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• W4200568296 abstract "•Presenting an interface between the data analysis platform and MAGIC algorithm•MAGIC-Net methyl network analysis for selection of methyl labeling scheme•MAGIC-Act semi-automated preparation of experimental data•MAGIC-View for unbiased comparison of different NOE-based assignment protocols Methyl-TROSY spectroscopy has extended the reach of solution-state NMR to supra-molecular machineries over 100 kDa in size. Methyl groups are ideal probes for studying structure, dynamics, and protein-protein interactions in quasi-physiological conditions with atomic resolution. Successful implementation of the methodology requires accurate methyl chemical shift assignment, and the task still poses a significant challenge in the field. In this work, we outline the current state of technology for methyl labeling, data collection, data analysis, and nuclear Overhauser effect (NOE)-based automated methyl assignment approaches. We present MAGIC-Act and MAGIC-View, two Python extensions developed as part of the popular NMRFAM-Sparky package, and MAGIC-Net a standalone structure-based network analysis program. MAGIC-Act conducts statistically driven amino acid typing, Leu/Val pairing guided by 3D HMBC-HMQC, and NOESY cross-peak symmetry checking. MAGIC-Net provides model-based NOE statistics to aid in selection of a methyl labeling scheme. The programs provide a versatile, semi-automated framework for rapid methyl assignment. Methyl-TROSY spectroscopy has extended the reach of solution-state NMR to supra-molecular machineries over 100 kDa in size. Methyl groups are ideal probes for studying structure, dynamics, and protein-protein interactions in quasi-physiological conditions with atomic resolution. Successful implementation of the methodology requires accurate methyl chemical shift assignment, and the task still poses a significant challenge in the field. In this work, we outline the current state of technology for methyl labeling, data collection, data analysis, and nuclear Overhauser effect (NOE)-based automated methyl assignment approaches. We present MAGIC-Act and MAGIC-View, two Python extensions developed as part of the popular NMRFAM-Sparky package, and MAGIC-Net a standalone structure-based network analysis program. MAGIC-Act conducts statistically driven amino acid typing, Leu/Val pairing guided by 3D HMBC-HMQC, and NOESY cross-peak symmetry checking. MAGIC-Net provides model-based NOE statistics to aid in selection of a methyl labeling scheme. The programs provide a versatile, semi-automated framework for rapid methyl assignment. During the past two decades, developments in methyl-TROSY spectroscopy have extended the reach of solution NMR to supra-molecular machines well-over 100 kDa in size (Gardner et al., 1997Gardner K.H. Rosen M.K. Kay L.E. Global folds of highly deuterated, methyl-protonated proteins by multidimensional NMR.Biochemistry. 1997; 36: 1389-1401Crossref PubMed Scopus (241) Google Scholar; Huang et al., 2016Huang C. Rossi P. Saio T. Kalodimos C.G. Structural basis for the antifolding activity of a molecular chaperone.Nature. 2016; 537: 202-206Crossref PubMed Scopus (106) Google Scholar; Rosenzweig and Kay, 2014Rosenzweig R. Kay L.E. Bringing dynamic molecular machines into focus by methyl-TROSY NMR.Annu. Rev. Biochem. 2014; 83: 291-315Crossref PubMed Scopus (163) Google Scholar; Sekhar and Kay, 2019Sekhar A. Kay L.E. An NMR view of protein dynamics in health and disease.Annu. Rev. Biophys. 2019; 48: 297-319Crossref PubMed Scopus (62) Google Scholar). Methyl groups have many advantages as NMR probes in larger systems: they can be leveraged to obtain three-dimensional (3D) structure of proteins and their complexes, probe interactions with partners, and quantitate internal dynamics (Alderson and Kay, 2021Alderson T.R. Kay L.E. NMR spectroscopy captures the essential role of dynamics in regulating biomolecular function.Cell. 2021; 184: 577-595Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar; Huang et al., 2016Huang C. Rossi P. Saio T. Kalodimos C.G. Structural basis for the antifolding activity of a molecular chaperone.Nature. 2016; 537: 202-206Crossref PubMed Scopus (106) Google Scholar; Jiang et al., 2019Jiang Y. Rossi P. Kalodimos C.G. Structural basis for client recognition and activity of Hsp40 chaperones.Science. 