Protein Structure Search Using Iterative ML Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing protein structure prediction methods using deep learning models do not effectively utilize additional compute resources to improve initial predictions, limiting the accuracy of protein structure search results.
Innovation Solution
A multi-track biological language reasoning model is employed to refine protein structure predictions by exploring a targeted search space using iterative search techniques and additional compute resources, incorporating geometric reasoning and evaluation functions to identify more accurate protein structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a conventional feed-forward network is used for protein structure prediction, then the prediction can be computed with a single pass, but additional compute resources cannot be utilized to improve the results
Solution Approach 1:
The system transitions from a static single-pass feed-forward network to a dynamic iterative refinement process. The biological language model performs multiple successive passes, where each pass refines the prediction based on the probability distribution from the previous pass, allowing the system to adaptively improve accuracy with additional compute resources.
Solution Approach 2:
The iterative refinement process incorporates feedback loops where the output of one pass becomes the input for the next pass. The model uses the probability distribution from each pass to guide subsequent predictions, creating a feedback mechanism that continuously improves prediction accuracy when additional compute resources are available.
2Measurement precision
If a multi-track biological language model is used for iterative refinement, then prediction accuracy can be improved with additional compute resources, but the computational complexity and time increase
Solution Approach 1:
The system performs preliminary action by conducting an initial single-pass prediction first to establish a baseline structure. This preliminary prediction serves as the foundation for subsequent iterative refinements, allowing the system to quickly obtain a reasonable initial result before investing additional compute resources in detailed refinement when needed.
Solution Approach 2:
The system applies partial action by allowing users to choose the number of iterative passes based on their specific needs. For applications requiring high accuracy, multiple passes are performed. For applications where speed is more important, a single pass or fewer passes suffice, providing a flexible trade-off between accuracy and computation time.
3Measurement precision
If iterative search techniques are employed to explore alternative structure candidates, then more optimal protein structures can be identified, but the computational resources and time required increase significantly
Solution Approach 1:
The iterative search process is segmented into multiple distinct passes, where each pass focuses on refining specific aspects of the protein structure. The multi-track biological language model divides the prediction task into separate tracks that can be independently refined, allowing systematic exploration of alternative structures without overwhelming computational complexity in a single step.
Data Source
AI summary
At least a portion of a protein sequence is received. Using a machine learning model, a plurality of candidates for a property of a selected amino acid position included in the protein are predicted. For each selected property candidate of the plurality of property candidates, using the selected property candidate as an input to the machine learning model, properties for one or more other amino acid positions of the protein are predicted into a corresponding candidate set of properties. The corresponding candidate sets of properties for the plurality of property candidates are evaluated, and one of the plurality of property candidates is selected as a determined result property of the selected amino acid position. Using the determined result property as an input to the machine learning model, a plurality of candidates for a property of a different selected amino acid position included in the protein are predicted.