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bioRxiv · 10.1101/2023.03.20.533501

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment

Malbranke, C.·Rostain, W.·Depardieu, F.·Cocco, S.·Monasson, R.·Bikard, D.
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Abstract

We present here an approach to protein design that enables to leverage (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learned a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then 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 functional, 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. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.

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Malbranke, C., Rostain, W., Depardieu, F., Cocco, S., Monasson, R., Bikard, D.. 2023-03-22. Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment. https://doi.org/10.1101/2023.03.20.533501

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