ArticlePloS one2013
How to use not-always-reliable binding site information in protein-protein docking prediction.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A computational model of ESAT-6 complex in membrane.Journal of theoretical & computational chemistry · 2020Article
- StructureMan: A Structure Manipulation Tool to Study Large Scale Biomolecular Interactions.Frontiers in molecular biosciences · 2020Article
- Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking.Journal of molecular biology · 2018Article
- Performance of ZDOCK and IRAD in CAPRI rounds 28-34.Proteins · 2017Article
- Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures.BMC structural biology · 2015Article
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Authors and funding
3 authors.
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Abstract
In many protein-protein docking algorithms, binding site information is used to help predicting the protein complex structures. Using correct and accurate binding site information can increase protein-protein docking success rate significantly. On the other hand, using wrong binding sites information should lead to a failed prediction, or, at least decrease the success rate. Recently, various successful theoretical methods have been proposed to predict the binding sites of proteins. However, the predicted binding site information is not always reliable, sometimes wrong binding site information could be given. Hence there is a high risk to use the predicted binding site information in current docking algorithms. In this paper, a softly restricting method (SRM) is developed to solve this problem. By utilizing predicted binding site information in a proper way, the SRM algorithm is sensitive to the correct binding site information but insensitive to wrong information, which decreases the risk of using predicted binding site information. This SRM is tested on benchmark 3.0 using purely predicted binding site information. The result shows that when the predicted information is correct, SRM increases the success rate significantly; however, even if the predicted information is completely wrong, SRM only decreases success rate slightly, which indicates that the SRM is suitable for utilizing predicted binding site information.
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