2019; 365: 1313-1319Crossref PubMed Scopus (57) Google Scholar; Saio et al., 2014Saio T. Guan X. Rossi P. Economou A. Kalodimos C.G. Structural basis for protein antiaggregation activity of the trigger factor chaperone.Science. 2014; 344: 1250494Crossref PubMed Scopus (194) Google Scholar; Sekhar and Kay, 2019Sekhar A. Kay L.E. An NMR view of protein dynamics in health and disease.Annu. Rev. Biophys. 2019; 48: 297-319Crossref PubMed Scopus (62) Google Scholar; Xie et al., 2020Xie T. Saleh T. Rossi P. Kalodimos C.G. Conformational states dynamically populated by a kinase determine its function.Science. 2020; 370: eabc2754Crossref PubMed Scopus (60) Google Scholar). The methyl-TROSY effect originates in isolated 13CH3 methyl spin system (Ollerenshaw et al., 2003Ollerenshaw J.E. Lidar D.A. Kay L.E. Magnetic resonance realization of decoherence-free quantum computation.Phys. Rev. Lett. 2003; 91: 217904Crossref PubMed Scopus (90) Google Scholar), where the methyl spin relaxation is governed by dipole-dipole (DD) cross-correlation terms. The favorable slow-relaxing components of those interactions are captured with a heteronuclear multiple quantum coherence experiment (HMQC) resulting in gains in sensitivity and resolution (Tugarinov et al., 2003Tugarinov V. Hwang P.M. Ollerenshaw J.E. Kay L.E. Cross-correlated relaxation enhanced 1H[bond]13C NMR spectroscopy of methyl groups in very high molecular weight proteins and protein complexes.J. Am. Chem. Soc. 2003; 125: 10420-10428Crossref PubMed Scopus (479) Google Scholar). Methyl-TROSY methodologies are implemented by selectively labeling the protein methyls with NMR active nuclei in a highly deuterated background (Schutz and Sprangers, 2020Schutz S. Sprangers R. Methyl TROSY spectroscopy: a versatile NMR approach to study challenging biological systems.Prog. Nucl. Magn. Reson. Spectrosc. 2020; 116: 56-84Crossref PubMed Scopus (50) Google Scholar). The targets of interest are overexpressed in 2H2O minimal media using 13CH3-labeled methyl amino acids or biosynthetic precursors (Goto et al., 1999Goto N.K. Gardner K.H. Mueller G.A. Willis R.C. Kay L.E. A robust and cost-effective method for the production of Val, Leu, Ile (delta 1) methyl-protonated 15N-, 13C-, 2H-labeled proteins.J. Biomol. NMR. 1999; 13: 369-374Crossref PubMed Scopus (439) Google Scholar). One drawback of selective methyl labeling is that it precludes most backbone-based assignment methodologies, thereby making methyl resonance assignment challenging. Three complementary approaches have been developed to assign methyl-TROSY samples: site-directed mutagenesis (Amero et al., 2011Amero C. Asuncion Dura M. Noirclerc-Savoye M. Perollier A. Gallet B. Plevin M.J. Vernet T. Franzetti B. Boisbouvier J. A systematic mutagenesis-driven strategy for site-resolved NMR studies of supramolecular assemblies.J. Biomol. NMR. 2011; 50: 229-236Crossref PubMed Scopus (55) Google Scholar), divide and conquer (Pickford and Campbell, 2004Pickford A.R. Campbell I.D. NMR studies of modular protein structures and their interactions.Chem. Rev. 2004; 104: 3557-3566Crossref PubMed Scopus (56) Google Scholar), and model-based computational approaches. Site-directed mutagenesis is costly and labor intensive. The divide-and-conquer approach simplifies the task by separating larger systems into smaller subdomains but is applicable only to multidomain proteins with protomers that are stable in isolation (Gelis et al., 2007Gelis I. Bonvin A.M. Keramisanou D. Koukaki M. Gouridis G. Karamanou S. Economou A. Kalodimos C.G. Structural basis for signal-sequence recognition by the translocase motor SecA as determined by NMR.Cell. 2007; 131: 756-769Abstract Full Text Full Text PDF PubMed Scopus (329) Google Scholar). Computational approaches that rely on experimentally determined distances are popular strategies to address the methyl assignment challenge. Distances from paramagnetic relaxation (John et al., 2007John M. Schmitz C. Park A.Y. Dixon N.E. Huber T. Otting G. Sequence-specific and stereospecific assignment of methyl groups using paramagnetic lanthanides.J. Am. Chem. Soc. 2007; 129: 13749-13757Crossref PubMed Scopus (54) Google Scholar; Venditti and Fawzi, 2018Venditti V. Fawzi