biochemistry copyright © 2018 a functional proteomics ......cornett et al., ci. adv. 2018 4 :...

Cornett et al., Sci. Adv. 2018; 4 : eaav2623 28 November 2018

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B I O C H E M I S T R Y

A functional proteomics platform to reveal the sequence determinants of lysine methyltransferase substrate selectivityEvan M. Cornett1, Bradley M. Dickson1, Krzysztof Krajewski2, Nicholas Spellmon3, Andrew Umstead4, Robert M. Vaughan1, Kevin M. Shaw1, Philip P. Versluis1, Martis W. Cowles5, Joseph Brunzelle6, Zhe Yang3, Irving E. Vega4, Zu-Wen Sun5, Scott B. Rothbart1*

Lysine methylation is a key regulator of histone protein function. Beyond histones, few connections have been made to the enzymes responsible for the deposition of these posttranslational modifications. Here, we debut a high-throughput functional proteomics platform that maps the sequence determinants of lysine methyltransferase (KMT) substrate selectivity without a priori knowledge of a substrate or target proteome. We demonstrate the predictive power of this approach for identifying KMT substrates, generating scaffolds for inhibitor design, and predicting the impact of missense mutations on lysine methylation signaling. By comparing KMT selectivity pro-files to available lysine methylome datasets, we reveal a disconnect between preferred KMT substrates and the ability to detect these motifs using standard mass spectrometry pipelines. Collectively, our studies validate the use of this platform for guiding the study of lysine methylation signaling and suggest that substantial gaps exist in proteome-wide curation of lysine methylomes.

INTRODUCTIONReversible protein posttranslational modifications (PTMs) (e.g., acetyl-ation, ubiquitination, phosphorylation, and methylation) are key regulators of protein activity, stability, subcellular localization, and molecular interactions (1–4). While lysine methylation was found more than a half century ago (5), the study of protein signaling through this PTM did not intensify until the early 2000s when his-tone lysine methylation was connected to transcriptional regulation (6). Since then, substantial effort has been devoted to the study of lysine methylation signaling, primarily in the context of histone proteins.

Methylation on the -amine of the lysine side chain is catalyzed by a family of approximately 60 lysine methyltransferase (KMT) en-zymes (4). While numerous KMTs are bona fide histone methyltrans-ferases, many have little or no activity toward histone proteins. The number of identified KMTs with nonhistone substrates is steadily growing (7) but is being outpaced by the discovery of methylated pro-teins by mass spectrometry (MS). More than 6000 unique sites of lysine methylation on more than 3000 unique human proteins have been identified by MS (8), but very few of these identified sites have been linked to a KMT. The inability to connect KMTs to their pre-ferred substrates is a barrier to fully appreciating the biological roles of lysine methylation.

Here, we report on the development of a functional proteomics platform to enable rapid mapping of KMT substrate selectivity with-out a priori knowledge of a substrate. Our approach uses a lysine-

oriented peptide library (K-OPL) to generate a KMT substrate selectivity profile (±3 amino acids from a fixed central lysine) for any KMT. Se-quence maps are used to rank all lysine-centered motifs in any pro-teome of interest by the likelihood of its use as a substrate. Variations of this positional scanning technology have been used to map the sub-strate selectivity of kinases and arginine methyltransferases (9, 10).

To validate the K-OPL approach for KMTs, we confirm and ex-pand upon the known substrate motifs of G9a (EHMT2/KMT1C), SET7/9 (SETD7/KMT7), and SMYD2 (KMT3C). We further demon-strate how K-OPL data can be used to reveal novel and kinetically distinct substrates for these enzymes, to discover inhibitor scaffolds, and to identify cancer-associated missense mutations that may mod-ulate lysine methylation signaling by altering, removing, or creating new KMT substrates. Notably, we discover that the substrates most preferred by the enzymes characterized in this study are difficult to detect using standard bottom-up MS proteomics pipelines. The im-plications of these observations are important for the future study of lysine methylation signaling and suggest that the current com-pendium of lysine methylation sites curated from MS proteomics datasets may be substantially underrepresented. Overall, this study validates the use of the K-OPL platform for mapping KMT substrate selectivity and demonstrates ways in which data generated with this platform can guide the biochemical and biological study of lysine methylation signaling.

RESULTSDevelopment of a KMT screening platform that queries K-OPLThe K-OPL used in this study consisted of approximately 47 million unique peptides. Each peptide was nine amino acids long, oriented around a lysine residue at the fifth position (Fig. 1A). Fixed N- and C-terminal glycine residues were included for spacing, and each peptide was C-terminally functionalized with triethylene glycol–biotin to enable surface immobilization. Peptides were divided into 114 sets.

1Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA. 2Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. 3Department of Microbiology, Immunolo-gy, and Biochemistry, Wayne State University School of Medicine, Detroit, MI 48201, USA. 4Department of Translational Science and Molecular Medicine and In-tegrated Mass Spectrometry Unit, College of Human Medicine, Michigan State Uni-versity, Grand Rapids, MI 49503, USA. 5EpiCypher Inc., Research Triangle Park, NC 27709, USA. 6Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA.*Corresponding author. Email: [email protected]

Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

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Each set was made by fixing an additional amino acid ±3 from the central lysine and varying the other five positions uniformly among 19 synthetically tractable natural amino acids (excluding cysteine). On the basis of structural information for numerous KMTs, lysine demeth-ylases (KDMs), and readers bound to histone peptides, we rationalized that three residues flanking either side of the central lysine provide a sufficient footprint for target engagement while also minimizing the total number of sets required to capture the selectivity of a KMT.

To measure enzyme activity, we developed a scintillation prox-imity assay (SPA), which quantified the transfer of tritium-labeled methyl groups from the cofactor and methyl donor S-adenosylme-thionine (SAM) to each K-OPL set (Fig. 1B). SPAs permit sensitive analysis of enzymatic activity and are advantageous when the reaction product is unknown. This platform enabled us to rapidly determine a KMT’s preference for any amino acid (except cysteine) at any position ± three positions from a target lysine.

Generation of KMT substrate selectivity profiles with K-OPLTo test whether the K-OPL platform could derive the sequence de-terminants of KMT substrate selectivity, we profiled G9a, SET7/9, and SMYD2; each of which have been reported to methylate distinct histone and nonhistone substrates. The substrate selectivity profiles obtained using K-OPL were unique for each of these three KMTs (Fig. 2, A to C). Each profile was consistent with the amino acid com-position of previously identified substrates, and new selectivity in-formation was also found.

