| Téléchargement | - Voir la version finale : Disentangled wasserstein autoencoder for T-cell receptor engineering (PDF, 7.1 Mio)
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| Lien | https://neurips.cc/virtual/2023/poster/72313 |
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| Auteur | Rechercher : Li, Tianxiao; Rechercher : Guo, Hongyu1Identifiant ORCID : https://orcid.org/0000-0002-7663-2421; Rechercher : Grazioli, Filippo; Rechercher : Gerstein, Mark; Rechercher : Min, Martin Renqiang |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 37th Conference on Neural Information Processing Systems (NeurIPS) 2023, December 10-16, 2023, New Orleans, Louisianna, USA |
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| Sujet | cell engineering; cell membranes; domain knowledge; learning systems; T-cells; active site; auto encoders; binding surface; data driven; functional sites; protein engineering; T cells receptors; proteins |
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| Résumé | In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires significant domain knowledge. To automate this process from a data-driven perspective, we propose a disentangled Wasserstein autoencoder with an auxiliary classifier, which isolates the function-related patterns from the rest with theoretical guarantees. This enables one-pass protein sequence editing and improves the understanding of the resulting sequences and editing actions involved. To demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a well-studied structure-function case. We show that our method can be used to alter the function of TCRs without changing the structural backbone, outperforming several competing methods in generation quality and efficiency, and requiring only 10% of the running time needed by baseline models. To our knowledge, this is the first approach that utilizes disentangled representations for TCR engineering. |
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| Date de publication | 2023-12-10 |
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| Maison d’édition | Neural Information Processing Systems Foundation |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | cf5d1096-2edb-40b5-abe1-4da6a86eb52d |
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| Enregistrement créé | 2024-07-18 |
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| Enregistrement modifié | 2024-11-19 |
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