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Clip width
Clip distance

Ligands

GG1_A_2001
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


GG1_B_2002
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


GG1_C_2003
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


GG1_D_2004
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


GG1_E_2005
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


GG1_F_2006
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


GG1_G_2007
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


GG1_H_2008
O=C1N(C)c2ccc(C#CCc3ccccc3)cc2C(=O)N1Cc4ccc(cc4)C(=O)[O-]


HAE_C_3001
ONC(=O)C


HAE_D_3002
ONC(=O)C


HAE_E_3003
ONC(=O)C


HAE_F_3004
ONC(=O)C


CA_A_4003
[Ca+2]


CA_A_4004
[Ca+2]


CA_A_4005
[Ca+2]


CA_B_4008
[Ca+2]


CA_B_4009
[Ca+2]


CA_B_4010
[Ca+2]


CA_C_4013
[Ca+2]


CA_C_4014
[Ca+2]


CA_C_4015
[Ca+2]


CA_D_4018
[Ca+2]


CA_D_4019
[Ca+2]


CA_D_4020
[Ca+2]


CA_E_4023
[Ca+2]


CA_E_4024
[Ca+2]


CA_E_4025
[Ca+2]


CA_F_4028
[Ca+2]


CA_F_4029
[Ca+2]


CA_F_4030
[Ca+2]


CA_G_4033
[Ca+2]


CA_G_4034
[Ca+2]


CA_G_4035
[Ca+2]


CA_H_4038
[Ca+2]


CA_H_4039
[Ca+2]


CA_H_4040
[Ca+2]


ZN_A_4001
[Zn+2]


ZN_A_4002
[Zn+2]


ZN_B_4006
[Zn+2]


ZN_B_4007
[Zn+2]


ZN_C_4011
[Zn+2]


ZN_C_4012
[Zn+2]


ZN_D_4016
[Zn+2]


ZN_D_4017
[Zn+2]


ZN_E_4021
[Zn+2]


ZN_E_4022
[Zn+2]


ZN_F_4026
[Zn+2]


ZN_F_4027
[Zn+2]


ZN_G_4031
[Zn+2]


ZN_G_4032
[Zn+2]


ZN_H_4036
[Zn+2]


ZN_H_4037
[Zn+2]


Pockets

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calculating Pockets, this may take a minute

Protoss is a fully automated hydrogen prediction tool for protein-ligand complexes. It adds missing hydrogen atoms to protein structures (PDB-format) and detects reasonable protonation states, tautomers, and hydrogen coordinates of both protein and ligand molecules. Protoss investigates hydrogen bonds, metal interactions and repulsive atom contacts for all possible states and calculates an optimal hydrogen bonding network within these degrees of freedom. Furthermore, alternative conformations or overlapping entries that might be annotated in the original protein structure are removed, as they could disturb the analysis of molecular interactions.1,2

1. Lippert, T.; Rarey, M., Fast automated placement of polar hydrogen atoms in protein-ligand complexes. J Cheminform 2009, 1 (1), 13. DOI: https://doi.org/10.1186/1758-2946-1-13
2. Bietz, S.; Urbaczek, S.; Schulz, B.; Rarey, M., Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes. J Cheminform 2014, 6, 12. DOI: https://doi.org/10.1186/1758-2946-6-12

Protoss

DoGSiteScorer is a grid-based method that uses a Difference of Gaussian filter to detect potential binding pockets1 - solely based on the 3D structure of the protein - and splits them into subpockets. Global properties, describing the size, shape and chemical features of the predicted (sub)pockets are calculated. Per default, a simple druggability score is provided for each (sub)pocket, based on a linear combination of the three descriptors describing volume, hydrophobicity and enclosure. Furthermore, a subset of meaningful descriptors is incorporated in a support vector machine (libsvm) to predict the (sub)pocket druggability score (values are between zero and one). The higher the score the more druggable the pocket is estimated to be.2

1. Volkamer, A.; Griewel, A.; Grombacher, T.; Rarey, M., Analyzing the topology of active sites: on the prediction of pockets and subpockets. J Chem Inf Model 2010, 50 (11), 2041-52. DOI: https://doi.org/10.1021/ci100241y
2. Volkamer, A.; Kuhn, D.; Grombacher, T.; Rippmann, F.; Rarey, M., Combining global and local measures for structure-based druggability predictions. J Chem Inf Model 2012, 52 (2), 360-72. DOI: https://doi.org/10.1021/ci200454v

DoGSiteScorer

PoseView automatically creates two-dimensional diagrams of complexes with known 3D structure according to chemical drawing conventions.1 Directed bonds between protein and ligand are drawn as dashed lines and the interacting protein residues and the ligand are visualized as structure diagrams. Hydrophobic contacts are represented more indirectly through spline sections highlighting the hydrophobic parts of the ligand and the label of the contacting residue. The generation of structure diagrams and their layout modifications are based on the library 2Ddraw.2 Interactions between the molecules are estimated by a builtin interaction model that is based on atom types and simple geometric criteria.

