Table 2.
Overview of databases and tools for microbial effectors
| Microbial effector | Name | Description | Source | |
|---|---|---|---|---|
| Virulence factors | Database | VFDB | - Virulence factors of bacterial pathogens | [12] |
| Tool | PATHOFact | - Prediction of AMR genes, virulence factors, toxins, and BGCs | [13] | |
| MetaVF | - Identification of pathobiont-carried VFGs at the species level | [14] | ||
| Toxins | Database | Toxinome | - Bacterial protein toxin | [15] |
| TADB | - Bacterial types I to VIII toxin-antitoxin loci | [16] | ||
| Tool | PathoFact | - Prediction of AMR genes, virulence factors, toxins and BGCs | [13] | |
| Antimicrobials | Database | AntibioticDB | - Antibacterial compounds (incl. discontinued agents and drugs under pre-clinical development or in clinical trials) | [17] |
| DrugBank | - Drugs, drug targets, and related pharmaceutical information | [18] | ||
| PubChem | - Chemical information on e.g., small molecules, siRNA, miRNA, lipids or carbohydrates | [19] | ||
| ChEMBL | - Bioactive molecules with drug-like properties | [20] | ||
| CARD | - Antibiotic resistance ontologies with curated AMR gene sequences and resistance-conferring mutations | [21] | ||
| Tool | antiSMASH | - Detecting and characterizing biosynthetic gene clusters (BGCs) | [22] | |
| ResFinder | - Identification of AMR genes in NGS-data | [23] | ||
| Antimicrobial/non-ribosomal peptides | Database | CAMPR4 | - Natural and synthetic AMPs | [24] |
| dbAMP | - Annotations on AMPs (incl. sequence information, functional activity data, or physicochemical properties) | [25] | ||
| DBAASP | - Sequences, chemical modifications, structures, bioactivities and toxicities of AMPs | [26] | ||
| DRAMP | - Antimicrobial, antifungal, antiviral, anticancer, antitumor, antiprotozoal, and insecticidal peptides | [27] | ||
| Tool | Macrel | - Prediction of AMP sequences from genomes and metagenomes | [28] | |
| SPEQ | - Identification of high-quality, not identified LC–MS spectra | [29] | ||
| Ensemble-AMPPred | - AMP prediction and recognition from sequence data | [30] | ||
| Deep-AmPEP30 | - Prediction of short-length (≤ 30 aa) AMP | [31] | ||
| SBSPKSv3 | - Prediction of macrocyclized structures of non-ribosomal peptide synthetase and polyketide synthase | [32] | ||
| NRPminer | - NRP discovery from (meta)genomic and mass spectrometry datasets | [33] | ||
| BiG-MEx | - Identification of BGC protein domains and assessment of diversity and novelty | [34] | ||
| BiG-SCAPE | - Analysis of sequence similarity networks of biosynthetic gene clusters and gene cluster families | [35] | ||
| NaPDos | - Assessment of secondary metabolite biosynthetic gene diversity and novelty of in organisms and environments | [36] | ||
| Bacteriophages/Archeophages | Database | PhageDive | - Experimental data (e.g., host range) and metadata (e.g., geographical origin) on bacteriophages | [37] |
| Gut Phage Database (GPD) | - Non-redundant viral genomes obtained by mining human gut metagenomes and reference genomes of cultured gut bacteria | [38] | ||
| Microbe Versus Phage (MVP) | - Phage–microbe interactions | [39] | ||
| PhagesDB | - Interactive website for discovery, characterization, and genomics of viruses that infect Actinobacteria | [40] | ||
| PhaLP | - Phage lytic proteins | [41] | ||
| Tool | phageAI | - Lifecycle prediction tasks based on bacteriophage nucleotide sequences | [42] | |
| What the Phage | - Identification and analysis of phage sequences | [43] | ||
| PHASTEST | - Identification, interactive visualization, and annotation of prophage sequences within bacterial genomes or plasmids | [44] | ||
| PEPGM | - Taxonomic inference of viral proteome samples | [45] | ||
| VirHostMatcher | - Prediction of virus-prokaryote interactions | [46] | ||