FLAMEnGO 2.0: An enhanced fuzzy logic algorithm for structure-based assignment of methyl group resonances

Chao, Fa-An; Kim, Jonggul J; Xia, Youlin; Milligan, Michael; Rowe, Nancy; Veglia, Gianluigi

doi:10.1016/j.jmr.2014.04.012

Cited by 41 publications

(56 citation statements)

References 22 publications

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“…Purification of PKA-C was performed as previously described using the His 6 -RIIα(R213K)(Hemmer et al, 1997) subunit and a second purification step was performed using the HiTrap SP cation exchange column as previously described(Chao et al, 2014). The most abundant isoform, corresponding to phosphorylation at S338, T197 and S10, was used for all experiments.…”

Section: Methodsmentioning

confidence: 99%

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Uncoupling Catalytic and Binding Functions in the Cyclic AMP-Dependent Protein Kinase A

Kim

Walters

et al. 2016

Structure

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SUMMARY The canonical function of kinases is to transfer a phosphoryl group to substrates, initiating a signaling cascade; while their non-canonical role is to bind other kinases or substrates, acting as scaffolds, competitors, and signal integrators. Here, we show how to uncouple kinases dual function by tuning the binding cooperativity between nucleotide (or inhibitors) and substrate allosterically. We demonstrate this new concept for the C-subunit of protein kinase A (PKA-C). Using thermocalorimetry and NMR, we found a linear correlation between the degree of cooperativity and the population PKA-C’s closed state. The non-hydrolysable ATP analog (ATPγC) does not follow this correlation, suggesting that changing the chemical groups around the phosphoester bond can uncouple kinases dual function. Remarkably, this uncoupling was also found for two ATP-competitive inhibitors, H89 and balanol. Since the mechanism for allosteric cooperativity is not conserved in different kinases, these results may suggest new approaches for designing selective kinase inhibitors.

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Section: Methodsmentioning

confidence: 99%

“…Samples for 13 C IVL 15 N labeled PKA-C were expressed and purified as previously described(Chao et al, 2014; Masterson et al, 2008). Effective final sample concentrations were 0.2–0.25 mM in 20 mM KH 2 PO 4 , 90 mM KCl, 10 mM DTT, 10 mM MgCl 2 1 mM NaN 3 at pH 6.5 with 12 mM of nucleotide.…”

Section: Methodsmentioning

confidence: 99%

Uncoupling Catalytic and Binding Functions in the Cyclic AMP-Dependent Protein Kinase A

Kim

Walters

et al. 2016

Structure

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“…Extensive comparison with the Monte Carlo-based algorithms 24,25 is already conducted in the MAGMA manuscript. MAGMA completes assignment for a proS -Leu/Val and Ile labeled MBP sample (VsLsI, 73 peaks) in 56 hours.…”

Section: Resultsmentioning

confidence: 99%

“…Two computational model-based approaches were also proposed in which the assignment is inferred by matching the nuclear Overhauser effect spectroscopy (NOESY) data with the connectivity expected from a known structure or structure model via a Monte Carlo-based swapping procedure to reach convergence according to a matching function 24,25 . Stochastic iterative swapping is implemented to overcome the computationally unfeasible exploration of the full assignment universe, which grows with the factorial of methyl number.…”

Section: Introductionmentioning

confidence: 99%

Automatic methyl assignment in large proteins by the MAGIC algorithm

et al. 2017

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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.

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“…Thus, various mathematical methods have been applied to help overcome this problem, including graph theory, fuzzy math/logic, nuclear vector representation, clustering algorithms, and probabilistic techniques. 12,13,15,17,18,59–62 A number of programs have been developed for automated structure-based assignment of methyl resonances, including MAP-XS (II), 13,17 FLAMEnGO(2.0), 15,18 and a PRE-based program. 63 All these programs first employ residue-type identification to reduce the complexity of the search and then employ NOE and/or PRE constraints as their primary experimental data.…”

Section: Discussionmentioning

confidence: 99%

Structure-Based Assignment of Ile, Leu, and Val Methyl Groups in the Active and Inactive Forms of the Mitogen-Activated Protein Kinase Extracellular Signal-Regulated Kinase 2

et al. 2015

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Resonance assignments are the first step in most NMR studies of protein structure, function, and dynamics. Standard protein assignment methods employ through-bond backbone experiments on uniformly 13C/15N-labeled proteins. For larger proteins, this through-bond assignment procedure often breaks down due to rapid relaxation and spectral overlap. The challenges involved in studies of larger proteins led to efficient methods for 13C labeling of side chain methyl groups, which have favorable relaxation properties and high signal-to-noise. These methyls are often still assigned by linking them to the previously assigned backbone, thus limiting the applications for larger proteins. Here, a structure-based procedure is described for assignment of 13C1H3-labeled methyls by comparing distance information obtained from three-dimensional methyl–methyl nuclear Overhauser effect (NOE) spectroscopy with the X-ray structure. The Ile, Leu, or Val (ILV) methyl type is determined by through-bond experiments, and the methyl–methyl NOE data are analyzed in combination with the known structure. A hierarchical approach was employed that maps the largest observed “NOE-methyl cluster” onto the structure. The combination of identification of ILV methyl type with mapping of the NOE-methyl clusters greatly simplifies the assignment process. This method was applied to the inactive and active forms of the 42-kDa ILV 13C1H3-methyl labeled extracellular signal-regulated kinase 2 (ERK2), leading to assignment of 60% of the methyls, including 90% of Ile residues. A series of ILV to Ala mutants were analyzed, which helped confirm the assignments. These assignments were used to probe the local and long-range effects of ligand binding to inactive and active ERK2.

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FLAMEnGO 2.0: An enhanced fuzzy logic algorithm for structure-based assignment of methyl group resonances

Cited by 41 publications

References 22 publications

Uncoupling Catalytic and Binding Functions in the Cyclic AMP-Dependent Protein Kinase A

Uncoupling Catalytic and Binding Functions in the Cyclic AMP-Dependent Protein Kinase A

Automatic methyl assignment in large proteins by the MAGIC algorithm

Structure-Based Assignment of Ile, Leu, and Val Methyl Groups in the Active and Inactive Forms of the Mitogen-Activated Protein Kinase Extracellular Signal-Regulated Kinase 2

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