G9a catalyzes monomethylation and dimethylation of lysine 9 on histone H3 (H3K9me1/me2) (11) and has several reported nonhis-tone substrates, including K1162me3 on WIZ, K135me3 on CDYL1, and K654me2 on ACINUS (12). The reported G9a substrate motif, XAR-K-SXX, corresponds to the sequence surrounding H3K9 and many of its nonhistone substrates. The K-OPL sets that correspond to this sequence (P−2 A, P−1 R, and P+1 S) were among the most methylated by G9a (Fig. 2A and fig. S1, A and B). Of all the sets with a fixed amino acid at the P−3 position, the set with fixed threonine (which corresponds to the sequence context surrounding H3K9) was the third most used. The G9a substrate selectivity profile derived by K-OPL also revealed preference for amino acids at the P+2 and P+3 positions, which did not match the sequence surrounding H3K9.

For example, the sets containing hydrophobic amino acids (I, L, V, M, and F) at the P+2 position were good substrates (Fig. 2A), a finding con-sistent with the previous analysis of G9a substrate preference (12). Preferential substitutions to the H3K9 sequence at the P+1, P+2, and P+3 positions (TARKRFK) created a more efficient substrate than H3K9 or WIZ (Fig. 2D). In addition, substitutions in the TARKRFK sequence to amino acids predicted by the K-OPL selectivity profile to be less favorable (threonine to proline in the P−3 position or alanine to glycine in the P−2 position) resulted in substrates with severely reduced methylation rates (Fig. 2D).

SET7/9 was first reported to monomethylate H3K4 (H3K4me1) (13) but was later shown to target K189me1 on TAF10, K372me1 on p53, and numerous other proteins (14). Based on validated substrates and structural studies, the consensus SET7/9 motif is X[KR][STA]-K-pXX, where p denotes a polar amino acid. Like G9a, the K-OPL substrate selectivity profile for SET7/9 was consistent with the re-ported motif (Fig. 2B and fig. S1, C and D). The P−1 serine K-OPL set was the most used at this position. In the P+2 position, several K-OPL sets with polar amino acids were well used, including lysine, arginine, histidine, asparagine, glutamine, and serine. The SET7/9 K-OPL footprint suggested that H3K4 is not an optimal substrate. The optimal SET7/9 substrate predicted by K-OPL screening was RRSKRRK, a sequence that maps to K477 of SCN5A (the subunit of the cardiac sodium channel). SCN5A K477 was methylated at a faster rate than p53 K372 (Fig. 2E). As predicted by the K-OPL sub-strate selectivity profile, substitution of the P−1 serine in RRSKRRK for glutamic acid resulted in loss of any detectable methylation.

SMYD2 has been reported to monomethylate or dimethylate histone H3 (H3K4me1 and H3K36me2) (15, 16), p53 (K370me1) (17), RB (K860me1) (18), MAPKAPK3 (K355me1) (19), and numer-ous other proteins (20). It has been reported that SMYD2 prefers a XX[LFM]-K-SXX motif, and all K-OPL sets corresponding to this reported motif were among the most methylated by SMYD2 (Fig. 2C and fig. S1, E and F). Despite initial reports that SMYD2 methylates H3K4 and H3K36, K-OPL analysis suggests that both would be poor substrates. In vitro methyltransferase assays using recombinant human mononucleosomes confirmed that unlike G9a, recombinant human nucleosomes are poor SMYD2 substrates compared to a pool of all K-OPL sets (fig. S2A).

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Fig. 1. K-OPL platform for mapping KMT substrate selectivity. (A) Composition and design of the K-OPL. (B) Cartoon depiction of the SPA developed for screening the activities of KMTs with K-OPL. TEG, triethylene glycol; SAM, S-adenosylmethionine.


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To validate the SMYD2 K-OPL screen, a series of peptides were synthesized on the basis of the optimal substrate, WKLKSKR. This exact sequence is not found in any human protein. However, substi-tution of the P−3 tryptophan with arginine corresponds to lysine 798 of PER2 (RKLKSKR), a transcriptional repressor and core component of the circadian clock. As predicted by the K-OPL substrate selectivity profile, a peptide corresponding to PER2 K798 was a more efficient substrate than peptides corresponding to previously reported SMYD2 substrates p53 K370 and MAPKAPK3 K355 (Fig. 2F). Substitution of aspartic acid in the P−1 position, glutamic acid in the P+1 position, or aspartic acid in the P−3 position reduced the rate of methylation by SMYD2, while substitution to tryptophan at the P−3 position in-creased the rate of methylation (Fig. 2F).

To further validate specific KMT peptide substrates revealed by K-OPL, we performed comparative rate measurements for the best SMYD2, G9a, and SET7/9 substrates with each enzyme (fig. S2B). These results demonstrate that sequence compositions revealed by K-OPL screening are highly enzyme specific.

All three KMTs screened in this study preferred basic residues near the central lysine. The resulting substrates predicted by the K-OPL selectivity map often contained more than one lysine residue, including the optimal substrates tested for SMYD2 (RKLKSKR), G9a (TARKRFK), and SET7/9 (RRSKRRK). To rule out the possi-

bility that multiple methylation events on single peptides contributed to the increased substrate utilization seen in our assays, we performed MS analysis on the products of these methylation reactions. G9a meth-ylation of TARKRFK resulted in both monomethyl and dimethyl products (Fig. 3A). Tandem MS (MS/MS) analysis of both products indicated that only the central lysine of this substrate was methylated (fig. S3, A and B). For SET7/9 and SMYD2, MS analysis detected a mass shift consistent with the addition of a single methyl group (Fig. 3, B and C), and MS/MS confirmed that only the central lysine was monomethylated (fig. S3, C and D).

Collectively, K-OPL analysis accurately identified and extended known sequence motifs for G9a, SET7/9, and SMYD2. In addition, K-OPL substrate selectivity profiles accurately predicted how changes in the substrate sequence negatively or positively affected its rate of methylation. These data validate the use of K-OPL for profiling KMT substrate selectivity and provide the first high-resolution maps of the amino acid preference for G9a, SET7/9, and SMYD2. Notably, these results suggest that the most studied substrates for these three en-zymes are not the most robust.

Structural and kinetic analysis of SMYD2-substrate interactionsWe focused downstream efforts on SMYD2, in part, because its profile revealed that its most well-appreciated substrate, p53 K370,

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Fig. 2. K-OPL reveals the substrate selectivity of G9a, SET7/9, and SMYD2. K-OPL substrate selectivity profiles for G9a (A), SET7/9 (B), and SMYD2 (C). Mean results of two independent K-OPL SPA screens for each enzyme are reported as position-normalized heat maps (see fig. S1 for global normalized heat maps and raw K-OPL data). The color code is proportional to the creation of enzyme product, where red (1) is most active and blue (0) is least active. Rows show the identity of each fixed residue, and columns show the position within the sequence. Initial rate measurements with peptides corresponding to known and newly identified substrates for G9a (D), SET7/9 (E), or SMYD2 (F). cpm, counts per minute. Point mutations predicted to decrease or increase the rate of methylation are indicated in red or green, respectively. Data points are shown as the mean of three independent measurements, and error is presented as ±SEM. For some data points, error bars are masked by the symbol weight. on July 31, 2021


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contains several nonoptimal residues. In addition, small-molecule in-hibitors for a variety of clinical indications are currently under de-velopment for SMYD2 (21). These therapeutic efforts are primarily based on the biological connection that SMYD2 methylates the tumor suppressor protein p53, leading to inhibition of p53 function (17). The knowledge of all SMYD2 targets will be important for under-standing the clinical outcomes of SMYD2 inhibition.

