Semi-supervised prediction of protein fitness for data-driven protein engineering

Semi-supervised prediction of protein fitness for data-driven protein engineering

Abstract Protein fitness prediction plays a crucial role in the advancement of protein engineering endeavours. However, the combinatorial complexity of the protein sequence space and the limited availability of assay-labelled data hinder the efficient optimization of protein properties. Data-driven...

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Bibliographic Details
Main Authors: Alicia Olivares-Gil, José A. Barbero-Aparicio, Juan J. Rodríguez, José F. Díez-Pastor, César García-Osorio, Mehdi D. Davari
Format: Article
Language:English
Published: BMC 2025-05-01
Series:Journal of Cheminformatics
Subjects:
Machine learning
Protein engineering
Directed evolution
Semi-supervised learning
Protein design
Tri-training regressor
Online Access:https://doi.org/10.1186/s13321-025-01029-w
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https://doi.org/10.1186/s13321-025-01029-w

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