4.7 Article

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

Journal

BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 6, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab220

Keywords

Cancer Gene Census 3D; hallmark mutations; modelling cancer genes census; mutational analyses of cancer drug targets

Funding

  1. King Abdullah scholarship by Saudi Arabia
  2. Cystic Fibrois Trust [PHZJ_395, RG 70975]
  3. Brazilian National Counsil for Scientific and Technological Development
  4. American Leprosy Mission [PHZJ_576 RG92035]
  5. Wellcome Trust [PHZJ/489 RG83114]

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Mutations in hallmark genes play a key role in cancer progression, and predicting their impacts on protein structure is crucial for drug discovery. Many cancer-related genes have been studied and their effects on protein stability and structure have been predicted, along with the prediction of intrinsically disordered regions.
Mutations in hallmark genes are believed to be the main drivers of cancer progression. These mutations are reported in the Catalogue of Somatic Mutations in Cancer (COSMIC). Structural appreciation of where these mutations appear, in protein-protein interfaces, active sites or deoxyribonucleic acid (DNA) interfaces, and predicting the impacts of these mutations using a variety of computational tools are crucial for successful drug discovery and development. Currently, there are 723 genes presented in the COSMIC Cancer Gene Census. Due to the complexity of the gene products, structures of only 87 genes have been solved experimentally with structural coverage between 90% and 100%. Here, we present a comprehensive, user-friendly, web interface (https://cancer-3d.com/) of 714 modelled cancer-related genes, including homo-oligomers, hetero-oligomers, transmembrane proteins and complexes with DNA, ribonucleic acid, ligands and co-factors. Using SDM and mCSM software, we have predicted the impacts of reported mutations on protein stability, protein-protein interfaces affinity and protein-nucleic acid complexes affinity. Furthermore, we also predicted intrinsically disordered regions using DISOPRED3.

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