Contacts-based prediction of binding affinity in protein–protein complexes

By Anna Vangone, Alexandre M. J. J. Bonvin. eLife, 2015

524 citations recorded by the source corpus.

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods. DOI: http://dx.doi.org/10.7554/eLife.07454.001

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b7add6f0344e6845c4552c8fea59bdf2c9624da5

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Citations in the corpus

Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem (2021, methodology): “PRODIGY, the PROtein binDIng enerGY Prediction tool (Vangone and Bonvin, 2015) allows to predict the binding affinity of protein-protein complexes from their three-dimensional structures.”
PRODIGY: a web server for predicting the binding affinity of protein-protein complexes (2016, methodology): “4 in Vangone and Bonvin, 2015).”
Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2 (2017, methodology): “Recently, we have introduced a residue–residue contact-based method for the prediction of the binding affinity in Target R M S D ( Å ) protein–protein complexes [40], implemented in the web-server PRODIGY (PROtein binDIng enerGY prediction) [41, 42].”
DisruPPI: structure-based computational redesign algorithm for protein binding disruption (2018, methodology): “…and Zhang, 2015; Guerois et al. , 2002; Kortemme and Baker, 2004), statistical contact potentials (Pons et al. , 2011; Tharakaraman et al. , 2013; Vangone and Bonvin, 2015), machine learning methods combining multiple such features (Dehouck et al. , 2013; Wang et al. , 2012) and target-specific…”
Identifying well-folded de novo proteins in the new era of accurate structure prediction (2022, methodology): “As an independent approach, we also estimated the binding free energy using the PROtein binDIng enerGY prediction (PRODIGY) tool (Vangone and Bonvin, 2015).”
Accurate structure prediction of CDR H3 loops enabled by a novel structure-based C-terminal constraint (2016, unknown): “affinities and dissociation free energies (11, 12).”