28 research outputs found
Utilizing Protein Structure to Identify Non-Random Somatic Mutations
Motivation: Human cancer is caused by the accumulation of somatic mutations
in tumor suppressors and oncogenes within the genome. In the case of oncogenes,
recent theory suggests that there are only a few key "driver" mutations
responsible for tumorigenesis. As there have been significant pharmacological
successes in developing drugs that treat cancers that carry these driver
mutations, several methods that rely on mutational clustering have been
developed to identify them. However, these methods consider proteins as a
single strand without taking their spatial structures into account. We propose
a new methodology that incorporates protein tertiary structure in order to
increase our power when identifying mutation clustering.
Results: We have developed a novel algorithm, iPAC: identification of Protein
Amino acid Clustering, for the identification of non-random somatic mutations
in proteins that takes into account the three dimensional protein structure. By
using the tertiary information, we are able to detect both novel clusters in
proteins that are known to exhibit mutation clustering as well as identify
clusters in proteins without evidence of clustering based on existing methods.
For example, by combining the data in the Protein Data Bank (PDB) and the
Catalogue of Somatic Mutations in Cancer, our algorithm identifies new
mutational clusters in well known cancer proteins such as KRAS and PI3KCa.
Further, by utilizing the tertiary structure, our algorithm also identifies
clusters in EGFR, EIF2AK2, and other proteins that are not identified by
current methodology
Leveraging protein quaternary structure to identify oncogenic driver mutations.
BACKGROUND: Identifying key "driver" mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Due to multiple pharmacological successes in treating cancers that are caused by such driver mutations, a large body of methods have been developed to differentiate these mutations from the benign "passenger" mutations which occur in the tumor but do not further progress the disease. Under the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of algorithms that identify these clusters has become a critical area of research. RESULTS: We have developed a novel methodology, QuartPAC (Quaternary Protein Amino acid Clustering), that identifies non-random mutational clustering while utilizing the protein quaternary structure in 3D space. By integrating the spatial information in the Protein Data Bank (PDB) and the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), QuartPAC is able to identify clusters which are otherwise missed in a variety of proteins. The R package is available on Bioconductor at: http://bioconductor.jp/packages/3.1/bioc/html/QuartPAC.html . CONCLUSION: QuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.This work was supported in part by NSF Grant DMS 1106738 (GR, HZ); NIH Grants GM59507 and CA154295 (HZ), and GM102869 (YM); and Wellcome Trust Grant 101908/Z/13/Z (YM)
A Spatial Simulation Approach to Account for Protein Structure When Identifying Non-Random Somatic Mutations
Background: Current research suggests that a small set of "driver" mutations
are responsible for tumorigenesis while a larger body of "passenger" mutations
occurs in the tumor but does not progress the disease. Due to recent
pharmacological successes in treating cancers caused by driver mutations, a
variety of of methodologies that attempt to identify such mutations have been
developed. Based on the hypothesis that driver mutations tend to cluster in key
regions of the protein, the development of cluster identification algorithms
has become critical.
Results: We have developed a novel methodology, SpacePAC (Spatial Protein
Amino acid Clustering), that identifies mutational clustering by considering
the protein tertiary structure directly in 3D space. By combining the
mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and
the spatial information in the Protein Data Bank (PDB), SpacePAC is able to
identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In
addition, SpacePAC is better able to localize the most significant mutational
hotspots as demonstrated in the cases of BRAF and ALK. The R package is
available on Bioconductor at:
http://www.bioconductor.org/packages/release/bioc/html/SpacePAC.html
Conclusion: SpacePAC adds a valuable tool to the identification of mutational
clusters while considering protein tertiary structureComment: 16 pages, 8 Figures, 4 Table
A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations
Background: It is well known that the development of cancer is caused by the
accumulation of somatic mutations within the genome. For oncogenes
specifically, current research suggests that there is a small set of "driver"
mutations that are primarily responsible for tumorigenesis. Further, due to
some recent pharmacological successes in treating these driver mutations and
their resulting tumors, a variety of methods have been developed to identify
potential driver mutations using methods such as machine learning and
mutational clustering. We propose a novel methodology that increases our power
to identify mutational clusters by taking into account protein tertiary
structure via a graph theoretical approach.
Results: We have designed and implemented GraphPAC (Graph Protein Amino Acid
Clustering) to identify mutational clustering while considering protein spatial
structure. Using GraphPAC, we are able to detect novel clusters in proteins
that are known to exhibit mutation clustering as well as identify clusters in
proteins without evidence of prior clustering based on current methods.
Specifically, by utilizing the spatial information available in the Protein
Data Bank (PDB) along with the mutational data in the Catalogue of Somatic
Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in
well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory
to account for the tertiary structure, GraphPAC identifies clusters in DPP4,
NRP1 and other proteins not identified by existing methods. The R package is
available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html
Conclusion: GraphPAC provides an alternative to iPAC and an extension to
current methodology when identifying potential activating driver mutations by
utilizing a graph theoretic approach when considering protein tertiary
structure.Comment: 25 pages, 8 figures, 3 Table