Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment
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Abstract
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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Cites
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Past, present, and future of CRISPR genome editing technologies
Pacesa, M., Pelea, O., & Jinek, M. (2024) · Cell · doi:10.1016/j.cell.2024.01.042 · Cited by 609 · see citing works
- Authors
- Malbranke, C., Rostain, W., Depardieu, F., Cocco, S., Monasson, R., & Bikard, D.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Malbranke, Cyril
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2023-11-17
- Volume
- 19
- Issue
- 11
- Pages
- e1011621
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC10729993
- OpenAlex
- W4388775348
- Cited by
- 15
- References
- 15
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 3
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Update notices
- new_version (2023-12-19)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Agence Nationale de la Recherche (ANR-17-CE30-0021 RBMPro, ANR-19-CE30-0021 Decrypted, ANR-10-LABX-62-IBEID)
- École Polytechnique, Université Paris-Saclay
- Fondation Bettencourt Schueller
- European Research Council (677823, 101044479)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- Cas9, protein design, restricted Boltzmann machine, semi-supervised learning, FoldX, evolutionary information, protein structure prediction, CRISPR/Cas9
- MeSH terms
- Machine Learning, Amino Acid Sequence, Learning, Proteins, CRISPR-Cas Systems
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