PINE is a probailistic method for automated protein backbone and sidechain assignments, detection and correction of referencing and secondary structure determination from input protein sequence and NMR data set peak lists.  Expand the "SUBMISSION" tab below for free access to the PINE analysis web-server maintained by NMRFAM.

BACKGROUND

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination. 

SUBMISSION

Access the PINE webserver submission form here.

MANUAL

PINE  is freely available for use as a web-server, operated and maintained at NMRFAM.  Users can submit information and file inputs to the web-server via the submission form in the right column of this page.  Outputs of PINE analysis are promptly returned to the user via the input e-mail address.

INPUTS

PINE requires several types of input information be entered into the submission form:
  • User Information (Name, contact e-mail, principle investigator name, institution)
  • Protein Sequence
  • NMR Experiment Peaklists

For more details on these inputs, see below. 

Input fields denoted with an asterisk, *, are required for submission.

Once all inputs are entered into the submission form, click the "Submit" button at the bottom.

Protein sequence input file

The protein sequence input text file should contain the amino acid sequence in a single column, using either 1- or 3-letter amino acid codes.

Examples:

M   
T   
E   
V   
Y   
D   
L   
E   
I   
T   
T   
N   
A   
.......
.......
or
MET   
THR   
GLU   
VAL   
TYR   
ASP   
LEU   
GLU   
ILE   
THR   
THR   
ASN   
ALA
.......
....... 

OUTPUTS

PINE returns several types of output, detailing the probabilistic chemical shift assignments and secondary structure determined, in several different formats.

  • Protein backbone assignments with probabilities and sub-optimals (Native PINE format)
  • Protein sidechain assignments with probabilities and sub-optimals (Native PINE format)
  • Assignments in NMR-STAR format (ver. 2.1 and 3.1)
  • Graphical depictions (.jpg) of backbone assignment and secondary structure

For more details on these outputs, see below.

EXAMPLES

CITATION

 Bahrami, A., Assadi, A., Markley, J. L. & Eghbalnia, H., "Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak lists from Protein NMR Spectroscopy", PLoS Comput Biol. 2009 Mar;5(3):e1000307.