The methods are usually executed on a 64 CPU cluster. Our goal is to ensure that that the prediction time for each sequence is less than 24 hours, but this of course depends on how many people submit sequences in a given day.
All publications on the research are accessible from our ongoing areas of research page, and all or most of the software is accessible from our software distribution server.
Following the CASP convention, up to five models may be returned (in CASP format).
This method (published in Protein Science) uses neural networks to translate NMR chemical shifts into secondary structure information (somewhat similar to CSI) and combines it with sequence based predictions (à la Psipred). It has a sustained three-state average accuracy of 89% on a rigourously jack-knifed test set of 92 proteins for which NMR chemical shift information was publicly available. This is done before the hybrid NMR-de novo simulations are run to ensure more accurate secondary structure predictions (which helps in our simulations).
PsiCSI chemical shifts must be supplied in NMR-Star format (tools for converting to this format from other popular formats are available).
The chemical shifts must be supplied in NMR-Star format (tools for converting to this format from other popular formats are available). This method uses an iterative hybrid NMR-de novo protocol for tertiary structure predictions.
15N and 13C edited NOESY data must be supplied as two separate files, with at least one of them required. The first three lines should contain the following:
The rest are the list of peaks each specified in a single line as:
<peak-number> <proton-shift> <heteronuclear-correlated-proton-shift> <heteronuclear-shift>
15N 0.02 0.04 0.7 4.73 1 1.33 8.01 121.0 2 4.33 8.01 121.0 . . .
An optional set of one or more conformation files in the standard PDB format for NMR files (each model separated by "MODEL N" record where N is the number of the model) may also be input. These files will be used by the Bayesian constraint assignment protocol for constructing probabilities. This enables constraint assignment and structure generation in iterative cycles (using our own or other standalone software such as CYANA or XPLOR-NIH).
PROTINFO-NMR v0.1 RAMP v0.4H