发表机构
Allen Institute; Move37 Labs（艾伦研究所; Move37 实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出序列精简任务，即从现有功能DNA中删除碱基以保持活性，并构建了首个仅删除基准，通过编码智能体ERA改进设计器生成GRADASLIM，在多数实验设置中优于随机和贪心方法。
AI 中文摘要
紧凑的调控DNA可以释放载体载荷中的空间，减少合成和检测负担，并揭示哪些序列特征驱动预测活性。然而，大多数基于模型的核酸设计器通过替换来优化固定长度的序列；它们不会询问现有功能元件中哪些碱基可以在保持预测活性的同时被移除。我们将序列精简任务定义为在保持活性的同时选择一个精确长度、保序的子序列。参照设计基准NucleoBench，我们提出了一个用于精简的定量评估，该评估在序列缩减与维持功能之间取得平衡。每个精简器必须同时返回子序列及其源索引，这些索引可用于验证精简器是否遵守了任务要求。据我们所知，这是第一个专门针对这种仅删除问题的基准。编码智能体实证研究助手（ERA）随后搜索了可执行的设计器程序。ERA接收了任务提示和一个成功的仅替换设计器GrAdaBeam作为起始程序，并修改了该设计器以生成GRADASLIM。我们报告了针对五个转录因子结合靶点的留出评估，比较了在400和100 bp下的随机、贪心和ERA引导的精简。ERA在9/10的设置中具有最高的平均值。ERA减去贪心的配对自助置信区间在五个400-bp设置中全部高于零，在一个100-bp设置中低于零，在其余四个设置中与零重叠。
英文摘要
Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose which sequence features drive predicted activity. Yet most model-based nucleic-acid designers optimize fixed-length sequences through substitutions; they do not ask which bases of an existing functional element can be removed while retaining predicted activity. We define the task of sequence slimming as selecting an exact-length, order-preserving subsequence while retaining activity. Modeled on the design benchmark NucleoBench, we propose a quantitative evaluation for slimming that balances sequence reduction with maintaining function. Each slimmer must return both the subsequence and its source indices, which can be used to verify that the slimmer obeyed task requirements. To our knowledge, this is the first dedicated benchmark of this deletion-only problem. The coding agent Empirical Research Assistant (ERA) then searched over executable designer programs. ERA received the task prompt and a successful substitution-only designer GrAdaBeam as a starting program, and it modified the designer to produce GRADASLIM. We report held-out evaluations for five transcription-factor binding targets, comparing random, greedy, and ERA-guided slimming at 400 and 100 bp. ERA has the highest mean in 9/10 settings. Paired bootstrap intervals for ERA minus greedy are above zero in all five 400-bp settings, below zero in one 100-bp setting, and overlap zero in the remaining four.
Comments40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: Agentic AI for Biological Discovery
