Research group Samuel Flores' research group

Determining the impact of mutations on Protein-protein interaction energies

I have written a few papers on predicting the change in protein-protein interaction energy (ΔΔG) resulting from mutations. I want to develop the capability to increase affinity in silico, and as a second priority, alter specificity. Recently, I have developed a method which asks the user for a the Protein Data Bank (PDB) ID of a protein-protein complex, the chains in each of two interacting subunits, and a mutation to test. It not only computes ΔΔG using that structure (using the FoldX potential) but also searches the PDB for additional, sufficiently-similar structures on which to repeat the calculation. It then averages over the results of all such calculation, thus improving precision and (importantly) positive predictive value -- the fraction of mutations which are predicted to improve affinity, which successfully do so. You can try this out on our easy to use web server, http://biodesign.scilifelab.se/.

Toward designing RNA-complexes for nanotechnology

The flexibility of large RNAs presents a significant challenge to understanding large complexes such as the ribosome, and to designing complexes for nanotechnology. Previously, experiments have been performed which has successfully folded tRNA and the P4/P6 domain of the Group I Intron, threaded the Azoarcus Group I Intron, explained the role of disease associated mutations in telomerase, and modeled all-atoms trajectories of ribosomal translocation. The latter drove development of very fast morphing and low-resolution (e.g. cryoem) density map fitting methods. We also written about GTP hydrolysis on EF-G and ribosomal frameshifting. Current projects include deep learning for RNA structure prediction, and viral DNA genome packaging.

Current MS students (Spring 2022):

Kaouther Harbi

Floyd Jaggy

Emilio Skarwan