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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| Vydáno v: | PLoS computational biology Ročník 19; číslo 11; s. e1011621 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
01.11.2023
PLOS Public Library of Science (PLoS) |
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| ISSN: | 1553-7358, 1553-734X, 1553-7358 |
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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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| AbstractList | 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. Proteins are essential molecules in all living organisms, with their function largely determined by their sequence. Modifying a protein’s sequence to achieve a desired function remains a challenging endeavor, requiring careful consideration of factors such as the stability of the structure and interactions with molecular partners. In this study, we devised a protein design method that combines insights from experimental data, natural variation in protein sequences, and physics-based predictions. This approach provides a reliable and interpretable means of altering a protein’s sequence while maintaining its functionality. We applied our technique to a domain of the Cas9 protein, a key component in the CRISPR gene editing system. Our results demonstrate the possibility of generating functional protein domains with over 20% of their sequence modified. These findings underscore how the integration of diverse sources of information in a unified design process enhances the quality of engineered proteins. This advancement holds promise for creating valuable protein variants for applications in drug development and various industries. 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.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. 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. |
| Audience | Academic |
| Author | Malbranke, Cyril Depardieu, Florence Monasson, Rémi Bikard, David Rostain, William Cocco, Simona |
| AuthorAffiliation | University of Kansas, UNITED STATES 1 Laboratory of Physics of the Ecole Normale Superieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Paris, France 2 Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, Paris, France |
| AuthorAffiliation_xml | – name: University of Kansas, UNITED STATES – name: 2 Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, Paris, France – name: 1 Laboratory of Physics of the Ecole Normale Superieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Paris, France |
| Author_xml | – sequence: 1 givenname: Cyril orcidid: 0000-0002-4545-0801 surname: Malbranke fullname: Malbranke, Cyril – sequence: 2 givenname: William orcidid: 0000-0002-9960-4669 surname: Rostain fullname: Rostain, William – sequence: 3 givenname: Florence orcidid: 0000-0002-2680-0589 surname: Depardieu fullname: Depardieu, Florence – sequence: 4 givenname: Simona orcidid: 0000-0002-4459-0204 surname: Cocco fullname: Cocco, Simona – sequence: 5 givenname: Rémi orcidid: 0000-0002-1852-7789 surname: Monasson fullname: Monasson, Rémi – sequence: 6 givenname: David orcidid: 0000-0002-5729-1211 surname: Bikard fullname: Bikard, David |
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| CitedBy_id | crossref_primary_10_1209_0295_5075_ada636 crossref_primary_10_1016_j_cels_2025_101236 crossref_primary_10_1016_j_cell_2024_01_042 crossref_primary_10_1038_s41586_025_09021_y crossref_primary_10_1038_s41587_024_02127_0 crossref_primary_10_1093_nar_gkaf782 |
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| Copyright | Copyright: © 2023 Malbranke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2023 Public Library of Science 2023 Malbranke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. licence_http://creativecommons.org/publicdomain/zero 2023 Malbranke et al 2023 Malbranke et al |
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| SubjectTerms | 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 |
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| Title | Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment |
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