To determine the molecular basis for SMYD2 substrate selectiv-ity, we first solved a 2.7-Å x-ray crystal structure of SMYD2 bound to S-adenosylhomocysteine (SAH) and a peptide with the sequence GWKL-Nle-SKRG (Fig. 4, A and B; fig. S4, A and B; and table S1). This peptide corresponded to the optimal K-OPL–derived SMYD2 substrate with the central lysine substituted for norleucine (Nle), a methyl-lysine mimic that stabilizes KMT-substrate complexes (22). The P−3 tryptophan was not resolved in the structure, sug-gesting conformational flexibility in this position (fig. S4A). Un-expectedly, the peptide conformation was nearly identical to previous SMYD2-SAH-peptide structures (Fig. 4B and fig. S4B) (23–25), offer-ing little insight as to why PER2 or WKLKSKR are better substrates than p53.

Next, we collected a series of 0.5-s molecular dynamic (MD) tra-jectories (3 s in total) of SMYD2 in complex with various substrates (Fig. 4C). As a starting template, we used the structure of SMYD2 bound to a p53 peptide (PDB: 3TG5), which resolved residues be-tween the P−2 and P+3 positions relative to p53 K370. The unre-solved amino acid in the P−3 position was modeled, and all-atom MD simulations of this structure in complex with the peptides used for in vitro KMT assays allowed us to evaluate the dynamics of SMYD2- substrate interactions. The RMSD of the C atoms in the peptide back-bone throughout the simulation (Fig. 4C, x axis) provided a proxy for peptide stability, or off-rate, because simulations began in the bound state. Integration of this metric with in vitro KMT reaction rate mea-surements (Fig. 2F) led to the hypothesis that coordination of SMYD2 substrates and catalytic turnover are related by a quasi-concave function (Fig. 4C). In this model, loosely coordinated peptides (i.e., RKDKSKR) are poor substrates because the enzyme-substrate in-teraction is too weak to organize the substrate for catalysis. Tightly coordinated peptides (RKLKEKR) are also poor substrates because of their slow off-rates, leading to inefficient substrate turnover. Op-

timal substrates (KLKSKR and WKLKSKR) are organized to allow for both efficient catalysis and rapid turnover.

The MD simulations also identified unique interactions for the SMYD2 substrates PER2 and WKLKSKR. The PER2 peptide quick-ly moved to an alternate conformation to make stabilizing contacts at the P−3 position. In this conformation, the P−3 arginine formed a salt bridge with D151 of SMYD2 (fig. S4C). The P−3 tryptophan of the WKLKSKR peptide settled in a hydrophobic pocket (fig. S4D) near the helix that contained D151. This is the same pocket occu-pied by AZ506, a recently found small-molecule inhibitor of SMYD2 (fig. S4E) (26). Overall, the MD simulations revealed unique inter-actions for both PER2 and WKLKSKR, which likely contribute to the faster methylation rates observed for these peptides.

Next, we performed kinetic analysis to further investigate what makes PER2 and WKLKSKR better SMYD2 substrates than p53. SMYD2 methylation of p53 adhered to classical MM kinetics (Fig. 4D). However, SMYD2 methylation of PER2 and WKLKSKR had a strik-ingly different kinetic profile, consistent with substrate inhibition. In-creased concentrations of PER2 or WKLKSKR resulted in decreased rates of methylation.

Because the MD simulations revealed unique P−3 conformations for the non-MM substrates (Fig. 4C and fig. S4, C and D), we next questioned whether these interactions contribute to the observed substrate inhibition kinetics. In MD simulations, substitution of an aspartic acid in the P−3 position prevented the peptide from interact-ing with SMYD2 in the same manner as PER2 or WKLKSKR (Fig. 4C and fig. S4, C and D), and SMYD2 methylation of DKLKSKR followed a classical MM model (Fig. 4D). Together, these observations sug-gest that the amino acid composition of the P−3 position modulates the conformation of SMYD2 substrates and the kinetics of SMYD2.

K-OPL as a tool for discovering scaffolds for rational KMT inhibitor designReplacement of a substrate’s target lysine with Nle has been pre-viously used as an inhibitory strategy for KMTs (22). As a proof of concept for K-OPL–guided discovery of KMT inhibitors, we synthesized Nle-containing peptides based on the optimal SMYD2 substrate and found that the Nle derivative of WKLKSKR inhibited SMYD2 methylation of PER2 (Fig. 4E). RKDKSKR and RKLKEKR

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Nle derivatives were less effective, as predicted by K-OPL analysis. However, although RKLKEKR and RKDKSKR were equally poor SMYD2 substrates (Fig. 2F), RKLNleEKR was a more efficient com-petitive inhibitor than RKDNleSKR (Fig. 4E). This result was con-sistent with the MD simulation that showed that RKLKEKR formed a more stable complex with SMYD2 (Fig. 4C). Overall, these results suggest that optimal substrates identified by K-OPL screens can serve as scaffolds for further optimization toward more potent inhibitors.

Using K-OPL to identify new SMYD2 substratesTo identify new KMT substrates, we developed a lowest bin (LoB) scoring function based on K-OPL selectivity profiles to rank lysine-

centered 7-mer sequences from annotated proteomes. LoB scores are equal to the raw signal from the lowest K-OPL set used to construct an entire 7-mer sequence, e.g., consider PER2 K798 (RKLKSKR). The score for this sequence is assigned by the arginine in the P−3 posi-tion, as this set has the lowest signal in this sequence (fig. S1, E and F). The LoB score was purposely designed to minimize false positives and contains no positional weighting common to other motif scoring functions (27, 28).

A candidate list of six proteins (PER2, PRDM11, CDC5L, GDAP1, ZPK, and ATP6V1G3) from the top 50 LoB-scored sequences (table S2) was selected for in vitro validation as SMYD2 substrates. All six proteins were methylated by SMYD2 (Fig. 5A and fig. S5A). ZPK and GDAP1 were poor SMYD2 substrates (fig. S5A). Available structural data for ZPK showed that the target lysine is in a structured helix that likely prohibited methylation by SMYD2 (fig. S5B), but no struc-tural data were available to rationalize why GDAP1 is not a more robust SMYD2 substrate. To determine whether the lysine predicted by the K-OPL screen was methylated, we generated protein sub-strates with the target lysine substituted to arginine (K to R). All K to R mutant substrates had reduced methylation (Fig. 5A and fig. S5A). For CDC5L, ATP6V1G3, and PER2, the K to R mutation did not com-pletely abolish methylation, suggesting that additional residues are also being methylated. Consistent with rate measurements for the MAPKAPK3 K355 peptide (Fig. 2F), methylation of full-length re-combinant MAPKAPK3 was weaker than most of the newly identi-fied SMYD2 substrates (Fig. 5A), requiring a much longer exposure to detect methylation of this protein (fig. S5A).

