Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.03.20.533501v1?rss=1
Authors: Malbranke, C., Rostain, W., Depardieu, F., Cocco, S., Monasson, R., Bikard, D.
Abstract:
We present here an approach to protein design that combines evolutionary and physics-grounded modeling. Using a Restricted Boltzmann Machine, we learned a sequence model of a protein family and propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force field method (FoldX). This method was applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assessed the functionality of 71 variants that were generated to explore a range of RBM and FoldX energies. We show how a combination of structural and evolutionary information can identify functional variants with high accuracy. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Interestingly, some sequences (6/71) produced by our method showed an improved activity in comparison with the original wild-type proteins sequence. These results demonstrate the interest of further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.
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