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ProVolOne: protein volume prediction using a multi-attention, multi-resolution deep neural network and finite element analysis

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1007/978-3-031-53969-5_21
AuteurRechercher : 1Identifiant ORCID : https://orcid.org/0000-0001-6515-2556; Rechercher : Identifiant ORCID : https://orcid.org/0000-0003-1914-5077; Rechercher : Identifiant ORCID : https://orcid.org/0000-0002-9198-6439; Rechercher : Identifiant ORCID : https://orcid.org/0000-0002-8985-4920
Affiliation
  1. Conseil national de recherches Canada. Technologies numériques
FormatTexte, Article
ConférenceThe 9th Annual Conference on Machine Learning, Optimization and Data Science, LOD 2023, September 22–26, 2023, Grasmere, UK
Sujetdeep learning; multi-attention learning; protein volume prediction; multi-resolution learning
Résumé
Protein structural properties are often determined by experimental techniques such as X-ray crystallography and nuclear magnetic resonance. However, both approaches are time-consuming and expensive. Conversely, protein amino acid sequences may be readily obtained from inexpensive high-throughput techniques, although such sequences lack structural information, which is essential for numerous applications such as gene therapy, in which maximisation of the payload, or volume, is required. This paper proposes a novel solution to volume prediction, based on deep learning and finite element analysis. We introduce a multi-attention, multi-resolution deep learning architecture that predicts protein volumes from their amino acid sequences. Experimental results demonstrate the efficiency of the ProVolOne framework.
Date de publication
Maison d’éditionSpringer
Dans
  • Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part I (2024) : 274–287.
Série
  • Lecture Notes in Computer Science 14505.
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement34cfab5f-d7ab-4027-9b7a-077938310e96
Enregistrement créé2024-07-03
Enregistrement modifié2024-07-04
Date de modification :