Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining.

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Millions of proteomics articles exist, making manual summarization and relevance quantification challenging.
• Identifying key proteins for specific biological functions or diseases requires efficient prioritization methods.

Purpose of the Study:

• To develop a fully automated bioinformatics framework for identifying and prioritizing proteins associated with any biological entity.
• To create a cloud-based tool for real-time protein prioritization relevant to custom search terms.

Main Methods:

• Developed an automated bioinformatics framework utilizing the Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores.
• Applied the framework to the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) areas for protein prioritization.
• Validated prioritization results against a curated database and characterized top proteins in model organisms.
• Extended the workflow to cover all organ systems and human diseases.

Main Results:

• Successfully prioritized proteins associated with targeted areas of the B/D-Human Proteome Project.
• Demonstrated the framework's ability to identify relevant proteins across diverse biological contexts.
• Deployed a cloud-based tool enabling real-time protein prioritization for custom queries.

Conclusions:

• The automated framework and cloud tool facilitate the prioritization of proteins for specific organ systems or diseases.
• This approach can significantly contribute to the development of targeted proteomic studies for precision medicine.