Literature DB >> 19552372

E-novo: an automated workflow for efficient structure-based lead optimization.

Bradley C Pearce1, David R Langley, Jia Kang, Hongwei Huang, Amit Kulkarni.   

Abstract

An automated E-Novo protocol designed as a structure-based lead optimization tool was prepared through Pipeline Pilot with existing CHARMm components in Discovery Studio. A scaffold core having 3D binding coordinates of interest is generated from a ligand-bound protein structural model. Ligands of interest are generated from the scaffold using an R-group fragmentation/enumeration tool within E-Novo, with their cores aligned. The ligand side chains are conformationally sampled and are subjected to core-constrained protein docking, using a modified CHARMm-based CDOCKER method to generate top poses along with CDOCKER energies. In the final stage of E-Novo, a physics-based binding energy scoring function ranks the top ligand CDOCKER poses using a more accurate Molecular Mechanics-Generalized Born with Surface Area method. Correlation of the calculated ligand binding energies with experimental binding affinities were used to validate protocol performance. Inhibitors of Src tyrosine kinase, CDK2 kinase, beta-secretase, factor Xa, HIV protease, and thrombin were used to test the protocol using published ligand crystal structure data within reasonably defined binding sites. In-house Respiratory Syncytial Virus inhibitor data were used as a more challenging test set using a hand-built binding model. Least squares fits for all data sets suggested reasonable validation of the protocol within the context of observed ligand binding poses. The E-Novo protocol provides a convenient all-in-one structure-based design process for rapid assessment and scoring of lead optimization libraries.

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Year:  2009        PMID: 19552372     DOI: 10.1021/ci900073k

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  7 in total

1.  Transferable scoring function based on semiempirical quantum mechanical PM6-DH2 method: CDK2 with 15 structurally diverse inhibitors.

Authors:  Petr Dobeš; Jindřich Fanfrlík; Jan Rezáč; Michal Otyepka; Pavel Hobza
Journal:  J Comput Aided Mol Des       Date:  2011-02-01       Impact factor: 3.686

Review 2.  Application of NMR and molecular docking in structure-based drug discovery.

Authors:  Jaime L Stark; Robert Powers
Journal:  Top Curr Chem       Date:  2012

3.  De novo design by pharmacophore-based searches in fragment spaces.

Authors:  Tobias Lippert; Tanja Schulz-Gasch; Olivier Roche; Wolfgang Guba; Matthias Rarey
Journal:  J Comput Aided Mol Des       Date:  2011-09-16       Impact factor: 3.686

Review 4.  Receptor-ligand molecular docking.

Authors:  Isabella A Guedes; Camila S de Magalhães; Laurent E Dardenne
Journal:  Biophys Rev       Date:  2013-12-21

5.  Optimization of Benzoisothiazole dioxide inhibitory activity of the NS5B polymerase of HCV genotype 4 using ligand-steered homological modeling, reaction-driven scaffold-hopping and Enovo workflow.

Authors:  Amr Hamed Mahmoud; Khaled Abouzid Mohamed Abouzid; Dalal Abd El Rahman Abou El Ella; Mohamed Abdel Hamid Ismail
Journal:  Bioinformation       Date:  2011-12-10

6.  AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization.

Authors:  Jacob O Spiegel; Jacob D Durrant
Journal:  J Cheminform       Date:  2020-04-17       Impact factor: 5.514

7.  3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery.

Authors:  Albert J Kooistra; Márton Vass; Ross McGuire; Rob Leurs; Iwan J P de Esch; Gert Vriend; Stefan Verhoeven; Chris de Graaf
Journal:  ChemMedChem       Date:  2018-02-14       Impact factor: 3.466

  7 in total

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