LLM 驱动的文献抽取流程构建蛋白缓冲液与表达条件数据库 ProBufEx
An advanced LLM-powered article extraction pipeline delivers a database of buffers for protein studies and protein expression conditions
研究团队提出一套由大语言模型驱动的文献抽取流程，把非结构化科学文献系统转换为可查询的结构化数据库，抽取准确率约 97%。该流程结合先进的光学字符识别、自训练的 reranker 模型与大语言模型，处理了与 RCSB Protein Data Bank 相关的 100 353 篇结构生物学文献，构建出包含 947 153 条缓冲液条目、73 210 条表达方案、覆盖 35 690 个不同蛋白的数据库，作者称其为同类资源中规模最大者。配套的 ProBufEx 网页平台支持按蛋白性质或功能快速检索最优条件。
来源：Briefings in Bioinformatics · doi.org
该流程把十万余篇结构生物学文献转成可查询的缓冲液与表达条件库，做蛋白表达纯化的读者可据此减少试错。
Optimizing protein expression and buffer conditions is a major challenge in life-science research and biotechnology. We present an LLM-powered extraction pipeline that systematically converts unstructured scientific literature into a structured, queryable database. Leveraging advanced optical character recognition, custom reranker models, and large language models, our system achieves ~97% extraction accuracy. By processing 100 353 structural biology publications associated with the RCSB Protein Data Bank, we constructed a database containing 947 153 distinct buffer entries and 73 210 expression protocols for 35 690 unique proteins, establishing the largest resource of its kind. The ProBufEx (https://www.probufex.org/) web platform enables rapid exploration of optimal conditions by protein properties or functions. Case studies reveal distinct patterns in chemical usage on proteins with different post-translational modifications, and function-dependent expression strategies for proteins with different enzymatic activities. This resource transforms vast structural biology literature into actionable knowledge, accelerating evidence-based protocol design, reducing trial-and-error, and streamlining experiments in protein science.