Overall, SMYD2 methylated four of the newly identified substrates (PER2, PRDM11, CDC5L, and ATP6V1G3) at least as efficiently as the known substrate, p53. These results validate the use of K-OPL selectivity profiles to identify new KMT substrates.

K-OPL analysis predicts the impact of missense mutations on substrate usageMissense mutations have been shown to alter kinase signaling net-works, including mutations that cause amino acid substitutions in prox-imity to the modified residue (29). We sought to determine whether K-OPL selectivity profiles could predict the impact of missense mu-tations on a KMT substrate at the protein level. Guided by the SMYD2 K-OPL profile, we generated a PRDM11 mutant (K89D), predicted to render this robust SMYD2 substrate deficient. In an in vitro KMT assay, PRDM11 K89D methylation was reduced compared to the wild-type PRDM11 (Fig. 5B). In addition, mutations predicted to en-hance methylation of the weak SMYD2 substrate MAPKAPK3 (D353R and T356S) improved methylation of this protein (Fig. 5B). These results show that K-OPL–derived selectivity profiles accurately predict how single amino acid changes near the target lysine can significantly affect the efficiency of a KMT substrate at the protein level.

We next turned our attention to predicting how reported missense mutations in primary human cancer sequencing datasets might re-wire lysine methylation signaling networks because of substitutions within SMYD2 substrate motifs. To do so, we catalogued and analyzed K-centric 7-mer amino acid sequences on a proteome-wide scale. Comparison of the LoB scores for SMYD2 targets in the normal pro-teome (UniProt) with the oncoproteome (COSMIC) (30) resulted in the identification of four classes of missense mutations that may affect SMYD2 lysine methylation signaling. The four classes include mutations that (i) weakened, (ii) strengthened, (iii) created, or (iv) had no effect on a target of methylation (Fig. 5C and table S3). These

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Fig. 4. Structural and kinetic analysis of SMYD2. (A) Hybrid ribbon-surface rep-resentation of SMYD2 (white) bound to SAH and GWKLNleSKRG (Nle, norleucine) (blue sticks). Costructure has been deposited in the Protein Data Bank (PDB) as PDB: 6MON. (B) Overlay of peptide substrates from SMYD2-GWKLNleSKRG (PDB: 6MON) and SMYD2-p53K370 (PDB: 3TG5) structures. (C) Scatterplot comparing the relationship between the methylation rate calculated from Fig. 2F and the dynamics of substrate coordination by SMYD2. Root mean square displace-ment (RMSD) of the C atoms of the indicated peptides was calculated from 500-ns whole-atom MD simulations. Peptide orientations at several time points over the course of the MD simulations are shown, indicated by color with the corresponding color scale (top left). (D) Kinetic analysis of SMYD2 methylation of p53, PER2, and PER2 derivative substrates. Data points are the mean of three independent measurements, and error is presented as ±SEM. PER2 and WKLKSKR were fit to a substrate inhibition kinetic model. DKLKSKR and SHLKSKK were fit to a standard Michaelis-Menten (MM) model. (E) IC50 (median inhibitory concentration) measurements of Nle peptide in-hibitors of SMYD2 using PER2 as a substrate. Data points are the mean of three independent measurements, and error is presented as ±SEM.


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results demonstrate the utility of K-OPL datasets for prioritizing the study of cancer-associated missense mutations based on their predicted functional relationship with lysine methylation signaling. Further-more, these studies suggest that the lysine methylome of an individual cancer cell may be altered due to missense mutations.

K-OPL analysis reveals gaps in the lysine methylomeA recent study used MS to identify 35 proteins with monomethyla-tion sites that consistently decreased upon loss of SMYD2 activity (20). Surprisingly, these 35 proteins fall well outside our top 50 LoB-scored substrates (table S2). The physical properties of MS-identified substrates are distinct from the top 50 LoB-scored substrates. Most of the top 50 K-OPL–predicted substrates are enriched with lysine and arginine residues, whereas nearly all of the top 35 substrates from the Olsen et al. study (20) are depleted of these amino acids (Fig. 6A). The density of lysine and arginine positively correlates with hydrophilicity and is a proxy for the number of trypsin cut sites, all confounding the

detection of these substrate motifs by MS. Thus, either the proteins that K-OPL identifies as ideal substrates are never methylated in cells or they are undetectable using standard bottom-up MS pipelines.

To test the latter hypothesis, we performed bottom-up MS analysis of recombinant PER2. Despite achieving more than 70% sequence coverage of the recombinant protein (fig. S6, A and B), we were un-able to detect peptides corresponding to the region encompassing the newly identified K798 substrate for SMYD2. This suggests that even if PER2 or other basic motifs predicted by K-OPL analysis were meth-ylated in cells, they would not be detected by MS.

We note that in addition to SMYD2, SET7/9 and G9a also prefer lysine- and arginine-rich substrates (Fig. 2). While this is a small sam-pling of KMT activities, analysis of the lysine and arginine content of human lysine methylation proteomics data curated in PhosphoSitePlus (8) revealed that the bias seen in the Olsen et al. study is reflective of the annotated lysine methylome (Fig. 6B). While it is well appreciated that lysine- and arginine-rich sequences are challenging to detect

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Fig. 5. Novel SMYD2 substrates identified with K-OPL. (A) Representative in vitro SMYD2 methyltransferase assay with known and predicted protein substrates. K to R refers to a missense mutation (lysine to arginine) introduced at the target lysine. Coomassie-stained gel is shown on top in blue, and 3H fluorography is shown on the bottom. (B) Representative in vitro SMYD2 methyltransferase assay with mutant forms of PRDM11 and MAPKAPK3 substrates predicted to decrease or increase substrate efficiency, respectively. WT, wild-type. (C) Scatterplot of the LoB score for SMYD2 methylation motifs that are created (blue), weakened (red), strengthened (green), or unchanged (black) by missense mutations found in primary human cancer sequencing data.


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by MS (31), K-OPL analysis demonstrates that the enzymes studied here have a strong bias for substrates with the exact sequence com-positions that escape MS detection. This key discovery suggests that the current compendium of lysine methylation sites curated from MS proteomics datasets is incomplete.

