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The recent availability of large language models for protein sequences has spurred the development of deep learning models for prediction of protein functions, mainly in the form of gene ontology (GO) terms. We developed InterLabelGO+, a top performing deep learning–based protein GO term prediction model in the recent fifth Critical Assessment of Function Annotation (CAFA5) challenge. InterLabelGO+ uses the ESM2 protein language model to extract sequence features, which are then used as inputs to a deep learning model that was trained under a loss function that considers the potentially complex relations between different GO terms. The deep learning–predicted GO terms are then combined with GO terms from sequence homology search to derive consensus predictions. InterLabelGO+ is available at https://seq2fun.dcmb.med.umich.edu/InterLabelGO/ . In this chapter, we demonstrate procedures to perform protein GO term prediction with the InterLabelGO+ webserver and the standalone package, as well as how to retrain the model with up-to-date training data.

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Using InterLabelGO+ for Accurate Protein Language Model-Based Function Prediction

  • 实验方案

  • 发表日期：

    2025年

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  • Chengxin Zhang,
  • Quancheng Liu,
  • Lydia Freddolino

摘要

The recent availability of large language models for protein sequences has spurred the development of deep learning models for prediction of protein functions, mainly in the form of gene ontology (GO) terms. We developed InterLabelGO+, a top performing deep learning–based protein GO term prediction model in the recent fifth Critical Assessment of Function Annotation (CAFA5) challenge. InterLabelGO+ uses the ESM2 protein language model to extract sequence features, which are then used as inputs to a deep learning model that was trained under a loss function that considers the potentially complex relations between different GO terms. The deep learning–predicted GO terms are then combined with GO terms from sequence homology search to derive consensus predictions. InterLabelGO+ is available at https://seq2fun.dcmb.med.umich.edu/InterLabelGO/ . In this chapter, we demonstrate procedures to perform protein GO term prediction with the InterLabelGO+ webserver and the standalone package, as well as how to retrain the model with up-to-date training data.