ProtAlign: 用于序列和结构对齐的对比学习范式
ProtAlign: Contrastive learning paradigm for Sequence and structure alignment
AI总结：
ProtAlign通过对比学习范式，统一了蛋白质序列与结构的表示，提升跨模态检索和下游预测性能。
摘要：
蛋白质语言模型通常考虑蛋白质序列与其文本描述之间的对齐。然而，它们不考虑结构信息。传统方法将序列和结构分开处理，限制了利用结构和蛋白质序列嵌入之间对齐能力的能力。在本文中，我们介绍了一个序列结构对比对齐框架，该框架学习了一个共享的嵌入空间，其中蛋白质在不同模态下被一致地表示。通过在大规模序列和实验确定或预测的结构配对上进行训练，模型在匹配的序列结构对之间最大化一致性和在不相关对之间推远。这种对齐能够实现跨模态检索（例如，给定一个序列找到结构邻居），提高下游预测任务如功能注释和稳定性估计，并提供可解释的链接，将序列变异与结构组织联系起来。我们的结果表明，对比学习可以作为蛋白质序列和结构之间的强大桥梁，提供一种统一的表示方法，用于理解和工程化蛋白质。
英文摘要：
Protein language models often take into consideration the alignment between a protein sequence and its textual description. However, they do not take structural information into consideration. Traditional methods treat sequence and structure separately, limiting the ability to exploit the alignment between the structure and protein sequence embeddings. In this paper, we introduce a sequence structure contrastive alignment framework, which learns a shared embedding space where proteins are represented consistently across modalities. By training on large-scale pairs of sequences and experimentally resolved or predicted structures, the model maximizes agreement between matched sequence structure pairs while pushing apart unrelated pairs. This alignment enables cross-modal retrieval (e.g., finding structural neighbors given a sequence), improves downstream prediction tasks such as function annotation and stability estimation, and provides interpretable links between sequence variation and structural organization. Our results demonstrate that contrastive learning can serve as a powerful bridge between protein sequences and structures, offering a unified representation for understanding and engineering proteins.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
