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

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...

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Veröffentlicht in:PLoS computational biology Jg. 19; H. 11; S. e1011621
Hauptverfasser: Malbranke, Cyril, Rostain, William, Depardieu, Florence, Cocco, Simona, Monasson, Rémi, Bikard, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.11.2023
PLOS
Public Library of Science (PLoS)
Schlagworte:
Amino acid sequence
Amino acids
Analysis
Biochemistry, Molecular Biology
Biology and Life Sciences
Boltzmann constant
Information sources
Life Sciences
Ligands
Machine learning
Methods
Modelling
Mutation
Physics
Proteins
Quality assessment
Quality control
Quantitative Methods
Representations
Research and Analysis Methods
Structural Biology
Sucrose
France
ISSN:1553-7358, 1553-734X, 1553-7358
Online-Zugang:Volltext
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Zusammenfassung: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.
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PMCID: PMC10729993
The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1011621