Structural Stability Predicts the Binding Mode of Protein-Ligand Complexes.

Structural Stability Predicts the Binding Mode of Protein-Ligand Complexes. The prediction of a ligand's binding mode into its macromolecular target is essential in structure-based drug discovery. Even though tremendous effort has been made to address this problem, most of the developed tools work similarly, trying to predict the binding free energy associated with each particular binding mode. In this study, we decided to abandon this criterion, following structural stability instead. This view, implemented in a novel computational workflow, quantifies the steepness of the local energy minimum associated with each potential binding mode. Surprisingly, the protocol outperforms docking scoring functions in case of fragments (ligands with MW < 300 Da) and is as good as docking for drug-like molecules. It also identifies substructures that act as structural anchors, predicting their binding mode with particular accuracy. The results open a new physical perspective for binding mode prediction, which can be combined with existing thermodynamic-based approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of chemical information and modeling Pubmed

Structural Stability Predicts the Binding Mode of Protein-Ligand Complexes.

Journal of chemical information and modeling, Volume 60 (3): 8 – Mar 23, 2020
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Structural Stability Predicts the Binding Mode of Protein-Ligand Complexes.

Journal of chemical information and modeling, Volume 60 (3): 8 – Mar 23, 2020

Abstract

The prediction of a ligand's binding mode into its macromolecular target is essential in structure-based drug discovery. Even though tremendous effort has been made to address this problem, most of the developed tools work similarly, trying to predict the binding free energy associated with each particular binding mode. In this study, we decided to abandon this criterion, following structural stability instead. This view, implemented in a novel computational workflow, quantifies the steepness of the local energy minimum associated with each potential binding mode. Surprisingly, the protocol outperforms docking scoring functions in case of fragments (ligands with MW < 300 Da) and is as good as docking for drug-like molecules. It also identifies substructures that act as structural anchors, predicting their binding mode with particular accuracy. The results open a new physical perspective for binding mode prediction, which can be combined with existing thermodynamic-based approaches.
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DOI
10.1021/acs.jcim.9b01062
pmid
32052965

Abstract

The prediction of a ligand's binding mode into its macromolecular target is essential in structure-based drug discovery. Even though tremendous effort has been made to address this problem, most of the developed tools work similarly, trying to predict the binding free energy associated with each particular binding mode. In this study, we decided to abandon this criterion, following structural stability instead. This view, implemented in a novel computational workflow, quantifies the steepness of the local energy minimum associated with each potential binding mode. Surprisingly, the protocol outperforms docking scoring functions in case of fragments (ligands with MW < 300 Da) and is as good as docking for drug-like molecules. It also identifies substructures that act as structural anchors, predicting their binding mode with particular accuracy. The results open a new physical perspective for binding mode prediction, which can be combined with existing thermodynamic-based approaches.

Journal

Journal of chemical information and modelingPubmed

Published: Mar 23, 2020

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