Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA.

By Torsten Herrmann, Peter Güntert, Kurt Wüthrich. Journal of Molecular Biology, 2002

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Combined automated NOE assignment and structure determination module (CANDID) is a new software for efficient NMR structure determination of proteins by automated assignment of the NOESY spectra. CANDID uses an iterative approach with multiple cycles of NOE cross-peak assignment and protein structure calculation using the fast DYANA torsion angle dynamics algorithm, so that the result from each CANDID cycle consists of exhaustive, possibly ambiguous NOE cross-peak assignments in all available spectra and a three-dimensional protein structure represented by a bundle of conformers. The input for the first CANDID cycle consists of the amino acid sequence, the chemical shift list from the sequence-specific resonance assignment, and listings of the cross-peak positions and volumes in one or several two, three or four-dimensional NOESY spectra. The input for the second and subsequent CANDID cycles contains the three-dimensional protein structure from the previous cycle, in addition to the complete input used for the first cycle. CANDID includes two new elements that make it robust with respect to the presence of artifacts in the input data, i.e. network-anchoring and constraint-combination, which have a key role in de novo protein structure determinations for the successful generation of the correct polypeptide fold by the first CANDID cycle. Network-anchoring makes use of the fact that any network of correct NOE cross-peak assignments forms a self-consistent set; the initial, chemical shift-based assignments for each individual NOE cross-peak are therefore weighted by the extent to which they can be embedded into the network formed by all other NOE cross-peak assignments. Constraint-combination reduces the deleterious impact of artifact NOE upper distance constraints in the input for a protein structure calculation by combining the assignments for two or several peaks into a single upper limit distance constraint, which lowers the probability that the presence of an artifact peak will influence the outcome of the structure calculation. CANDID test calculations were performed with NMR data sets of four proteins for which high-quality structures had previously been solved by interactive protocols, and they yielded comparable results to these reference structure determinations with regard to both the residual constraint violations, and the precision and accuracy of the atomic coordinates. The CANDID approach has further been validated by de novo NMR structure determinations of four additional proteins. The experience gained in these calculations shows that once nearly complete sequence-specific resonance assignments are available, the automated CANDID approach results in greatly enhanced efficiency of the NOESY spectral analysis. The fact that the correct fold is obtained in cycle 1 of a de novo structure calculation is the single most important advance achieved with CANDID, when compared with previously proposed automated NOESY assignment methods that do not use network-anchoring and constraint-combination.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/604688a7cedfa1dcc821dbc0a0e77e33c988a621

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Citations in the corpus

Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N (2015, methodology): “The TALOS-N output can be converted into ϕ and ψ torsion angle restraints that then can be used directly as input for a conventional protein NMR structure calculation procedure [33, 34].”
A software tool for the prediction of Xaa-Pro peptide bond conformations in proteins based on 13C chemical shift statistics (2002, methodology): “The a priori knowledge of the presence of a cis XaaPro peptide bond is crucial for automated structure calculation programs, e.g., ARIA (Nilges et al., 1999; Linge et al., 2001) or CANDID/DYANA (Herrmann et al., 2002).”
Chemical shift-based methods in NMR structure determination (2018, methodology): “For instance, Auto-NOE-Rosetta (Automatic NOESY Assignment-Rosetta) [214] leverages the powerful RASREC-Rosetta sampling engine (see Section 4) together with an iterative NOE assignment algorithm which uses network anchoring [212], agreement with a pool of preliminary models (already built into the ”
Combined automated NOE assignment and structure calculation with CYANA (2015, methodology): “CYANA uses ‘‘constraint combination’’ (Herrmann et al. 2002a) to reduce structural distortions from erroneous distance restraints.”
A topology‐constrained distance network algorithm for protein structure determination from NOESY data (2005, neutral): “9,69 CANDID’s network anchoring and constraint-combination methods still require some 90% complete resonance assignments, almost complete aromatic side-chain assignments, low percentage of noise peaks, and small chemical shift variations.”
The structure of mouse cytomegalovirus m04 protein determined from sparse NMR data reveals a new fold of the m02-m06 family of viral immune modulators (2014, neutral): “…targets of known structures, the protocol showed improved sampling efficiency over both standard CSRosetta structure calculations (Shen et al., 2008) and conventional NMR structure determination programs (Herrmann et al., 2002) for b-rich proteins with complicated topologies (Lange et al., 2012).”
Assignment validation software suite for the evaluation and presentation of protein resonance assignment data (2004, methodology): “…and software for automated analysis provide a significant reduction in the time required to generate sequence-specific resonance assignments and 3D structures of proteins using solution NMR methods (Moseley and Montelione, 1999; Montelione et al., 2000; Herrmann et al., 2002; Huang et al., 2003).”
Automatic NOESY assignment in CS-RASREC-Rosetta (2014, methodology): “As suggested previously, peaks are also eliminated if the network anchoring score of their assignments remains low (Herrmann et al. 2002 where R o ( t , s ) denotes all network contributions between residues t , s .”
Evaluating protein structures determined by structural genomics consortia (2006, methodology): “19,32 DYANA/CYANA uses a minimal force field, with bond length and bond angle parameters for amino acid residues that differ from those used in programs such as XPLOR, CNS, or PROCHECK.”
ARIA: automated NOE assignment and NMR structure calculation (2003, methodology): “Several programs for automated NOE assignment have been developed: CLOUDS (Grishaev and Llinás, 2002), CANDID (Herrmann et al., 2002), NOAH (Mumenthaler et al.”
BioMagResBank (2007, background): “Promising approaches include the automation of NMR peak assignments (8–10) and computerized sorting of ambiguous structural restraints (11–13).”
Structural insights into the recruitment of SMRT by the corepressor SHARP under phosphorylative regulation. (2013, methodology): “0) was used with the CANDID protocol for structural restraint collection (Herrmann et al., 2002).”