Peptide Array Biomarker Identification via Proteome Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods are inadequate for identifying candidate biomarkers for autoimmune diseases and infections, as they fail to efficiently differentiate between various health conditions and disease activities through antibody binding patterns.
Innovation Solution
The use of peptide arrays to identify discriminating peptides that differentiate between autoimmune diseases and healthy conditions by aligning peptides to proteins in a proteome, obtaining protein scores, and ranking them for statistical significance, thereby identifying candidate biomarkers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large-scale comparative interrogation of the human proteome is performed, then insights into disease biology and biomarker discovery are improved, but the complexity and resource requirements of the analysis increase
Solution Approach 1:
The proteome is segmented into individual protein entries from the Uniprot database, allowing systematic comparison of protein sequences across different disease states. This segmentation enables the complex proteome analysis to be broken down into manageable units that can be processed and compared individually
Solution Approach 2:
Discriminating peptides serve as intermediaries between the complex proteome data and the final biomarker identification. These peptides are derived from protein sequences and used as queries to identify proteins that differentiate between disease states, simplifying the overall analysis pipeline
2Reliability
If discriminating peptides are used to differentiate autoimmune diseases from healthy conditions, then diagnostic accuracy is improved, but the computational methods and analysis time increase
Solution Approach 1:
Peptide sequences are pre-derived from protein sequences in the Uniprot database before the actual disease differentiation analysis. This preliminary preparation of peptide queries allows for faster comparison and identification during the actual biomarker discovery process, reducing analysis time while maintaining accuracy
Solution Approach 2:
The method employs statistical parameters such as p-values and area under the ROC curve to quantify differentiation accuracy. By changing and optimizing these statistical thresholds, the method achieves high reliability in disease differentiation while managing computational resources efficiently
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 effectively identifies candidate biomarkers that can differentiate between autoimmune diseases and healthy conditions, predicting disease occurrence and activity with high accuracy, as demonstrated by area under the receiver operator characteristic (ROC) curve values ranging from 0.60 to 1.00.
Implementation Method 1
identifying a set of discriminating peptides bound to antibodies in the biological sample from the plurality of subjects that differentiate the autoimmune disease from at least one different health condition
Data Source
AI summary
The disclosed embodiments concern methods, devices, and systems for identifying candidate biomarkers useful for the diagnosis, prognosis, monitoring and screening and/or as targets for the treatment of diseases and conditions in subjects, in particular autoimmune and infectious diseases. The identification of candidate biomarkers is predicated on identifying discriminating peptides present on a peptide array, which can distinguish samples from different subjects having different health conditions by the binding patterns of antibodies present in the samples.