Neural Network for Aptamer Binding Strength Prediction
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Solution Overview
Problem
Conventional methods for identifying the binding strength between aptamers and target molecules are labor-intensive and often fail to yield aptamers with strong specific binding, as they rely on iterative SELEX processes that may not include the best aptamer in the initial random pool.
Innovation Solution
A neural network is configured to process aptamer sequences and predict their binding strength to a target molecule, allowing for the identification of aptamers with strong specific binding without the need for multiple SELEX rounds. The neural network can model binding to multiple targets and identify aptamers that bind strongly to the target molecule while avoiding background molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If iterative SELEX processes are used to identify aptamers with strong binding, then binding strength may be improved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The neural network is trained in advance on a dataset of aptamer sequences and their corresponding binding strengths to SELEX targets. This preliminary training enables the network to predict binding strengths of new aptamers without requiring iterative SELEX experiments, thus resolving the contradiction by performing the analytical work beforehand rather than through repeated experimental cycles
Solution Approach 2:
The patent replaces the mechanical/experimental SELEX process with a computational neural network model. Instead of physically performing multiple rounds of SELEX selection and analysis, the system uses the trained neural network to computationally predict binding strengths, substituting wet-lab experimentation with in-silico modeling to reduce time and labor
2Reliability
If conventional SELEX methods are used, then aptamers may be identified, but they often fail to yield aptamers with strong specific binding
Solution Approach 1:
The neural network incorporates feedback mechanisms where prediction results are used to guide further aptamer design and selection. The network learns from training data that includes actual SELEX outcomes, allowing it to refine its predictions and identify aptamers with higher specific binding capability while maintaining efficient identification throughput
Solution Approach 2:
The system changes the approach from experimental parameter optimization (through iterative SELEX) to computational parameter prediction. By using the neural network to evaluate multiple aptamer candidates in silico based on sequence features and predicted binding strengths, the system can identify high-specificity aptamers more efficiently without relying on conventional SELEX productivity limitations
3Strength
If multiple SELEX rounds are performed to ensure strong binding, then binding strength improves, but costs increase
Solution Approach 1:
The neural network creates a computational copy or model of the SELEX selection process. Instead of performing multiple expensive and time-consuming experimental SELEX rounds, the system uses the trained network to simulate and predict the outcomes of multiple selection rounds, thereby identifying strong-binding aptamers at a fraction of the cost while maintaining binding strength quality
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for obtaining data defining a sequence for an aptamer, the aptamer comprising a string of nucleobases; encoding the data defining the sequence for the aptamer as a neural network input; and processing the neural network input using a neural network to generate an output that characterizes how strongly the aptamer binds to a particular target molecule, wherein the neural network has been configured through training to receive the data defining the sequence and to process the data to generate predicted outputs that characterize how strongly the aptamer binds to the particular target molecule.