Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?
<|reference_start|>Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?: Deep learning has become a crucial tool in studying proteins. While the significance of modeling protein structure has been discussed extensively in the literature, amino acid types are typically included in the input as a default operation for many inference tasks. This study demonstrates with structure alignment task that embedding amino acid types in some cases may not help a deep learning model learn better representation. To this end, we propose ProtLOCA, a local geometry alignment method based solely on amino acid structure representation. The effectiveness of ProtLOCA is examined by a global structure-matching task on protein pairs with an independent test dataset based on CATH labels. Our method outperforms existing sequence- and structure-based representation learning methods by more quickly and accurately matching structurally consistent protein domains. Furthermore, in local structure pairing tasks, ProtLOCA for the first time provides a valid solution to highlight common local structures among proteins with different overall structures but the same function. This suggests a new possibility for using deep learning methods to analyze protein structure to infer function.<|reference_end|>
@article{tan2024protein,
    title={Protein Representation Learning with Sequence Information Embedding:
  Does it Always Lead to a Better Performance?},
    author={Yang Tan, Lirong Zheng, Bozitao Zhong, Liang Hong, Bingxin Zhou},
    journal={arXiv preprint arXiv:2406.19755},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19755},
    primaryClass={q-bio.QM cs.AI}
}