COSMIC Cancer Gene Census 3D database: understanding the impacts of mutations on cancer targets

Area of Science:

• Genomics and Bioinformatics
• Structural Biology
• Computational Biology

Background:

• Hallmark gene mutations drive cancer progression and are cataloged in COSMIC.
• Understanding mutation locations (e.g., protein interfaces, active sites) is vital for drug discovery.
• Experimental structures exist for only 87 of 723 COSMIC genes, leaving a gap in structural knowledge.

Purpose of the Study:

• To present a comprehensive web interface for 714 modelled cancer-related genes.
• To predict the functional impacts of mutations on protein stability and binding affinities.
• To provide a user-friendly resource for cancer genomics and drug development.

Main Methods:

• Utilized SDM and mCSM software for predicting mutation impacts on protein stability and binding affinities.
• Modeled 714 cancer-related genes, including various protein complexes and DNA/RNA interactions.
• Employed DISOPRED3 to predict intrinsically disordered regions within proteins.

Main Results:

• Developed Cancer-3D, a web interface offering structural models for 714 cancer genes.
• Predicted mutation effects on protein stability, protein-protein interactions, and protein-nucleic acid interactions.
• Identified intrinsically disordered regions, providing further insights into protein function.

Conclusions:

• Cancer-3D provides valuable structural and functional insights into cancer-related mutations.
• The resource facilitates drug discovery and development by predicting mutation impacts.
• This comprehensive modeling approach addresses the limitations of experimentally determined structures.