N.L. Probing the atomic structure of transient protein contacts by paramagnetic relaxation enhancement solution NMR.Methods Mol. Biol. 2018; 1688: 243-255Crossref PubMed Scopus (7) Google Scholar) or nuclear Overhauser effect (NOE) are used to correlate structure to methyl chemical shifts. In this manuscript, we focus exclusively on methyl assignments by NOE-based methodologies. There are currently six NOE-based approaches available: MAP-XSII (Xu and Matthews, 2013Xu Y. Matthews S. MAP-XSII: an improved program for the automatic assignment of methyl resonances in large proteins.J. Biomol. NMR. 2013; 55: 179-187Crossref PubMed Scopus (32) Google Scholar), FLAMEnGO2.0 (Chao et al., 2014Chao F.A. Kim J. Xia Y. Milligan M. Rowe N. Veglia G. FLAMEnGO 2.0: an enhanced fuzzy logic algorithm for structure-based assignment of methyl group resonances.J. Magn. Reson. 2014; 245: 17-23Crossref PubMed Scopus (35) Google Scholar), MAGMA (Pritisanac et al., 2017Pritisanac I. Degiacomi M.T. Alderson T.R. Carneiro M.G. Ab E. Siegal G. Baldwin A.J. Automatic assignment of methyl-NMR spectra of supramolecular machines using graph theory.J. Am. Chem. Soc. 2017; 139: 9523-9533Crossref PubMed Scopus (38) Google Scholar), MAGIC (Monneau et al., 2017Monneau Y.R. Rossi P. Bhaumik A. Huang C. Jiang Y. Saleh T. Xie T. Xing Q. Kalodimos C.G. Automatic methyl assignment in large proteins by the MAGIC algorithm.J. Biomol. NMR. 2017; 69: 215-227Crossref PubMed Scopus (32) Google Scholar), Methyl-FLYA (Pritisanac et al., 2019Pritisanac I. Wurz J.M. Alderson T.R. Guntert P. Automatic structure-based NMR methyl resonance assignment in large proteins.Nat. Commun. 2019; 10: 4922Crossref PubMed Scopus (19) Google Scholar), and MAUS (Nerli et al., 2021Nerli S. De Paula V.S. McShan A.C. Sgourakis N.G. Backbone-independent NMR resonance assignments of methyl probes in large proteins.Nat. Commun. 2021; 12: 691Crossref PubMed Scopus (12) Google Scholar). Limited performance and accuracy comparisons have been conducted previously (Nerli et al., 2021Nerli S. De Paula V.S. McShan A.C. Sgourakis N.G. Backbone-independent NMR resonance assignments of methyl probes in large proteins.Nat. Commun. 2021; 12: 691Crossref PubMed Scopus (12) Google Scholar; Pritisanac et al., 2020Pritisanac I. Alderson T.R. Guntert P. Automated assignment of methyl NMR spectra from large proteins.Prog. Nucl. Magn. Reson. Spectrosc. 2020; 118-119: 54-73Crossref PubMed Scopus (14) Google Scholar). 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). Additionally, NOE-based programs require extensive expert data curation but lack visualization tools to facilitate the process.Table 1Input data required by NOE-based automated methyl resonance assignment protocolsInput data typeMAP-XSIIFLAMEnGO2.0MAGMAMAGICMethyl-FLYAMAUSProtein structure (PDB format)yesyesyesyesyesyesUnassigned methyl-methyl NOEs (peak list with intensity)3D3DaManually generated two-column list with arbitrary methyl group IDs that annotate each NOE cross peak between two methyls resonances in the 2D HMQC; IDs contain methyl residue type.3D4D3D/4D2D peaks list with residue types indicatedyesyesaManually generated two-column list with arbitrary methyl group IDs that annotate each NOE cross peak between two methyls resonances in the 2D HMQC; IDs contain methyl residue type.yesyesyesSupported methyl typesILVMATILVMATILVMATILVMATILVAILVMAAmbiguity in methyl residue typeoptionalbAmbiguity in LV only.optionaloptionalcAmbiguity in ALV only.optionalLV-geminal paring informationoptionaloptionaloptionaloptionaloptionalKnow residue-specific assignmentsoptionaloptionaloptionaloptionaloptionalPrograms listed in order of publication date.a Manually generated two-column list with arbitrary methyl group IDs that annotate each NOE cross peak between two methyls resonances in the 2D HMQC; IDs contain methyl residue type.b Ambiguity in LV only.c Ambiguity in ALV only. Open table in a new tab Programs listed in order of publication date. Herein, we discuss the current NOE-based methyl assignment knowledge base, visualize key concepts that govern methyl networks in proteins, and show how their size and complexity should be evaluated to decide the most appropriate choice of labeling for best results. We provide details and specific examples of the protocol adopted in our laboratory for sample preparation and data collection. We introduce MAGIC-Net, MAGIC-Act, and MAGIC-View, a suite of Python-based programs to aid the assignment process. MAGIC-Net, a standalone program, generates comprehensive model statistics to aid in the choice of optimal methyl labeling scheme(s). MAGIC-Act, a companion to our MAGIC program, pre-processes the experimental data, exports MAGIC input files, and imports MAGIC output directly to and from NMRFAM-Sparky (Lee et al., 2015Lee W. Tonelli M. Markley J.L. NMRFAM-SPARKY: enhanced software for biomolecular NMR spectroscopy.Bioinformatics. 