Clip width
Clip distance

Ligands

4F1_A_601
Clc1cccc(c1C(=O)N2N=C(c3ccc(cc3)C(O)=O)c4ccccc24)C(F)(F)F

 4f1 a 601


GOL_A_605
OCC(O)CO

 gol a 605


GOL_A_604
OCC(O)CO

 gol a 604


GOL_A_603
OCC(O)CO

 gol a 603


GOL_A_602
OCC(O)CO

 gol a 602


SO4_A_606
S(O)(O)(=O)=O

 so4 a 606


Pockets

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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 which might be annotated in the original protein structure are removed, as they could disturb the analysis of molecular interactions12.

1. Lippert, T., Rarey, M.: Fast automated placement of polar hydrogen atoms in protein-ligand complexes. Journal of Cheminformatics 2009, 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. Journal of Cheminformatics 2014, 6:12.

Protoss

DoGSiteScorer is a grid-based method which uses a Difference of Gaussian filter to detect potential binding pockets - 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. 1

1. A. Volkamer, D. Kuhn, T. Grombacher, F. Rippmann, M. Rarey. Combining global and local measures for structure-based druggability predictions. J. Chem. Inf. Model. 2012,52,360-372.

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 by means of spline sections highlighting the hydrophobic parts of the ligand and the label of the contacting amino acid. 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., Rarey, M. (2006) Molecular Complexes at a Glance: Automated Generation of two-dimensional Complex Diagrams. Bioinformatics, 22, 1710-1716.
2. Fricker, P., Gastreich, M., and Rarey, M. (2004) Automated Generation of Structural Molecular Formulas under Constraints. Journal of Chemical Information and Computer Sciences, 44, 1065-1078.

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 of 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 in order 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 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 of 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. (2015). ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations. Journal of Chemical Information and Modeling, 55(8):1747–1756.
2. Bietz, S.; Rarey, M. (2015). SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. Journal of Chemical Information and Modeling, submitted.

SIENA

HyPPI Prediction Server 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 form as well as in the monomeric depending of the necessary function of the complex. Crystal artifacts have no biological function and are artifically formed during 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. (2013). A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function. Journal of Computer-Aided Molecular Design, 27(1):15-29.

HyPPI
The electron density score for individual atoms (EDIA) quantifies the electron density fit of an atom. Multiple EDIA 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. Due to the power mean, an EDIAm below 0.8 has at least three atoms with an EDIA below 0.8.
EDIA is calculated in 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 soon to be published publication. EDIA above 0.8 mark well supported, EDIA in range 0.4 to 0.8 mark medium supported and EDIA 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

1. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M. (2017). Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. Journal of Chemical Information and Modeling, 57(10): 2437–2447.

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 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. (2016). SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. Journal of Chemical Information and Modeling, 56 (1), pp 248–259.
2. Meyder A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M. (2017). Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. Journal of Chemical Information and Modeling, 57 (10), pp 2437-2447.

METALizer

The activity finder establishes a connection between crystallographic data stored in the PDB database and the activity values that can be found in the ChEMBL database. The activity finder links structural data of the PDB to 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 a 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

tructureChecker was developed as an all-in-one tool to screen structures based on selection criteria typically used upon dataset assemblage for structure-based design methods. Test configurations based on either the Astex1, the Iridium HT2, the Platinum3, or the combination of all three criteria catalogs. (Note that RSCC as an electron density validation criteria was replaced by EDIAm4 scoring).

1. Hartshorn, M. J. et al. (2007) Diverse, high-quality test set for the validation of protein-ligand docking performance. Journal of Medicinal Chemistry, 50(4): 726–741.
2. Warren, G. L.; Do, T. D.; Kelley, B. P.; Nicholls, A.; Warren, S. D.; (2012). Essential considerations for using protein-ligand structures in drug discovery. Drug Discovery Today, 17(23-24), 1270–1281.
3. Friedrich, N.-O. et al. (2017). High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators. Journal of Chemical Information and Modeling, 57(3): 529–539.
4. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M. (2017). Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. Journal of Chemical Information and Modeling, 57(10): 2437–2447.

StructureChecker

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 which might be annotated in the original protein structure are removed, as they could disturb the analysis of molecular interactions1 2.

