ArticleBMC bioinformatics2011
Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams.
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 22 papers.
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22 citing papers in PubMed.
- Computational prediction of multiple antigen epitopes.Bioinformatics (Oxford, England) · 2024Article
- Computational Methods to Predict Conformational B-Cell Epitopes.Biomolecules · 2024Review
- Integrated structure-based protein interface prediction.BMC bioinformatics · 2022Article
- Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures.Methods in molecular biology (Clifton, N.J.) · 2022Review
- 2D Zernike polynomial expansion: Finding the protein-protein binding regions.Computational and structural biotechnology journal · 2021Article
- A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.Frontiers in genetics · 2021Article
- A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers.Molecules (Basel, Switzerland) · 2020Article
- CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information.Biomolecules · 2020Article
- BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.Bioinformatics (Oxford, England) · 2019Article
- Article
- Protein-protein interaction specificity is captured by contact preferences and interface composition.Bioinformatics (Oxford, England) · 2018Article
- Ebola virus VP24 interacts with NP to facilitate nucleocapsid assembly and genome packaging.Scientific reports · 2017Article
- Protein social behavior makes a stronger signal for partner identification than surface geometry.Proteins · 2017Article
- Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins.Scientific reports · 2016Article
- Progress and challenges in predicting protein interfaces.Briefings in bioinformatics · 2016Review
- Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions.PLoS computational biology · 2015Article
- Predicting protein interface residues using easily accessible on-line resources.Briefings in bioinformatics · 2015Review
- Article
- Algorithmic approaches to protein-protein interaction site prediction.Algorithms for molecular biology : AMB · 2015Article
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3 authors.
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Abstract
backgroundProtein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i) which proteins interact and (ii) how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces.
resultsHere we present VORFFIP, a novel method for protein binding site prediction. The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors) used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance) have been analyzed in order to maximize the performance of the method.
conclusionsThe integration of different forms information such as structural features, energy term, evolutionary conservation and crystallographic B-factors, improves the performance of binding site prediction. Including the information of neighbouring residues also improves the prediction of protein interfaces. Among the different approaches that can be used to define the environment of exposed residues, Voronoi Diagrams provide the most accurate description. Finally, VORFFIP compares favourably to other methods reported in the recent literature.
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