2015; 31: 1325-1327Crossref PubMed Scopus (978) Google Scholar), enabling rapid result validation and iterative runs. In addition, MAGIC-Act generates and displays NMR data and model statistics, produces a summary of the data, estimates the methyl network uniqueness, and gives the assignment feasibility range (as a percentage) based on labeling scheme and data quality. MAGIC-View is a flexible tool to visualize assignment on the structure and compile accuracy statistics from competing lists. Importantly, MAGIC-View offers a standard platform for performance evaluations and comparisons with other assignment programs. Overall, we propose a computer-aided strategy that minimizes sample preparation, standardizes data collection, and gives rapid, accurate, and reproducible methyl assignments within the MAGIC algorithm framework. All NOE-based approaches frame the methyl assignment problem as a network correlation problem and set out to match the experimental NOE peak network to a model-based connectivity network. In this section, we describe the construction of the model-based connectivity network and its relation to the experimental NOE peak network. The experimental NOE is the result of the through-space proton-proton interactions and scales with 1/r6, where r is the distance between protons (Kumar et al., 1980Kumar A. Ernst R.R. Wüthrich K. A two-dimensional nuclear Overhauser enhancement (2D NOE) experiment for the elucidation of complete proton-proton cross-relaxation networks in biological macromolecules.Biochem. Biophysical Res. Commun. 1980; 95: 1-6Crossref PubMed Scopus (2023) Google Scholar). 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-Å range covers most methyl-methyl interactions in proteins (Pritisanac et al., 2020Pritisanac I. Alderson T.R. Guntert P. Automated assignment of methyl NMR spectra from large proteins.Prog. Nucl. Magn. Reson. Spectrosc. 2020; 118-119: 54-73Crossref PubMed Scopus (14) Google Scholar). The upper distance limit depends on the choice of labeling, residual protonation levels, the solvent, and the length of the NOESY mixing time. Model connectivity networks are constructed by extracting the coordinates for all methyl carbons and calculating the C-C distance for all possible combinations of methyl groups, retaining only those with distances below a specified cutoff. There are three types of theoretical connectivity networks that can be constructed for comparison with experimental data: the global network (GN), the subnetworks (SNs), and the local networks (LNs). A local network is defined by the number of direct connections between a given methyl group (acceptor) and other methyl groups (donors) within a specified distance cutoff. LNs that are linked to each other form SNs, and the GN represents the ensemble of all separate SNs. Graphical representations of each of these networks are shown in Figure 1 for the FGFR3 kinase domain, a 34-kDa protein fragment containing 174 methyl groups. In this system, we observe 159 LNs within a 6-Å cutoff (Figures 1A and S1) with an average of seven methyl groups each. A total nine SNs can be constructed for the system, containing between 2 and 67 methyl groups (Figure 1B). The global FGRF3 network contains all 174 methyl groups, including 15 isolated methyl groups and a total of 1,104 possible connections (Figure 1C). These analyses were also performed using a 7- and 10-Å cutoff, as shown in Figures S2 and S3. The experimentally derived network is populated based on the observation of cross peaks in multi-dimensional NOESY spectra. The NOE peak list is parsed using the chemical shifts observed in a 2D HMQC spectrum, usually containing methyl type