一种基于蛋白质语言模型（PLMs）的蛋白质检索框架
A PLMs based protein retrieval framework
AI总结：
本文提出一种基于蛋白质语言模型和高维向量索引的蛋白质检索框架，缓解传统方法对序列相似性的偏置，能够同时检索相似与不相似蛋白质，并发现传统方法遗漏的蛋白质。
摘要：
蛋白质检索旨在解构序列、结构与功能之间的关系，从而推动生物学的发展。基于序列相似性的算法——基本局部比对搜索工具（BLAST）已经证明了该领域的有效性。尽管已有多种蛋白质检索工具，但它们优先考虑序列相似性，可能忽略那些序列不相似但具有同源性或功能相似性的蛋白质。为了解决这一问题，我们提出了一种新颖的蛋白质检索框架，以缓解对序列相似性的偏置。我们的框架率先利用蛋白质语言模型（PLMs）将蛋白质序列嵌入到高维特征空间中，从而增强后续分析的表征能力。随后，构建了一个加速的索引向量数据库，以便于快速访问和检索稠密向量。大量实验表明，我们的框架能够同等地检索相似和不相似的蛋白质。此外，该方法能够识别传统方法无法发现的蛋白质。该框架将有效辅助蛋白质挖掘，并推动生物学的发展。
英文摘要：
Protein retrieval, which targets the deconstruction of the relationship between sequences, structures and functions, empowers the advancing of biology. Basic Local Alignment Search Tool (BLAST), a sequence-similarity-based algorithm, has proved the efficiency of this field. Despite the existing tools for protein retrieval, they prioritize sequence similarity and probably overlook proteins that are dissimilar but share homology or functionality. In order to tackle this problem, we propose a novel protein retrieval framework that mitigates the bias towards sequence similarity. Our framework initiatively harnesses protein language models (PLMs) to embed protein sequences within a high-dimensional feature space, thereby enhancing the representation capacity for subsequent analysis. Subsequently, an accelerated indexed vector database is constructed to facilitate expedited access and retrieval of dense vectors. Extensive experiments demonstrate that our framework can equally retrieve both similar and dissimilar proteins. Moreover, this approach enables the identification of proteins that conventional methods fail to uncover. This framework will effectively assist in protein mining and empower the development of biology.
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。
