Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses
Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We prop...
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doaj-dc0fb0b879ff440c9c405f5299ec41fe2020-11-25T01:52:33ZengMDPI AGMolecules1420-30492019-07-012414261010.3390/molecules24142610molecules24142610Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking PosesCélien Jacquemard0Viet-Khoa Tran-Nguyen1Malgorzata N. Drwal2Didier Rognan3Esther Kellenberger4Laboratoire D’innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, FranceLaboratoire D’innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, FranceLaboratoire D’innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, FranceLaboratoire D’innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, FranceLaboratoire D’innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, FranceLigand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.https://www.mdpi.com/1420-3049/24/14/2610scoringprotein ligand interactionbenchmarking |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Célien Jacquemard Viet-Khoa Tran-Nguyen Malgorzata N. Drwal Didier Rognan Esther Kellenberger |
spellingShingle |
Célien Jacquemard Viet-Khoa Tran-Nguyen Malgorzata N. Drwal Didier Rognan Esther Kellenberger Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses Molecules scoring protein ligand interaction benchmarking |
author_facet |
Célien Jacquemard Viet-Khoa Tran-Nguyen Malgorzata N. Drwal Didier Rognan Esther Kellenberger |
author_sort |
Célien Jacquemard |
title |
Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses |
title_short |
Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses |
title_full |
Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses |
title_fullStr |
Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses |
title_full_unstemmed |
Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses |
title_sort |
local interaction density (lid), a fast and efficient tool to prioritize docking poses |
publisher |
MDPI AG |
series |
Molecules |
issn |
1420-3049 |
publishDate |
2019-07-01 |
description |
Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time. |
topic |
scoring protein ligand interaction benchmarking |
url |
https://www.mdpi.com/1420-3049/24/14/2610 |
work_keys_str_mv |
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