Structural databases and biological networks from our publications, available for bulk download.
ProBiS-Dock Database dataset
The ProBiS-Dock Database dataset contains docking-ready protein binding sites for small molecules and cofactors, covering the Protein Data Bank. Download the full collection or choose a subset by organism or protein class.
Binding-site preparation and dataset contents
The ProBiS-Dock Database dataset consists of docking-ready binding sites on proteins for small molecules classified as cofactors (ex. ATP, NAD...) and other small molecules (ex. substrates, receptor agonists...). It covers the Protein Data Bank and is ready for use with the ProBiS-Dock (inverse) docking software enabling proteome-wide target prediction. With minor modifications it can also be used with other docking tools. The binding sites on proteins are accurately defined for each query protein using multiple centroids (x,y,z,radius) that capture different binding site shapes. Centroids are defined based on (multiple) template ligands obtained from similar binding sites in the PDB, so that the space occupied by all these template ligands is considered as a binding site. Each dataset entry contains a receptor, centroids, and template ligands. Receptor can be a single chain or a multi-protein complex, while centroids define the location of the binding site on the receptor. Template ligands have been transposed from similar binding sites and are used in the scoring function of ProBiS-Dock. The dataset uniquely enables docking into substrate OR cofactor binding sites to facilitate discovery of both types of competitive inhibitors. It is further divided into different subsets, enabling selective docking against different species or different protein classes.
| Subset | Description / source | Small molecules* | Cofactors |
|---|---|---|---|
| All | All proteins in the Protein Data Bank | all.pro.gz | cof_all.pro.gz |
| Human | Human proteins determined as those where source organism is HOMO SAPIENS | human.pro.gz | cof_human.pro.gz |
| Cancer | Cancer-related human genes obtained from The Human Protein Atlas (https://www.proteinatlas.org) | cancer.pro.gz | cof_cancer.pro.gz |
| Human kinases | View EC numbers1.1.1.3, 1.2.1.38, 2.1.2.-, 2.1.7.127, 2.3.1.1, 2.3.1.48, 2.4.2.9, 2.5.1.15, 2.5.1.3, 2.7.-.-, 2.7.1.-, 2.7.10.-, 2.7.10.1, 2.7.10.2, 2.7.1.1, 2.7.11.-, 2.7.1.100, 2.7.1.105, 2.7.1.107, 2.7.1.11, 2.7.11.1, 2.7.11.10, 2.7.11.11, 2.7.1.112, 2.7.11.12, 2.7.1.113, 2.7.11.13, 2.7.11.14, 2.7.1.115, 2.7.11.15, 2.7.11.16, 2.7.11.17, 2.7.11.19, 2.7.1.12, 2.7.11.2, 2.7.11.21, 2.7.11.22, 2.7.1.123, 2.7.11.23, 2.7.11.24, 2.7.11.25, 2.7.1.126, 2.7.11.26, 2.7.1.127, 2.7.11.27, 2.7.1.130, 2.7.11.30, 2.7.11.31, 2.7.11.32, 2.7.1.134, 2.7.1.137, 2.7.11.4, 2.7.1.140, 2.7.1.143, 2.7.1.144, 2.7.1.145, 2.7.1.146, 2.7.1.147, 2.7.1.148, 2.7.1.149, 2.7.1.15, 2.7.11.5, 2.7.1.151, 2.7.1.153, 2.7.1.154, 2.7.1.158, 2.7.1.159, 2.7.1.16, 2.7.1.161, 2.7.1.162, 2.7.1.163, 2.7.1.164, 2.7.1.167, 2.7.1.17, 2.7.11.7, 2.7.1.170, 