融合全局结构与局部氨基酸信息，提升蛋白质功能预测精度
GLProtein: Global-and-Local Structure Aware Protein Representation Learning
- 联合建模蛋白三维结构、分子局部特征与全局相似性
- 在蛋白质互作和接触预测任务上超越现有方法
- 适合需要精细结构理解的生物信息学研究者
蛋白质是生命系统的核心组成部分，广泛参与各类生物过程。尽管通过序列分析已取得显著进展，但整合蛋白质结构信息仍有潜力可挖。我们认为，蛋白质结构信息不仅包括三维空间构型，还涵盖氨基酸分子细节（局部信息）及蛋白-蛋白结构相似性（全局信息）。为此，我们提出首个在预训练中同时融合全局结构相似性与局部氨基酸特征的框架——GLProtein。该模型创新性地结合了蛋白质掩码建模、三元组结构相似性评分、3D距离编码以及基于子结构的氨基酸分子编码。实验表明，GLProtein在蛋白质-蛋白质互作预测、接触预测等多个生物信息学任务中优于现有方法。
原文摘要 · Abstract (English)
Proteins are central to biological systems, participating as building blocks across all forms of life. Despite advancements in understanding protein functions through protein sequence analysis, there remains potential for further exploration in integrating protein structural information. We argue that the structural information of proteins is not only limited to their 3D information but also encompasses information from amino acid molecules (local information) to protein-protein structure similarity (global information). To address this, we propose \textbf{GLProtein}, the first framework in protein pre-training that incorporates both global structural similarity and local amino acid details to enhance prediction accuracy and functional insights. GLProtein innovatively combines protein-masked modelling with triplet structure similarity scoring, protein 3D distance encoding and substructure-based amino acid molecule encoding. Experimental results demonstrate that GLProtein outperforms previous methods in several bioinformatics tasks, including predicting protein-protein interaction, contact prediction, and so on.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。