Sampling-based Continuous Optimization with Coupled Variables for RNA Design

TL;DR

This paper introduces a novel sampling-based continuous optimization method with coupled variables for RNA design, outperforming existing heuristics especially on complex, long structures by modeling nucleotide correlations and using gradient-based improvements.

Contribution

It proposes a new continuous optimization framework with coupled variable distributions and sampling, enabling effective RNA design for complex structures.

Findings

Outperforms state-of-the-art methods on Eterna100 benchmark

Achieves higher Boltzmann probability and lower ensemble defect

Effective on long, hard-to-design RNA structures

Abstract

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.