1. Stierand, K.; Maass, P. C.; Rarey, M., Molecular complexes at a glance: automated generation of two-dimensional complex diagrams. Bioinformatics 2006, 22 (14), 1710-6. DOI: https://doi.org/10.1093/bioinformatics/btl150
2. Fricker, P. C.; Gastreich, M.; Rarey, M., Automated drawing of structural molecular formulas under constraints. J Chem Inf Comput Sci 2004, 44 (3), 1065-78. DOI: https://doi.org/10.1021/ci049958u

PoseView

SIENA has been developed for the automated assembly and preprocessing of protein binding site ensembles. Starting with a single binding site, SIENA searches the PDB for alternative conformations of the same or sequentially closely related binding sites. The method is based on an indexed database for identifying perfect k-mer matches and a new algorithm for the detection of protein binding site conformations. Furthermore SIENA provides a variety of different filters for pruning the resulting conformational ensemble to meet a user’s case specific requirements. This involves a new algorithm for the interaction-based selection of binding site conformations as well as an RMSD-based clustering for ensemble reduction. SIENA provides the user with a sequence alignment of the binding site as well as superimposed PDB structures which are, apart from the transferred coordinates, equal to the original files from the PDB and thus contain all structural details and further information. 1,2

1. Bietz, S.; Rarey, M., ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations. J Chem Inf Model 2015, 55 (8), 1747-56. DOI: https://doi.org/10.1021/acs.jcim.5b00210
2. Bietz, S.; Rarey, M., SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. J Chem Inf Model 2016, 56 (1), 248-59. DOI: https://doi.org/10.1021/acs.jcim.5b00588

SIENA

HyPPI classifies a protein-protein complex concerning its interaction type into permanent, transient or crystal artifact. Permanent protein-protein complexes are only stable in their complexed state and the subunits would denature upon complex dissociation. Transient protein-protein complexes are stable in the complexed as well as in the monomeric form depending on the necessary function of the complex. Crystal artifacts have no biological function and are artifically formed during the crystallization process. The discrimination is performed using two characteristics of the protein-protein complex, the hydrophobicity of the interface (ΔGHydrophobic) and the quotient of interface area ratios (IF-quotient). ΔGHydrophobic represents the energy emerging exclusively from the hydrophobic effect upon binding of two protein subunits and was calculated according to the desolvation term of the HYDE scoring function1. The IF-quotient takes the symmetry of the protein-protein interface into account. 1

1. Schneider, N.; Lange, G.; Hindle, S.; Klein, R.; Rarey, M., A consistent description of HYdrogen bond and Dehydration energies in protein-ligand complexes: methods behind the HYDE scoring function. J Comput Aided Mol Des 2013, 27 (1), 15-29. DOI: https://doi.org/10.1007/s10822-012-9626-2

HyPPI
EDIA - the electron density score for individual atoms quantifies the electron density fit of an atom. Multiple EDIA values can be combined with the help of the power mean to compute EDIAm, the electron density score for multiple atoms to score a set of atoms such as a ligand or residue. Substructures such as residues and ligands can be automatically analyzed with EDIAm.
EDIA is calculated by computing the weighted sum over all relevant grid points in the sphere of interest around the atom. The radius of the sphere of interest is two times the resolution dependent electron density sphere radius. More details can be found in our publication.1 EDIA values above 0.8 mark well supported, EDIA values in the range of 0.4 to 0.8 mark moderately supported and EDIA values below 0.4 mark badly supported atoms. Due to the power mean, an EDIAm below 0.8 is based on at least three atoms with EDIA below 0.8.

1. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M., Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. J Chem Inf Model 2017, 57 (10), 2437-2447. DOI: https://doi.org/10.1021/acs.jcim.7b00391

EDIA

METALizer predicts the coordination geometry of metals in metalloproteins. Potential coordination geometries of metals are matched onto the found metal interactions in the examined structure. The predicted coordination geometries and the observed metal interaction distances can be compared interactively to statistics calculated based on the PDB.
Furthermore, METALizer is combined with other tools in the ProteinsPlus server: Using SIENA1, ensembles of proteins with sequentially and structurally closely related metal binding sites can be retrieved from the PDB, superimposed and visualized. This allows the comparison of the predicted coordination geometries and metal interaction distances to statistics calculated only on related metal binding sites. Furthermore, different binding modes of ligands to the metal and of the metal within the protein can be explored.
Another option is the EDIA2 filter to detect atoms that are poorly supported by electron density. These are then excluded from the METALizer analysis.

