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#Sparse Ensemble Selection (SES)
Sparse Ensemble Selection (SES) method is a computational method for recovering multiple conformations from a limited number of observations. SES is more general and accurate than previously published minimum-ensemble methods is applicable to any experimental observables that can be expressed as a weighted linear combination of data for individual states.
Instructions on using SES:
- Make sure you have Java 1.7+ installed
- Download and place the sesgeneral.jar file into a directory
- Unzip the "lib" zip file, and place the lib directory in the same directory as the SES jar file
- Open command prompt (in Windows go to start/run and type "cmd")
- Switch into the directory where SES is unzipped
- Type "java -jar sesgeneral-1.1.jar -help", help should appear.
You will probably need to increase java memory size to about 90% of system memory using "-Xms" flag, in order to run for large number of K.
Program flags: <program name> <flag> [<value>] <flag> [<value>] ARMOR Version: 1.1, Build Time: 06/24/2014 12:53 PM Boolean flags do not contain a value key. Flag options are as follows: -K, default = 100000 Number of top solutions to store per iteration of M-OMP. -align, default = "CA" Name of the atom type (ex. H, CA, etc) to use for alignment and RMSD computation during postprocess clustering. Set to * for any -best, default = false Output the best possible x>0 (NNLS) solution. -data, *required, Experimental data size filename. -help, default = true Displays the help menu. -l0max, default = 2147483647 Maximum ensemble size to compute. -matrix, *required, File name of the data matrix file. -maxsum, default = Infinity Maximum possible sum of the column weights. -out, default = "solution" Output directory file name. -outalign, default = 0 Type of alignment for models inside the output pdb file. 0) no alignment 1) align based on whole struct. 2) Align by first chain. -pdb, default = "" Directory of PDB files. Ignore, if not pdb files available. -precond, default = 1 Preconditioning of the linear system before M-OMP: 0) None, 1) Rotation and Compression (cutoff=reltol*0.1). -reltol, default = 5.0E-4 M-OMP solvers relative error tolerance for termination. -rmsd, default = 4.0 Cluster RMSD value for solution pdb files (see align, outalign options). -storejava, default = false Output the java object with all results. Can be used for parsing using ARMOR API. -top, default = 0.005 Max relative error of top solutions relative to input. All output solutions have abs. error <= best sol abs. error + top*||(input vector)./(error vector)||_2.
REFERENCES
If you use the software please cite:
K. Berlin, C. A. Castaneda, D. Schneidman-Duhovny, A. Sali, A. Nava-Tudela, and D. Fushman, “Recovering a representative conformational ensemble from underdetermined macromolecular structural data,” Journal of the American Chemical Society, vol. 135, no. 44, pp. 16595–16609, 2013.
Example
- Step 1: Make an Lx2 text matrix file of the experimental data, "y_data.txt". The first column is the data, the second column is the associated error. The columns are separated by spaces, and the rows by return character.
- Step 2: Make an LxN text matrix of predicted data, A_data.txt. The ith column represents the predicted data for the ith conformer.
- Step 3: Make the directory "outputdir".
- Step 4: Run SES: java -Xmx<memory size> -jar sesgeneral-1.1.jar -out "outputdir" -matrix "A_data.txt" -data "y_data.txt"
Here <memory size> is the RAM memory size that you want to use for the Java virtual machine. Ex. -Xmx10g if you would like to give 10 GB of RAM to Java.
If you want to see more options, use the -help flag
java -jar sesgeneral-1.1.jar -help
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