Automated assignment of methyl NMR spectra from large proteins

Pritišanac, Iva; Alderson, T. Reid; Güntert, Peter

doi:10.1016/j.pnmrs.2020.04.001

Cited by 24 publications

(13 citation statements)

References 163 publications

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“…Since the methyl protons are magnetically equivalent, it is convenient to model the distance between methyls as a C-C atoms distance. The consensus in the literature is that a $4-10-A ˚range covers most methyl-methyl interactions in proteins (Pritisanac et al, 2020). The upper distance limit depends on the choice of labeling, residual protonation levels, the solvent, and the length of the NOESY mixing time.…”

Section: Methyl Network In Proteinsmentioning

confidence: 99%

“…To establish general trends, we employed MAGIC-Net to query 10 methyl-labeled datasets, including seven standard datasets in the literature (Nerli et al, 2021;Pritisanac et al, 2020). Networks are analyzed at 6-A ˚(Figure 2), 7-A ˚(Figure S4), and 10-A ˚(Figure S5) methyl-methyl distance cutoff.…”

Section: Methyl Network In Proteinsmentioning

confidence: 99%

“…There are currently six NOE-based approaches available: MAP-XSII (Xu and Matthews, 2013), FLAMEnGO2.0 (Chao et al, 2014), MAGMA (Pritisanac et al, 2017), MAGIC (Monneau et al, 2017), Methyl-FLYA (Pritisanac et al, 2019), and MAUS (Nerli et al, 2021). Limited performance and accuracy comparisons have been conducted previously (Nerli et al, 2021;Pritisanac et al, 2020). Differences in input requirement and the lack of standard dataset for indiscriminate use across all software platforms make unbiased evaluation difficult and true blind tests or meta-analysis of the results arduous (Table 1).…”

Section: Introductionmentioning

confidence: 99%

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Progress toward automated methyl assignments for methyl-TROSY applications

et al. 2022

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Section: Methyl Network In Proteinsmentioning

confidence: 99%

Section: Methyl Network In Proteinsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Progress toward automated methyl assignments for methyl-TROSY applications

et al. 2022

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“…CYANA includes all of the steps of NMR experiments such as data processing, peak picking, chemical shift assignment, NOE assignment, and structure calculation. The chemical shift assignment step of CYANA is realized with the FLYA algorithm that has been applied for a variety of different NMR assignment problems [ 7 , 19 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. The program PINE assigns chemical shift values automatically by using evidence estimates from empirical distributions and consistency measures.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

2021

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An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. Several improvement steps and repetitions on these algorithms are performed and their combinations are also created as a hyper-heuristic approach to the problem. Additionally, a hill-climbing algorithm is also applied after the evolutionary algorithm steps. The algorithms are tested on several different datasets with a set of 11 commonly used spectra. The test results showed that our algorithm could assign both sidechain and backbone atoms fully automatically without any manual interactions. Our approaches could provide around a 65% success rate and could assign some of the atoms that could not be assigned by other methods.

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“…Moreover, specific mutations may interfere with the ability of the protein to fold properly and/or affect expression yields. Recently, developed computational techniques use inter-methyl distance restraints derived from nuclear overhauser effect (NOE) experiments, compared with distances extracted from the protein structure as a means to obtain resonance assignments for methyl residues [19][20][21][22][23][24][25][26] . The central idea is to systematically compare and match candidate assignments for consistency between the known set of inter-methyl distances and NOE distance estimates and choose the best overall match.…”

mentioning

confidence: 99%

Nearest-neighbor NMR spectroscopy: categorizing spectral peaks by their adjacent nuclei

et al. 2020

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Methyl-NMR enables atomic-resolution studies of structure and dynamics of large proteins in solution. However, resonance assignment remains challenging. The problem is to combine existing structural informational with sparse distance restraints and search for the most compatible assignment among the permutations. Prior classification of peaks as either from isoleucine, leucine, or valine reduces the search space by many orders of magnitude. However, this is hindered by overlapped leucine and valine frequencies. In contrast, the nearest-neighbor nuclei, coupled to the methyl carbons, resonate in distinct frequency bands. Here, we develop a framework to imprint additional information about passively coupled resonances onto the observed peaks. This depends on simultaneously orchestrating closely spaced bands of resonances along different magnetization trajectories, using principles from control theory. For methyl-NMR, the method is implemented as a modification to the standard fingerprint spectrum (the 2D-HMQC). The amino acid type is immediately apparent in the fingerprint spectrum. There is no additional relaxation loss or an increase in experimental time. The method is validated on biologically relevant proteins. The idea of generating new spectral information using passive, adjacent resonances is applicable to other contexts in NMR spectroscopy.

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Automated assignment of methyl NMR spectra from large proteins

Cited by 24 publications

References 163 publications

Progress toward automated methyl assignments for methyl-TROSY applications

Progress toward automated methyl assignments for methyl-TROSY applications

Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

Nearest-neighbor NMR spectroscopy: categorizing spectral peaks by their adjacent nuclei

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