ArticleBioinformatics (Oxford, England)2020
iScore: a novel graph kernel-based function for scoring protein-protein docking models.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes.Journal of chemical information and modeling · 2025Article
- Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.Journal of chemical information and modeling · 2025Article
- A comprehensive survey of scoring functions for protein docking models.BMC bioinformatics · 2025Article
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces.Bioinformatics (Oxford, England) · 2024Article
- ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.Biomolecules · 2024Review
- DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model.Bioinformatics advances · 2024Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- A gated graph transformer for protein complex structure quality assessment and its performance in CASP15.Bioinformatics (Oxford, England) · 2023Article
- MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations.Biomolecules · 2023Article
- Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs.Frontiers in medicine · 2023Review
- GDockScore: a graph-based protein-protein docking scoring function.Bioinformatics advances · 2023Article
- G-RANK: an equivariant graph neural network for the scoring of protein-protein docking models.Bioinformatics advances · 2023Article
- Improving classification of correct and incorrect protein-protein docking models by augmenting the training set.Bioinformatics advances · 2023Article
- DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces.Bioinformatics (Oxford, England) · 2023Article
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- Protein-protein interaction prediction methods: from docking-based to AI-based approaches.Biophysical reviews · 2022Review
- Prediction of the structural interface between fibroblast growth factor23 and Burosumab using alanine scanning and molecular docking.Scientific reports · 2022Article
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6 authors.
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Abstract
motivationProtein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge.
resultsHere we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. AVAILABILITY AND IMPLEMENTATION: The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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