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 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. We explore several semi-supervised learning strategies capable of including the homologous sequences (unlabelled) to the protein of interest in the training process. Among them, we present two new methods to exploit the information in the homologous sequences: i) a new generalised version of MERGE capable of employing any regressor as a base estimator; ii) the Tri-Training Regressor method, an adaptation of the Tri-Training method for regression problems. We find that the information inherent in the homologous sequences has the ability to improve the predictive capacity of models when the number of available sequences is scarce, especially when using the DCA encoding together with MERGE and an SVM regressor.