CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Accurate prediction of protein subcellular localization is crucial for understanding cellular functions.
• Existing tools may lack comprehensive functional annotation capabilities.
• Integrating localization prediction with functional analysis can enhance biological insights.

Purpose of the Study:

• To introduce CELLO2GO, a web-based platform for predicting protein subcellular localization and functional properties.
• To describe the methodology combining CELLO localization prediction and BLAST homology searching for Gene Ontology (GO) annotation.
• To highlight CELLO2GO's utility in addressing research questions related to complex subcellular systems.

Main Methods:

• Utilizing the BLAST algorithm to search for homologous sequences in an in-house UniProt KnowledgeBase-derived database.
• Employing the CELLO algorithm for predicting subcellular localization based on protein sequence and species information.
• Integrating BLAST and CELLO outputs to generate GO-type categories (cellular compartment, molecular function, biological process).

Main Results:

• CELLO2GO provides GO-type functional categories, including subcellular localization(s), for queried proteins.
• The platform generates pie charts summarizing functional annotations based on identified homologs.
• CELLO2GO can confidently suggest subcellular localization even when experimental data is unavailable.

Conclusions:

• CELLO2GO is a valuable, publicly available tool for protein analysis.
• The integration of CELLO and BLAST within a single platform offers a streamlined approach to functional annotation.
• CELLO2GO facilitates research by providing easily interpretable and manipulable data for complex biological systems.