ArticleProteins2013
Using the concept of transient complex for affinity predictions in CAPRI rounds 20-27 and beyond.
Article in Proteins, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.Nucleic acids research · 2018Article
- Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation.Chemical reviews · 2018Review
- A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.Proteins · 2017Article
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2 authors.
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Abstract
Predictions of protein-protein binders and binding affinities have traditionally focused on features pertaining to the native complexes. In developing a computational method for predicting protein-protein association rate constants, we introduced the concept of transient complex after mapping the interaction energy surface. The transient complex is located at the outer boundary of the bound-state energy well, having near-native separation and relative orientation between the subunits but not yet formed most of the short-range native interactions. We found that the width of the binding funnel and the electrostatic interaction energy of the transient complex are among the features predictive of binders and binding affinities. These ideas were very promising for the five affinity-related targets (T43-45, 55, and 56) of CAPRI rounds 20-27. For T43, we ranked the single crystallographic complex as number 1 and were one of only two groups that clearly identified that complex as a true binder; for T44, we ranked the only design with measurable binding affinity as number 4. For the nine docking targets, continuing on our success in previous CAPRI rounds, we produced 10 medium-quality models for T47 and acceptable models for T48 and T49. We conclude that the interaction energy landscape and the transient complex in particular will complement existing features in leading to better prediction of binding affinities.
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