2022).
8. UniProt: The universal protein knowledgebase contains lots of protein modification information including PTMs (The, et al., 2019).
9. PhosphoSitePlus: PhosphoSitePlus provides comprehensive information and tools for the study of protein post-translational modifications (PTMs) including phosphorylation, acetylation, and more (Hornbeck, et al., 2015).
10. EPSD: EPSD is a comprehensive data resource of eukaryotic phosphorylation sites (Lin, et al., 2020).
11. PTMcode 2.0: A database containing post-translational regulations and protein-protein interactions (Minguez, et al., 2015).
(2) Genetic variation & mutation
12. COSMIC: A comprehensive database contains cancer mutations and cancer mRNA expression data (Forbes, et al., 2015).
13. ICGC: A public resource that provides great information of cancer mutation and gene expression data (Zhang, et al., 2019).
14. TCGA: A well-known data resource for a huge number of cancer mutations detected from clinical samples (Cancer, et al., 2017).
15. dbSNP: The NCBI database of single nucleotide polymorphisms (SNPs) (Sherry, et al., 2001).
16. Varcards: An integrated resource for maintaining coding variants in the human genome (Li, et al., 2018).
17. KinaseMD: A database containing SNPs in kinases (Hu, et al., 2021).
18. IntOGen: An online database that integrated cancer genomic data, which including cancer mutations (Gundem, et al., 2010).
19. BioMuta: A knowledgebase of cancer-associated single-nucleotide variations for cancer biomarker discovery (Dingerdissen, et al., 2018).
20. ActiveDriverDB: An online database developed to visualize and explore mutations affecting post-translational modification (PTM) sites in human proteins/genes (Krassowski, et al., 2021).
(3) Protein-protein interaction
21. iRefIndex: An integrative resource of PPIs (Razick, et al., 2008).
22. Mentha: A well curated PPI database (Calderone, et al., 2013).
23. DifferentialNet: A novel database that provides differential protein-protein networks of human tissues (Basha, et al., 2018).
24. MIST: A helpful resource for annotating gene-protein and protein-protein interactions (Hu, et al., 2018).
25. HIPPIE: An integrated database of protein-protein interactions (Lobato, et al., 2017).
26. PINA: A well curated PPI database (Du, et al., 2021).
27. HINT: A curated compilation of high-quality protein-protein interactions (Das, et al., 2012).
28. inBio MapTM: A scored human protein-protein interaction network database (Li, et al., 2017).
29. STRING: A database of known and predicted protein-protein interactions, covers 67.6 million proteins from 14,094 organisms (Szklarczyk, et al., 2023).
30. BioGRID: A public database that curated genetic, chemical interactions and proteins with a large number of PPIs (Oughtred, et al., 2019).
31. HPRD: A centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome (Prasad, et al., 2009).
(4) Protein functional annotation
32. DrLLPS: A comprehensive data resource that contained known and computationally detected LLPS-associated proteins (Ning, et al., 2019).
33. THANATOS: A database of proteins and PTMs invovled in autophagy and cell death pathways (Deng, et al., 2018).
34. iEKPD: A database contains phosphorylation regulators including protein kinases, protein phosphatases and PPBD-containing proteins (Guo, et, et al., 2019).
35. iUUCD: A database contained ubiquitination associated enzymes including ubiquitin activating enzymes (E1s), ubiquitin-conjugating enzymes (E2s), ubiquitin-protein ligases (E3s), deubiquitinating enzymes (DUBs), ubiquitin/ubiquitin-like binding domains (UBDs) and ubiquitin-like domains (ULDs) (Zhou, et al., 2018).
36. CGDB: A database resource provides validated circadian genes collected from small-scale and high-throughput studies (Li, et al., 2016).
37. WERAM: A database of writers, erasers and readers of histone acetylation and methylation in eukaryotes (Xu, et, et al., 2016).
38. AmyPro: A data resource of experimentally identified amyloid precursor proteins and their amyloidogenic sequence regions (Varadi, et, et al., 2018).
39. MultitaskProtDB-II: A open access that contains multitasking and moonlighting proteins (Serrano, et, et al., 2018).
40. GPCRdb: A open resource that contains G protein-coupled receptors (GPCRs) and GPCR ligands with data on biological activities (Szekeres, et, et al., 2023).
41. neXtProt: A human protein-centric knowledge platform (Zabal, et al., 2020).
42. CORUM: The comprehensive resource of mammalian protein complexes (Tsitsiridis, et al., 2023).
43. HAMAP: A database for classification of protein families (Pedruzzi, et al., 2015).
44. CellMarker 2.0: A curated resource of cell biomarkers in human and mouse (Hu, et, et al., 2023).
45. MoonDB 2.0: An updated database of extreme multifunctional and moonlighting proteins (Ribeiro, et, et al., 2019).
46. Membranome 2.0: Database for proteome-wide profiling of single-pass membrane proteins (Lomize, et, et al., 2018).
47. AnimalTFDB 4.0: A comprehensive resource for annotation and prediction of animal transcription factors (Shen, et al., 2023).
