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Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.


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Authors

Méndez-Lucio, Oscar 
Kooistra, Albert J 
de Graaf, Chris 
Medina-Franco, José L 

Abstract

Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.

Description

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.

Keywords

Algorithms, Drug Discovery, Hydrogen Bonding, Ligands, Models, Molecular, Peptide Mapping, Phosphotransferases, Protein Conformation, Protein Kinase Inhibitors, Structure-Activity Relationship

Journal Title

J Chem Inf Model

Conference Name

Journal ISSN

1549-9596
1549-960X

Volume Title

55

Publisher

American Chemical Society (ACS)
Sponsorship
European Research Council (336159)
M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government.