CAP - Catabolite gene Activating Protein (1BER)
DNA - GATTACAGATTACAGATTACA Endonuclease PvuII bound to palindromic DNA recognition site CAGCTG (1PVI) DNA - GATTACAGATTACAGATTACA TBP - TATA box Binding Protein (1C9B)
CAP - Catabolite gene Activating Protein (1BER)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
TBP - TATA box Binding Protein (1C9B)

NMR structure determination with YASARA

Taking the step from experimental NMR data to a three dimensional protein structure should be straightforward, given the countless years of expertise accumulated in the many programs developed for this purpose. In practice however, it turns out that the path is cluttered with potential pitfalls, some of which may even redirect the search to the wrong answer[1].

The key problem is that experimental NMR data provide only part of the answer - the rest has to be filled in by the software, relying on a priori knowledge about the look of a protein. This knowledge is both essential and dangerous: essential because it provides the only way to extract a concise 3D structure from the often noisy and contradictory raw data, and dangerous because it may suppress novel structural features that have not been seen before.

All the crucial knowledge is contained in the force field - which is also the main distinguishing feature between different programs. Ever since the release of YASARA's first own force field in 1997, we have focused on getting the a priori knowledge right and improving the force field accuracy. Recent advances included the self-parameterizing NOVA force field[3] in 2002, which derived its own parameters while energy minimizing protein structures, and the extension of this approach to entire high resolution protein crystals in 2004, leading to the YAMBER force fields[4]. In 2009, the YASARA force field incorporated knowledge-based torsion potentials in a way that increased the accuracy without preventing the protein from adapting novel or unusual conformations[7] (last image on the right).

While YASARA's optimized force fields form the core of the NMR structure determination module, the following additional features are noteworthy:

  • Fully automatic: Requires a FASTA sequence file, a restraint file and five mouse clicks to generate the result. Force field parameters for unusual amino acids and other non-standard residues are also derived automatically from semi-empirical quantum chemistry.
  • YASARA reads distance-, dihedral-angle and RDC restraints[8] in standard X-PLOR format and provides the same restraining functions (biharmonic, square well, soft-square) and averaging methods (R-3, R-6, sum, center) as X-PLOR, so that you can easily use existing data.
  • Real-time structure generation: watch the protein fold on screen, show the NOEs, differentiate between fulfilled and violated ones, identify regions of conformational stress and potential misassignments.
  • The entire structure determination protocol has been implemented in YASARA's trivial Yanaconda macro language, which makes it easy to adapt everything to very specific requirements.

The NMR structure determination module is available as an add-on to YASARA Structure.


[1] Traditional biomolecular structure determination by NMR spectroscopy allows for major errors
Nabuurs SB, Spronk CA, Vuister GW, Vriend G (2006) PLoS Comput Biol. 2(2)
[2] GTP-Ras Disrupts the Intramolecular Complex of C1 and RA Domains of Nore1
Harjes E, Harjes S, Wohlgemuth S, Muller KH, Krieger E, Herrmann C, Bayer P (2006) Structure 14, 881-888.
[3] Increasing the precision of comparative models with YASARA NOVA - a self-parameterizing force field
Krieger E, Koraimann G, Vriend G (2002) Proteins 47, 393-402.
[4] Making optimal use of empirical energy functions: Force-field parameterization in crystal space
Krieger E, Darden T, Nabuurs SB, Finkelstein A, Vriend G (2004) Proteins 57, 678-683
[5] Structure of the Tyrosine-sulfated C5a Receptor N Terminus in Complex with Chemotaxis Inhibitory Protein of Staphylococcus aureus
Ippel JH, de Haas CJ, Bunschoten A, van Strijp JA, Kruijtzer JA, Liskamp RM, Kemmink J (2009) J Biol Chem 284, 12363-72
[6] NMR structure of the human prion protein with the pathological Q212P mutation reveals unique structural features
Ilc G, Giachin G, Jaremko M, Jaremko L, Benetti F, Plavec J, Zhukov I, Legname G (2010) PLoS One 5, e11715
[7] Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8
Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, Tyka M, Baker D, Karplus K (2009) Proteins 77 Suppl 9, 114-122
[8] Direct measurement of distances and angles in biomolecules by NMR in a dilute liquid crystalline medium
Tjandra N and Bax A (1997) Science 278, 1111-1114

Structure coverFigure 1: An NMR structure solved with YASARA by Elena Harjes et al. [2]: The C1 domain of mNore1, a novel Ras effector. The cover of Structure Vol. 14/5 has also been created with YASARA and shows the C1 domain in the foreground, after it has lost the intraaction with the RA domain (yellow), which is now bound to a Ras protein (orange) attached to the membrane surface in the background.

Ras effector Nore1
Figure 2: The C1 domain of mNore1 as determined by Elena Harjes et al. [2]. Click here to download the top five ensemble structures, with no NOE violations > 0.5 Å and no torsion violations > 5°.
Chemotaxis inhibitory protein, 2K3U
Figure 3: The structure of tyrosine-sulfated C5a receptor (N-terminus) with bound  chemotaxis inhibitory protein (shown in grey) from S. aureus. Ippel et al. used YASARA to dock the two proteins based on NOE restraints[5], PDB file 2K3U. Other examples (not shown) include a human prion protein mutant[6].
Knowledge based potentials
Figure 4: Knowledge based torsion potentials helping to increase force field accuracy. Arrows indicate the current torsion angles in one or two dimensions.