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Cancer3D: understanding cancer mutations through protein structures.

Porta-Pardo E, Hrabe T, Godzik A - Nucleic Acids Res. (2014)

Bottom Line: This approach allows users to find novel candidate driver regions or drug biomarkers that cannot be found when similar analyses are done on the whole-gene level.In addition, it displays mutations from over 14,700 proteins mapped to more than 24,300 structures from PDB.This helps users visualize the distribution of mutations and identify novel three-dimensional patterns in their distribution.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.

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Database sources, content and main view. The database allows users to simultaneously access two types of cancer data: mutation frequency (from TCGA) and pharmacogenomic profiles (from CCLE). When a user queries the database with a protein name, Cancer3D retrieves these data and analyzes them using e-Driver and e-Drug, respectively. The user can also view where the mutations are located in different structures from PDB and navigate through the different protein regions and structures using the protein viewer. Finally, Cancer3D also provides information on which proteins are interacting with the query according to Human Protein Reference Database (HPRD), allowing users to either go to references describing the interaction or to query Cancer3D with those proteins.
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Figure 1: Database sources, content and main view. The database allows users to simultaneously access two types of cancer data: mutation frequency (from TCGA) and pharmacogenomic profiles (from CCLE). When a user queries the database with a protein name, Cancer3D retrieves these data and analyzes them using e-Driver and e-Drug, respectively. The user can also view where the mutations are located in different structures from PDB and navigate through the different protein regions and structures using the protein viewer. Finally, Cancer3D also provides information on which proteins are interacting with the query according to Human Protein Reference Database (HPRD), allowing users to either go to references describing the interaction or to query Cancer3D with those proteins.

Mentions: Cancer3D integrates data from TCGA and CCLE and allows users to explore the biomarker and driver problems at the same time through two novel algorithms: e-Driver (13) and e-Drug (15). These algorithms are unique in using information about the modular structure of a protein to predict novel cancer drivers or drug biomarkers, respectively. Statistics are calculated separately for each region in each protein, including known PFAM domains (16), predicted intrinsically disordered regions and over 1300 potential novel domains in the human proteome detected by AIDA (17). Another important feature of Cancer3D is that it maps somatic missense mutations from over 18 000 human proteins to, wherever available, experimental or predicted protein three-dimensional structures. The Cancer3D database not only displays the mutated positions of a protein in their corresponding structures, but also conveys important information such as drug activity or mutation frequency by color-coding. This helps users to quickly identify three-dimensional patterns in data such as clustering of mutation hotspots around particular regions (Figure 1).


Cancer3D: understanding cancer mutations through protein structures.

Porta-Pardo E, Hrabe T, Godzik A - Nucleic Acids Res. (2014)

Database sources, content and main view. The database allows users to simultaneously access two types of cancer data: mutation frequency (from TCGA) and pharmacogenomic profiles (from CCLE). When a user queries the database with a protein name, Cancer3D retrieves these data and analyzes them using e-Driver and e-Drug, respectively. The user can also view where the mutations are located in different structures from PDB and navigate through the different protein regions and structures using the protein viewer. Finally, Cancer3D also provides information on which proteins are interacting with the query according to Human Protein Reference Database (HPRD), allowing users to either go to references describing the interaction or to query Cancer3D with those proteins.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383948&req=5

Figure 1: Database sources, content and main view. The database allows users to simultaneously access two types of cancer data: mutation frequency (from TCGA) and pharmacogenomic profiles (from CCLE). When a user queries the database with a protein name, Cancer3D retrieves these data and analyzes them using e-Driver and e-Drug, respectively. The user can also view where the mutations are located in different structures from PDB and navigate through the different protein regions and structures using the protein viewer. Finally, Cancer3D also provides information on which proteins are interacting with the query according to Human Protein Reference Database (HPRD), allowing users to either go to references describing the interaction or to query Cancer3D with those proteins.
Mentions: Cancer3D integrates data from TCGA and CCLE and allows users to explore the biomarker and driver problems at the same time through two novel algorithms: e-Driver (13) and e-Drug (15). These algorithms are unique in using information about the modular structure of a protein to predict novel cancer drivers or drug biomarkers, respectively. Statistics are calculated separately for each region in each protein, including known PFAM domains (16), predicted intrinsically disordered regions and over 1300 potential novel domains in the human proteome detected by AIDA (17). Another important feature of Cancer3D is that it maps somatic missense mutations from over 18 000 human proteins to, wherever available, experimental or predicted protein three-dimensional structures. The Cancer3D database not only displays the mutated positions of a protein in their corresponding structures, but also conveys important information such as drug activity or mutation frequency by color-coding. This helps users to quickly identify three-dimensional patterns in data such as clustering of mutation hotspots around particular regions (Figure 1).

Bottom Line: This approach allows users to find novel candidate driver regions or drug biomarkers that cannot be found when similar analyses are done on the whole-gene level.In addition, it displays mutations from over 14,700 proteins mapped to more than 24,300 structures from PDB.This helps users visualize the distribution of mutations and identify novel three-dimensional patterns in their distribution.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.

Show MeSH
Related in: MedlinePlus