1. Bietz, S.; Rarey, M., SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. J Chem Inf Model 2016, 56 (1), 248-59. DOI: https://doi.org/10.1021/acs.jcim.5b00588
2. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M., Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. J Chem Inf Model 2017, 57 (10), 2437-2447. DOI: https://doi.org/10.1021/acs.jcim.7b00391

METALizer

ActivityFinder establishes a connection between crystallographic data stored in the PDB database and the activity values stored in the ChEMBL database. It utilizes information published by the platforms Ligand Expo, Swiss-Prot and ChEMBL. Ligands are extracted from the PDB and stored as unique SMILES (uSMILES). The ChEMBL ligand information is translated to uSMILES and matched with the data from PDB. Entries for which a link between PDB ID, UniProt ID and ChEMBL target ID exists are retained and saved to an SQLite database. Version 23 of ChEMBL was used. PDB and Swiss-Prot data are only as up to date as the published files (access date: 15.7.2017).

ActivityFinder

LifeSoaks calculates solvent channels and their corresponding bottleneck radii by construcing a three dimensional Voronoi diagram. You can use it to compute a bottleneck radius for a complete crystal structure as well as a ccp4 density file storing local bottleneck radii in order to analyze the structure with respect to specific regions of interest. 1

1. Pletzer, J. (Paper in preparation).

LifeSoaks

WarPP places water molecules for the active site of a given PDB file.1 Based on interaction geometries previously derived from protein crystal structures potential water positions are generated for free interaction directions (lone pairs of acceptors or hydrogen atoms of donors). Every small molecule present in the PDB structure is used to generate an active site in a 6.5 Å radius of each atom. Within these active sites water molecules are placed.
In the corresponding publication we showed that WarPP places 80% of water molecules within 1.0 Å distance to a crystallographically observed one.

1. Nittinger, E.; Flachsenberg, F.; Bietz, S.; Lange, G.; Klein, R.; Rarey, M., Placement of Water Molecules in Protein Structures: From Large-Scale Evaluations to Single-Case Examples. J Chem Inf Model 2018, 58 (8), 1625-1637. DOI: https://doi.org/10.1021/acs.jcim.8b00271

WarPP

StructureProfiler was developed as an all-in-one tool to screen structures based on selection criteria typically used upon dataset assembly for structure-based design methods.1 Test configurations based on either the Astex2, the Iridium HT3, the Platinum4, or the combination of all three criteria catalogs. (Note that real-space correlation coefficient (RSCC) as an electron density validation criteria was replaced by EDIAm5 scoring).

1. Meyder, A.; Kampen, S.; Sieg, J.; Fahrrolfes, R.; Friedrich, N. O.; Flachsenberg, F.; Rarey, M., StructureProfiler: an all-in-one tool for 3D protein structure profiling. Bioinformatics 2019, 35 (5), 874-876. DOI: https://doi.org/10.1093/bioinformatics/bty692
2. Hartshorn, M. J.; Verdonk, M. L.; Chessari, G.; Brewerton, S. C.; Mooij, W. T.; Mortenson, P. N.; Murray, C. W., Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem 2007, 50 (4), 726-41. DOI: https://doi.org/10.1021/jm061277y
3. Warren, G. L.; Do, T. D.; Kelley, B. P.; Nicholls, A.; Warren, S. D., Essential considerations for using protein-ligand structures in drug discovery. Drug Discov Today 2012, 17 (23-24), 1270-81. DOI: https://doi.org/10.1016/j.drudis.2012.06.011
4. Friedrich, N. O.; Meyder, A.; de Bruyn Kops, C.; Sommer, K.; Flachsenberg, F.; Rarey, M.; Kirchmair, J., High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators. J Chem Inf Model 2017, 57 (3), 529-539. DOI: https://doi.org/10.1021/acs.jcim.6b00613
5. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M., Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. J Chem Inf Model 2017, 57 (10), 2437-2447. DOI: https://doi.org/10.1021/acs.jcim.7b00391

StructureProfiler

GeoMine enables textual, numerical and 3D searching with full chemical awareness in binding sites of the entire PDB dataset. 1,2 The tool is based on the desktop application Pelikan.3 It enables various user-defined queries for on-the-fly binding site comparison for predicted and ligand-based binding sites in the PDB. Data from PDB files is preprocessed and stored in a PostgreSQL database.

