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CATHe2: Enhanced CATH superfamily detection using ProstT5 and structural alphabets.

Orfeú Mouret1,2, Jad Abbass1

  • 1School of Computer Science and Mathematics, Kingston University, Kingston-upon-Thames KT1 2EE, United Kingdom.

Biology Methods & Protocols
|November 24, 2025
PubMed
Summary

CATHe2 enhances protein domain classification by integrating advanced protein language models and 3D structural information. This new model significantly improves accuracy and F1 scores for predicting CATH superfamilies.

Keywords:
3DiCATHCATHeProstT5Protein Language Model (pLM)Structural Alphabetclassifierprotein

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in genomics

Background:

  • The CATH database classifies protein domain structures and evolutionary relationships.
  • AlphaFold has accelerated protein structure prediction, necessitating automated classification methods.
  • The previous CATHe classifier used protein Language Model (pLM) embeddings for superfamily prediction.

Purpose of the Study:

  • To develop an improved automated classifier, CATHe2, for CATH superfamilies.
  • To enhance protein domain classification accuracy and efficiency.
  • To leverage recent advancements in protein Language Models and incorporate 3D structural data.

Main Methods:

  • Utilized updated pLM embeddings (ProstT5) and 3Di sequence embeddings representing 3D structure.
  • Implemented a fine-tuned feed-forward neural network (FNN) classifier architecture.
  • Trained and evaluated models on a dataset of remote protein homologues with a 20% sequence identity threshold.

Main Results:

  • The best CATHe2 model achieved 92.2% accuracy and 82.3% F1 score on the largest dataset (∼1700 superfamilies).
  • This represents a substantial improvement over the previous CATHe version (85.6% accuracy, 72.4% F1 score).
  • Even a simplified CATHe2 version using only amino acid sequences showed significant performance gains.

Conclusions:

  • CATHe2 offers a more accurate and robust method for automated CATH superfamily classification.
  • The integration of advanced pLMs and 3D structural information is crucial for improving protein classification.
  • This advancement aids in understanding protein evolution and function in the era of large-scale structure prediction.