DISCUSSIONOur study establishes K-OPL as a functional proteomics platform for mapping KMT substrate selectivity by quantitatively measuring ami-no acid preference ± three positions from the target lysine. In addi-tion to the identification of new substrates, K-OPL can be used as a tool for discovering new KMT inhibitor scaffolds. K-OPL analysis of SMYD2 substrate selectivity revealed that the optimal substrate is not found in the human proteome (WKLKSKR) due to the trypto-phan in the P−3 position. A Nle derivative of the optimal substrate with no further modification was a modest inhibitor of SMYD2 activity. MD simulations showed that the tryptophan settles in a hydrophobic pocket near the SMYD2 active site, and a recently found small-molecule inhibitor of SMYD2 contains a bulky aromatic group that also binds in this same pocket. Thus, the K-OPL screen-ing platform identified a SMYD2-substrate interaction that would never have been found through the analysis of previously identi-fied SMYD2 substrates. Use of K-OPL analysis to generate scaf-folds for inhibitor development is an exciting future application, especially for KMTs with no known inhibitors.

In addition, the K-OPL platform can be applied to understand the impact of missense mutations on lysine methylation signaling. Our analysis revealed that missense mutations in human cancer can have a substantial impact on the lysine methylation signaling network for SMYD2. Missense mutations that strengthen, weaken, create, or destroy new SMYD2 substrates are abundant in human cancer cells. Already, this limited analysis motivates future work mapping how the lysine methylome changes as a consequence of missense mutations. As K-OPL profiling of additional KMTs is completed, this integrative analysis will become increasingly useful for formulating hypotheses on the role of lysine methylation in human disease.

K-OPL profiling of SMYD2 enabled the identification of new sub-strates. Identifying KMT substrates is a major challenge, and previ-

ously reported approaches have limitations that the K-OPL platform circumvents. For example, qualitative tolerance assays have been used to determine the impact of individual amino acids on a known KMT substrate and have been used successfully to identify new targets for G9a, SET7/9, and other KMTs (12, 32). The K-OPL platform does not require any known substrate, which may help identify substrates of orphan KMTs, such as members of the enigmatic PR domain–containing family. We note that the K-OPL screening approach does not consider some significant contributions to substrate selectivity that may be present in a cell, such as complex membership, expression levels, tissue specificity, and subcellular localization. Careful consid-eration of these additional contributions to substrate selectivity can be used to further prioritize candidate substrates.

In addition to tolerance assays, new KMT substrates have been identified using MS analysis coupled with genetic or chemical genetic approaches. MS-based approaches suffer from two technical limita-tions. First, pan–methyl-lysine affinity reagents, required for enrich-ment before MS analysis, often contain a sequence bias, masking some of the lysine methylome (10, 33). The K-OPL approach does not require an affinity enrichment step. Second, MS-based proteomics pipelines analyze peptides from primarily trypsin-digested cell lysates. A recent analysis of proteomics data deposited in the Global Proteome Machine Database (GPBdb) showed that 96% of all data were derived from trypsin-digested samples (31). All three of the KMTs screened in this study prefer arginine and lysine residues surrounding the substrate lysine. Tryptic digestion of the optimal sequence motifs produces pep-tides that are not detected by MS. Our analysis of a recent MS-based study of SMYD2 (20) demonstrates that the identified substrates are lysine and arginine deficient. Furthermore, we could not detect PER2 K798 by MS despite excellent sequence coverage, exemplifying the difficulty to detect lysine- and arginine-rich sequences by MS. K-OPL analysis of G9a, SET7/9, and SMYD2 reported in this study, and analy-sis of reported nonhistone substrates for other KMTs (fig. S6C), suggest that the annotated human lysine methylome is likely incomplete due to the basic sequence composition of the most preferred methylation motifs for these enzymes. This study highlights the need for develop-ing new MS-based methods to detect lysine methylation in sequence compositions that are preferentially modified by KMTs but are un-detectable by standard bottom-up MS pipelines.

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Fig. 6. K-OPL analysis reveals a gap in MS-based lysine methylation datasets. (A) Comparison of the arginine and lysine content between the top 50 K-OPL–predicted SMYD2 substrates (blue) and the 35 substrates identified using MS (red) (Olsen et al.). (B) Lysine and arginine content in the entire lysine methylome as curated by PhosphoSitePlus (8).


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Our knowledge of the enzymes responsible for the addition of nonhistone lysine methylation is lacking. Comprehensive mapping of KMT substrates is a critical step toward the goal of understand-ing the many biological roles of lysine methylation. Progress toward this important and challenging goal will require the development of new technologies and techniques to study lysine methylation. This report validates the use of K-OPL as a robust substrate selectivity screening platform that can now be used to further guide the study of lysine methylation signaling.

MATERIALS AND METHODSRecombinant protein productionSET7/9 was purchased from New England BioLabs (catalog no. M0223). G9a (amino acids 913 to 1210) was produced as a 6XHis N-terminal fusion, and SMYD2 (full length) was expressed as a N-terminal glu-tathione S-transferase (GST) fusion. MAPKAPK3 (amino acids 1 to 520) (catalog no. 131688) and GDAP1 (full length) (catalog no. 162725) were obtained from Abcam. PRDM11 (amino acids 79 to 314) (Addgene plasmid no. 32858) and full-length human TP53 (Addgene plasmid no. 24859) were gifts from C. Arrowsmith. PER2 (amino acids 691 to 900) was subcloned into a modified pQE vector as an N-terminal maltose-binding protein (MBP)–His fusion. MAPKAPK3 (full length), ATP6V1G3 (full length), and CDC5L (amino acids 150 to 280) were subcloned into pGEX-6P2 (GE Healthcare) as N-terminal GST fu-sions. Point mutations were generated by QuikChange Site-Directed Mutagenesis (Stratagene). All expression constructs were transformed into Escherichia coli BL21(DE3) and grown in LB media (Caisson) at 37°C. When the OD600 (optical density at 600 nm) reached 0.6 to 0.8, the temperature was lowered to 16°C, isopropyl--d-thiogalactopyranoside was added (0.5 mM), and incubation was continued overnight with shaking. Bacteria were harvested by centrifugation and either frozen at −80°C or used immediately. Protein was purified with either gluta-thione agarose (GE Healthcare) or TALON resin (Clontech) accord-ing to the manufacturer’s protocol.

K-OPL synthesisAll 114 peptide sets were synthesized on a PTI Symphony peptide synthesizer using Fmoc chemistry. The sets were synthesized on Biotin- PEG NovaTag resin (10 mol per set; MillporeSigma no. 855055) using a single 70-min coupling with 12-fold excess of coupling mix-ture (amino acids/HATU/3-eq N-methylmorpholine) and 2× 10-min deprotection with 20% piperidine in N,N′-dimethylformamide (DMF). Degenerate positions were synthesized using a mixture of 19 Fmoc- protected l-amino acids (cysteine was excluded) at molar ratios con-sistent with coupling efficiency, as previously described. After final Fmoc deprotection, resins were washed (3× DMF, 3× dichlorometh-ane, and 3× methanol) and left overnight under high vacuum. Resins were mixed 2 hours with 0.5 ml of cleavage mixture (92.5% triflu-oroacetic acid, 2.5% H2O, 2.5% triisopropylsilane, and 2.5% 1,2- ethanedithiol) and precipitated with cold diethyl ether. Precipitates were washed with diethyl ether and separated by centrifugation. The washing procedure was repeated five times. After separation, the precipitates were air dried for 5 min, dissolved in 1 ml of 50% acetonitrile, frozen at −80°C, and dried on a speed-vac overnight. To assess the quality of the libraries, matrix-assisted laser desorption/ionization–time-of-flight (MALDI-TOF) MS spectra were collected for each library using SCIEX TOF/TOF 5800 MALDI MS spectrometer and compared with theoret-ical mass distributions (analytical data are available upon request).

