Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?

• URL: http://arxiv.org/abs/2406.19755v1
• Date: Fri, 28 Jun 2024 08:54:37 GMT
• Title: Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?
• Authors: Yang Tan, Lirong Zheng, Bozitao Zhong, Liang Hong, Bingxin Zhou,
• Abstract summary: We propose ProtLOCA, a local geometry alignment method based solely on amino acid structure representation. Our method outperforms existing sequence- and structure-based representation learning methods by more quickly and accurately matching structurally consistent protein domains.
• Score: 4.7077642423577775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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