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Ligands

processing Ligands

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

PPI 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.

PPI Interactions
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 EDIA_m, 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 EDIA_m below 0.8 is based on at least three atoms with EDIA below 0.8.

EDIA

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 :

processing

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. (2015). SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles. Journal of Chemical Information and Modeling, 2016, 56 (1), pp 248–259.

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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.

Ajax loader ac87229a9d77ed14f5e6af8315ab265b7f3a1a5bc2262e7d291fcd18004b89e4

Protein-protein interactions

PPI 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

Interacting chain 1 :

Interacting chain 2 :

Sequence alignment :

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. More details can be found in our soon to be published publication.

Letzte Änderung: 31 Januar 2017
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Universität Hamburg