2.7.1.173, 2.7.1.175, 2.7.1.183, 2.7.1.189, 2.7.1.19, 2.7.1.2, 2.7.1.20, 2.7.1.21, 2.7.12.1, 2.7.1.22, 2.7.12.2, 2.7.1.23, 2.7.1.24, 2.7.1.25, 2.7.1.26, 2.7.1.27, 2.7.1.29, 2.7.1.3, 2.7.1.30, 2.7.1.31, 2.7.13.1, 2.7.1.32, 2.7.1.33, 2.7.13.3, 2.7.1.35, 2.7.1.36, 2.7.1.37, 2.7.1.38, 2.7.1.39, 2.7.1.4, 2.7.1.40, 2.7.1.45, 2.7.1.48, 2.7.1.49, 2.7.1.5, 2.7.1.50, 2.7.1.51, 2.7.1.55, 2.7.1.56, 2.7.1.58, 2.7.1.59, 2.7.1.6, 2.7.1.60, 2.7.1.63, 2.7.1.64, 2.7.1.67, 2.7.1.68, 2.7.1.69, 2.7.1.71, 2.7.1.74, 2.7.1.76, 2.7.1.78, 2.7.1.82, 2.7.1.85, 2.7.1.86, 2.7.1.90, 2.7.1.91, 2.7.1.95, 2.7.1.99, 2.7.2.-, 2.7.2.1, 2.7.2.11, 2.7.2.12, 2.7.2.15, 2.7.2.2, 2.7.2.3, 2.7.2.4, 2.7.2.7, 2.7.2.8, 2.7.3.-, 2.7.3.1, 2.7.3.2, 2.7.3.3, 2.7.3.4, 2.7.3.5, 2.7.3.9, 2.7.4.-, 2.7.4.1, 2.7.4.10, 2.7.4.13, 2.7.4.14, 2.7.4.16, 2.7.4.2, 2.7.4.21, 2.7.4.22, 2.7.4.24, 2.7.4.25, 2.7.4.27, 2.7.4.3, 2.7.4.4, 2.7.4.6, 2.7.4.7, 2.7.4.8, 2.7.4.9, 2.7.6.1, 2.7.6.2, 2.7.6.3, 2.7.6.5, 2.7.7.10, 2.7.7.12, 2.7.7.2, 2.7.7.4, 2.7.7.6, 2.7.7.62, 2.7.9.1, 2.7.9.2, 2.7.9.3, 3.1.26.-, 3.1.3.-, 3.1.3.46, 3.1.3.48, 3.1.3.5, 3.4.21.-, 3.4.24.57, 3.6.-.-, 3.6.4.12, 3.6.5.2, 4.-.-.-, 4.1.1.32, 4.1.1.33, 4.1.2.25, 4.1.2.40, 5.3.1.8, 5.3.1.9 |
kinases.pro.gz | cof_kinases.pro.gz |
| Animals | NCBI taxonomy: invertebrates, vertebrates, mammals, primates, rodents | animals.pro.gz | cof_animals.pro.gz |
| Bacteria | NCBI taxonomy: bacteria | bacteria.pro.gz | cof_bacteria.pro.gz |
| Pathogenic bacteria | View source organismsBACILLUS ANTHRACIS, BACILLUS CEREUS, BARTONELLA HENSELAE, BARTONELLA QUINTANA, BORDETELLA PERTUSSIS, BORRELIA BURGDORFERI, BORRELIA GARINII, BORRELIA AFZELII, BORRELIA RECURRENTIS, BRUCELLA ABORTUS, BRUCELLA CANIS, BRUCELLA MELITENSIS, BRUCELLA SUIS, CAMPYLOBACTER JEJUNI, CHLAMYDIA PNEUMONIAE, CHLAMYDIA TRACHOMATIS, CHLAMYDOPHILA PSITTACI, CLOSTRIDIUM BOTULINUM, CLOSTRIDIUM DIFFICILE, CLOSTRIDIUM PERFRINGENS, CLOSTRIDIUM TETANI, CORYNEBACTERIUM DIPHTHERIAE, ENTEROCOCCUS FAECALIS, ENTEROCOCCUS FAECIUM, ESCHERICHIA COLI, FRANCISELLA TULARENSIS, HAEMOPHILUS INFLUENZAE, HELICOBACTER PYLORI, LEGIONELLA PNEUMOPHILA, LEPTOSPIRA INTERROGANS, LEPTOSPIRA SANTAROSAI, LEPTOSPIRA WEILII, LEPTOSPIRA NOGUCHII, MYCOBACTERIUM LEPRAE, MYCOBACTERIUM TUBERCULOSIS, MYCOBACTERIUM ULCERANS, MYCOPLASMA PNEUMONIAE, NEISSERIA GONORRHOEAE, NEISSERIA MENINGITIDIS, PSEUDOMONAS AERUGINOSA, RICKETTSIA RICKETTSII, SALMONELLA TYPHI, SALMONELLA TYPHIMURIUM, SHIGELLA SONNEI, STAPHYLOCOCCUS AUREUS, STAPHYLOCOCCUS EPIDERMIDIS, STAPHYLOCOCCUS SAPROPHYTICUS, STREPTOCOCCUS AGALACTIAE, STREPTOCOCCUS PNEUMONIAE, STREPTOCOCCUS PYOGENES, TREPONEMA PALLIDUM, UREAPLASMA UREALYTICUM, VIBRIO CHOLERAE, YERSINIA PESTIS, YERSINIA ENTEROCOLITICA, YERSINIA PSEUDOTUBERCULOSIS |
pathogenic_bacteria.pro.gz | cof_pathogenic_bacteria.pro.gz |
| Viruses | NCBI taxonomy: viruses | viruses.pro.gz | cof_viruses.pro.gz |
| Plants and Fungi | NCBI taxonomy: plants and fungi | plants_fungi.pro.gz | cof_plants_fungi.pro.gz |
* Small-molecule binding sites exclude cofactor binding sites.