48. EuRBPDB: A comprehensive and user-friendly database for eukaryotic RNA binding proteins (RBPs) (Liao, et al., 2020).
49. ATtRACT: A database of RNA-binding proteins and associated motifs (Giudice, et al., 2016).
50. TISSUES 2.0: A database of tissue-specific expressions of mammalian genes (Palasca, et al., 2018).
51. PlantTFDB 4.0: A central hub for transcription factors and regulatory interactions in plants (Jin, et al., 2017).
(5) DNA & RNA element
52. TargetScanHuman: TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA (Agarwal, et al., 2015).
53. miRWalk: The new version of miRWalk stores predicted data obtained with a maschine learning algorithm including experimentally verified miRNA-target interactions (Sticht, et al., 2018).
54. miRcode: miRcode provides whole transcriptome human microRNA target predictions based on the comprehensive GENCODE gene annotation, including >10,000 long non-coding RNA genes (Jeggari, et al., 2012).
55. LncRNADisease: LncRNADisease collected more than 200,000 experimental supported lncRNA-disease associations (Bao, et al., 2019).
56. somamiR 2.0: SomamiR contains cancer somatic mutations in microRNAs (miRNA) with their target sites affectting the interactions between miRNAs and competing endogenous RNAs (ceRNA) (Bhattacharya, et al., 2016).
57. RISE: A database collecting expetimental RNA interactome. RNA-RNA interactions are important for RNA regulation and function (Gong, et al., 2018).
58. RNAInter: A database of RNA interactome with annotation, containing great unique molecules and RNA-protein interactions (Kang, et al., 2022).
59. circBase: A database for circular RNAs (circRNAs) providing their expression with supporting evidence (Glažar, et al., 2014).
60. RAIN: A database integrated ncRNA-RNA and ncRNA-protein association, also providing a confidence score for each interaction (Junge, et al., 2017).
61. RegNetwork: An open resource that provides transcriptional networks of human and mouse (Liu, et al., 2015).
62. TRRUST: A reference resource of human and mouse transcriptional regulatory interactions (Han, et al., 2018).
63. YTRP: A database of the collection of transcription factor (TF)-gene regulatory pairs (Yang, et al., 2014).
64. UTRdb 2.0: A curated database of 5' and 3' untranslated sequences of eukaryotic mRNAs (Giudice, et al., 2023).
(6) mRNA expression
65. TCGA: TCGA is a public resource that cotains major cancer-causing genomic alterations and gene expressions, aming to provide a comprehensive cancer genomic profiles (Cancer, et al., 2017).
66. COSMIC: Besides cancer mutations, COSMIC also contains cancer mRNA expression data (Tate, et al., 2019).
67. The Human Protein Atlas: Besides the proteomic data, the Human Protein Atlas also provided RNA gene data for RNA levels in 64 cell lines and 37 tissues based on RNA-seq (Pontén, et al., 2011).
68. Human Proteome Map: Besides the proteomic data, the Human Proteome Map (HPM) also provides mRNA expression data (Zhang, et al., 2019).
69. ICGC: Maintains a huge number of cancer mutations detected from clinical samples (Zhang, et al., 2011).
70. TissGDB: A database containing tissue-specific gene expression data in cancer (Kim, et al., 2018).
71. TISSUES 2.0: A database of tissue-specific expressions of mammalian genes (Palasca, et al., 2018).
(7) Protein expression/Proteomics
72. Human Proteome Map: Containing proteins encoded by 17,294 genes that were detected in 30 histologically normal human samples (Kim, et al., 2014).
73. The Human Protein Atlas: Besides the proteomic data, the Human Protein Atlas also provided RNA gene data for RNA levels in 64 cell lines and 37 tissues based on RNA-seq (Pontén, et al., 2011).
(8) Subcellular localization
74. COMPARTMENTS: A database that provides annotation and visualization of protein subcellular localizations with confidence scores to the localization evidence (Binder, et al., 2014).
75. ComPPI: A cellular compartment-specific database for protein–protein interaction network analysis with eight subcellular localization (Veres, et al., al.,2015).
76. NLSdb: A database collecting experimentally nuclear and non-nuclear proteins with annotations and nuclear export signals (NES) and nuclear localization signals (NLS) (Bernhofer, et al., 2018).
77. Translocatome: A database of manually curated data set of 213 human translocating proteins with several translocation mechanism, local compartmentalized interactome and involvement in signalling pathways and disease development (Mendik, et al., 2019).
78. MiCroKiTS: A database of midbody, centrosome, kinetochore, telomere and spindle (Huang, et al., 2015).
(9) Biological pathway
79. SignaLink: An integrated resource to analyze signaling pathway cross-talks, transcription factors, miRNAs and regulatory enzymes (Csabai, et al., 2022).
80. Reactome: Provides molecular details of signal transduction, transport, DNA replication, metabolism, and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model (Fabregat, et al., 2018).
