MassNet

MassNet provides comprehensive functional annotation of proteins identified by mass spectrometry, combining physico-chemical characterization, KEGG pathway assignment, Gene Ontology (GO) mapping, and protein-protein interaction prediction to support biological interpretation of proteomic datasets.

Key Features:

• Physico-Chemical Analysis: Reports physico-chemical properties of proteins relevant to stability, solubility, and overall behavior in biological systems.
• KEGG Pathway Assignment: Maps identified proteins to KEGG pathways to contextualize their roles in metabolic and signaling processes.
• Gene Ontology (GO) Mapping: Assigns GO terms covering biological process, cellular component, and molecular function for functional categorization.
• Protein-Protein Interaction (PPI) Prediction: Predicts PPIs using 3D structural interaction analysis and by integrating experimental interaction data from PSIMAP, BIND, DIP, HPRD, IntAct, MINT, CYGD, and BioGrid.

Scientific Applications:

• Pathway Analysis: Enables exploration of metabolic and signaling pathways to identify pathway membership and potential regulatory proteins.
• Functional Annotation: Supports functional characterization of proteins identified by mass spectrometry through combined KEGG, GO, and physico-chemical annotations.
• Interaction Networks: Facilitates construction and analysis of protein-protein interaction networks using predicted interactions and integrated experimental database evidence.

Methodology:

Processes outputs from mass spectrometry search engines MASCOT, Prospector, and Profound and integrates these outputs with annotation analyses for physico-chemical properties, KEGG pathway mapping, GO mapping, and PPI prediction.