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Bioinformatics. 2018 Feb 15;34(4):592-598. doi: 10.1093/bioinformatics/btx616.

On the mechanisms of protein interactions: predicting their affinity from unbound tertiary structures.

Author information

1
Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona 08003, Spain.
2
Division of Metabolic and Vascular Health, University of Warwick, Coventry CV4?7AL, UK.
3
Laboratory of Protein Design and Immunoenginneering, School of Engineering, Ecole Polytechnique Federale de Lausanne, Lausanne 1015, Switzerland.
4
Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23?3DA, UK.

Abstract

Motivation:

The characterization of the protein-protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein-protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures.

Results:

We present a new approach that relies on the unbound protein structures and protein docking to predict protein-protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol.

Availability and implementation:

The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock.

Contact:

j.planas-iglesias@warwick.ac.uk or baldo.oliva@upf.edu.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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