Quick guide

Using RASP - A quick guide

RASP runs on the command-line, or from a simple GUI. Run without any command-line options, RASP will attempt to start the GUI, allowing you to interactively select input and output files and access a limited set of options. If the GUI cannot be started, RASP will fall back to the following defaults: predicted chemical shifts will be read from a file named rasp_pred.tab and experimental spin systems from rasp_ss.tab. 100 RASP iterations will be run, and the calculated assignment ensemble will be output to rasp_ss.out and rasp_res.out (organised by spin system and by residue, respectively). A typical command line invocation of RASP might be:

> rasp –n runname –i 50 –nproc 6 –w

This can be expected to run for minutes for a small protein up to a day or two for very large/difficult problems. See here for an explanation of these and many more options.

Predicted chemical shifts can be read directly from the output of ShiftX2 (1) (run with the –f BMRB flag), SPARTA+ (2), or CamShift (3). The file format for spin system input is the same as that used by MARS: each spin system is listed on a single line: the first field is a unique integer spin system identifier, followed by the list of shifts associated with that spin system. The first line is a header, listing the chemical shift types, in the order they appear in each subsequent line. RASP input files are described in more detail here.

The resulting assignment ensemble will be output in two files. rasp_ss.out lists the assignment ensemble by spin system. Each line presents the assignment results for a single spin system. A leading asterisk (*) denotes spin system assignments that are considered robustly determined. The next term is the spin system identifier from the input file. Subsequent terms are residue numbers to which that spin system is assigned, followed by (in brackets) the frequency of that assignment in the ensemble. RASP output files are described in more detail here.

(1) Beomsoo Han, Yifeng Liu, Simon Ginzinger, and David Wishart. (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol. NMR, 50: 43-57.
(2) Yang Shen and Ad Bax. (2010) SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J. Biomol. NMR, 48: 13-22.
(3) K. J. Kohlhoff, P. Robustelli, A. Cavalli, X. Salvatella and M. Vendruscolo. (2009) J. Am. Chem. Soc., 131: 13894-13895.