and geminal pairing information, to produce the experimental network (see example strips of a 3D methyl-NOESY for FGFR3 in Figure 1A). Unfortunately, poor data quality, chemical shift degeneracy, and other experimental imperfections result in ambiguous and incomplete experimental connectivity networks. Importantly, proton spin diffusion can create spurious NOEs that extend and alter the networks beyond the prescribed cutoff in unpredictable ways. Irrespective of the format, information content, and degree of manual preparation required to generate the input files, all NOE-based approaches require the inclusion of amino acid type, or methyl type, information (Table 1). This is because, in the absence of any additional information, a system containing N methyl group has N! possible solutions, which is computationally untenable even for small systems (N < 20). Inclusion of methyl type information allows for the identification of LNs based on the number and type of methyls it contains (Figure 1C). This limits the number of possible solutions for each residue addressing the sampling problem. Unambiguous assignment requires the identity of the LN to be unique (ULN) compared to all other ambiguous LNs (ALN). It follows that a larger array of labeled methyl types should produce more ULNs and thus a higher number of accurate residue-specific assignments, while minimizing the number of unassignable, unconnected, or isolated methyls. Please note that we use the terms unique and ambiguous interchangeably when classifying methyl type and LNs. Still, the terms are related in that experimental LNs can be defined as unique only when the methyl type of all donors is uniquely defined (e.g., as L only or T only) It is also common practice to include geminal pairing information as part of the input data for most NOE-based assignment methods (see Table 1). This effectively halves the number of Leu/Val LNs that must be examined to arrive at a unique assignment. Pairing is used to impose symmetry on interactions involving geminal methyls, further reducing the search space. Connections to either one of the geminal methyls are automatically observed by the partner, even if slightly outside of the distance cutoff. This similarity of the NOE correlations of geminal methyls is utilized during expert manual assignment. Thus, inclusion of both methyl types and geminal pairing information is crucial to the accuracy of NOE-based assignment methods. Ultimately, the success of NOE-based assignment approaches hinges on the quality of the experimental data and agreement with the model provided. The models can be derived from crystallography, homology (Bordoli et al., 2009Bordoli L. Kiefer F. Arnold K. Benkert P. Battey J. Schwede T. Protein structure homology modeling using SWISS-MODEL workspace.Nat. Protoc. 2009; 4: 1-13Crossref PubMed Scopus (975) Google Scholar), comparative (Song et al., 2013Song Y. DiMaio F. Wang R.Y. Kim D. Miles C. Brunette T. Thompson J. Baker D. High-resolution comparative modeling with RosettaCM.Structure. 2013; 21: 1735-1742Abstract Full Text Full Text PDF PubMed Scopus (642) Google Scholar), or artificial intelligence modeling (Baek et al., 2021Baek M. DiMaio F. Anishchenko I. Dauparas J. Ovchinnikov S. Lee G.R. Wang J. Cong Q. Kinch L.N. Schaeffer R.D. et al.Accurate prediction of protein structures and interactions using a three-track neural network.Science. 2021; 373: 871-876Crossref PubMed Scopus (855) Google Scholar; Jumper et al., 2021Jumper J. Evans R. Pritzel A. Green T. Figurnov M. Ronneberger O. Tunyasuvunakool K. Bates R. Zidek A. Potapenko A. et al.Highly accurate protein structure prediction with AlphaFold.Nature. 2021; 596: 583-589Crossref PubMed Scopus (4264) Google Scholar). Computational optimization of side chain rotamers in experimentally derived models improves the agreement with the solution-state data and the accuracy of the resulting assignments (Nerli et al., 2021Nerli S. De Paula V.S. McShan A.C. Sgourakis N.G. Backbone-independent NMR resonance assignments of methyl probes in large proteins.Nat. Commun. 