MOKCa-3D database: functional and structural analysis of missense mutations in cancer

Area of Science:

• Bioinformatics
• Cancer Genomics
• Structural Biology

Background:

• Millions of cancer missense mutations lack functional annotation.
• Understanding mutation consequences is vital for cancer evolution and therapeutic vulnerability.
• Existing databases offer limited functional and structural context for cancer mutations.

Purpose of the Study:

• To introduce the MOKCa-3D database for contextualizing and interpreting cancer somatic missense mutations.
• To provide insights into the structural impact and functional consequences (gain/loss) of mutations.
• To facilitate the interpretation of missense mutations for cancer research.

Main Methods:

• Development of the MOKCa-3D database (https://bioinformaticslab.sussex.ac.uk/MOKCa-3D/).
• Integration of a sequence feature viewer for interactive visualization of protein sequence data.
• Utilization of the SAAP pipeline for structural impact analysis and MOL* viewer for 3D structure visualization.
• Inclusion of AlphaFold protein structural models.

Main Results:

• MOKCa-3D provides interactive visualization of protein sequences, mutations, and functional sites.
• The database offers concise functional insights and structural impacts for individual mutations.
• Mutated residues are highlighted on AlphaFold models, aiding structural interpretation.

Conclusions:

• MOKCa-3D enhances the interpretation of cancer somatic missense mutations.
• The database offers freely accessible, navigable tools for researchers.
• It supports understanding cancer evolution and identifying therapeutic vulnerabilities.