2026-04-18T13:52:20Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/114302026-02-26T01:05:59Zcom_10259_5377com_10259_5086com_10259_2604com_10259_4219col_10259_5378col_10259_4220

00925njm 22002777a 4500 dc Olivares Gil, Alicia author Barbero Aparicio, José Antonio author Rodríguez Diez, Juan José author Diez Pastor, José Francisco author García Osorio, César author Davari, Mehdi D. author 2025-12 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 strategies utilizing machine learning methods have emerged as a promising solution, yet their dependence on labelled training datasets poses a significant obstacle. To overcome this challenge, in this work, we explore various ways of introducing the latent information present in evolutionarily related sequences (homologous sequences) into the training process. To do so, we establish several strategies based on semi-supervised learning (unsupervised pre-processing and wrapper methods) and perform a comprehensive comparison using 19 datasets containing protein-fitness pairs. Our findings reveal that using the information present in the homologous sequences can improve the performance of the models, especially when the number of available labelled sequences is considerably low. Specifically, the combination of a sequence encoding method based on Direct Coupling Analysis (DCA), with MERGE (a hybrid regression framework that combines evolutionary information with supervised learning) and an SVM regressor, outperforms other encodings (PAM250, UniRep, eUniRep) and other semi-supervised wrapper methods (Tri-Training Regressor, Co-Training Regressor). In summary, the demonstrated performance gains of this strategy mark a substantial leap towards more robust and reliable predictive models for protein engineering tasks. This advancement holds the potential to streamline the design and optimisation of proteins for diverse applications in biotechnology and therapeutics. 1758-2946 https://hdl.handle.net/10259/11430 10.1186/s13321-025-01029-w 1758-2946 Machine learning Protein engineering Directed evolution Semi-supervised learning Protein design Tritraining regressor Generalized MERGE Semi-supervised prediction of protein fitness for data-driven protein engineering