Rapid prediction of multi-dimensional NMR data sets

Abstract

We present a computational environment for Fast Analysis of multidimensional NMR DAta Sets (FANDAS) that allows assembling multidimensional data sets from a variety of input parameters and facilitates comparing and modifying such "in silico" data sets during the various stages of the NMR data analysis. The input parameters can vary from (partial) NMR assignments directly obtained from experiments to values retrieved from in silico prediction programs. The resulting predicted data sets enable a rapid evaluation of sample labeling in light of spectral resolution and structural content, using standard NMR software such as Sparky. In addition, direct comparison to experimental data sets can be used to validate NMR assignments, distinguish different molecular components, refine structural models or other parameters derived from NMR data. The method is demonstrated in the context of solid-state NMR data obtained for the cyclic nucleotide binding domain of a bacterial cyclic nucleotide-gated channel and on membrane-embedded sensory rhodopsin II. FANDAS is freely available as web portal under WeNMR ( http://www.wenmr.eu/services/FANDAS ).

Conformational analysis of the full‐length M2 protein of the influenza A virus using solid‐state NMR

“…This chemical-shift-prediction approach has recently seen increasing use in SSNMR. 73,74 Our simulations are not able to fully reproduce the extreme b-sheet Ca and Cb chemical shifts of Ser, Ala and Leu. We attribute this to the difficulty of creating a true b-sheet with the relevant hydrogen bonds and other tertiary structure constraints, 64 since the limited chemical shifts and the lack of intermolecular distances make it too speculative to model a multistrand b-sheet.…”

“…The verification of the chemical‐shift‐predicted β‐strand location by the truncated M2(21–97) construct suggests that chemical‐shift prediction and back‐calculation of experimental spectra represent a viable approach for obtaining global conformational constraints of disordered proteins prior to full resonance assignment. This chemical‐shift‐prediction approach has recently seen increasing use in SSNMR . Our simulations are not able to fully reproduce the extreme β‐sheet Cα and Cβ chemical shifts of Ser, Ala and Leu.…”

Bacteriophage SPP1 Tail Tube Protein Self-assembles into β-Structure-rich Tubes

“…All experiments were performed at a magic angle spinning rate of 10 kHz at Ϫ20°C with SPINAL 64 (17) 1 H decoupling during evolution and detection periods. The liquid state NMR chemical shift-based cross-peaks were generated using the program FANDAS (18) and were analyzed with Sparky3. The average chemical shifts of the amino acid residues in a ␤-structure were obtained from the work of Wang and Jardetsky (19).…”

Probing a cell-embedded megadalton protein complex by DNP-supported solid-state NMR

“…Signals stemming from Braun's lipoprotein (Lpp) were predicted using the Lpp crystal structure (PDB 1EQ7 30 ). Chemical shift predictions for the crystal part of T4SScc was made using ShiftX 31 and NMR correlations were derived by FANDAS 18 . Analysis of the NMR/ DNP spectra was performed using Sparky (https://www.cgl.ucsf.…”

“…6) confirmed that T4SScc and Braun's lipoprotein (Lpp) are dominant. Subsequently, we adapted the NMR software package FANDAS 18 to predict the spectra of T4SScc, Lpp and the other dominating cellular envelope compounds 9 -lipopolysaccharides, peptidoglycans and lipids (phosphatidylethanolamine)-that all contribute to the ssNMR spectrum of uniformly 13 C, 15 N-labeled cellular envelope preparations (Supplementary Fig. 7).…”