QuartPAC (version 1.4.0)

QuartPAC-package: Identifying mutational clusters while incorporating protein quaternary structure.

Description

QuartPAC is a companion package to iPAC, GraphPAC and iPAC. It allows one to use the methodologies proposed by each of those packages to be applied to the protein quaternary structure.

Details

QuartPAC is designed to identify mutational clustering in 3D space when looking at the entire quaternary protein structure. It does this by applying the algorithms proposed in iPAC, GraphPAC and SpacePAC over the entire assembly after correctly preprocessing the mutational and positional data. QuartPAC typically follows a three step process. Step 1 involves reading the mutational data - see getMutations for more information. Step 2 involves reading in the structural information to create the protein assembly and is explained in makeAlignedSuperStructure. Finally, Step 3 performs the statistical analysis and reports the significant p-values -- see QuartCluster for more information.

The clustering results give the serial number values from the *.pdb1 file.

References

Gregory Ryslik and Hongyu Zhao (2012). iPAC: Identification of Protein Amino acid Clustering. R package version 1.8.0. http://www.bioconductor.org/.

Gregory Ryslik and Hongyu Zhao (2013). GraphPAC: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. R Package version 1.6.0 http://www.bioconductor.org/.

Gregory Ryslik and Hongyu Zhao (2013). SpacePAC: Identification of Mutational Clusters in 3D Protein Space via Simulation. R package version 1.2.0. http://www.bioconductor.org/.

The UniProt Consortium. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res. 42: D191-D198 (2014).

Examples

#read the mutational data
mutation_files <- list(
        system.file("extdata","HFE_Q30201_MutationOutput.txt", package = "QuartPAC"),
        system.file("extdata","B2M_P61769_MutationOutput.txt", package = "QuartPAC")
					)
uniprots <- list("Q30201","P61769")
mutation.data <- getMutations(mutation_files = mutation_files, uniprots = uniprots)

#read the pdb file
pdb.location <- "http://www.rcsb.org/pdb/files/1A6Z.pdb"
assembly.location <- "http://www.rcsb.org/pdb/files/1A6Z.pdb1"
structural.data <- makeAlignedSuperStructure(pdb.location, assembly.location)

#Perform Analysis
#We use a very high alpha level here with no multiple comparison adjustment
#to make sure that each method provides shows a result.
#Lower alpha cut offs are typically used.
(quart_results <- quartCluster(mutation.data, structural.data, perform.ipac = "Y", perform.graphpac = "Y",
                              perform.spacepac = "Y", create.map = "N", MultComp = "None",
                              alpha = .3, radii.vector = c(1:3), show.low.level.messages = "Y"))