ArticleBMC structural biology2010
Designing coarse grained-and atom based-potentials for protein-protein docking.
Article in BMC structural biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Increasing the fraction of correct solutions in ensembles of protein-protein docking models by an iterative consensus algorithm.Protein science : a publication of the Protein Society · 2025Article
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- MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations.Biomolecules · 2023Article
- Improving classification of correct and incorrect protein-protein docking models by augmenting the training set.Bioinformatics advances · 2023Article
- An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants.Structure (London, England : 1993) · 2021Article
- Wnt Binding Affinity Prediction for Putative Frizzled-Type Cysteine-Rich Domains.International journal of molecular sciences · 2019Article
- Benchmarking of different molecular docking methods for protein-peptide docking.BMC bioinformatics · 2019Article
- Predictive and Experimental Approaches for Elucidating Protein-Protein Interactions and Quaternary Structures.International journal of molecular sciences · 2017Review
- IRaPPA: information retrieval based integration of biophysical models for protein assembly selection.Bioinformatics (Oxford, England) · 2017Article
- A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.Proteins · 2017Article
- Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.PloS one · 2017Article
- Evaluation of the coarse-grained OPEP force field for protein-protein docking.BMC biophysics · 2016Article
- Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2.Journal of molecular biology · 2015Article
- Article
- DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking.PloS one · 2014Article
- Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.Proteins · 2013Article
- The scoring of poses in protein-protein docking: current capabilities and future directions.BMC bioinformatics · 2013Article
- Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization.PLoS computational biology · 2013Article
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Abstract
backgroundProtein-protein docking is a challenging computational problem in functional genomics, particularly when one or both proteins undergo conformational change(s) upon binding. The major challenge is to define a scoring function soft enough to tolerate these changes and specific enough to distinguish between near-native and "misdocked" conformations.
resultsUsing a linear programming (LP) technique, we developed two types of potentials: (i) Side chain-based and (ii) Heavy atom-based. To achieve this we considered a set of 161 transient complexes and generated a large set of putative docked structures (decoys), based on a shape complementarity criterion, for each complex. The demand on the potentials was to yield, for the native (correctly docked) structure, a potential energy lower than those of any of the non-native (misdocked) structures. We show that the heavy atom-based potentials were able to comply with this requirement but not the side chain-based one. Thus, despite the smaller number of parameters, the capability of heavy atom-based potentials to discriminate between native and "misdocked" conformations is improved relative to those of the side chain-based potentials. The performance of the atom-based potentials was evaluated by a jackknife test on a set of 50 complexes taken from the Zdock2.3 decoys set.
conclusionsOur results show that, using the LP approach, we were able to train our potentials using a dataset of transient complexes only the newly developed potentials outperform three other known potentials in this test.
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