Local-Global Alignment for 3D Protein Structure Similarity
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Solution Overview
Problem
Current methods for determining three-dimensional protein structures from primary sequences are labor-intensive, slow, and expensive, limiting the rapid progression of structural genomics, as experimental techniques like x-ray crystallography are inefficient for the growing number of sequenced genomes.
Innovation Solution
The Local-Global Alignment (LGA) method compares protein structures using Longest Continuous Segments (LCS) and Global Distance Test (GDT) analyses, combined with a scoring function, to identify regions of 3D similarities and generate accurate structural models, even for proteins with no significant amino acid sequence similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental methods like x-ray crystallography are used to determine protein structures, then structural accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent uses computational homology modeling to create copies of protein structures based on template structures. Instead of determining each protein structure experimentally, the method copies structural information from homologous proteins with known structures, significantly reducing time and cost while maintaining accuracy for proteins with sequence similarity to templates
Solution Approach 2:
The patent replaces mechanical experimental methods (x-ray crystallography, NMR, cryo-EM) with computational algorithms. The LGA method uses mathematical optimization and scoring functions to predict structures in silico, eliminating the need for labor-intensive laboratory procedures while providing rapid structure determination
2Productivity
If sequence homology modeling is used to predict protein structures, then productivity is improved, but accuracy decreases for proteins with low sequence similarity
Solution Approach 1:
The patent segments the protein structure comparison into local regions using the LGA method. Instead of requiring global sequence similarity, the method identifies and aligns locally similar structural segments, allowing accurate prediction even when overall sequence similarity is low. The scoring function evaluates local structural alignments independently
Solution Approach 2:
The patent applies local quality by focusing on locally similar regions rather than requiring global similarity. The LGA method identifies specific local structural motifs and domains that are conserved between proteins, using these local similarities to build accurate structural models even when the rest of the sequence diverges
3Device complexity
If traditional alignment methods are used to compare protein structures, then device complexity is reduced, but the ability to detect distant homologs decreases
Solution Approach 1:
The patent transitions from sequence-space alignment to structure-space alignment. Instead of comparing amino acid sequences directly, the LGA method compares three-dimensional structural coordinates, adding a spatial dimension to the comparison. This allows detection of distant homologs that have diverged beyond sequence recognition but retain structural similarity
Data Source
AI summary
A method of finding 3D similarities in protein structures of a first molecule and a second molecule. The method comprises providing preselected information regarding the first molecule and the second molecule. Comparing the first molecule and the second molecule using Longest Continuous Segments (LCS) analysis. Comparing the first molecule and the second molecule using Global Distance Test (GDT) analysis. Comparing the first molecule and the second molecule using Local Global Alignment Scoring function (LGA_S) analysis. Verifying constructed alignment and repeating the steps to find the regions of 3D similarities in protein structures.