1. Graef, J.; Ehrt, C.; Diedrich, K.; Poppinga, M.; Ritter, N.; Rarey, M., Searching Geometric Patterns in Protein Binding Sites and Their Application to Data Mining in Protein Kinase Structures. J Med Chem 2022, 65 (2), 1384-1395. DOI: https://doi.org/10.1021/acs.jmedchem.1c01046
2. Diedrich, K.; Graef, J.; Schoning-Stierand, K.; Rarey, M., GeoMine: interactive pattern mining of protein-ligand interfaces in the Protein Data Bank. Bioinformatics 2021, 37 (3), 424-425. DOI: https://doi.org/10.1093/bioinformatics/btaa693
3. Inhester, T.; Bietz, S.; Hilbig, M.; Schmidt, R.; Rarey, M., Index-Based Searching of Interaction Patterns in Large Collections of Protein-Ligand Interfaces. J Chem Inf Model 2017, 57 (2), 148-158. DOI: https://doi.org/10.1021/acs.jcim.6b00561

GeoMine

JAMDA is a novel and fully automated protein-ligand docking tool.1 It combines the TrixX docking algorithm 2,3 with the JAMDA scoring function.4 The docking and scoring performance of JAMDA is in line with the state of the art in the field. JAMDA has an unprecedented level of automation enabling molecular docking for everyone on the web. Furthermore, the JAMDA scoring function is combined with a new gradient-based optimizer achieving high numerical stability. Starting from a protein structure and the molecules to be docked, the structures are preprocessed and docked automatically. The docking site can be defined by a reference ligand or by a pocket from the pockets tab (that has been either built by hand or predicted by DoGSiteScorer5). The molecules for docking can be uploaded without any preprocessing i.e., neither coordinates nor protonation states are required.

This is a pre-release version of JAMDA and docking results may change in the future.

1. DOI: in preparation
2. Schellhammer, I.; Rarey, M., TrixX: structure-based molecule indexing for large-scale virtual screening in sublinear time. J Comput Aided Mol Des 2007, 21 (5), 223-38. DOI: https://doi.org/10.1007/s10822-007-9103-5
3. Henzler, A. M.; Urbaczek, S.; Hilbig, M.; Rarey, M., An integrated approach to knowledge-driven structure-based virtual screening. J Comput Aided Mol Des 2014, 28 (9), 927-39. DOI: https://doi.org/10.1007/s10822-014-9769-4
4. Flachsenberg, F.; Meyder, A.; Sommer, K.; Penner, P.; Rarey, M., A Consistent Scheme for Gradient-Based Optimization of Protein-Ligand Poses. J Chem Inf Model 2020, 60 (12), 6502-6522. DOI: https://doi.org/10.1021/acs.jcim.0c01095
5. Volkamer, A.; Griewel, A.; Grombacher, T.; Rarey, M., Analyzing the topology of active sites: on the prediction of pockets and subpockets. J Chem Inf Model 2010, 50 (11), 2041-52. DOI: https://doi.org/10.1021/ci100241y

JAMDA

MicroMiner searches mutations in protein structure databases like the PDB. Retrieved mutant structures can be easily analysed and compared to the wild-type through automatically superposed structures. Local mutation sites can be filtered by RMSD to identify structurally deviations upon mutation. The method uses the local 3D micro-environment of single residues in a query protein structure to search for similar local environments in a protein structure database where the central query residue is mutated. MicroMiner returns structures with similar environments that are identical in sequence -except for the position of the mutation- and have a similar structural arrangement. These mutant structures are automatically superposed to the query wild-type protein for easy comparison and analysis of the structural effects of mutations. The method originates from the ASCONA1/SIENA2 technology.

We are working on a publication. In the meantime the tool has already been presented at the ISMB/ECCB 2021 conference. Watch the talk here.

This is a BETA version of MicroMiner and results may change in the future.

1. Bietz, S.; Rarey, M., ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations. J Chem Inf Model 2015, 55 (8), 1747-56. DOI: https://doi.org/10.1021/acs.jcim.5b00210
2. Bietz, S.; Rarey, M., SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. J Chem Inf Model 2016, 56 (1), 248-59. DOI: https://doi.org/10.1021/acs.jcim.5b00588

MicroMiner

Protoss

Protoss is a fully automated hydrogen prediction tool for protein-ligand complexes. It adds missing hydrogen atoms to protein structures (PDB-format) and detects reasonable protonation states, tautomers, and hydrogen coordinates of both protein and ligand molecules. Protoss investigates hydrogen bonds, metal interactions and repulsive atom contacts for all possible states and calculates an optimal hydrogen bonding network within these degrees of freedom. Furthermore, alternative conformations or overlapping entries that might be annotated in the original protein structure are removed, as they could disturb the analysis of molecular interactions.1,2

1. Lippert, T.; Rarey, M., Fast automated placement of polar hydrogen atoms in protein-ligand complexes. J Cheminform 2009, 1 (1), 13. DOI: https://doi.org/10.1186/1758-2946-1-13
2. Bietz, S.; Urbaczek, S.; Schulz, B.; Rarey, M., Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes. J Cheminform 2014, 6, 12. DOI: https://doi.org/10.1186/1758-2946-6-12

Check the help pages for Protoss and its REST API and the video tutorial for further information.

