Automatic Assignment of Methyl-NMR Spectra of Supramolecular Machines Using Graph Theory

Pritišanac, Iva; Degiacomi, Matteo T.; Alderson, T. Reid; Carneiro, Marta G.; Ab, Eiso; Siegal, Gregg; Baldwin, Andrew J.

doi:10.1021/jacs.6b11358

Cited by 48 publications

(94 citation statements)

References 54 publications

(130 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We examined performance differences between our program and the exhaustive search, classic graph theory-based program MAGMA 28 . Extensive comparison with the Monte Carlo-based algorithms 24,25 is already conducted in the MAGMA manuscript.…”

Section: Resultsmentioning

confidence: 99%

“…Graph theory and exhaustive search were previously applied to backbone amides assignment from 3D 15 N-edited NOESY-HSQC 26,27 and during the preparation of our manuscript a method appeared that used McGregor graph matching algorithm and exhaustive search to address the methyl assignment problem 28 . The success of these approaches is highly dependent upon accurate a priori NOE-based peak network selection that is labor intensive to construct.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic methyl assignment in large proteins by the MAGIC algorithm

et al. 2017

View full text Add to dashboard Cite

Selective methyl labeling is an extremely powerful approach to study the structure, dynamics and function of biomolecule systems by NMR. Despite spectacular progress in the field, such studies are still rather limited in number. One of the main obstacles remains the assignment of the methyl resonances, which is labor intensive and error prone. Typically, NOESY crosspeak patterns are manually correlated to the available crystal structure or an in silico template model of the protein. Here, we propose Methyl Assignment by Graphing Inference Construct (MAGIC), an exhaustive search algorithm with no peak network definition requirement. In order to overcome the combinatorial problem, the exhaustive search is performed locally, i.e. for a small number of methyls connected through-space according to experimental 3D methyl NOESY data. The local network approach drastically reduces the search space. Only the best local assignments are combined together to provide the final output. Assignments that match the data with equivalent – or slightly lower - scores are made available to the user for cross-validation by additional experiments such as methyl-amide NOEs. Several NMR datasets for proteins in the 25–50 kDa range were used during development and for performance evaluation against the manually assigned data. We show that the algorithm is robust, reliable and greatly speeds up the methyl assignment task.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic methyl assignment in large proteins by the MAGIC algorithm

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The method was demonstrated on several different complexes with strong or weak binders and will potentially compete with X-ray crystallography for rapid complex structure determination. Furthermore, the development of specific methyl labelling schemes and automatic methyl resonance assignment methods have opened an avenue toward the study of large molecular complexes [81][82][83]. Since our method strongly relies on sharp methyl NMR signals, the path to structure-based drug design on a large system, where classical NMR methods are limited, is wide open.…”

Section: Discussionmentioning

confidence: 99%

The NMR2 Method to Determine Rapidly the Structure of the Binding Pocket of a Protein–Ligand Complex with High Accuracy

Wälti

Orts

2018

Magnetochemistry

View full text Add to dashboard Cite

Structural characterization of complexes is crucial for a better understanding of biological processes and structure-based drug design. However, many protein-ligand structures are not solvable by X-ray crystallography, for example those with low affinity binders or dynamic binding sites. Such complexes are usually targeted by solution-state NMR spectroscopy. Unfortunately, structure calculation by NMR is very time consuming since all atoms in the complex need to be assigned to their respective chemical shifts. To circumvent this problem, we recently developed the Nuclear Magnetic Resonance Molecular Replacement (NMR 2 ) method. NMR 2 very quickly provides the complex structure of a binding pocket as measured by solution-state NMR. NMR 2 circumvents the assignment of the protein by using previously determined structures and therefore speeds up the whole process from a couple of months to a couple of days. Here, we recall the main aspects of the method, show how to apply it, discuss its advantages over other methods and outline its limitations and future directions.

show abstract

“…[25][26][27] For instance, PRE-ASSIGN 27 uses paramagnetic relaxation enhancements (PREs), whereas PARAssign 26 relies on pseudo-contact shifts (PCSs). NOESYbased automatic approaches match a network of measured methyl-methyl distances to the network of short inter-methyl contacts predicted from the protein structure, using Monte Carlo [28][29][30][31] or graphbased 32,33 algorithms. For example, MAGMA 32 uses exact graph matching algorithms to generate confident assignments for a subset of well inter-connected methyls.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we here adopt the FuLlY Automated assignment algorithm FLYA, 34 which is integrated in the CYANA structure calculation software, 35 and has been shown capable to assign proteins exclusively from NOESY data, 36 for structure-and NOESY-based methyl resonance assignment. We apply the resulting MethylFLYA algorithm to a benchmark 32 of five large proteins and protein complexes and show that, on the basis of methyl-methyl NOEs alone, MethylFLYA can assign significantly more methyl resonances with high accuracy than the previously introduced methods MAGMA, 32 MAP-XSII, 29 and FLAMEnGO2.0 31 operating on the same input data. To demonstrate its robustness with respect to ambiguous and imperfect experimental information, MethylFLYA is applied also to unrefined peak lists, reduced input data sets, and peak lists obtained by automated peak picking with the CYPICK algorithm.…”

Section: Introductionmentioning

confidence: 99%

Structure-based methyl resonance assignment with MethylFLYA

Pritišanac

Würz

Alderson

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Isotope-labeled methyl groups provide NMR probes that can be observed in very large, otherwise deuterated systems and enable investigations of protein structure, dynamics and mechanisms.However, the assignment of resonances to specific methyls in the protein is expensive and timeconsuming, which limits the use of methyl-based NMR for large proteins. To resolve this bottleneck, methyl assignment methods have been developed. However, these remain limited regarding complete automation, computational feasibility, and/or the extent and accuracy of the assignments. Here, we present the automated MethylFLYA method for the assignment of methyl groups that is based on methyl-methyl nuclear Overhauser effect spectroscopy (NOESY) peak lists. MethylFLYA was applied to five proteins (28-358 kDa) comprising a total of 708 isotopelabeled methyl groups, of which 674 had manually determined 1 H/ 13 C reference assignments and 614 showed cross peaks in the available NOESY peak lists. MethylFLYA confidently assigned 488 methyl groups, i.e. 79% of those with NOESY data. Of these, 460 agreed with the reference, 5 were different, and 23 concerned methyls without reference assignment. For high-quality NOESY spectra, automatic NOESY peak picking followed by resonance assignment with MethylFLYA can yield results comparable to those obtained from manually prepared peak lists, indicating the feasibility of unbiased, fully automatic methyl resonance assignment starting directly from the NMR spectra. Overall, MethylFLYA assigns significantly more methyl groups than other algorithms, has an average error rate of 1%, modest runtimes of 0.4-1.2 h, and flexibility to handle arbitrary isotope labeling patterns and include data from other types of NMR spectra.

show abstract

Automatic Assignment of Methyl-NMR Spectra of Supramolecular Machines Using Graph Theory

Cited by 48 publications

References 54 publications

Automatic methyl assignment in large proteins by the MAGIC algorithm

Automatic methyl assignment in large proteins by the MAGIC algorithm

The NMR2 Method to Determine Rapidly the Structure of the Binding Pocket of a Protein–Ligand Complex with High Accuracy

Structure-based methyl resonance assignment with MethylFLYA

Contact Info

Product

Resources

About