1. Lippert, T., Rarey, M.: Fast automated placement of polar hydrogen atoms in protein-ligand complexes. Journal of Cheminformatics 2009, 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. Journal of Cheminformatics 2014, 6:12.

DoGSiteScorer

DoGSiteScorer is a grid-based method which uses a Difference of Gaussian filter to detect potential binding pockets - 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. 1

1. A. Volkamer, D. Kuhn, T. Grombacher, F. Rippmann, M. Rarey. Combining global and local measures for structure-based druggability predictions. J. Chem. Inf. Model. 2012,52,360-372.

Settings

Analysis detail:

Binding site prediction granularity :

Ligands :

Chain:

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 of 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 in order 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 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 of 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. (2015). ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations. Journal of Chemical Information and Modeling, 55(8):1747–1756.
2. Bietz, S.; Rarey, M. (2016). SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. Journal of Chemical Information and Modeling, 56 (1), pp 248–259.

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 by means of spline sections highlighting the hydrophobic parts of the ligand and the label of the contacting amino acid. 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., Rarey, M. (2006) Molecular Complexes at a Glance: Automated Generation of two-dimensional Complex Diagrams. Bioinformatics, 22, 1710-1716.
2. Fricker, P., Gastreich, M., and Rarey, M. (2004) Automated Generation of Structural Molecular Formulas under Constraints. Journal of Chemical Information and Computer Sciences, 44, 1065-1078.

Settings

Ligands :

Protein-protein interactions

HyPPI Prediction Server 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 form as well as in the monomeric depending of the necessary function of the complex. Crystal artifacts have no biological function and are artifically formed during 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.1 The IF-quotient takes the symmetry of the protein-protein interface into account.

1.Schneider, N., Lange, G., Hindle, S., Klein, R., Rarey, M. (2013). A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function. Journal of Computer-Aided Molecular Design, 27(1):15-29.

Settings

The calculation of protein-protein interactions is only possible for pdb-entries with 2 or more chains.

EDIA

The electron density score for individual atoms (EDIA) quantifies the electron density fit of an atom. Atomic EDIA values can be combined with the help of the power mean to compute EDIAm, the electron density score for small molecules, fragments, or residues.

EDIA values are easy to interpret. For single atoms, values above 0.8 mark well supported, values in range 0.4 to 0.8 mark medium supported and values below 0.4 mark badly supported atoms. Due to the power mean, an EDIAm value for a molecular substructure below 0.8 results if at least three atoms with EDIA values below 0.8. appear. EDIA is calculated for each atom by computing a weighted sum over an oversampled electron density grid in the proximity of the atom. The proximity is defined element and resolution dependent, covalent bonds are considered.1

1. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M. (2017). Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. Journal of Chemical Information and Modeling, 57(10): 2437–2447.

Settings

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 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. (2016). SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. Journal of Chemical Information and Modeling, 56 (1), pp 248–259.
2. Meyder A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M. (2017). Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. Journal of Chemical Information and Modeling, 57 (10), pp 2437-2447.

Settings


Query metal atom:

No metal atom selected!

EDIA filter :

SIENA ensemble calculation :

ActivityFinder

The activity finder establishes a connection between crystallographic data stored in the PDB database and the activity values that can be found in the ChEMBL database. The activity finder links structural data of the PDB to 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 a 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).

StructureChecker

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

Test configurations based on either the Astex1, the Iridium HT2, the Platinum3, or the combination of all three criteria catalogs are available (Note that RSCC as an electron density validation criteria was replaced by EDIAm4 scoring).


1. Hartshorn, M. J. et al. (2007) Diverse, high-quality test set for the validation of protein-ligand docking performance. Journal of Medicinal Chemistry, 50(4): 726–741.
2. Warren, G. L.; Do, T. D.; Kelley, B. P.; Nicholls, A.; Warren, S. D.; (2012). Essential considerations for using protein-ligand structures in drug discovery. Drug Discovery Today, 17(23-24), 1270–1281.
3. Friedrich, N.-O. et al. (2017). High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators. Journal of Chemical Information and Modeling, 57(3): 529–539.
4. Meyder, A.; Nittinger, E.; Lange, G.; Klein, R.; Rarey, M. (2017). Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. Journal of Chemical Information and Modeling, 57(10): 2437–2447.

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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