DoGSiteScorer

DoGSiteScorer is a grid-based method that uses a Difference of Gaussian filter to detect potential binding pockets1 - solely based on the 3D structure of the protein - and splits them into subpockets. Global properties, describing the size, shape and chemical features of the predicted (sub)pockets are calculated. Per default, a simple druggability score is provided for each (sub)pocket, based on a linear combination of the three descriptors describing volume, hydrophobicity and enclosure. Furthermore, a subset of meaningful descriptors is incorporated in a support vector machine (libsvm) to predict the (sub)pocket druggability score (values are between zero and one). The higher the score the more druggable the pocket is estimated to be.2

1. Volkamer, A.; Griewel, A.; Grombacher, T.; Rarey, M., Analyzing the topology of active sites: on the prediction of pockets and subpockets. J Chem Inf Model 2010, 50 (11), 2041-52. DOI: https://doi.org/10.1021/ci100241y
2. Volkamer, A.; Kuhn, D.; Grombacher, T.; Rippmann, F.; Rarey, M., Combining global and local measures for structure-based druggability predictions. J Chem Inf Model 2012, 52 (2), 360-72. DOI: https://doi.org/10.1021/ci200454v

Check the help pages for DoGSiteScorer and its REST API and the video tutorial for further information.

Settings

Analysis detail:

Binding site prediction granularity :

Ligands :

Chain:


Info: DNA and RNA are not considered during DoGSite calculations.

SIENA

SIENA has been developed for the automated assembly and preprocessing of protein binding site ensembles. Starting with a single binding site, SIENA searches the PDB for alternative conformations of the same or sequentially closely related binding sites. The method is based on an indexed database for identifying perfect k-mer matches and a new algorithm for the detection of protein binding site conformations. Furthermore SIENA provides a variety of different filters for pruning the resulting conformational ensemble to meet a user’s case specific requirements. This involves a new algorithm for the interaction-based selection of binding site conformations as well as an RMSD-based clustering for ensemble reduction. SIENA provides the user with a sequence alignment of the binding site as well as superimposed PDB structures which are, apart from the transferred coordinates, equal to the original files from the PDB and thus contain all structural details and further information. 1,2

1. Bietz, S.; Rarey, M., ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations. J Chem Inf Model 2015, 55 (8), 1747-56. DOI: https://doi.org/10.1021/acs.jcim.5b00210
2. Bietz, S.; Rarey, M., SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. J Chem Inf Model 2016, 56 (1), 248-59. DOI: https://doi.org/10.1021/acs.jcim.5b00588

Check the help pages for SIENA and its REST API and the video tutorial for further information.

Settings


Query binding site:

no binding site selected


Search Options:

Binding site radius :

Alignment options:

Minimum fragment length :

Flexibility sensitivity :

Maximum fragment distance :

Filter Options:

Resolution threshold :

Earliest deposition year :

EC Number :

Maximum backbone RMSD :

Minimum Site Identity :

Minimum Site Coverage :

Maximum number of mutations :

Electron density available :

No mutations in the global alignment :

Identical global sequence :

Remove sequence duplicates :

Holo structures only :

Complete residues only :

Reduction Procedures:

Remove sites with ligand duplicates :

You can only choose one of the following reduction procedures. The other two will be deactivated.

None

Ensemble size for backbone clustering :

Ensemble size for all atom clustering :

Ensemble size for interaction-based selection :


PoseView

PoseView automatically creates two-dimensional diagrams of complexes with known 3D structure according to chemical drawing conventions.1 Directed bonds between protein and ligand are drawn as dashed lines and the interacting protein residues and the ligand are visualized as structure diagrams. Hydrophobic contacts are represented more indirectly through spline sections highlighting the hydrophobic parts of the ligand and the label of the contacting residue. The generation of structure diagrams and their layout modifications are based on the library 2Ddraw.2 Interactions between the molecules are estimated by a builtin interaction model that is based on atom types and simple geometric criteria.

1. Stierand, K.; Maass, P. C.; Rarey, M., Molecular complexes at a glance: automated generation of two-dimensional complex diagrams. Bioinformatics 2006, 22 (14), 1710-6. DOI: https://doi.org/10.1093/bioinformatics/btl150
2. Fricker, P. C.; Gastreich, M.; Rarey, M., Automated drawing of structural molecular formulas under constraints. J Chem Inf Comput Sci 2004, 44 (3), 1065-78. DOI: https://doi.org/10.1021/ci049958u

Check the help pages for PoseView and its REST API and the video tutorial for further information.

