用AI生成全新萜类合酶，实验验证有效。
De novo generation of functional terpene synthases using TpsGPT
- 基于7.9万条萜类合酶数据微调语言模型，生成新蛋白序列。
- 从2.8万个候选中筛选出7个符合标准的酶，其中2个经实验证实有活性。
- 适合做酶设计、合成生物学或AI药物研发的研究者参考。
萜类合酶（TPS）是一类关键酶，负责生成多种萜类骨架，这些骨架是抗癌药物紫杉醇等天然产物的基础。然而，通过定向进化进行新TPS设计成本高且耗时长。本文提出TpsGPT，一种基于ProtGPT2微调的生成模型，训练数据为从UniProt中挖掘的7.9万条TPS序列。TpsGPT在体外生成了新酶候选序列，并通过多维度评估：包括EnzymeExplorer分类、ESMFold结构置信度（pLDDT）、序列多样性、CLEAN分类、InterPro域检测及Foldseek结构比对。从初始2.8万个生成序列中，筛选出7个满足所有标准的潜在TPS酶，其中至少两个经实验验证具有酶活性。结果表明，针对特定酶类精心构建的数据集结合严格过滤，可实现功能型、进化上远缘酶的从头生成。
原文摘要 · Abstract (English)
Terpene synthases (TPS) are a key family of enzymes responsible for generating the diverse terpene scaffolds that underpin many natural products, including front-line anticancer drugs such as Taxol. However, de novo TPS design through directed evolution is costly and slow. We introduce TpsGPT, a generative model for scalable TPS protein design, built by fine-tuning the protein language model ProtGPT2 on 79k TPS sequences mined from UniProt. TpsGPT generated de novo enzyme candidates in silico and we evaluated them using multiple validation metrics, including EnzymeExplorer classification, ESMFold structural confidence (pLDDT), sequence diversity, CLEAN classification, InterPro domain detection, and Foldseek structure alignment. From an initial pool of 28k generated sequences, we identified seven putative TPS enzymes that satisfied all validation criteria. Experimental validation confirmed TPS enzymatic activity in at least two of these sequences. Our results show that fine-tuning of a protein language model on a carefully curated, enzyme-class-specific dataset, combined with rigorous filtering, can enable the de novo generation of functional, evolutionarily distant enzymes.
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