PMconv: How to Compare Proteomes and Metabolomes?

Area of Science:

• Biochemistry
• Bioinformatics
• Systems Biology

Background:

• Integrating proteomic and metabolomic data is complex due to intricate molecular relationships and cellular compartmentalization.
• Existing methods struggle to bridge the gap between protein and metabolite datasets effectively.

Purpose of the Study:

• To develop a novel web-based application, PMconv, for bidirectional knowledge-based mapping of proteomic and metabolomic datasets.
• To facilitate hypothesis generation and feature engineering in multi-omics research by inferring potential biochemical connections.

Main Methods:

• PMconv leverages curated associations from the Human Metabolome Database (HMDB) and protein interaction data from STRING.
• The application infers biochemical connections between detected molecules and pathway-annotated partners.
• It supports interactive network visualization and integrates compartment annotations from the Human Protein Atlas.

Main Results:

• PMconv enables the bidirectional mapping of proteomic and metabolomic data, addressing integration challenges.
• The tool infers potential biochemical links by integrating protein interaction and metabolite databases.
• Spatial contextualization is enhanced through the export of compartment annotations.

Conclusions:

• PMconv serves as a valuable exploratory resource for multi-omics research, aiding hypothesis generation.
• The application facilitates the understanding of complex molecular interactions within cellular contexts.
• Knowledge-derived associations require experimental validation for compartment-specific interpretation.