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.<sup>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</sup>

DoGSite3¹ 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 macromolecules (protein and nucleic acid chains with > 50 atoms are considered) - and splits them into subpockets. It is a reimplementation of the DoGSite algorithm^{2, 3} with optimized parameters and an optional method to make use of a ligand bias. Global properties, describing the size, shape and chemical features of the predicted (sub)pockets are calculated.

We are working on a publication.

<sup>1. DOI: in preparation
2. 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
3. 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.</sup>

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.

<sup>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</sup>

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. <sup>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</sup>

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. <sup>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</sup>

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.

<sup>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</sup>

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).

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. <sup>1

1. Pletzer, J. (Paper in preparation).</sup>

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}

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 EDIA_m⁵ scoring).

<sup>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</sup>

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.

<sup>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</sup>

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.

<sup>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</sup>

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.

<sup>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</sup>