Proteomics Descriptor Sets for Pathogen-Host Interactome Analysis
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Solution Overview
Problem
Current high-throughput proteomics-derived interactome information for pathogens shows limited overlap, making it challenging to develop effective countermeasures against pandemics, as existing methodologies struggle to interpret and utilize this data for identifying relevant treatments.
Innovation Solution
Development of novel proteomics-based descriptor sets, such as 11KTSPDS and PATHPPI, which analyze pathogen-host interactome information to identify substance combinations like Niclosamide and other compounds for treating a broad range of pathogens, and constructing protein-protein interaction networks to pinpoint affected biological processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If high throughput proteomics screening methodologies are used to identify pathogen-host interactions, then information on how pathogens affect cellular machinery can be obtained, but the derived interactome information from different studies shows very little overlap, causing uncertainty in assessing relevance for developing countermeasures
Solution Approach 1:
The patent combines multiple high throughput proteomics datasets from different studies into a unified interactome map. By merging these datasets and applying normalization techniques, the invention increases the overlap and consistency of pathogen-host interaction information, thereby improving reliability for countermeasure development.
Solution Approach 2:
The patent creates a universal interactome framework that can be applied across different pathogen types and study methodologies. This multi-functional approach allows the same analytical framework to process diverse proteomics data, ensuring consistent and reliable assessment of interactome relevance regardless of the specific study or pathogen.
2Reliability
If novel proteomics-based descriptor sets are constructed to analyze interactome information, then effective substance combinations can be identified, but the complexity of analyzing and interpreting high throughput proteomics data increases
Solution Approach 1:
The patent segments the complex proteomics data into distinct functional modules and descriptor sets. By dividing the interactome information into manageable components (e.g., host factors, pathogen factors, interaction types), the invention simplifies analysis while maintaining identification accuracy through systematic evaluation of each segment.
Solution Approach 2:
The patent introduces proteomics-based descriptor sets as intermediary structures between raw proteomics data and treatment identification. These descriptor sets act as mediators that translate complex interaction data into interpretable features, reducing analysis complexity while preserving the information needed for accurate substance combination identification.
Data Source
AI summary
The disclosed subject matter relates to the construction and use of novel proteomics-based descriptor sets for analyzing high throughput proteomics derived pathogen-host interactome information and the use of these descriptor sets for identifying substances and substance combinations that have utility for treating and or preventing infections and diseases caused by a broad range of pathogens of diverse origins.