ArticleBMC bioinformatics2011
Prediction of protein-binding areas by small-world residue networks and application to docking.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Improve hot region prediction by analyzing different machine learning algorithms.BMC bioinformatics · 2021Article
- iScore: a novel graph kernel-based function for scoring protein-protein docking models.Bioinformatics (Oxford, England) · 2020Article
- Computational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction.RSC advances · 2019Article
- Molecular Dynamics Simulations of Kir2.2 Interactions with an Ensemble of Cholesterol Molecules.Biophysical journal · 2018Article
- Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling.Biophysical journal · 2016Article
- Molecular docking as a popular tool in drug design, an in silico travel.Advances and applications in bioinformatics and chemistry : AABC · 2016Review
- Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures.BMC structural biology · 2015Article
- Cutoff lensing: predicting catalytic sites in enzymes.Scientific reports · 2015Article
- Article
- The scoring of poses in protein-protein docking: current capabilities and future directions.BMC bioinformatics · 2013Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- Small world network strategies for studying protein structures and binding.Computational and structural biotechnology journal · 2013Review
- A modified amino acid network model contains similar and dissimilar weight.Computational and mathematical methods in medicine · 2013Article
- Scoring function based on weighted residue network.International journal of molecular sciences · 2011Article
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3 authors.
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Abstract
backgroundProtein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.
resultsHere we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved.
conclusionsThe network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.
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