Protein Target Prioritization via Multi-Relational Network Analysis
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Solution Overview
Problem
Current bioinformatics tools face challenges in accurately identifying and prioritizing similar proteins, particularly in drug discovery and repositioning, due to limitations in integrating diverse data sources and balancing multiple scientific approaches.
Innovation Solution
A system and method that integrates diverse data sources to identify similar proteins by using a database arrangement and processor to determine secondary target proteins based on similarity criteria, performing matrix analysis, building a multi-relational directed network, and applying clustering algorithms to prioritize relevant proteins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple scientific approaches are used for protein identification, then the accuracy and coverage of protein targets improve, but the complexity of balancing and integrating these approaches increases
Solution Approach 1:
The patent merges multiple scientific approaches (database searching, de novo sequencing, peptide sequence tag) into a unified protein identification system. The system integrates these different methods to collectively identify proteins, allowing each approach to contribute its strengths while managing the overall complexity through a coordinated framework that processes results from all methods.
Solution Approach 2:
The invention creates a universal protein identification system that can handle multiple identification approaches through a single platform. The system is designed to accommodate various scientific methods (mass spectrometry-based proteomics, database searching, de novo sequencing) within one multi-functional framework, enabling it to perform diverse protein identification tasks without requiring separate specialized systems for each approach.
2Reliability
If diverse data sources are integrated for protein identification, then the completeness and accuracy of protein target enrichment improve, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent combines diverse data sources including protein sequences, spectral data, database information, and experimental results into a unified analysis framework. This merging of multiple data types allows comprehensive protein target enrichment while the system manages processing complexity through integrated data structures and coordinated analysis procedures that handle heterogeneous data sources together.
Solution Approach 2:
The invention creates a composite data structure that integrates multiple types of biological data (amino acid sequences, spectral fingerprints, database entries, experimental conditions) into a unified composite representation. This composite approach allows the system to process diverse data sources as an integrated whole, improving completeness while managing complexity through a standardized composite data model.
3Ease of operation
If traditional single-approach protein identification methods are used, then the simplicity of the method is maintained, but the accuracy and ability to identify indirect relevant relations deteriorates
Solution Approach 1:
The patent combines multiple identification methods (database searching, de novo sequencing, peptide sequence tag) into an integrated system that maintains operational simplicity through unified input-output interfaces. Users can operate the system as a single coherent tool while internally the system merges results from multiple approaches, thereby improving accuracy without significantly increasing operational complexity for the end user.
Data Source
AI summary
The present invention discloses a system and method for determination and prioritization of a plurality of secondary target protein. According to the invention, the present invention ensures the abstraction and authentication of possible scientific data for the primary target protein and introduces a statistical analysis of multiple similarity criteria to determine similar proteins in the form of the plurality of secondary target protein for an input query. Furthermore, the identified plurality of secondary target protein is analysed via matrix and multi-relational directed network analysis to prioritize and depict relationships among similarity concepts. Beneficially, the present invention ensures a high level of coverage and precision to descript protein-protein similarity.