Geometric Deep Learning for RNA Structure Prediction
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Solution Overview
Problem
Predicting the 3D structure of RNA molecules remains a challenging task due to limited availability of template structures and insufficient understanding of energetically favorable structural characteristics, making it difficult to distinguish accurate from less accurate structural models, especially with limited experimental data.
Innovation Solution
The development of a geometric deep learning approach using equivariant neural networks that learn directly from atomic structures without pre-defined features, encoding geometric patterns and recognizing them at different positions and orientations, and optimizing model parameters based on lowest root mean square deviation (RMSD) between predicted and experimentally determined structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational prediction methods are used for RNA structure, then the approach is simple and requires less data, but the prediction accuracy is insufficient and cannot distinguish accurate from less accurate structural models
Solution Approach 1:
The patent replaces traditional mechanical/physical-based computational prediction methods with a machine learning system that uses geometric deep learning neural networks. This substitution enables the system to achieve state-of-the-art prediction accuracy by learning from experimentally determined RNA structures, thereby resolving the contradiction between prediction accuracy and method complexity.
Solution Approach 2:
The patent creates a computational model that copies and learns from experimentally determined RNA structures to predict new structures. By training the neural network on known structure-data pairs, the system replicates the structural characteristics of accurate RNA folds, enabling high-accuracy predictions without requiring complex manual analysis methods.
2Quantity of substance
If more experimental RNA structure data is collected, then the training data quantity increases, but the cost and time required for structure determination increases
Solution Approach 1:
The system performs self-service by automatically training on available experimental structures and continuously improving its prediction capability without requiring additional manual intervention or time-consuming experimental procedures. The neural network learns structural patterns autonomously from the training data, eliminating the need for human experts to analyze each structure individually.
Solution Approach 2:
The patent performs preliminary action by pre-training the neural network on experimentally determined RNA structures before making predictions on new sequences. This preliminary training phase allows the system to internalize structural principles and geometric relationships, enabling rapid and accurate predictions on target structures without requiring time-consuming experimental determination for each case.
3Measurement precision
If geometric deep learning with equivariant neural networks is used, then prediction accuracy improves, but the computational resources and model complexity increase
Solution Approach 1:
The patent applies parameter changes by optimizing the neural network architecture and training parameters to achieve high accuracy while managing computational resource consumption. The equivariant neural network uses specific parameter transformations that maintain geometric invariance, allowing the model to capture complex structural relationships efficiently without excessive computational overhead.
Data Source
AI summary
Embodiments herein describe systems and methods to determine RNA structure and uses thereof. Many embodiments utilize one or more machine learning models to determine an RNA structure. In various embodiments, the machine learning model is trained using experimentally determined RNA structures. Certain embodiments identify one or more ligands or drugs that bind to an RNA structure, which can be used to treat an individual for a disease, disorder, or infection. Various embodiments determine structure of other molecules, including DNA, proteins, small molecules, etc. Further embodiments determine interactions between multiple molecules and/or molecule types (e.g., RNA-RNA interactions, RNA-DNA interactions, DNA-protein interactions, etc.)