Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Saturday Speaker Abstracts

In Silico Identification of Rescue Sites by Double Force Scanning Matteo Tiberti 1 , Alessandro Pandini 2 , Franca Fraternali 3,4,5 , Arianna Fornili 1,5 . 1 Queen Mary University of London, London, United Kingdom, 2 Brunel University London, London, United Kingdom, 3 King’s College London, London, United Kingdom, 4 The Francis Crick institute, London, United Kingdom, 5 The Thomas Young Centre for Theory and Simulation of Materials, London, United Kingdom. Deleterious amino acid changes in proteins can be compensated by second-site rescue mutations. These compensatory mechanisms can be mimicked by the binding of small molecules, so that the position of rescue mutations can be used to identify possible druggable regions on the protein surface for the reactivation of damaged mutants 1 . Here we present the Double Force Scanning (DFS) method 2 , the first general computational approach to detect rescue sites that use compensatory mechanisms mediated by backbone dynamics. The method is based on an elastic network model and on the application of external forces to mimic the effect of mutations. All the possible residue pairs in the protein are scanned and a rescue effect is detected when the simultaneous application of forces at the two sites affects the protein structure less than a force at a single site. The second-site residues that make the protein structure most resilient to the effect of single mutations are then identified. We tested DFS predictions against two datasets containing experimentally validated and putative evolutionary-related rescue sites, finding a remarkably good agreement between predictions and reference data. Indeed, half of the experimental rescue sites in the tumour suppressor protein p53 was correctly predicted by DFS, with 65% of remaining sites in contact with DFS predictions. Similar results were found for other proteins in the evolutionary dataset. Finally, we show how the prediction of rescue sites can be used to identify potential pockets for the binding of reactivating drugs.

1. Wassman C.D., et al. (2013) Nat Commun, 4, 1407-1409 2. Tiberti M., Pandini A., Fraternali F., Fornili A., submitted.

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