2021; 12: 691Crossref PubMed Scopus (12) Google Scholar). Care must be taken to select a methyl labeling scheme that produces the most unique LNs without compromising data quality. In the next section we discuss our strategy for selecting isotope labeling and sample preparation. The goal of any labeling strategy is to produce the highest number of unambiguous assignments with the fewest possible samples. Therefore, the strategy that maximize both the sensitivity and resolution of the spectra and the number of unique LNs is preferred. In a highly deuterated sample, sensitivity and resolution are dictated by the local proton density surrounding each methyl probe. Reagents are available to introduce Ala (Kerfah et al., 2015Kerfah R. Plevin M.J. Pessey O. Hamelin O. Gans P. Boisbouvier J. Scrambling free combinatorial labeling of alanine-beta, isoleucine-delta1, leucine-proS and valine-proS methyl groups for the detection of long range NOEs.J. Biomol. NMR. 2015; 61: 73-82Crossref PubMed Scopus (29) Google Scholar), Ileδ1/Ileγ2 (Ruschak et al., 2010Ruschak A.M. Velyvis A. Kay L.E. A simple strategy for (1)(3)C, (1)H labeling at the Ile-gamma2 methyl position in highly deuterated proteins.J. Biomol. NMR. 2010; 48: 129-135Crossref PubMed Scopus (59) Google Scholar), Met (Gelis et al., 2007Gelis I. Bonvin A.M. Keramisanou D. Koukaki M. Gouridis G. Karamanou S. Economou A. Kalodimos C.G. Structural basis for signal-sequence recognition by the translocase motor SecA as determined by NMR.Cell. 2007; 131: 756-769Abstract Full Text Full Text PDF PubMed Scopus (329) Google Scholar), Thr (Monneau et al., 2016Monneau Y.R. Ishida Y. Rossi P. Saio T. Tzeng S.R. Inouye M. Kalodimos C.G. Exploiting E. coli auxotrophs for leucine, valine, and threonine specific methyl labeling of large proteins for NMR applications.J. Biomol. NMR. 2016; 65: 99-108Crossref PubMed Scopus (24) Google Scholar; Velyvis et al., 2012Velyvis A. Ruschak A.M. Kay L.E. An economical method for production of (2)H, (13)CH3-threonine for solution NMR studies of large protein complexes: application to the 670 kDa proteasome.PLoS One. 2012; 7: e43725Crossref PubMed Scopus (70) Google Scholar), Leu and Val methyls (Goto et al., 1999Goto N.K. Gardner K.H. Mueller G.A. Willis R.C. Kay L.E. A robust and cost-effective method for the production of Val, Leu, Ile (delta 1) methyl-protonated 15N-, 13C-, 2H-labeled proteins.J. Biomol. NMR. 1999; 13: 369-374Crossref PubMed Scopus (439) Google Scholar). Reagents for pro-chiral (13CH3/12CD3) (Tugarinov and Kay, 2004Tugarinov V. Kay L.E. An isotope labeling strategy for methyl TROSY spectroscopy.J. Biomol. NMR. 2004; 28: 165-172Crossref PubMed Scopus (194) Google Scholar) or stereospecific (ProS or ProR) labeling of Leu and Val (Gans et al., 2010Gans P. Hamelin O. Sounier R. Ayala I. Dura M.A. Amero C.D. Noirclerc-Savoye M. Franzetti B. Plevin M.J. Boisbouvier J. Stereospecific isotopic labeling of methyl groups for NMR spectroscopic studies of high-molecular-weight proteins.Angew. Chem. Int. Ed. Engl. 2010; 49: 1958-1962Crossref PubMed Scopus (163) Google Scholar) are available to reduce the proton density and improve data quality in systems >100 kDa. Escherichia coli strains have been engineered to achieve new combinations of methyl labeling for spectral editing or relaxation studies (Monneau et al., 2016Monneau Y.R. Ishida Y. Rossi P. Saio T. Tzeng S.R. Inouye M. Kalodimos C.G. Exploiting E. coli auxotrophs for leucine, valine, and threonine specific methyl labeling of large proteins for NMR applications.J. Biomol. NMR. 2016; 65: 99-108Crossref PubMed Scopus (24) Google Scholar). Lastly, isotopomeric reagents with 13CD2H, 13CDH2 methyl for quantitative relaxation studies have been developed (Ollerenshaw et al., 2005Ollerenshaw J.E. Tugarinov V. Skrynnikov N.R. Kay L.E. Comparison of 13CH3, 13CH2D, and 13CHD2 methyl labeling strategies in proteins.J. Biomol. NMR. 2005; 33: 25-41Crossref PubMed Scopus (53) Google Scholar; Tugarinov and Kay, 2004Tugarinov V. Kay L.E. An isotope labeling strategy for methyl TROSY spectroscopy.J. Biomol. NMR. 2004; 28: 165-172Crossref PubMed Scopus (194) Google Scholar). In order to identify the best labeling strategy for accurate methyl assignment, we developed MAGIC-Net. This Python program calculates methyl connectivity network using common methyl labeling schemes (from simplest ILV to the full ILV plus AMT methyl complement) at 6-, 7-, and 10-Å cutoffs and including the pro-chiral and stereospecific Leu/Val