Multiplexed Proteomics for Drug Efficacy Prediction via PPI Outliers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for proteomic and phosphoproteomic analysis, particularly in mass spectrometry, are limited in identifying and quantifying phosphopeptides, leading to only a small fraction of the estimated phosphoproteome being detected, and lack robust methods for studying protein-protein co-regulation dynamics after perturbation.
Innovation Solution
The use of dual fragmentation schemes in mass spectrometry, such as collision-induced dissociation (CID) and high-energy collision dissociation (HCD), to identify and quantify phosphopeptides, along with the generation of basal protein-protein interaction networks, allows for the identification of deregulated proteins and prediction of drug efficacy by analyzing relative concentration values and protein-protein interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional mass spectrometry techniques are used for phosphoproteomic analysis, then some phosphopeptides can be identified, but only a small fraction of the estimated phosphoproteome is detected
Solution Approach 1:
The patent applies segmentation by dividing the phosphoproteome analysis into multiple fractions using strong cation exchange chromatography. This separates the complex phosphoproteome into manageable fractions that can be analyzed individually, increasing the overall detection capability without overwhelming the mass spectrometry system.
Solution Approach 2:
The patent introduces a new dimension to phosphoproteome analysis by implementing a two-stage mass spectrometry approach: first identifying phosphopeptides using one fragmentation method, then quantifying them using a different fragmentation method. This multi-dimensional approach allows simultaneous identification and quantification with higher completeness.
2Quantity of substance
If dual fragmentation schemes are used to identify phosphopeptides, then more phosphopeptides can be identified, but the analysis time and complexity increase
Solution Approach 1:
The patent applies preliminary action by first identifying phosphopeptides using collision-induced dissociation (CID) before proceeding to quantification using high-energy collision dissociation (HCD). This sequential approach allows the system to filter and prioritize phosphopeptides that need quantification, reducing the overall analysis time compared to analyzing all peptides with both methods.
Solution Approach 2:
The patent segments the mass spectrometry analysis into two distinct stages: identification stage using CID and quantification stage using HCD. This segmentation allows optimization of each stage for its specific purpose, preventing the time loss that would result from applying both methods simultaneously to all peptides.
3Loss of information
If comprehensive protein-protein interaction networks are generated, then more biological insights are obtained, but the complexity of data analysis increases significantly
Solution Approach 1:
The patent extracts specific biological information from the complex protein-protein interaction network by focusing on phosphoproteome-derived interactions. Rather than analyzing the entire interactome, the method selectively extracts phosphorylation-related interactions, reducing analysis complexity while maintaining biological relevance.
Solution Approach 2:
The patent creates a multi-functional analysis framework that integrates phosphoproteomic data with protein-protein interaction data. This universal approach allows the same dataset to be used for multiple analytical purposes: identifying phosphopeptides, constructing interaction networks, and predicting drug responses, thereby managing complexity through data reusability.
4Reliability
If robust methods for studying protein-protein co-regulation dynamics are developed, then better understanding of cellular responses is achieved, but the methodological complexity increases
Solution Approach 1:
The patent implements feedback by using phosphoproteomic data to refine and update protein-protein interaction networks. The identified phosphopeptides provide feedback information about active signaling pathways, which is then used to adjust the interaction network model, improving its reliability for studying co-regulation dynamics without requiring completely new methodologies.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly increases the detection of phosphopeptides and proteins, enabling a more comprehensive understanding of cellular responses to agents like kinase inhibitors and predicting drug efficacy by identifying dysregulated proteins and interactions.
Implementation Method 1
ionizing peptides derived from the biological samples to generate peptide ions
Implementation Method 2
fragmenting a first portion of the peptide ions by collision-induced dissociation to generate a first population of peptide ion fragments
Implementation Method 3
fragmenting a second portion of the peptide ions by high-energy collision dissociation to generate a second population of peptide ion fragments
Data Source
AI summary
The disclosure features methods of identifying specific drug candidates for disease treatment, the methods including: for pairs of associated expressed proteins in a protein-protein interaction network for a plurality of biological samples, comparing relative concentration values of the associated proteins in each of the biological samples to identify outliers among a distribution of the relative concentration values, identifying a set of proteins involved in dysregulated protein-protein interactions in the network based on the outliers, and predicting an efficacy of one or more drug candidates from among a set of drug candidates for treating a disease based on the identified set of proteins and information about one or more of proteins, protein complexes, biological pathways, and functional modules targeted by the set of drug candidates.