sparta_logo From the Bax Group at the National Institutes of Health ...
SPARTA+: Shifts Prediction from Analogy in Residue type and Torsion Angle by an artificial neural network
As described in the paper:
SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network
Yang Shen and Ad Bax, J. Biomol. NMR, 48, 13-22 (2010)
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A compressed package containing all required binary/scripts/parameters to run SPARTA+ program is available below for download:

       RedHat Linux/Unix/MacOS/Win32 Package (v2.90, last updated@May 23th, 2017, changeLog)

To install the SPARTA+ program, an automated script "" is provided in the download package. Users require to:

  1. Copy the package to the installation directory, uncompress the package by a command line:
    tar -zxvf sparta+.tar.Z 
  2. type "" to set up the program

In order to use the program, users MUST first execute an initialization script "" to import SPARTA+ installation information into the system path, for instance by adding the following command to their .cshrc file:

   if (-e $SPARTAP_Dir/ then
      source $SPARTAP_Dir/

If the package is installed successfully and environmental variables are set up correctly, typing "sparta+" at any directory will print the SPARTA+ help information to the screen.

Moreover, a list of 580 proteins (BMRB and PDB codes) used to train SPARTA+ can be downloaded here.

What is SPARTA+?
Reliability of SPARTA+
Components of the SPARTA+ Package
How to Use SPARTA+
Preparing the PDB Coordinates

What is SPARTA+?

SPARTA+ employs a well-trained neural network algorithm to make rapid chemical shift prediction on the basis of known structure. The input parameters for the neural network training procedure are similar to those used by the previous program SPARTA, hence the naming of this new program. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and 13CB chemical shifts are available. The neural network is well trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid and accurate chemical shift prediction for backbone and 13CB atoms.


Reliability of SPARTA+

The reliability of SPARTA+ approach was tested by a three-fold training-and-validation procedure during the neural network training procedure, as well as by a second validation of eleven proteins which are not present in the training database, for which the RMS deviations between the SPARTA+ predicted and experimental shifts are 2.45, 1.07, 0.92, 1.13, 0.25 and 0.49 ppm for 15N, 13C', 13CA, 13CB, 1HA and 1HN, respectively. Moreover, SPARTA+ performs equally well for proteins without any homolog in the database.  


Components of the SPARTA+ Package

The SPARTA+ program is implemented using C++. The compiled executable files ("$SPARTAP_DIR/bin/SPARTA+.linux" for Linux, "$SPARTAP_DIR/bin/SPARTA+.winxp" for Windows, "$SPARTAP_DIR/bin/SPARTA+.mac" for Mac) or the starting script ("$SPARTAP_DIR/sparta+" for Linux/Mac) can be invoked with command-line arguments. A complete list of options can be invoked and generated with a "-help" command-line argument.

Use of SPARTA+ requires definition of an environment variable "SPARTAP_DIR" or a command-line argument "-spartaDir" to specify the SPARTA installation directory; it will be established automatically if run SPARTA from the starting script ("$SPARTAP_DIR/sparta+" in Linux/Mac), which includes the following lines:

   setenv SPARTAP_DIR /disk1/SPARTA+/
   $SPARTAP_DIR/bin/SPARTA+ $argv[1-$#argv]

Note that the definition of $SPARTAP_DIR in the starting script MUST be modified by users according to their SPARTA installation directory and the default "$SPARTA_DIR" is the current directory if not specified.

Other files of the SPARTA+ package include:

The compiled SPARTA+ binary files for multiple platforms, such as Linux (SPARTA+.linux), MacOS (SPARTA+.mac), SGI (SPARTA+.sgi6x) and WindowsXP (SPARTA+.winxp).

The tables of random coil shifts and adjustments values used in the shifts prediction process. (The same tables as those used in TALOS/SPARTA)

The weighting factors and biases of the neural network used in the prediction process.

The sample input PDB files and their SPARTA+ prediction result files.


How to Use SPARTA+

Use of SPARTA+ to predict backbone chemical shifts for one protein

  1. Create a directory for the prediction session; all subsequent commands will be executed from this directory.
  2. Prepare an input PDB coordinate file (for example "protein.pdb"), according to the format given below. Note that the hydrogen atoms must be present in the PDB coordinate file, a prediction with 'non-protonated' PDB coordinate input will yield significantly degraded accuracy!
  3. Run SPARTA+ to perform the chemical shifts calculation. Most commonly, this will simply require a command such as:
       sparta+ -in protein.pdb
    SPARTA+ will first generate a "" file from PDB coordinates, which contains of the phi, psi, chi1 and chi2 angles, H-bonding information and other structural parameters. Before exiting, a file "" (defined by "-sum" option) will be created, this file includes a summary of the prediction results. The chemical shift calculation (after loading the parameters) will typically take <1 second for a 100-residue protein on a Linux PC with a 2.4GHz CPU.


Use of SPARTA+ to predict backbone chemical shifts for multiple proteins

  1. Create a directory for the prediction session; all subsequent commands will be executed from this directory.
  2. Prepare input PDB coordinate files (for example "protein1.pdb", ..., "proteinN.pdb"), according to the format given below.
  3. Run the following SPARTA+ command line
       sparta+ -in protein1.pdb protein2.pdb ... proteinN.pdb

    to perform the chemical shifts calculation for each of the input proteins. As results, a structural parameter table and a final prediction table file will be generated with an output name of {$PROTEIN_NAME} and  {$PROTEIN_NAME}, respectively, for each input structure with a name of {$PROTEIN_NAME}.


Use of SPARTA+ to identify possible chemical shift referencing offsets and chemical shift outliers

If the experimental chemical shifts of the query protein are available (with a name "", for example, and with TALOS format), SPARTA+ prediction can be performed by a command such as:

   sparta+ -in protein.pdb -ref

SPARTA would compare the predicted chemical shifts and experimental shifts before exiting, and a prediction summary file "" will be generated to store both the prediction results and the comparison between the experimental and SPARTA+ predicted shifts. If the average prediction error for a given chemical shift type exceeds 3 times expected errors (the standard deviation of the prediction errors divide the square root of the number of shifts), a warning will be printed to the screen with a suggestion of a reference correction for this type of chemical shift. A chemical shift outlier can also be (manually) identified if the predicted chemical shift (listed as column "SHIFT" in summary file deviates its experimental shift (column "CS_OBS") by more than five (estimated) standard deviation (column "SIGMA").


Preparing the Input PDB Coordinates

The input PDB coordinates should be prepared carefully, so that it has the proper format and naming conventions. SPARTA+ accepts standard PDB coordinates file, but ONLY the FIRST conformer/chain will be used if more than one exist. The hydrogen atoms must be present in the PDB coordinate file, a prediction with 'non-protonated' PDB coordinate input will yield significantly degraded accuracy! For the PDB coordinates without hydrogen atoms, the hydrogen atoms are required to be added (by using the programs such as DYNAMO, REDUCE, MOLMOL, or any other similar programs) in order to get the hydrogen bonding information and ring current shifts. The standard "HA2/HA3" names are required for the GLY HA atoms.


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last updated:  May 23 2017 / ys