Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment.
Autor: | Cyril Malbranke, William Rostain, Florence Depardieu, Simona Cocco, Rémi Monasson, David Bikard |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: | |
Zdroj: | PLoS Computational Biology, Vol 19, Iss 11, p e1011621 (2023) |
Druh dokumentu: | article |
ISSN: | 1553-734X 1553-7358 |
DOI: | 10.1371/journal.pcbi.1011621 |
Popis: | We present here an approach to protein design that combines (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 learn 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). Our approach is applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assess the functionality of 71 variants generated to explore a range of RBM and FoldX energies. 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. |
Databáze: | Directory of Open Access Journals |
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