Frog2: Efficient 3D conformation ensemble generator for small compounds

Frog is a web tool dedicated to small compound 3D generation. Here we present the new version, Frog2, which allows the generation of conformation ensembles of small molecules starting from either 1D, 2D or 3D description of the compounds. From a compound description in one of the SMILES, SDF or mol2 formats, the server will return an ensemble of diverse conformers generated using a two stage Monte Carlo approach in the dihedral space. When starting from 1D or 2D description of compounds, Frog2 is capable to detect the sites of ambiguous stereoisomery, and thus to sample different stereoisomers. Frog2 also embeds new energy minimization and ring generation facilities that solve the problem of some missing cycle structures in the Frog1 ring library. Finally, the optimized generator of conformation ensembles in Frog2 results in a gain of computational time permitting Frog2 to be up to 20 times faster that Frog1, while producing satisfactory conformations in terms of structural quality and conformational diversity. The high speed and the good quality of generated conformational ensembles makes it possible the treatment of larger compound collections using Frog2. The server and documentation are freely available at http://bioserv.rpbs.univ-paris-diderot.fr/Frog2.

[1]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[2]  D. E. Clark.  What has virtual screening ever done for drug discovery? , 2008, Expert opinion on drug discovery.

[3]  Jacques Chomilier,et al.  Frog: a FRee Online druG 3D conformation generator , 2007, Nucleic Acids Res..

[4]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[5]  Tania Pencheva,et al.  BMC Bioinformatics BioMed Central Methodology article AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening , 2022 .

[6]  Mark S. Johnson,et al.  Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm , 2007, J. Chem. Inf. Model..

[7]  Pierre Tufféry,et al.  FAF-Drugs: free ADME/tox filtering of compound collections , 2006, Nucleic Acids Res..

[8]  Thierry Langer,et al.  Comparative Performance Assessment of the Conformational Model Generators Omega and Catalyst: A Large-Scale Survey on the Retrieval of Protein-Bound Ligand Conformations , 2006, J. Chem. Inf. Model..

[9]  Olivier Sperandio,et al.  Free resources to assist structure-based virtual ligand screening experiments. , 2007, Current protein & peptide science.

[10]  J. Andrew Grant,et al.  Small Molecule Shape-Fingerprints , 2005, J. Chem. Inf. Model..

[11]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[12]  J. Gasteiger,et al.  FROM ATOMS AND BONDS TO THREE-DIMENSIONAL ATOMIC COORDINATES : AUTOMATIC MODEL BUILDERS , 1993 .

[13]  D. Douguet.  Ligand-Based Approaches in Virtual Screening , 2008 .

[14]  Arthur Dalby,et al.  Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited , 1992, J. Chem. Inf. Comput. Sci..

[15]  Andreas Bender,et al.  How Similar Are Similarity Searching Methods? A Principal Component Analysis of Molecular Descriptor Space , 2009, J. Chem. Inf. Model..

[16]  Markus H J Seifert,et al.  Targeted scoring functions for virtual screening. , 2009, Drug discovery today.

[17]  Martin Stahl,et al.  Small Molecule Conformational Preferences Derived from Crystal Structure Data. A Medicinal Chemistry Focused Analysis , 2008, J. Chem. Inf. Model..

[18]  I. Kuntz.  Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[19]  David Lagorce,et al.  MS-DOCK: Accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening , 2008, BMC Bioinformatics.

[20]  Maria A Miteva,et al.  DG-AMMOS: A New tool to generate 3D conformation of small molecules using Distance Geometry and Automated Molecular Mechanics Optimization for in silico Screening , 2009, BMC chemical biology.

[21]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[22]  F. Guerlesquin,et al.  Protein–protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein , 2007, Proceedings of the National Academy of Sciences.

[23]  Olivier Sperandio,et al.  MED-3DMC: a new tool to generate 3D conformation ensembles of small molecules with a Monte Carlo sampling of the conformational space. , 2009, European journal of medicinal chemistry.

[24]  Egon L. Willighagen,et al.  The Blue Obelisk—Interoperability in Chemical Informatics , 2006, J. Chem. Inf. Model..

[25]  Paul N. Mortenson,et al.  Diverse, high-quality test set for the validation of protein-ligand docking performance. , 2007, Journal of medicinal chemistry.

[26]  Christoph Globisch,et al.  Combined Pharmacophore Modeling, Docking, and 3D QSAR Studies of ABCB1 and ABCC1 Transporter Inhibitors , 2009, ChemMedChem.