81. KEGG: A database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (Kanehisa, et al., 2019).
82. PathBank: A comprehensive database of more than 110,000 annatated pathways of 10 model organisms (Wishart, et al., 2020).
(10) Domain annotation
83. InterPro: A database provides functional analysis of proteins by familiy classifications and predicted domains (Mitchell, et al., 2019).
84. SMART: An integrative database for the identification and annotation of protein domain architectures (Letunic, et, et al., 2015).
85. CDD: An NCBI database for annotating conserved domains in proteins (Marchler-Bauer, et, et al., 2015).
86. Pfam: A widely used database of protein families, containing 14,831 manually curated entries in the current release (Gebali, et al., 2019).
87. PIRSF: Reflects evolutionary relationships of full-length proteins and domains (Nikolskaya, et al., 2007).
88. PROSITE: Consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them (Sigrist, et al., 2013).
(11) Physicochemical property
89. Compute pI/Mw: A tool for computing the theoretical pI and Mw of proteins (Wilkins, et, et al., 1999).
90. AAindex: A database of various indices for physicochemical and biochemical properties of amino acids and pairs of amino acids (Kawashima, et, et al., 2008).
(12) Protein structural annotation
91. PDB: A leading resource of structural data of biological macromolecules (Berman, et al., 2000).
92. SCOP: A database provides the framework for protein structure classifications and annotations (Andreeva, et al., 2014).
93. DNAproDB: A database contains biochemical features and structural information of DNA-protein complexes (Sagendorf, et al., 2019).
94. MobiDB 5.0: A database of protein disorder and mobility annotations (Piovesan, et al., 2023).
(13) Target-herb relation
95. SymMap: SymMap integrates traditional chinese medicine (TCM) with modern medicine (MM) through both internal molecular mechanism and external symptom mapping, thus provides massive information on herbs/ingredients, targets, as well as the clinical symptoms and diseases they are used to treat for drug screening efforts (Wu, et al., 2019).
96. ETCM: ETCM includes comprehensive and standardized information for the commonly used herbs and formulas of TCM, as well as their ingredients (Xu, et al., 2018).
97. TCMSP: TCMSP is a database of systems pharmacology for drug discovery from herbal medicines (Ru, et al., 2014).
98. HERB: HERB: a high-throughput experiment- and reference-guided database of traditional chinese medicine (Fang, et al., 2021).
99. TCMSID: TCMSID is a simplified integrated database for drug discovery from traditional chinese medicine (Zhang, et al., 2022).
100. HIT 2.0: HIT 2.0: an enhanced platform for herbal ingredients' targets (Yan, et al., 2022).
(14) Disease-associated information
101. PTMD: A database of human disease-associated PTM events (Xu, et al., 2018).
102. OMIM: A resource of relations between curated human genes and phenotypes (Amberger, et al., 2015).
103. HGV&TB: An open resource for human genetic variants corresponding with tuberculosis (Sahajpal, et al., 2014).
104. ClinVar: A public resource to maintain relations between human variations and phenotypes with supporting evidence (Landrum, et al., 2018).
105. DiseaseEnhancer: A database of human disease-associated enhancers (Zhang, et al., 2018).
106. SympGAN: The construction of SympGAN filled in the gap and built millions of associations among symptoms, genotypes, diseases and drugs, which has a great prospect to promote precision medicine and health care.
107. CTD: CTD is a database of comparative toxicogenomics. The digital ecosystem information is integrated for genes (Davis, et al., 2021).
108. DisGeNET: DisGeNET provides knowledge about genetic variation in health and disease (Piñero, et al., 2020).
109. Diseases 2.0: Diseases 2.0 is a database to provide information about disease-gene associations by integrating biomedical leteratures, GWAS and text mining (Grissa, et al., 2022).
110. GAAD: GAAD is a database of gene and autoimmune disease association (Lu, et al., 2018).
(15) Ingredient information
111. ETCM: ETCM includes comprehensive and standardized information for the commonly used herbs and formulas of TCM, as well as their ingredients (Xu, et al., 2018).
112. PubChem: Pubchem is a comprehensive databse, including the database of substance, compound and bioassay (Kim, et al., 2023).
113. SymMap: SymMap integrates traditional chinese medicine (TCM) with modern medicine (MM) through both internal molecular mechanism and external symptom mapping, thus provides massive information on herbs/ingredients, targets, as well as the clinical symptoms and diseases they are used to treat for drug screening efforts (Wu, et al., 2019).
(16) Herb information
114. ETCM: ETCM includes comprehensive and standardized information for the commonly used herbs and formulas of TCM, as well as their ingredients (Xu, et al., 2018).
115. SymMap: SymMap integrates traditional chinese medicine (TCM) with modern medicine (MM) through both internal molecular mechanism and external symptom mapping, thus provides massive information on herbs/ingredients, targets, as well as the clinical symptoms and diseases they are used to treat for drug screening efforts (Wu, et al., 2019).
116. TCMSID: TCMSID is a simplified integrated database for drug discovery from traditional chinese medicine (Zhang, et al., 2022).