StructureProfiler: an all-in-one tool for 3D protein structure profiling

Self Cite

111

Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledgebased algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research.

Section: ■ Methodsmentioning

confidence: 99%

“…NAOMI was also utilized to determine the deviation of atom angles and bond lengths from known optimal values as well as the divergence of aromatic rings and ring systems (up to six bonds per relevant cycle) from planarity. 44 Runtimes of conformer ensemble generation were measured for SD files containing single molecules.…”

Section: ■ Methodsmentioning

confidence: 99%

Conformator: A Novel Method for the Generation of Conformer Ensembles

Friedrich

Flachsenberg

Meyder

et al. 2019

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111

“…Regarding the ligand descriptors, we define an applicability range for molecules with 10 to 30 heavy atoms, a clogP between −5 and 5, and at most 9 rotatable bonds. If we apply these filters and the complex filters by Meyder et al, 112 which only include global quality criteria of crystal structures, to the PDBScan22 data set, we achieve a redocking performance of 77.4% for the top 32 poses (48.7% for the top-ranked pose).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…The complete JAMDA docking pipeline (structure preprocessing and docking tool), described and evaluated in this paper, is available as a free-to-use web tool within our ProteinsPlus web server (https://proteins.plus). The standalone command line tools for the novel JAMDA protein structure preprocessing and docking, i.e., the JamdaPreprocessor and the JAMDA docking tool, as well as the already published Conformator, 90 UNICON, 109 and StructureProfiler, 112 are part of our NAOMI ChemBio Suite for all common platforms (https:// uhh.de/naomi). A free-of-charge license is available for academic and noncommercial use.…”

Section: Data Availability Statementmentioning

confidence: 99%

Redocking the PDB

Flachsenberg,

Ehrt,

Gutermuth

et al. 2023

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Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

“…One significant change was moving away from a proprietary subset of the PDB in favor of automatically extracting high-quality ligands directly from the PDB . The StructureProfiler was used to perform common PDB structure quality filtering and the EDIA tool employed to estimate the electron density fit of torsion atoms. CSD molecules were extracted using the CSD Python API and closely following the rules defined by the original publication .…”

Section: Methodsmentioning

confidence: 99%

The Torsion Library: Semiautomated Improvement of Torsion Rules with SMARTScompare

Penner

Guba

Schmidt

et al. 2022

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The Torsion Library is a collection of torsion motifs associated with angle distributions, derived from crystallographic databases. It is used in strain assessment, conformer generation, and geometry optimization. A hierarchical structure of expert curated SMARTS defines the chemical environments of rotatable bonds and associates these with preferred angles. SMARTS can be very complex and full of implications, which make them difficult to maintain manually. Recent developments in automatically comparing SMARTS patterns can be applied to the Torsion Library to ensure its correctness. We specifically discuss the implementation and the limits of such a procedure in the context of torsion motifs and show several examples of how the Torsion Library benefits from this. All automated changes are validated manually and then shown to have an effect on the angle distributions by correcting matching behavior. The corrected Torsion Library itself is available including both PDB as well as CSD histograms in the Supporting Information and can be used to evaluate rotatable bonds at .