Abstract
Summary: SPARKY (Goddard and Kneller, SPARKY 3) remains the most popular software program for NMR data analysis, despite the fact that development of the package by its originators ceased in 2001. We have taken over the development of this package and describe NMRFAM-SPARKY, which implements new functions reflecting advances in the biomolecular NMR field. NMRFAM-SPARKY has been repackaged with current versions of Python and Tcl/Tk, which support new tools for NMR peak simulation and graphical assignment determination. These tools, along with chemical shift predictions from the PACSY database, greatly accelerate protein side chain assignments. NMRFAM-SPARKY supports automated data format interconversion for interfacing with a variety of web servers including, PECAN , PINE, TALOS-N, CS-Rosetta, SHIFTX2 and PONDEROSA-C/S.
Availability and implementation: The software package, along with binary and source codes, if desired, can be downloaded freely from http://pine.nmrfam.wisc.edu/download_packages.html. Instruction manuals and video tutorials can be found at http://www.nmrfam.wisc.edu/nmrfam-sparky-distribution.htm.
Contact: whlee@nmrfam.wisc.edu or markley@nmrfam.wisc.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
1 Introduction
SPARKY (https://www.cgl.ucsf.edu/home/sparky) remains the most popular computer program for NMR operations, such as peak-picking and peak assignment (Supplementary Fig. S1), despite that fact that its originators have not released a new version since 2001 (Goddard and Kneller, 2008, SPARKY 3). SPARKY supports user-defined enhancements, and we have used these to develop new tools in support of our packages for automated protein assignment and structure determination. The added features support (i) interfacing with servers offering new technologies, (ii) tools for data visualization and verification and (iii) new protocols for maximizing the efficiency of NMR data analysis. We have refined these SPARKY enhancements through their use in our annual workshops by participants with varying experience in protein NMR spectroscopy. We describe here the new package, which we have named ‘NMRFAM-SPARKY’.
2 Implementation
We first replaced Python 2.4 by 2.7 and Tcl/Tk 8.4 by 8.6 to enable the implementation of features related to web services and user interfaces to platforms including Windows, Mac OS X, and Linux. We had to use Tcl/Tk 8.5 for the Mac OS X version, because the 8.6 version was incompatible with X11 libraries. Several modifications of the C++ and Python codes were needed to achieve stability. Once the new version compiled with Python and Tcl/Tk, we updated old functions and implemented new functions (Supplementary Table S1) found to be desirable from our workshops and research projects. New functions are located under the NMRFAM menu and are categorized by sub-menus identified by two-letter codes (Table 1).
Table 1.
New and updated NMRFAM-SPARKY functions
| Sub-menu | Functions | Two-letter-code |
|---|---|---|
| Automated BB and SC assignment: PINE | >>> Automated BB and SC assignment: PINE | n1 |
| Export to PINE(BMRB) input file | ep | |
| ARECA list | ar | |
| PINE visualization: PINE-SPARKY | >>> PINE validation: PINE-SPARKY | n2 |
| Run PINE2SPARKY converter | p2 | |
| Pine assigner | ab | |
| Pine graph assigner | pp | |
| Assign the best by Pine | pr | |
| Select all floating labels | se | |
| Superfast assignment with PACSY | >>> Superfast assignment on-the-fly referencing PACSY | n3 |
| Dummy graph | dg | |
| Transfer and simulate assignments | ta | |
| Untag _s | ut | |
| Center and Untag _s | cu | |
| >>> Structure based chemical shift predictor: SHIFTX2 from Wishart Lab. | E1 | |
| Automated 3D structure calculation with PONDEROSA | >>> Automated structure calculation: PONDEROSA | n4 |
| Run Ponderosa Client | cp | |
| Update Ponderosa | up | |
| Generate Distance Constraints for PONDEROSA | gd | |
| Cyana2Sparky format | cy | |
| XEASY, DYANA format | xe | |
| Extract phi-psi and accessible surface info from PDB with STRIDE | sr | |
| Structural predictions | >>> Structural predictions | n5 |
| Export to PECAN and go PECAN webserver | n6 | |