SPA for KMTsReactions (10 l) containing 1 g of KMT, 1 g of a K-OPL set, and 1 Ci of 3H-SAM (PerkinElmer) in KMT reaction buffer [50 mM tris (pH 8.8), 5 mM MgCl2, and 4 mM dithiothreitol] were incubated for 1 hour at room temperature. Reactions were stopped by adding trifluoracetic acid to a final concentration of 0.5%, neutralized by di-luting with 135 l of 50 mM NaHCO3, and transferred to streptavidin- coated FlashPlates (PerkinElmer). Plates were incubated for 15 min, sealed, and counted in a MicroBeta2 liquid scintillation counter (PerkinElmer) for 1 min per sample. Initial rate measure and kinetic analysis were performed using the same procedure with 200 nM SMYD2, 50 M SAM (5:1 cold/hot ratio), and 80 M substrate (initial rate measurements) or as indicated (kinetic analysis). IC50 measure-ments were performed using the same conditions with 5 M PER2 peptide as a substrate.

In vitro KMT reactionsReactions (10 l) containing 1 g of KMT, 1 g of the indicated sub-strates, and 1 Ci of 3H-SAM (PerkinElmer) in KMT reaction buf-fer were incubated for 1 hour at room temperature. Reactions were quenched by the addition of SDS loading buffer and resolved by SDS–polyacrylamide gel electrophoresis. Following the detection of total protein by Coomassie staining, gels were treated with EN3HANCE (PerkinElmer) and dried, and methylated proteins were detected by autoradiography.

MALDI-TOF-MS analysis of KMT reactionsFor MS experiments, 200 nM KMT, 80 M peptide, and 50 M SAM were incubated in KMT reaction buffer for 1 hour at room temperature. Reactions were quenched with 0.5% trifluoroacetic acid and analyzed by MS. KMT-reacted samples were deposited on a MALDI target plate (4 l per spot) and mixed with 1 l of matrix solution (-cyano- 4-hydroxycinnamic acid in 50% acetonitrile). MALDI-TOF-MS and MS/MS (positive ion mode at 1 kV) spectra were collected using SCIEX TOF/TOF 5800 MALDI MS spectrometer. The peptide fragmenta-tion modeling and peak assignments were done using the Peptide Sequence Fragmentation Modeling tool (https://omics.pnl.gov/software/molecular-weight-calculator).

LC-MS/MS analysis of PER2Recombinant MBP-tagged PER2 was expressed and purified as described above. Three micrograms of MBP-PER2 was buffer exchanged into 25 mM ammonium bicarbonate (pH 8.0). The sample was dried using a speed-vac, reconstituted in 25 mM ammonium bicarbonate (pH 8.0): 50% acetonitrile, and incubated at 37°C for 1 hour. Trypsin, Arg-C, or Asp-N (500 ng each; Promega) was added, and samples were digested overnight at 37°C. The resulting peptides were dried and reconsti-tuted in 25 mM ammonium bicarbonate:5% acetonitrile. Samples were loaded onto a C18 column (2-m particles, 25-cm by 75-m inner diameter) and eluted using a 2-hour acetonitrile gradient into a Q Exactive HF-X mass spectrometer, equipped with a nanospray source (flow at 350 nl/min). Full MS resolutions were set to 60,000 at 200 m/z (mass/charge ratio), full MS automatic gain control (AGC) target was 3 × 106, and mass range was set to 300 to 1400. AGC target value for fragment spectra was set at 1 × 105, intensity threshold was set at 2 × 105, and isolation width was at 1.3 m/z. Normalized collision energy was set at 28% (34). The mass spectra from each sample were searched against the UniProt human database and a custom database containing the sequence of our MBP-PER2 construct using Proteome Discoverer


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(version 2.2). Precursor mass tolerance was set to 10 parts per mil-lion, fragment mass tolerance was set at 0.02 Da, Delta Cn of 0.05, false discovery rate of 0.01, minimum peptide length of 6, and a min-imum number of peptides of 2.

MD simulationsFor SMYD2-peptide simulations, each peptide substrate consisted of seven amino acids. Simulations were solvated in TIP3P (transferable intermolecular potential with 3 points) water, and sodium chloride ions were used to bring the system to physiological salt. Individual systems were each energy minimized, relaxed in the canonical ensemble, equili-brated to atmospheric pressure, and run without restraint in the ca-nonical ensemble. All GROMACS inputs, topology files, and initial coordinates can be downloaded at https://github.com/BradleyDickson.

Protein crystallization, data collection, and structure determinationFor structure determination, full-length human SMYD2 was expressed, purified, and crystallized as described previously (25). Briefly, SMYD2 (10 mg/ml) was incubated with 600 M SAH and crystallized at 20°C in a solution containing 0.1 M tris (pH 7.5), 20.5% polyethylene glycol (PEG) 3350, and 5% ethanol. Crystals were then crushed to generate seeds for growing SMYD2-peptide complex crystals in a solution containing SMYD2 (1.5 mg/ml), 2 mM GWKLNleSKRG pep-tide, 600 M SAH, 0.1 M tris (pH 7.5), 20.5% PEG 3350, and 5.9% ethanol. Crystals suitable for diffraction were cryoprotected in a solution containing 0.1 M tris (pH 7.5), 25% PEG 3350, and 5.0% ethanol and then flash cooled in liquid nitrogen. X-ray diffraction was collected at the Advanced Photon Source at beamline 21-ID-F. Dif-fraction images were processed and scaled using autoPROC and AIMLESS (35, 36). Crystals belong to a tetragonal space group P42 with two molecules per asymmetric unit. The structure was solved by molecular replacement using human SMYD2 (PDB: 5KJK) as a search model. Model building and refinement were carried out in Coot and PHENIX, respectively. The final model was validated by MolProbility (37). Structural figures were prepared in PyMOL. Coordinates and structure factors were deposited in the PDB with the accession num-ber 6MON.

SUPPLEMENTARY MATERIALSSupplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/11/eaav2623/DC1Fig. S1. G9a, SET7/9, and SMYD2 K-OPL substrate selectivity profiles.Fig. S2. G9a, SET7/9, and SMYD2 enzyme assays.Fig. S3. MS/MS analysis of G9a, SET7/9, and SMYD2 reaction products.Fig. S4. Density map for SMYD2-SAH-GWKLNleSKRG structure and comparison of peptide conformation from previous structures.Fig. S5. In vitro SMYD2 assays with protein substrates.Fig. S6. Liquid chromatography (LC)–MS/MS analysis of recombinant PER2.Table S1. Crystallographic data and refinement statistics.Table S2. LoB scores for the human proteome based off SMYD2 K-OPL selectivity profile.Table S3. Missense mutations predicted to affect SMYD2 lysine methylation signaling.Table S4. Recombinant PER2 peptides identified by LC-MS/MS.

