不依赖EC编号，直接预测酶催化反应，提升注释精度与可解释性。
RXNRECer Enables Fine-grained Enzymatic Function Annotation through Active Learning and Protein Language Models
- 用蛋白质语言模型+主动学习，直接预测酶催化反应
- F1提升16.54%，准确率提高15.43%，优于6个基准方法
- 适合需要精细功能注释的酶研究与工业应用
酶功能注释的核心挑战在于识别蛋白质催化的生化反应。现有方法多以酶委员会（EC）编号为中介：先预测EC号，再映射反应。该间接策略因蛋白、EC号与反应间复杂的多对多关系而引入歧义，且受数据库更新频繁和不一致的影响。为此，我们提出RXNRECer，一个基于Transformer的集成框架，直接预测酶催化反应，无需依赖EC编号。它融合蛋白质语言建模与主动学习，捕捉序列高层语义与细微转化模式。在精心构建的交叉验证与时间测试集上，相比六个基于EC的基线模型，表现持续领先，F1分数提升16.54%，准确率提高15.43%。除精度提升外，该框架还支持全基因组范围反应注释、细化通用反应模板、系统注释未注释蛋白，并可靠识别酶的多能性。通过引入大语言模型，还可提供预测的可解释依据。这些能力使RXNRECer成为一种强大且通用的免EC精细酶功能预测方案，适用于酶研究与工业多个领域。
原文摘要 · Abstract (English)
A key challenge in enzyme annotation is identifying the biochemical reactions catalyzed by proteins. Most existing methods rely on Enzyme Commission (EC) numbers as intermediaries: they first predict an EC number and then retrieve the associated reactions. This indirect strategy introduces ambiguity due to the complex many-to-many mappings among proteins, EC numbers, and reactions, and is further complicated by frequent updates to EC numbers and inconsistencies across databases. To address these challenges, we present RXNRECer, a transformer-based ensemble framework that directly predicts enzyme-catalyzed reactions without relying on EC numbers. It integrates protein language modeling and active learning to capture both high-level sequence semantics and fine-grained transformation patterns. Evaluations on curated cross-validation and temporal test sets demonstrate consistent improvements over six EC-based baselines, with gains of 16.54% in F1 score and 15.43% in accuracy. Beyond accuracy gains, the framework offers clear advantages for downstream applications, including scalable proteome-wide reaction annotation, enhanced specificity in refining generic reaction schemas, systematic annotation of previously uncurated proteins, and reliable identification of enzyme promiscuity. By incorporating large language models, it also provides interpretable rationales for predictions. These capabilities make RXNRECer a robust and versatile solution for EC-free, fine-grained enzyme function prediction, with potential applications across multiple areas of enzyme research and industrial applications.
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。