| 3D structure prediction with CS-Rosetta (BMRB) | ce | |
| phi-psi prediction with TALOS-N (NIH) | tl | |
| Secondary structure prediction with PSIPRED (UCL) | PP | |
| Utilities | Backbone peak picking by APES | ae |
| PINE sequence formatting | fp | |
| Easy pipe2ucsf | Pu | |
| Easy bruk2ucsf | Bu | |
Dummy Graph (two-letter-code: dg) offers a generalized version of the Pine Graph Assigner utility from PINE-SPARKY (Lee et al., 2009) that does not require use of PINE (Bahrami et al., 2009) or the PINE2SPARKY converter. Sequence information and assignment situations are illustrated graphically in real time so as to notify users of any missing or wrong assignments that need to be fixed. Another useful function is Transfer and Simulate Assignments (two-letter-code: ta). We used statistics from the PACSY database Lee et al., 2012), a relational database management system incorporating PDB (Berman et al., 2007), BMRB (Ulrich et al., 2008), SCOP (Murzin et al., 1995), STRIDE (Frishman and Argos, 1995) and MolProbity (Chen et al., 2010), to populate this Python module, which simulates resonance frequencies dynamically on the basis of conditions such as amino acid and atom type, preceding and following residues, secondary structure, pH and temperature. This tool enables a new predict-and-confirm approach to resonance assignments that is much faster than the traditional pick-and-assign method because the user no longer needs to refer to a table of BMRB-derived chemical shift. Furthermore, if a 3D structure is available, it is possible to determine assignments by using SHIFTX2 prediction to simulate the possible assignments to be checked. NMRFAM-SPARKY contains a structure predictions menu that includes PECAN (Eghbalnia et al., 2005), TALOS-N (Shen and Bax, 2013) and CS-Rosetta (Shen et al., 2009). These tools can be used to predict the 3D structure so that side chain and NOE assignments can be refined through combined use of CS-Rosetta and SHIFTX2 (Han et al., 2011). In favorable cases, the user can complete the chemical shift assignments and then use the NMRFAM-SPARKY interface to PONDEROSA-C/S (Lee et al., 2014) to carry out an NOE-based structure determination in seamless fashion.
NMRFAM-SPARKY contains updated chemical shift statistics which greatly improve the accuracy and reliability of predictions. We expanded the number of resonance types (amino acid type plus atom type) from 273 to 276 (Supplementary Table S2A). By basing the statistics on the current BMRB chemical shift archive, we achieved statistical significance for 27 of these resonance types, which were not considered to be statistically meaningful because they previously were based on fewer than 30 examples (Supplementary Table S2B). The average chemical shifts of three of the resonance types changed by more than one SD (Supplementary Table S2C). The SDs for chemical shifts of 28 resonance types were refined by ≥30% (Supplementary Table S2D), whereas those of an equivalent number of resonances types were broadened by ≥30% (Table Supplementary S2E).
3 Results and Conclusions
Subsequent to announcing the availability of NMRFAM-SPARKY at workshops, at scientific meetings, and on the NMRFAM website, the package was downloaded 155 times from sites that provided unique e-mail addresses over the period May 21, 2014 to September 17, 2014. We expect that use of NMRFAM-SPARKY will increase the success rate of automated assignment runs on the PINE server and automated structure determinations on the PONDEROSA-C/S server, because our analysis shows that most failures stem from incorrectly formatted input. For similar reasons, data input through NMRFAM-SPARKY should improve the reliability and successful use of other programs or web servers, such as TALOS-N and CS-Rosetta.
Supplementary Material
Acknowledgements
We are grateful to Dr Thomas Goddard for making the source code availability for SPARKY3. We also thank the participants in the NMRFAM workshops who inspired us to develop many of the new functions described here.
Funding
United States National Institutes of Health NIH [P41GM103399].
Conflict of Interest: none declared.
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