ArticleBMC bioinformatics2023
TEMPROT: protein function annotation using transformers embeddings and homology search.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- DualNetGO: a dual network model for protein function prediction via effective feature selection.Bioinformatics (Oxford, England) · 2024Article
- Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.Briefings in bioinformatics · 2024Article
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3 authors.
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Abstract
backgroundAlthough the development of sequencing technologies has provided a large number of protein sequences, the analysis of functions that each one plays is still difficult due to the efforts of laboratorial methods, making necessary the usage of computational methods to decrease this gap. As the main source of information available about proteins is their sequences, approaches that can use this information, such as classification based on the patterns of the amino acids and the inference based on sequence similarity using alignment tools, are able to predict a large collection of proteins. The methods available in the literature that use this type of feature can achieve good results, however, they present restrictions of protein length as input to their models. In this work, we present a new method, called TEMPROT, based on the fine-tuning and extraction of embeddings from an available architecture pre-trained on protein sequences. We also describe TEMPROT+, an ensemble between TEMPROT and BLASTp, a local alignment tool that analyzes sequence similarity, which improves the results of our former approach.
resultsThe evaluation of our proposed classifiers with the literature approaches has been conducted on our dataset, which was derived from CAFA3 challenge database. Both TEMPROT and TEMPROT+ achieved competitive results on [Formula: see text], [Formula: see text], AuPRC and IAuPRC metrics on Biological Process (BP), Cellular Component (CC) and Molecular Function (MF) ontologies compared to state-of-the-art models, with the main results equal to 0.581, 0.692 and 0.662 of [Formula: see text] on BP, CC and MF, respectively.
conclusionsThe comparison with the literature showed that our model presented competitive results compared the state-of-the-art approaches considering the amino acid sequence pattern recognition and homology analysis. Our model also presented improvements related to the input size that the model can use to train compared to the literature methods.
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