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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Mapping Dysfunctional Protein-Protein Interactions in Disease

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

Oscar Méndez-Lucio¹, Albert J Kooistra, Chris de Graaf

¹Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom.

Journal of Chemical Information and Modeling

|January 24, 2015

Summary

Activity landscape modeling is enhanced by incorporating protein-ligand interaction fingerprints (IFPs). This approach improves the analysis and interpretation of activity cliffs in drug discovery by providing structure-based insights.

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Structural Biology

Background:

Activity landscape modeling aids in rationalizing structure-activity relationships (SARs).
Interpreting specific landscape features like activity cliffs can be challenging.
Understanding both ligand and target properties is crucial for analyzing activity cliffs.

Purpose of the Study:

To enhance activity landscape modeling by integrating protein-ligand interaction fingerprints (IFPs).
To facilitate the analysis and interpretation of activity cliffs using IFPs.
To introduce novel analysis tools like structure-activity-interaction similarity (SAIS) maps and interaction cliffs.

Main Methods:

Modeling activity landscapes for 507 ligand-kinase complexes from the KLIFS database.
Incorporating protein-ligand interaction fingerprints (IFPs) into the modeling process.
Developing and applying structure-activity-interaction similarity (SAIS) maps and defining interaction cliffs.

Main Results:

IFP integration improves the analysis and interpretation of activity cliffs.
SAIS maps incorporate ligand-target contact similarity.
Interaction cliffs are identified as complexes with high structural/interaction similarity but large potency differences.
Activity cliff hot spots are identified, offering target-centric explanations for activity cliffs.

Conclusions:

IFPs provide a structure-based understanding of activity landscape features.
The integration of IFPs enhances the interpretability of activity cliffs.
Novel concepts like SAIS maps and interaction cliffs offer new perspectives in drug discovery analysis.