REFERENCES AND NOTES 1. E. Verdin, M. Ott, 50 years of protein acetylation: From gene regulation to epigenetics,

metabolism and beyond. Nat. Rev. Mol. Cell Biol. 16, 258–264 (2015). 2. R. Yau, M. Rape, The increasing complexity of the ubiquitin code. Nat. Cell Biol. 18,

579–586 (2016). 3. Y. L. Deribe, T. Pawson, I. Dikic, Post-translational modifications in signal integration.

Nat. Struct. Mol. Biol. 17, 666–672 (2010).

4. J. Murn, Y. Shi, The winding path of protein methylation research: Milestones and new frontiers. Nat. Rev. Mol. Cell Biol. 18, 517–527 (2017).

5. R. P. Ambler, M. W. Rees, Epsilon-N-Methyl-lysine in bacterial flagellar protein. Nature 184, 56–7 (1959).

6. S. Rea, F. Eisenhaber, D. O’Carroll, B. D. Strahl, Z. W. Sun, M. Schmid, S. Opravil, K. Mechtler, C. P. Ponting, C. D. Allis, T. Jenuwein, Regulation of chromatin structure by site-specific histone H3 methyltransferases. Nature 406, 593–599 (2000).

7. K. K. Biggar, Z. Wang, S. S. C. Li, SnapShot: Lysine methylation beyond histones. Mol. Cell. 68, 1016–1016.e1 (2017).

8. P. V. Hornbeck, B. Zhang, B. Murray, J. M. Kornhauser, V. Latham, E. Skrzypek, PhosphoSitePlus, 2014: Mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).

9. Z. Songyang, S. Blechner, N. Hoagland, M. F. Hoekstra, H. Piwnica-Worms, L. C. Cantley, Use of an oriented peptide library to determine the optimal substrates of protein kinases. Curr. Biol. 4, 973–982 (1994).

10. S. Gayatri, M. W. Cowles, V. Vemulapalli, D. Cheng, Z. W. Sun, M. T. Bedford, Using oriented peptide array libraries to evaluate methylarginine-specific antibodies and arginine methyltransferase substrate motifs. Sci. Rep. 6, 28718 (2016).

11. M. Tachibana, K. Sugimoto, T. Fukushima, Y. Shinkai, Set domain-containing protein, G9a, is a novel lysine-preferring mammalian histone methyltransferase with hyperactivity and specific selectivity to lysines 9 and 27 of histone H3. J. Biol. Chem. 276, 25309–25317 (2001).

12. P. Rathert, A. Dhayalan, M. Murakami, X. Zhang, R. Tamas, R. Jurkowska, Y. Komatsu, Y. Shinkai, X. Cheng, A. Jeltsch, Protein lysine methyltransferase G9a acts on non-histone targets. Nat. Chem. Biol. 4, 344–346 (2008).

13. H. Wang, R. Cao, L. Xia, H. Erdjument-Bromage, C. Borchers, P. Tempst, Y. Zhang, Purification and functional characterization of a histone H3-lysine 4-specific methyltransferase. Mol. Cell 8, 1207–1217 (2001).

14. S. Chuikov, J. K. Kurash, J. R. Wilson, B. Xiao, N. Justin, G. S. Ivanov, K. McKinney, P. Tempst, C. Prives, S. J. Gamblin, N. A. Barlev, D. Reinberg, Regulation of p53 activity through lysine methylation. Nature 432, 353–360 (2004).

15. M. A. Brown, R. J. Sims III, P. D. Gottlieb, P. W. Tucker, Identification and characterization of Smyd2: A split SET/MYND domain-containing histone H3 lysine 36-specific methyltransferase that interacts with the Sin3 histone deacetylase complex. Mol. Cancer 5, 26 (2006).

16. M. Abu-Farha, J. P. Lambert, A. S. Al-Madhoun, F. Elisma, I. S. Skerjanc, D. Figeys, The tale of two domains: Proteomics and genomics analysis of SMYD2, a new histone methyltransferase. Mol. Cell. Proteomics 7, 560–572 (2008).

17. J. Huang, L. Perez-Burgos, B. J. Placek, R. Sengupta, M. Richter, J. A. Dorsey, S. Kubicek, S. Opravil, T. Jenuwein, S. L. Berger, Repression of p53 activity by Smyd2-mediated methylation. Nature 444, 629–632 (2006).

18. L. A. Saddic, L. E. West, A. Aslanian, J. R. Yates, S. M. Rubin, O. Gozani, J. Sage, Methylation of the retinoblastoma tumor suppressor by SMYD2. J. Biol. Chem. 285, 37733–37740 (2010).

19. N. Reynoird, P. K. Mazur, T. Stellfeld, N. M. Flores, S. M. Lofgren, S. M. Carlson, E. Brambilla, P. Hainaut, E. B. Kaznowska, C. H. Arrowsmith, P. Khatri, C. Stresemann, O. Gozani, J. Sage, Coordination of stress signals by the lysine methyltransferase SMYD2 promotes pancreatic cancer. Genes Dev. 30, 772–785 (2016).

20. J. B. Olsen, X. J. Cao, B. Han, L. H. Chen, A. Horvath, T. I. Richardson, R. M. Campbell, B. A. Garcia, H. Nguyen, Quantitative profiling of the activity of protein lysine methyltransferase SMYD2 using SILAC-based proteomics. Mol. Cell. Proteomics 15, 892–905 (2016).

21. E. Eggert, R. C. Hillig, S. Koehr, D. Stöckigt, J. Weiske, N. Barak, J. Mowat, T. Brumby, C. D. Christ, A. Ter Laak, T. Lang, A. E. Fernandez-Montalvan, V. Badock, H. Weinmann, I. V. Hartung, D. Barsyte-Lovejoy, M. Szewczyk, S. Kennedy, F. Li, M. Vedadi, P. J. Brown, V. Santhakumar, C. H. Arrowsmith, T. Stellfeld, C. Stresemann, Discovery and characterization of a highly potent and selective aminopyrazoline-based in vivo probe (BAY-598) for the protein lysine methyltransferase SMYD2. J. Med. Chem. 59, 4578–4600 (2016).

22. H. Jayaram, D. Hoelper, S. U. Jain, N. Cantone, S. M. Lundgren, F. Poy, C. D. Allis, R. Cummings, S. Bellon, P. W. Lewis, S-adenosyl methionine is necessary for inhibition of the methyltransferase G9a by the lysine 9 to methionine mutation on histone H3. Proc. Natl. Acad. Sci. U.S.A. 113, 6182–6187 (2016).

