BAnG：双向锚定生成用于条件RNA设计
BAnG: Bidirectional Anchored Generation for Conditional RNA Design
AI总结：
本文提出BAnG方法，通过双向锚定生成技术实现无需先验知识的条件RNA序列设计，解决了传统方法依赖已知序列和结构的限制。
摘要：
设计能够与特定蛋白质相互作用的RNA分子是实验和计算生物学中的关键挑战。现有的计算方法需要为每个特定蛋白质提供大量已知的相互作用RNA序列或对RNA结构有详细的知识，限制了它们的实际应用。为了解决这一限制，我们开发了RNA-BAnG，一种基于深度学习的模型，旨在在不需这些要求的情况下生成蛋白质相互作用的RNA序列。我们的方法核心是一种新的生成方法，即双向锚定生成（BAnG），它利用了蛋白质结合RNA序列通常包含的功能结合位点嵌入在更广泛序列背景中的观察。我们首先在涉及类似局部位点的通用合成任务上验证了我们的方法，证明其优于现有生成方法。然后我们在生物序列上评估我们的模型，展示了其在给定结合蛋白时对条件RNA序列设计的有效性。
英文摘要：
Designing RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Existing computational approaches require a substantial amount of previously known interacting RNA sequences for each specific protein or a detailed knowledge of RNA structure, restricting their utility in practice. To address this limitation, we develop RNA-BAnG, a deep learning-based model designed to generate RNA sequences for protein interactions without these requirements. Central to our approach is a novel generative method, Bidirectional Anchored Generation (BAnG), which leverages the observation that protein-binding RNA sequences often contain functional binding motifs embedded within broader sequence contexts. We first validate our method on generic synthetic tasks involving similar localized motifs to those appearing in RNAs, demonstrating its benefits over existing generative approaches. We then evaluate our model on biological sequences, showing its effectiveness for conditional RNA sequence design given a binding protein.
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。
