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 k off. 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.
| Original language | English |
|---|---|
| Pages (from-to) | 636-645 |
| Number of pages | 10 |
| Journal | Structure |
| Volume | 22 |
| Issue number | 4 |
| DOIs | |
| State | Published - 8 Apr 2014 |
Bibliographical note
Funding Information:We thank Prof. Israel Silman and Dr. Yaakov Ashani for providing us with samples of hAChE and tAChE. We also thank Prof. Joel Sussman and Prof. Israel Silman for scientific discussions. We thank Dr. Yoav Peleg for his constant help with molecular biology and Royee Navon for help with some activity assays. This work was supported by the Deutsche Forschungsgemeinschaft grant EI 423/2-1, the Abisch Frenkel foundation, and ISF grant 1372/10.