labeling. MAGIC-Net uses the model to construct the methyl connectivity network and extract statistics regarding methyl type frequency, impact of each methyl type on protonation level, which methyl types are most often isolated, and the LN composition (ULN versus ALN) to determine the solvability as a function of distance cutoff and labeling scheme. To establish general trends, we employed MAGIC-Net to query 10 methyl-labeled datasets, including seven standard datasets in the literature (Nerli et al., 2021Nerli S. De Paula V.S. McShan A.C. Sgourakis N.G. Backbone-independent NMR resonance assignments of methyl probes in large proteins.Nat. Commun. 2021; 12: 691Crossref PubMed Scopus (12) Google Scholar; Pritisanac et al., 2020Pritisanac I. Alderson T.R. Guntert P. Automated assignment of methyl NMR spectra from large proteins.Prog. Nucl. Magn. Reson. Spectrosc. 2020; 118-119: 54-73Crossref PubMed Scopus (14) Google Scholar). Networks are analyzed at 6-Å (Figure 2), 7-Å (Figure S4), and 10-Å (Figure S5) methyl-methyl distance cutoff. Leu and Val are the most abundant methyl-bearing residues, followed by Ala, Ile, Thr, and Met (Figure 2A). This also reflects the relative impact on the proton density (Figure 2B). Ala and Thr are the most frequently isolated methyl groups, while Met is least likely to be isolated. As expected, the analyses showed that the number of ULNs increases as more methyl types are included (Figure 2D). The fraction of ULN drops from ∼60% of the total methyls to ∼35% in going from full ILVMAT labeling to ILV labeling. The discrete contributions to isolated, ALN, and ULN by methyl type are shown in Figures 2E–2G, S4E–S4G, and S5E–S5G. Ileδ1, Leu, and Val contribute the highest number of unique networks, followed closely by Met. Iδ1LVM methyls contribute to the highest fraction of ULN across all labeling schemes. Met, the least abundant methyl type, stands out as the highest contributor to methyl network uniqueness per unit residue. When pro-chiral (13CH3/12CD3) LV labeling is chosen, Met has the absolute highest fractional contribution to ULNs (Figure 2F). The number of ULNs and the total number of LNs is used to establish the minimal percentage assignment feasibility range. The 6-Å cutoff represents the “must-see” methyl-methyl contacts and is a conservative estimate of the number of ULNs and assignment feasibility for a given sample. Obviously, the 7- and 10-Å cutoffs (Figures S4 and S5) progressively reduce isolated methyls (iso) and expand the number of ULNs. However, this also increases the corresponding number of experimental NOEs and the spectral resolution required to define them. This number far exceeds the theoretical 1,104 NOE expected at 6 Å, and the 1,220 experimental NOEs we have measured in the FGFR3 example and limits the maximum percentage assignment obtainable. From these analyses we draw the following conclusions: (1) Met methyls should always be labeled in any preparation as they increase ULNs with a minimal impact on protonation levels and scale well to the largest systems. (2) Ala and Thr inclusion should be evaluated on a case-by-case basis as their contribution to ULNs is somewhat compromised by their high propensity for being isolated and generally low NOE content. The high order parameter of Ala and poor chemical shift dispersion, coupled with its high abundance, makes this amino acid unsuitable for high-molecular-weight systems. (3) The Leu/Val pro-chiral labeling effectively controls the DD relaxation of the geminal pair in large systems while maintaining high information content and high percentage of ULN but with sensitivity penalty since only 50% of the Leu/Val are labeled. (4) The 6–7-Å range gives the most conservative representation of methyl network versus experimental NOE data and is the most reliable predictor of assignment feasibility. In summ" @default.
• W4200568296 created "2021-12-31" @default.
• W4200568296 creator A5017276788 @default.
• W4200568296 creator A5018749514 @default.
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• W4200568296 date "2022-01-01" @default.
• W4200568296 modified "2023-10-13" @default.
• W4200568296 title "Progress toward automated methyl assignments for methyl-TROSY applications" @default.
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