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In the field of bioinformatics, automated function prediction (AFP) for proteins is a significant issue. We propose a computational framework based on a protein language model to provide accurate functional predictions for proteins. By integrating multiple component methods, our approach effectively improves prediction performance. Additionally, we have developed a user-friendly online platform that allows users to obtain prediction results simply by submitting protein sequences, freely available at https://dmiip.sjtu.edu.cn/ng3.0/?returning=true . We provide a detailed guide on how to use the web server and correctly interpret the prediction results. Finally, through a practical example, we demonstrate the superior performance of NetGO 3.0 in predicting protein functions, further showcasing the potential of this framework for protein functional annotation.

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NetGO 3.0: A Recent Protein Function Prediction Tool Based on Protein Language Model

  • 实验方案

  • 发表日期：

    2025年

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  • Shaojun Wang,
  • Hancheng Liu,
  • Ronghui You,
  • Yunjia Liu,
  • Yi Xiong,
  • Shanfeng Zhu

摘要

In the field of bioinformatics, automated function prediction (AFP) for proteins is a significant issue. We propose a computational framework based on a protein language model to provide accurate functional predictions for proteins. By integrating multiple component methods, our approach effectively improves prediction performance. Additionally, we have developed a user-friendly online platform that allows users to obtain prediction results simply by submitting protein sequences, freely available at https://dmiip.sjtu.edu.cn/ng3.0/?returning=true . We provide a detailed guide on how to use the web server and correctly interpret the prediction results. Finally, through a practical example, we demonstrate the superior performance of NetGO 3.0 in predicting protein functions, further showcasing the potential of this framework for protein functional annotation.