RNA Sequence Design via Reinforcement Learning
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Solution Overview
Problem
Current RNA design methods restrict their solution space by requiring structural priority on the entire molecule or full form, making it difficult to handle unbalanced parentheses and partial structures, and limiting the exploration of versatile candidate sequences.
Innovation Solution
A method using a neural network-based strategy that initializes weights randomly, incorporates task representations of structural and sequential restrictions, applies reinforcement learning to optimize the placement of nucleotides within a primary RNA structure, and adapts to find RNA sequences that satisfy both sequence and structural features, allowing for the exploration of a larger search space and knowledge transfer between RNA design tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If structural priority on the entire molecule is required, then the RNA design method can ensure structural accuracy, but the solution space is restricted and cannot handle partial structures
Solution Approach 1:
The patent divides the RNA design problem into segments by allowing structural restrictions to be applied to only certain regions (partial structures) rather than requiring complete structural priority on the entire molecule. This segmentation enables the method to handle unbalanced parentheses and partial structures while maintaining structural accuracy where needed.
Solution Approach 2:
The patent applies structural restrictions partially rather than fully across the entire RNA sequence. By allowing some regions to have structural constraints while leaving other regions flexible, the method expands the solution space while still ensuring structural accuracy in the constrained regions.
2Manufacturing precision
If complete structural restrictions are imposed, then the RNA structure can be precisely controlled, but the exploration of versatile candidate sequences is limited
Solution Approach 1:
The patent implements partial structural restrictions where only specific regions of the RNA sequence are constrained structurally. This partial application of structural control maintains precision where needed while allowing the reinforcement learning algorithm to explore a broader range of candidate sequences in unrestricted regions, thereby improving sequence exploration efficiency.
Solution Approach 2:
The patent introduces dynamic flexibility by allowing the degree of structural restriction to vary across different regions of the RNA sequence. The reinforcement learning algorithm can adaptively explore sequences with varying levels of structural constraint, enabling both precise structural control in critical regions and efficient exploration in less critical regions.
3Reliability
If traditional RNA design methods are used, then the algorithm can handle complete structures, but it cannot deal with unbalanced parentheses and partial structures
Solution Approach 1:
The patent segments the structural representation into complete and partial components, allowing the algorithm to handle unbalanced parentheses and partial structures by treating them as valid intermediate states rather than requiring complete balanced structures throughout the entire design process.
Solution Approach 2:
The patent introduces dynamic handling of structural completeness by allowing the RNA design algorithm to transition between states with different levels of structural completion. The reinforcement learning framework can navigate through unbalanced parentheses and partial structures dynamically, eventually reaching complete valid structures without being constrained by the requirement of complete structures at all intermediate steps.
Data Source
AI summary
A method for creating a strategy, which is configured to determine a placement of nucleotides within a primary RNA structure as a function of a detail of a predefined secondary structure. The method includes the following steps: initializing the strategy; providing a task representation, the task representation including structural restrictions of the secondary RNA structure and sequential restrictions of the primary RNA structure; determining a primary candidate RNA sequence with the aid of the strategy as a function of the task representation; adapting the strategy with the aid of a reinforcement learning algorithm in such a way that a total loss is optimized.