TEMPROT: protein function annotation using transformers embeddings and homology search

Author(s)

Oliveira, Gabriel B. ; Pedrini, Helio ; Dias, Zanoni

• Document type: Journal article
• Source: BMC Bioinformatics; v. 24, n. 1, p. 16-pg., 2023-06-08.

Abstract

Background: Although 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.Results: The 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 F-max, S-min, 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 F-max on BP, CC and MF, respectively.Conclusions: The 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. (AU)

• FAPESP's process: 17/16246-0 - Sensitive media analysis through deep learning architectures
• Grantee: Sandra Eliza Fontes de Avila
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/20875-8 - Chest X-ray image classification using deep neural networks
• Grantee: Vinicius Teixeira de Melo
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
• Grantee: Anderson de Rezende Rocha
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points
• Grantee: Flávio Keidi Miyazawa
• Support Opportunities: Research Projects - Thematic Grants