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• W2017310049 abstract "Our understanding of protein evolution would greatly benefit from mapping of binding landscapes, i.e., changes in protein-protein binding affinity due to all single mutations. However, experimental generation of such landscapes is a tedious task due to a large number of possible mutations. Here, we use a simple computational protocol to map the binding landscape for two homologous high-affinity complexes, involving a snake toxin fasciculin and acetylcholinesterase from two different species. To verify our computational predictions, we experimentally measure binding between 25 Fas mutants and the 2 enzymes. Both computational and experimental results demonstrate that the Fas sequence is close to the optimum when interacting with its targets, yet a few mutations could further improve Kd, kon, and koff. Our computational predictions agree well with experimental results and generate distributions similar to those observed in other high-affinity PPIs, demonstrating the potential of simple computational protocols in capturing realistic binding landscapes." @default.
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• W2017310049 date "2014-04-01" @default.
• W2017310049 modified "2023-10-14" @default.
• W2017310049 title "Mapping of the Binding Landscape for a Picomolar Protein-Protein Complex through Computation and Experiment" @default.
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• W2017310049 doi "https://doi.org/10.1016/j.str.2014.01.012" @default.
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