quartCluster: Quaternary Protein Structure Clustering Analysis

In QuartPAC: Identification of mutational clusters in protein quaternary structures.

Description

Perform the clustering analysis over the protein quaternary structure. One can select to use the iPAC, GraphPAC or SpacePAC methods. The output will show the relevant information from each algorithm that was selected.

Usage

quartCluster(mutation_data, alignment, perform.ipac = "Y", perform.graphpac = "Y",
			 perform.spacepac = "Y", insertion.type = "cheapest_insertion", 
			 MultComp = "Bonferroni", alpha = 0.05, show.low.level.messages = "N", 
			 ipac.method = "MDS", spacepac.method = "SimMax", create.map = "Y",
			 Show.Graph = "Y", Graph.Output.Path = NULL, Graph.File.Name = "Map.pdf",
			 Graph.Title = "Mapping", fix.start.pos = "Y", numsims = 1000, 
			 simMaxSpheres = 3, radii.vector = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 
			 OriginX = "", OriginY = "", OriginZ = "")

Arguments

mutation_data	The mutation data in the format outputted by getMutations.
alignment	The assembly structural information outputted by makeAlignedSuperStructure.
perform.ipac	Whether or not to perform the iPAC algorithm. Either a "Y" or a "N".
perform.graphpac	Whether or not to perform the GraphPAC algorithm. Either a "Y" or a "N".
perform.spacepac	Whether or not to perform the SpacePAC algorithm. Either a "Y" or a "N".
insertion.type	Specifies the type of insertion method used in the GraphpAC package. Please see the GraphPAC for more details.
MultComp	Specifies the multiple comparison adjustment required by the iPAC and GraphPAC packages. Options are: "Bonferroni", "BH", or "None". Please see the iPAC and GraphPAC packages for details.
alpha	The significance level required in order to find a mutational cluster significant using the iPAC and GraphPAC algorithms.
show.low.level.messages	Whether to display the output messages generated by the iPAC, GraphPAC and SpacePAC algorithms. Either a "Y" or a "N". Commonly used for debugging.
ipac.method	The type of approach used by iPAC to map the protein to 1D space. This parameter usually set to "MDS", but can be set to "linear" as well. See the iPAC package for more details.
spacepac.method	The type of approach used by SpacePAC to identify clustering. The options are either "SimMax" or "Poisson. This parameter usually set to "SimMax". See the SpacePAC package for more details.
create.map	Whether a graphical representation of the iPAC algorithm's dimension reduction from 3D to 1D space should be diplsayed. Either "Y" or "N".
Show.Graph	Whether to show the iPAC package dimension reduction chart on the screen. Warning: You must be running R in a GUI environment, otherwise, an error will occur.
Graph.Output.Path	Where to save the dimension reduction chart. This is useful if you want to save the chart automatically or can't display it on the screen (for instance, you are running R in a terminal window).The Graph.File.Name variable must be set as well.
Graph.File.Name	If you would like the chart saved automatically to the disk, specify the output file name. The Graph.Output.Path variable must be set as well.
Graph.Title	The title of the graph to be created.
fix.start.pos	For the GraphPAC package, the heuristic solver for the traveling salesman problem starts the path at a random amino acid. In order to make the results easily reproducible, the default starts the path on the first amino acid in the protein. Please see the GraphPAC package for more details.
numsims	The number of times to simulate the distribution of mutations over the protein quaternary structure for the SpacePAC algorithm. For each simulation, given m total mutations and n total amino acids, each amino acid has a m/n probability of mutation.
simMaxSpheres	For the SpacePAC algorithm, the maximum number of spheres to consider. Currently, the implementation allows for simMaxspheres to be either 1, 2 or 3.
radii.vector	This applies to the SpacePAC algorithm and denotes the vector of radii that will be considered. At each sphere radius, the best sphere combination is found. See the SpaceClust method in the SpacePAC package for further details
OriginX	If the "Linear" method is chosen for the iPAC algorithm, this specifies the x-coordinate part of the fixed point. See the vignette in the iPAC package for more details.
OriginY	If the "Linear" method is chosen for the iPAC algorithm, this specifies the y-coordinate part of the fixed point. See the vignette in the iPAC package for more details.
OriginZ	If the "Linear" method is chosen for the iPAC algorithm, this specifies the z-coordinate part of the fixed point. See the vignette in the iPAC package for more details.

Value

ipac	The clustering results using the iPAC algorithm. See the iPAC packagee for more details of each sub item.
graphpac	The clustering results using the GraphPAC algorithm. See the GraphPAC package for more details of each sub item.
spacepac	The clustering results using the SpacePAC algorithm. See the SpacePAC package for more details of each sub item.
ipac_messages	Any messages that might of been reported by the iPAC algorithm. Typically, warning or error messages are displayed here.
graphpac_messages	Any messages that might of been reported by the GraphPAC algorithm. Typically, warning or error messages are displayed here.
spacepac_messages	Any messages that might of been reported by the SpacePAC algorithm. Typically, warning or error messages are displayed here.

Note

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

Most of the parameters simply pass the requisite values to the underlying iPAC, GraphPAC and SpacePAC algorithms. The user should be aware of the parameters for these algorithms as this package is designed to extend them to quaternary structures.

References

Gregory Ryslik and Hongyu Zhao (2012). iPAC: Identification of Protein Amino acid Clustering. R package version 1.8.0. Gregory Ryslik and Hongyu Zhao (2012). GraphPAC: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach.. R package version 1.6.0. Gregory Ryslik and Hongyu Zhao (2013). SpacePAC: Identification of Mutational Clusters in 3D Protein Space via Simulation.. R package version 1.2.0. Michael Hahsler and Kurt Hornik (2014). TSP: Traveling Salesperson Problem (TSP). R package version 1.0-9. http://CRAN.R-project.org/package=TSP

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 <- "https://files.rcsb.org/view/1A6Z.pdb"
assembly.location <- "https://files.rcsb.org/download/1A6Z.pdb1"
structural.data <- makeAlignedSuperStructure(pdb.location, assembly.location)
## Not run: 
#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"))

## End(Not run)

QuartPAC documentation built on Nov. 8, 2020, 6:58 p.m.