Integrating Transformers and AutoML for Protein Function Prediction

The next-generation sequencing technology and the decreasing cost of experimental verification of proteins made the accumulation of sequenced proteins in recent years possible. However, determining protein function is still difficult due to the cost and time required for this analysis. For that reason, computational methods have been developed to automatically assign annotations to proteins. In this work, we present MAGO, an approach based on Transformers and AutoML, and MAGO+, an ensemble of MAGO with BLASTp, to deal with this task. MAGO and MAGO+ surpassed state-of-the-art methods based on machine learning and ensemble methods combining local alignment tools and machine learning algorithms, improving the results based on Fmax and presenting statistically significant differences with the compared approaches.