23. A. D. Ferguson, N. A. Larsen, T. Howard, H. Pollard, I. Green, C. Grande, T. Cheung, R. Garcia-Arenas, S. Cowen, J. Wu, R. Godin, H. Chen, N. Keen, Structural basis of substrate methylation and inhibition of SMYD2. Structure 19, 1262–1273 (2011).

24. L. Wang, L. Li, H. Zhang, X. Luo, J. Dai, S. Zhou, J. Gu, J. Zhu, P. Atadja, C. Lu, E. Li, K. Zhao, Structure of human SMYD2 protein reveals the basis of p53 tumor suppressor methylation. J. Biol. Chem. 286, 38725–38737 (2011).

25. Y. Jiang, L. Trescott, J. Holcomb, X. Zhang, J. Brunzelle, N. Sirinupong, X. Shi, Z. Yang, Structural insights into estrogen receptor methylation by histone methyltransferase SMYD2, a cellular event implicated in estrogen signaling regulation. J. Mol. Biol. 426, 3413–3425 (2014).


ag.org/D

ownloaded from

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10 of 10

26. S. D. Cowen, S. D. Cowen, D. Russell, L. A. Dakin, H. Chen, N. A. Larsen, R. Godin, S. Throner, X. Zheng, A. Molina, J. Wu, T. Cheung, T. Howard, R. Garcia-Arenas, N. Keen, C. S. Pendleton, J. A. Pietenpol, A. D. Ferguson, Design, synthesis, and biological activity of substrate competitive SMYD2 inhibitors. J. Med. Chem. 59, 11079–11097 (2016).

27. J. C. Obenauer, L. C. Cantley, M. B. Yaffe, Scansite 2.0: Proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res. 31, 3635–3641 (2003).

28. K. Fujii, G. Zhu, Y. Liu, J. Hallam, L. Chen, J. Herrero, S. Shaw, Kinase peptide specificity: Improved determination and relevance to protein phosphorylation. Proc. Natl. Acad. Sci. U.S.A. 101, 13744–13749 (2004).

29. P. Creixell, P. Creixell, E. M. Schoof, C. D. Simpson, J. Longden, C. J. Miller, H. J. Lou, L. Perryman, T. R. Cox, N. Zivanovic, A. Palmeri, A. Wesolowska-Andersen, M. Helmer-Citterich, J. Ferkinghoff-Borg, H. Itamochi, B. Bodenmiller, J. T. Erler, B. E. Turk, R. Linding, Kinome-wide decoding of network-attacking mutations rewiring cancer signaling. Cell 163, 202–217 (2015).

30. S. A. Forbes, D. Beare, H. Boutselakis, S. Bamford, N. Bindal, J. Tate, C. G. Cole, S. Ward, E. Dawson, L. Ponting, R. Stefancsik, B. Harsha, C. Y. Kok, M. Jia, H. Jubb, Z. Sondka, S. Thompson, T. De, P. J. Campbell, COSMIC: Somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777–D783 (2017).

31. L. Tsiatsiani, A. J. R. Heck, Proteomics beyond trypsin. FEBS J. 282, 2612–2626 (2015). 32. S. Lanouette, J. A. Davey, F. Elisma, Z. Ning, D. Figeys, R. A. Chica, J. F. Couture, Discovery

of substrates for a SET domain lysine methyltransferase predicted by multistate computational protein design. Structure 23, 206–215 (2015).

33. K. E. Moore, S. M. Carlson, N. D. Camp, P. Cheung, R. G. James, K. F. Chua, A. Wolf-Yadlin, O. Gozani, A general molecular affinity strategy for global detection and proteomic analysis of lysine methylation. Mol. Cell 50, 444–456 (2013).

34. C. D. Kelstrup, D. B. Bekker-Jensen, T. N. Arrey, A. Hogrebe, A. Harder, J. V. Olsen, Performance evaluation of the Q exactive HF-X for shotgun proteomics. J. Proteome Res. 17, 727–738 (2018).

35. C. Vonrhein, C. Flensburg, P. Keller, A. Sharff, O. Smart, W. Paciorek, T. Womack, G. Bricogne, Data processing and analysis with the autoPROC toolbox. Acta Crystallogr. D Biol. Crystallogr. 67, 293–302 (2011).

36. P. R. Evans, G. N. Murshudov, How good are my data and what is the resolution? Acta Crystallogr. D Biol. Crystallogr. 69, 1204–1214 (2013).

37. V. B. Chen, W. B. Arendall III, J. J. Headd, D. A. Keedy, R. M. Immormino, G. J. Kapral, L. W. Murray, J. S. Richardson, D. C. Richardson, MolProbity: All-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010).

Acknowledgments: We thank A. Nelson for administrative support and members of the Rothbart Laboratory for helpful comments and suggestions on this study. Funding: This work was supported by the Van Andel Research Institute and grants from the National Institutes of Health to S.B.R. (R35GM124736) and Z.-W.S. (R43GM110869 and R44GM112234). Author contributions: E.M.C., B.M.D., M.W.C., Z.-W.S., and S.B.R. designed all studies and discussed results. E.M.C. performed and analyzed enzyme assays with input from M.W.C. and Z.-W.S. B.M.D. performed and analyzed MD simulation and created software to map K-OPL data onto any proteome. N.S., J.B., and Z.Y. solved the SMYD2 structure. K.K. synthesized and analyzed peptides. E.M.C., P.P.V., K.M.S., and R.M.V. produced recombinant proteins. A.U. performed MS analysis of recombinant PER2 under the direction of I.E.V. E.M.C. and S.B.R. wrote the manuscript with input from all authors. Competing interests: EpiCypher is commercializing use cases of OPL-related platforms similar to those reported in this study. S.B.R. has served in a compensated consulting role to EpiCypher. All authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. SMYD2-peptide structure was deposited in the PDB with the accession number 6MON.

Submitted 30 August 2018Accepted 30 October 2018Published 28 November 201810.1126/sciadv.aav2623

Citation: E. M. Cornett, B. M. Dickson, K. Krajewski, N. Spellmon, A. Umstead, R. M. Vaughan, K. M. Shaw, P. P. Versluis, M. W. Cowles, J. Brunzelle, Z. Yang, I. E. Vega, Z.-W. Sun, S. B. Rothbart, A functional proteomics platform to reveal the sequence determinants of lysine methyltransferase substrate selectivity. Sci. Adv. 4, eaav2623 (2018).


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RothbartM. Shaw, Philip P. Versluis, Martis W. Cowles, Joseph Brunzelle, Zhe Yang, Irving E. Vega, Zu-Wen Sun and Scott B. Evan M. Cornett, Bradley M. Dickson, Krzysztof Krajewski, Nicholas Spellmon, Andrew Umstead, Robert M. Vaughan, Kevin

DOI: 10.1126/sciadv.aav2623 (11), eaav2623.4Sci Adv

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