GenProBiS dataset
The GenProBiS dataset helps determine whether a sequence variant is in a binding site and if it could disrupt ligand-binding. The dataset consists of mappings of sequence variants → protein structures from the Protein Data Bank → predicted (ProBiS) and experimental binding sites → predicted and experimental ligands classified as proteins, nucleic acids, compounds, ions and conserved waters.
| Resource | Description | Download / browse |
|---|---|---|
| GenProBiSdb | Whole dataset dump in TSV format. | genprobisdb.tsv.gz |
| Sequence variant mappings | Mappings of sequence variants from UniProt numbering to PDB numbering each protein structure separately in JSON format | Browse files |
ProBiS-ligands dataset
ProBiS-ligands dataset consists of all-against-all local structural alignments of the proteins in the Protein Data Bank and derived information. For each protein chain it contains its local superimpositions with every protein structural chain (these can go far below 30 % seq. id.). It contains ligands (proteins, nucleic acids, compounds, cofactors, ions and waters) transposed from the locally similar structures onto the query protein chain; binding site residues; evolutionary conservation for each protein residue calculated based on the structural alignments; interactions between transposed ligands and the query protein chain; grids and centroids of predicted binding sites.
| Resource | Description | Browse files |
|---|---|---|
| Local structural alignments | Local structural alignments of protein chains for the entire PDB calculated using ProBiS algorithm | Browse files |
| Transposed ligands | Predicted ligands (by binding site similarity) for each protein chain in the PDB. The ligands are clustered by spatial distance and each cluster represents one binding site | Browse files |
| Binding-site residues | Binding site residues on proteins based on binding sites defined as clusters of transposed ligands | Browse files |
| Evolutionary conservation | Evolutionary conservation level for each protein residue; levels go from 0 - not conserved to 9 - highly conserved residue | Browse files |
| Ligand–protein interactions | Interactions between proteins and predicted (transposed) ligands | Browse files |
| Binding-site grids | Binding site grids that accurately describe the identified binding sites | Browse files |
| Binding-site centroids | Centroids that (similar to grids but more sparsely) describe the space taken by identified binding sites | Browse files |
| Protein superimpositions | Local superimpositions of protein chains as defined by the local alignments | Browse files |
| Binding-site surfaces | Surfaces (triangulated) of identified binding sites | Browse files |
BoBER dataset
This is the BoBER (Base of Bioisosteric Replacements) dataset. This dataset includes bioisosteres (different fragments that should have similar binding properties) that were detected by mining the Protein Data Bank using ProBiS algorithm. Two fragments are defined as bioisosteric if they were found to bind to structurally (geometrically & physicochemically) similar binding sites.
| Resource | Description | Download |
|---|---|---|
| BoBERdb | Bioisosteric replacements discovered by mining the PDB | BoBER.zip |
Complex biological networks
Biological networks constructed in our studies, suitable as benchmarks for network exploration algorithms. Each line in the network files represents a weighted edge:
Vertex#1 Vertex#2 Weight| Study | Description | Citation | Download |
|---|---|---|---|
| Insights from Ion Binding Site Network Analysis into Evolution and Functions of Proteins | Many biological phenomena can be represented as complex networks. Using a protein binding site comparison approach, we generated a network of ion binding sites on the scale of all known protein structures from the Protein Data Bank. | Skrlj et al. Mol. Inform., 2018 | Link to the network (Table S6) |
| Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins | The global organization of protein binding sites is analyzed by constructing a weighted network of binding sites based on their structural similarities and detecting communities of structurally similar binding sites based on the minimum description length principle. | Lee et al. Sci. Rep., 2017 | Link to the network |
Citing the datasets
If you use these datasets, please cite the relevant tool paper and consider including this general dataset citation.
Show BibTeX citation
@misc{insidata,
author = {Blaž Škrlj and Janez Konc},
title = {{Insidrug.org: Datasets}: Structural databases for drug design},
howpublished = {\url{http://insilab.org/datasets}},
month = sep,
year = 2018
}