Settings

Ligands :

Protein-protein interactions

HyPPI classifies a protein-protein complex concerning its interaction type into permanent, transient or crystal artifact. Permanent protein-protein complexes are only stable in their complexed state and the subunits would denature upon complex dissociation. Transient protein-protein complexes are stable in the complexed as well as in the monomeric form depending on the necessary function of the complex. Crystal artifacts have no biological function and are artifically formed during the crystallization process. The discrimination is performed using two characteristics of the protein-protein complex, the hydrophobicity of the interface (ΔGHydrophobic) and the quotient of interface area ratios (IF-quotient). ΔGHydrophobic represents the energy emerging exclusively from the hydrophobic effect upon binding of two protein subunits and was calculated according to the desolvation term of the HYDE scoring function. The IF-quotient takes the symmetry of the protein-protein interface into account.1

1. Schneider, N.; Lange, G.; Hindle, S.; Klein, R.; Rarey, M., A consistent description of HYdrogen bond and Dehydration energies in protein-ligand complexes: methods behind the HYDE scoring function. J Comput Aided Mol Des 2013, 27 (1), 15-29. DOI: https://doi.org/10.1007/s10822-012-9626-2

Check the help pages for HyPPI and its REST API and the video tutorial for further information.

Settings

Interacting chain 1 :

Interacting chain 2 :

Sequence alignment :

EDIA

EDIA - the electron density score for individual atoms quantifies the electron density fit of an atom. Multiple EDIA values can be combined with the help of the power mean to compute EDIAm, the electron density score for multiple atoms to score a set of atoms such as a ligand or residue. Substructures such as residues and ligands can be automatically analyzed with EDIAm.

EDIA is calculated by computing the weighted sum over all relevant grid points in the sphere of interest around the atom. The radius of the sphere of interest is two times the resolution dependent electron density sphere radius. More details can be found in our publication.1 EDIA values above 0.8 mark well supported, EDIA values in the range of 0.4 to 0.8 mark moderately supported and EDIA values below 0.4 mark badly supported atoms. Due to the power mean, an EDIAm below 0.8 is based on at least three atoms with EDIA below 0.8.

1. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M., Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. J Chem Inf Model 2017, 57 (10), 2437-2447. DOI: https://doi.org/10.1021/acs.jcim.7b00391

Check the help pages for EDIA and its REST API and the video tutorial for further information.

METALizer

METALizer predicts the coordination geometry of metals in metalloproteins. Potential coordination geometries of metals are matched onto the found metal interactions in the examined structure. The predicted coordination geometries and the observed metal interaction distances can be compared interactively to statistics calculated based on the PDB.
Furthermore, METALizer is combined with other tools in the ProteinsPlus server: Using SIENA1, ensembles of proteins with sequentially and structurally closely related metal binding sites can be retrieved from the PDB, superimposed and visualized. This allows the comparison of the predicted coordination geometries and metal interaction distances to statistics calculated only on related metal binding sites. Furthermore, different binding modes of ligands to the metal and of the metal within the protein can be explored.
Another option is the EDIA2 filter to detect atoms that are poorly supported by electron density. These are then excluded from the METALizer analysis.

1. Bietz, S.; Rarey, M., SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. J Chem Inf Model 2016, 56 (1), 248-59. DOI: https://doi.org/10.1021/acs.jcim.5b00588
2. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M., Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. J Chem Inf Model 2017, 57 (10), 2437-2447. DOI: https://doi.org/10.1021/acs.jcim.7b00391

Check the help pages for METALizer and its REST API and the video tutorial for further information.

Settings


Query metal atom:

No metal atom selected!


 

Consider crystallographic symmetry :

EDIA calculation :

SIENA ensemble calculation :

    Use per-metal distance cutoff :

ActivityFinder Alpha-Version

ActivityFinder establishes a connection between crystallographic data stored in the PDB database and the activity values stored in the ChEMBL database. It utilizes information published by the platforms Ligand Expo, Swiss-Prot and ChEMBL. Ligands are extracted from the PDB and stored as unique SMILES (uSMILES). The ChEMBL ligand information is translated to uSMILES and matched with the data from PDB. Entries for which a link between PDB ID, UniProt ID and ChEMBL target ID exists are retained and saved to an SQLite database. Version 23 of ChEMBL was used. PDB and Swiss-Prot data are only as up to date as the published files (access date: 15.7.2017).

Check the help pages for ActivityFinder and its REST API for further information.

MicroMinerBETA

MicroMiner searches mutations in protein structure databases like the PDB. Retrieved mutant structures can be easily analysed and compared to the wild-type through automatically superposed structures. Local mutation sites can be filtered by RMSD to identify structurally deviations upon mutation.


The method uses the local 3D micro-environment of single residues in a query protein structure to search for similar local environments in a protein structure database where the central query residue is mutated. MicroMiner returns structures with similar environments that are identical in sequence -except for the position of the mutation- and have a similar structural arrangement. These mutant structures are automatically superposed to the query wild-type protein for easy comparison and analysis of the structural effects of mutations. The method originates from the ASCONA1/SIENA2 technology.

