用连续优化方法设计RNA序列，提升长难结构设计效果。
Sampling-based Continuous Optimization with Coupled Variables for RNA Design
- 将RNA设计转为连续优化，用耦合变量分布排除无效序列
- 在Eterna100上对长序列的折叠概率、能量差等指标全面领先
- 适合需要高精度设计长RNA结构的研究者使用
给定目标结构的RNA序列设计任务旨在找到能折叠成该结构的序列。该问题计算复杂，部分版本已被证明是NP难问题。传统启发式方法如局部搜索仅探索有限候选序列，难以应对设计空间的指数增长，尤其在长且难设计的结构上表现不佳。本文将离散问题转化为连续优化：从所有可能序列的分布出发，利用梯度下降优化目标函数的期望值。提出基于耦合变量的新分布，以排除不满足目标结构的无效序列，并建模核苷酸间的相关性。为适配任意目标函数，采用采样近似期望值、估计梯度并选择最终候选序列。相比当前最优方法，本工作在玻尔兹曼概率、集合缺陷和能量间隙等关键指标上均表现更优，尤其在Eterna100基准中长且难设计的难题上优势显著。代码已公开于http://github.com/weiyutang1010/ncrna_design。
原文摘要 · Abstract (English)
The task of RNA design given a target structure aims to find a sequence that can fold into that structure. It is a computationally hard problem where some version(s) have been proven to be NP-hard. As a result, heuristic methods such as local search have been popular for this task, but by only exploring a fixed number of candidates. They can not keep up with the exponential growth of the design space, and often perform poorly on longer and harder-to-design structures. We instead formulate these discrete problems as continuous optimization, which starts with a distribution over all possible candidate sequences, and uses gradient descent to improve the expectation of an objective function. We define novel distributions based on coupled variables to rule out invalid sequences given the target structure and to model the correlation between nucleotides. To make it universally applicable to any objective function, we use sampling to approximate the expected objective function, to estimate the gradient, and to select the final candidate. Compared to the state-of-the-art methods, our work consistently outperforms them in key metrics such as Boltzmann probability, ensemble defect, and energy gap, especially on long and hard-to-design puzzles in the Eterna100 benchmark. Our code is available at: http://github.com/weiyutang1010/ncrna_design.
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