Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions.
Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based op...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010874 |
| _version_ | 1811160746421125120 |
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| author | Jérôme Tubiana Lucia Adriana-Lifshits Michael Nissan Matan Gabay Inbal Sher Marina Sova Haim J Wolfson Maayan Gal |
| author_facet | Jérôme Tubiana Lucia Adriana-Lifshits Michael Nissan Matan Gabay Inbal Sher Marina Sova Haim J Wolfson Maayan Gal |
| author_sort | Jérôme Tubiana |
| collection | DOAJ |
| description | Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators. |
| first_indexed | 2024-04-10T06:03:50Z |
| format | Article |
| id | doaj.art-5d1591ca5d284bbcae28a5fd134a7ad8 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-10T06:03:50Z |
| publishDate | 2023-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-5d1591ca5d284bbcae28a5fd134a7ad82023-03-03T05:31:04ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-02-01192e101087410.1371/journal.pcbi.1010874Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions.Jérôme TubianaLucia Adriana-LifshitsMichael NissanMatan GabayInbal SherMarina SovaHaim J WolfsonMaayan GalDesign of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.https://doi.org/10.1371/journal.pcbi.1010874 |
| spellingShingle | Jérôme Tubiana Lucia Adriana-Lifshits Michael Nissan Matan Gabay Inbal Sher Marina Sova Haim J Wolfson Maayan Gal Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions. PLoS Computational Biology |
| title | Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions. |
| title_full | Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions. |
| title_fullStr | Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions. |
| title_full_unstemmed | Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions. |
| title_short | Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions. |
| title_sort | funneling modulatory peptide design with generative models discovery and characterization of disruptors of calcineurin protein protein interactions |
| url | https://doi.org/10.1371/journal.pcbi.1010874 |
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