1. Bietz, S.; Rarey, M., ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations. J Chem Inf Model 2015, 55 (8), 1747-56. DOI: https://doi.org/10.1021/acs.jcim.5b00210
2. Bietz, S.; Rarey, M., SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. J Chem Inf Model 2016, 56 (1), 248-59. DOI: https://doi.org/10.1021/acs.jcim.5b00588

Run a Query

  1. Search for all residue positions of the protein at once by just clicking "Calculate".
  2. Select a list of interesting residues. This will usually run faster for larger protein complexes with >1000 residues.
For "normal" sized proteins just click 'Calculate' to search for all residues.
Enter residue identifier(s)

LifeSoaks BETA

LifeSoaks calculates solvent channels and their corresponding bottleneck radii by constructing a three dimensional Voronoi diagram. You can use it to compute a bottleneck radius for a complete crystal structure as well as a ccp4 grid file storing local bottleneck radii for analyzing the structure with respect to specific regions of interest.

1. Pletzer-Zelgert......

Settings

Search Options:

Unified Radius :

Minimal Radius :

Reference Ligand :

Ligands to treat as fixed :

JAMDA BETA

JAMDA docks small molecules into protein binding sites.

JAMDA is a novel and fully automated protein-ligand docking tool.1 It combines the TrixX docking algorithm2, 3 with the JAMDA scoring function.4 The docking and scoring performance of JAMDA is in line with the state of the art in the field. JAMDA has an unprecedented level of automation enabling molecular docking for everyone on the web. Furthermore, the JAMDA scoring function is combined with a new gradient-based optimizer achieving high numerical stability. Starting from a protein structure and the molecules to be docked, the structures are preprocessed and docked automatically. The docking site can be defined by a reference ligand or by a pocket from the pockets tab (that has been either built by hand or predicted by DoGSiteScorer5). The molecules for docking can be uploaded without any preprocessing i.e., neither coordinates nor protonation states are required.

This is a pre-release version of JAMDA and docking results may change in the future.

1. DOI: in preparation
2. Schellhammer, I.; Rarey, M., TrixX: structure-based molecule indexing for large-scale virtual screening in sublinear time. J Comput Aided Mol Des 2007, 21 (5), 223-38. DOI: https://doi.org/10.1007/s10822-007-9103-5
3. Henzler, A. M.; Urbaczek, S.; Hilbig, M.; Rarey, M., An integrated approach to knowledge-driven structure-based virtual screening. J Comput Aided Mol Des 2014, 28 (9), 927-39. DOI: https://doi.org/10.1007/s10822-014-9769-4
4. Flachsenberg, F.; Meyder, A.; Sommer, K.; Penner, P.; Rarey, M., A Consistent Scheme for Gradient-Based Optimization of Protein-Ligand Poses. J Chem Inf Model 2020, 60 (12), 6502-6522. DOI: https://doi.org/10.1021/acs.jcim.0c01095
5. Volkamer, A.; Griewel, A.; Grombacher, T.; Rarey, M., Analyzing the topology of active sites: on the prediction of pockets and subpockets. J Chem Inf Model 2010, 50 (11), 2041-52. DOI: https://doi.org/10.1021/ci100241y

Check the help pages for JAMDA for further information.

Settings

JAMDA needs a binding site (as reference ligand or pocket definition) and the molecules to be docked as input. More advanced options can be set below. Point at the signs to get help and more information about the parameters.
Binding site

no binding site selected

Molecules

no molecule selected

Only the first 50 molecules in a file are shown and at most 5 molecules may be docked!
Please select molecules for docking

Protein preparation

Use ligand
Ligands
Water
Protonation
Cofactor/water whitelist

Molecule preparation

Protonation states/tautomers

Docking

Precision level
Site radius
Restrict site

WarPP

WarPP is a fully automated procedure to place water molecules in the active site of a protein structure.

WarPP places water molecules for the active site of a given PDB file.1 Based on interaction geometries previously derived from protein crystal structures potential water positions are generated for free interaction directions (lone pairs of acceptors or hydrogen atoms of donors). Every small molecule present in the PDB structure is used to generate an active site in a 6.5 Å radius of each atom. Within these active sites water molecules are placed.
In the corresponding publication we showed that WarPP places 80% of water molecules within 1.0 Å distance to a crystallographically observed one.

1. Nittinger, E.; Flachsenberg, F.; Bietz, S.; Lange, G.; Klein, R.; Rarey, M., Placement of Water Molecules in Protein Structures: From Large-Scale Evaluations to Single-Case Examples. J Chem Inf Model 2018, 58 (8), 1625-1637. DOI: https://doi.org/10.1021/acs.jcim.8b00271

Check the help pages for WarPP and its REST API for further information.

