SUMMARY: ADAPT-nuclear magnetic resonance (ADAPT-NMR) offers an automated approach to the concurrent acquisition and processing of protein NMR data with the goal of complete backbone and side chain assignments. What the approach lacks is a useful graphical interface for reviewing results and for searching for missing peaks that may have prevented assignments or led to incorrect assignments. Because most of the data ADAPT-NMR collects are 2D tilted planes used to find peaks in 3D spectra, it would be helpful to have a tool that reconstructs the 3D spectra. The software package reported here, ADAPT-NMR Enhancer, supports the visualization of both 2D tilted planes and reconstructed 3D peaks on each tilted plane. ADAPT-NMR Enhancer can be used interactively with ADAPT-NMR to automatically assign selected peaks, or it can be used to produce PINE-SPARKY-like graphical dialogs that support atom-by-atom and peak-by-peak assignment strategies. Results can be exported in various formats, including XEASY proton file (.prot), PINE pre-assignment file (.str), PINE probabilistic output file, SPARKY peak list file (.list) and TALOS+ input file (.tab). As an example, we show how ADAPT-NMR Enhancer was used to extend the automated data collection and assignment results for the protein Aedes aegypti sterol carrier protein 2. AVAILABILITY: The program, in the form of binary code along with tutorials and reference manuals, is available at http://pine.nmrfam.wisc.edu/adapt-nmr-enhancer.

SUMMARY: PINE-SPARKY supports the rapid, user-friendly and efficient visualization of probabilistic assignments of NMR chemical shifts to specific atoms in the covalent structure of a protein in the context of experimental NMR spectra. PINE-SPARKY is based on the very popular SPARKY package for visualizing multidimensional NMR spectra (T. D. Goddard and D. G. Kneller, SPARKY 3, University of California, San Francisco). PINE-SPARKY consists of a converter (PINE2SPARKY), which takes the output from an automated PINE-NMR analysis and transforms it into SPARKY input, plus a number of SPARKY extensions. Assignments and their probabilities obtained in the PINE-NMR step are visualized as labels in SPARKY's spectrum view. Three SPARKY extensions (PINE Assigner, PINE Graph Assigner, and Assign the Best by PINE) serve to manipulate the labels that signify the assignments and their probabilities. PINE Assigner lists all possible assignments for a peak selected in the dialog box and enables the user to choose among these. A window in PINE Graph Assigner shows all atoms in a selected residue along with all atoms in its adjacent residues; in addition, it displays a ranked list of PINE-derived connectivity assignments to any selected atom. Assign the Best-by-PINE allows the user to choose a probability threshold and to automatically accept as "fixed" all assignments above that threshold; following this operation, only the less certain assignments need to be examined visually. Once assignments are fixed, the output files generated by PINE-SPARKY can be used as input to PINE-NMR for further refinements. AVAILABILITY: The program, in the form of source code and binary code along with tutorials and reference manuals, is available at http://pine.nmrfam.wisc.edu/PINE-SPARKY.

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.