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This article is part of the supplement: 6th German Conference on Chemoinformatics, GCC 2010

Open Access Poster presentation

Predicting protein-protein interactions with DrugScorePPI: fully-flexible docking, scoring, and in silico alanine-scanning

DM Krüger1*, JI Garzón2, PC Montes2 and H Gohlke1

  • * Corresponding author: DM Krüger

Author Affiliations

1 Department of Mathematics and Natural Sciences, Heinrich-Heine-University, 40225 Düsseldorf, Germany

2 Centro de Investigaciones Biológicas (CSIC), 28040 Madrid, Spain

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Journal of Cheminformatics 2011, 3(Suppl 1):P36  doi:10.1186/1758-2946-3-S1-P36

The electronic version of this article is the complete one and can be found online at: http://www.jcheminf.com/content/3/S1/P36


Published:19 April 2011

© 2011 Krüger et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

Protein-protein complexes play key roles in all cellular signal transduction processes. Here, we present a fast and accurate computational approach to predict protein-protein interactions. The approach is based on DrugScorePPI, a knowledge-based scoring function for which pair potentials were derived from 851 complex structures and adapted against 309 experimental alanine scanning results. We developed the DrugScorePPI webserver [1], accessible at http://cpclab.uni-duesseldorf.de/dsppi webcite, that is intended for identifying hotspot residues in protein-protein interfaces. For this, it allows performing computational alanine scanning of a protein-protein interface within a few minutes. Our approach has been successfully validated by application to an external test set of 22 alanine mutations in the interface of Ras/RalGDS and outperformed the widely used CC/PBSA, FoldX, and Robetta methods [1].

Next, DrugScorePPI was teamed with FRODOCK [2], a fast FFT-based protein-protein docking tool, in order to predict 3D structures of protein-protein complexes. When applied to datasets of 54 bound-bound (I) and 54 unbound-unbound (II) test cases, convincing results were obtained (docking success rate for complexes with rmsd < 10 Å: I: ~80%; II: ~50%). Thus, we set out to evaluate whether our approach of deformable potential grids [3], previously developed for protein-ligand docking, also provides an accurate and efficient means for representing intermolecular interactions in fully-flexible protein-protein docking. The underlying idea is to adapt a 3D grid of potential field values, pre-calculated from an initial protein conformation by DrugScorePPI, to another conformation by moving grid intersection points in space, but keeping the potential field values constant. Protein movements are thereby translated into grid intersection displacements by coupling protein atoms to nearby grid intersection points by means of harmonic springs and modelling the irregular, deformable 3D grid as a homogeneous linear elastic body applying elasticity theory. Thus, new protein conformations can be sampled during a docking run without the need to re-calculate potential field values.

References

  1. Krüger DM, Gohlke H: DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein-protein interactions.

    Nucl Acids Res 2010, 38:W480-W486. PubMed Abstract | PubMed Central Full Text OpenURL

  2. Garzon JI, Lopez-Blanco JR, Pons C, Kovacs J, Abagyan R, Fernandez-Recio J, Chacon P: FRODOCK: a new approach for fast rotational protein-protein docking.

    Bioinformatics 2009, 25:2544-2551. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Kazemi S, Krüger DM, Sirockin F, Gohlke H: Elastic potential grids: accurate and efficient representation of intermolecular interactions for fully-flexible docking.

    ChemMedChem 2009, 4:1264-1268. PubMed Abstract | Publisher Full Text OpenURL