Protein Structure Prediction With Cropped Distance Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining protein structures using machine learning models are limited in accuracy and efficiency, particularly in predicting the three-dimensional configuration of amino acid sequences and optimizing the quality of predicted structures.
Innovation Solution
A system utilizing multiple neural networks, including structure prediction, distance prediction, and geometry scoring, to iteratively refine protein structure parameters through gradient descent and optimization techniques, incorporating features from multiple sequence alignments and physical constraints to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to predict protein structures, then prediction speed increases, but prediction accuracy decreases
Solution Approach 1:
The patent implements an iterative optimization process where predicted structures are continuously evaluated against a quality score and refined through gradient descent. The system uses feedback from quality scoring neural networks to progressively improve prediction accuracy, transforming initial rapid predictions into highly accurate final structures through multiple refinement cycles.
Solution Approach 2:
The system dynamically adjusts multiple parameters including structure parameters, quality scores, and optimization steps. By changing parameters such as the number of optimization iterations, learning rates, and quality score thresholds, the system achieves both speed and accuracy - generating initial predictions quickly then refining them through controlled parameter adjustments.
2Measurement precision
If multiple neural networks and optimization iterations are used to improve accuracy, then prediction quality improves, but computational complexity increases
Solution Approach 1:
The patent divides the protein structure prediction task into multiple independent segments: sequence input processing, distance map generation, quality scoring, and optimization. Each component is handled by specialized neural networks that process specific aspects independently, making the overall complex system more manageable and efficient through modular architecture.
Solution Approach 2:
The quality score neural networks serve multiple functions simultaneously - they evaluate prediction accuracy, guide optimization iterations, and provide feedback for structure refinement. This multi-functionality reduces the need for separate specialized components, thereby managing computational complexity while maintaining high prediction quality.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing protein structure prediction. In one aspect, a method comprises generating a distance map for a given protein, wherein the given protein is defined by a sequence of amino acid residues arranged in a structure, wherein the distance map characterizes estimated distances between the amino acid residues in the structure, comprising: generating a plurality of distance map crops, wherein each distance map crop characterizes estimated distances between (i) amino acid residues in each of one or more respective first positions in the sequence and (ii) amino acid residues in each of one or more respective second positions in the sequence in the structure of the protein, wherein the first positions are a proper subset of the sequence; and generating the distance map for the given protein using the plurality of distance map crops.