GeoMine

GeoMine enables textual, numerical and 3D searching with full chemical awareness in binding sites of the entire PDB dataset. 1,2

The tool is based on the desktop application Pelikan.3 It enables various user-defined queries for on-the-fly binding site comparison for predicted and ligand-based binding sites in the PDB. Data from PDB files is preprocessed and stored in a PostgreSQL database.

1. Graef, J.; Ehrt, C.; Diedrich, K.; Poppinga, M.; Ritter, N.; Rarey, M., Searching Geometric Patterns in Protein Binding Sites and Their Application to Data Mining in Protein Kinase Structures. J Med Chem 2022, 65 (2), 1384-1395. DOI: https://doi.org/10.1021/acs.jmedchem.1c01046
2. Diedrich, K.; Graef, J.; Schoning-Stierand, K.; Rarey, M., GeoMine: interactive pattern mining of protein-ligand interfaces in the Protein Data Bank. Bioinformatics 2021, 37 (3), 424-425. DOI: https://doi.org/10.1093/bioinformatics/btaa693
3. Inhester, T.; Bietz, S.; Hilbig, M.; Schmidt, R.; Rarey, M., Index-Based Searching of Interaction Patterns in Large Collections of Protein-Ligand Interfaces. J Chem Inf Model 2017, 57 (2), 148-158. DOI: https://doi.org/10.1021/acs.jcim.6b00561

Check the help pages for GeoMine and its REST API for further information.

Settings


A 3D query, a PDB subselection, which restricts the search to a user-defined set of PDB codes, and filters for textual/numerical properties of any component of the PDB structure or the final results can be added to the search. All query types can be used independently or in combination.
Click "Toggle Viewer Size" and go to fullscreen for an easier 3D query selection.
Click "Export query"/"Import query" for saving and reloading a query.
Click "Rest-service XML" for saving a query as XML usable by the GeoMine REST-serivce.
Click "Search" in any of the query tabs in order to start the GeoMine search.

3D query selection

Selection Mode:

Point selection with all properties
Point ID Element Type Molecule ChildRowData NglID LigandType Color
Distance ID Point ID 1 Point ID 2 Length Tolerance NglID Color
Interaction ID Point ID 1 Point ID 2 Type NglID Color
Angle ID Point-Point ID 1 Point-Point ID 2 Size Tolerance NglID Color

PDB filter selection

Filtertype

PDB subselection

Number of PDBs in List:

StructureProfiler

StructureProfiler was developed as an all-in-one tool to screen structures based on selection criteria typically used upon dataset assembly for structure-based design methods.1

Test configurations based on either the Astex2, the Iridium HT3, the Platinum4, or the combination of all three criteria catalogs. (Note that real-space correlation coefficient (RSCC) as an electron density validation criteria was replaced by EDIAm5 scoring).

1. Meyder, A.; Kampen, S.; Sieg, J.; Fahrrolfes, R.; Friedrich, N. O.; Flachsenberg, F.; Rarey, M., StructureProfiler: an all-in-one tool for 3D protein structure profiling. Bioinformatics 2019, 35 (5), 874-876. DOI: https://doi.org/10.1093/bioinformatics/bty692
2. Hartshorn, M. J.; Verdonk, M. L.; Chessari, G.; Brewerton, S. C.; Mooij, W. T.; Mortenson, P. N.; Murray, C. W., Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem 2007, 50 (4), 726-41. DOI: https://doi.org/10.1021/jm061277y
3. Warren, G. L.; Do, T. D.; Kelley, B. P.; Nicholls, A.; Warren, S. D., Essential considerations for using protein-ligand structures in drug discovery. Drug Discov Today 2012, 17 (23-24), 1270-81. DOI: https://doi.org/10.1016/j.drudis.2012.06.011
4. Friedrich, N. O.; Meyder, A.; de Bruyn Kops, C.; Sommer, K.; Flachsenberg, F.; Rarey, M.; Kirchmair, J., High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators. J Chem Inf Model 2017, 57 (3), 529-539. DOI: https://doi.org/10.1021/acs.jcim.6b00613
5. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M., Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. J Chem Inf Model 2017, 57 (10), 2437-2447. DOI: https://doi.org/10.1021/acs.jcim.7b00391

Check the help pages for StructureProfiler and its REST API and the video tutorial for further information.

Settings

Filtering mode:

Depending on the application, different quality checks should be applied. The Astex criteria catalog analyses the quality and the drug likeliness of the ligands with additional checks of the active site and the overall model. The Iridium HT set of criteria is geared toward overall model and active site quality and was originally applied on top of the Astex quality tests. The Platinum criteria scan for model and ligand quality while ignoring the respective active site to find high qualitative ligands for conformer generation validation.

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