A Chemoenzymatic Method Enables Global Enrichment and Characterization of Protein Arginine Methylation.

Arginine methylation is one of the most important post-translational modifications involved in the regulation of numerous biological processes. To better understand the biological significance of arginine methylation, enrichment methods need to be developed to analyze the methylated proteome at large-scale. Unfortunately, the prevailing enrichment method based on immunoaffinity purification can only enrich a subset of them due to the lack of pan-specific antibodies. Therefore, it is crucial to develop a stable and efficient antibody-free approach for the global analysis of arginine methylation. In this study, we developed a chemoenzymatic method for the simultaneous identification of mono- and dimethylated arginine. Totally, we identified 1006 arginine methylation events in Jurkat T cells, corresponding to 645 dimethylated sites and 361 monomethylated sites. We further applied the developed approach to global identification of the substrate proteins regulated by type I protein arginine methyltransferases (PRMTs) and identified 49 substrate proteins of type I PRMTs, which will facilitate a better understanding of PRMTs-regulated biological processes. Given the robust performance of this method, it would have broad application in methylproteomics analysis.

Metabolic labeling based methylome profiling enables functional dissection of histidine methylation in C3H1 zinc fingers.

Protein methylation is a functionally important post-translational modification that occurs on diverse amino acid residues. The current proteomics approaches are inefficient to discover the methylation on residues other than Arg and Lys, which hinders the deep understanding of the functional role of rare protein methylation. Herein, we present a methyl-specific metabolic labeling approach for global methylome mapping, which enable the acquisition of methylome dataset covering diverse methylation types. Interestingly, of the identified methylation events, His methylation is found to be preferably occurred in C3H1 zinc fingers (ZFs). These His methylation events are determined to be Nπ specific and catalyzed by CARNMT1. The His methylation is found to stabilize the structure of ZFs. U2AF1 is used as a proof-of-concept to highlight the functional importance of His methylation in ZFs in RNA binding and RNA metabolism. The results of this study enable novel understanding of how protein methylation regulates cellular processes.

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.

Introduction: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve patient prognosis. Methods: Electronic medical records from patients with CKD at a single center from 2015 to 2020 were used to develop machine learning models for the prediction of CVD. Least absolute shrinkage and selection operator (LASSO) regression was used to select important features predicting the risk of developing CVD. Seven machine learning classification algorithms were used to build models, which were evaluated by receiver operating characteristic curves, accuracy, sensitivity, specificity, and F1-score, and Shapley Additive explanations was used to interpret the model results. CVD was defined as composite cardiovascular events including coronary heart disease (coronary artery disease, myocardial infarction, angina pectoris, and coronary artery revascularization), cerebrovascular disease (hemorrhagic stroke and ischemic stroke), deaths from all causes (cardiovascular deaths, non-cardiovascular deaths, unknown cause of death), congestive heart failure, and peripheral artery disease (aortic aneurysm, aortic or other peripheral arterial revascularization). A cardiovascular event was a composite outcome of multiple cardiovascular events, as determined by reviewing medical records. Results: This study included 8,894 patients with CKD, with a composite CVD event incidence of 25.9%; a total of 2,304 patients reached this outcome. LASSO regression identified eight important features for predicting the risk of CKD developing into CVD: age, history of hypertension, sex, antiplatelet drugs, high-density lipoprotein, sodium ions, 24-h urinary protein, and estimated glomerular filtration rate. The model developed using Extreme Gradient Boosting in the test set had an area under the curve of 0.89, outperforming the other models, indicating that it had the best CVD predictive performance. Conclusion: This study established a CVD risk prediction model for patients with CKD, based on routine clinical diagnostic and treatment data, with good predictive accuracy. This model is expected to provide a scientific basis for the management and treatment of patients with CKD.

Validity and applicability of the global leadership initiative on malnutrition criteria in non-dialysis patients with chronic kidney disease.

Introduction: There are no standardized assessment criteria for selecting nutritional risk screening tools or indicators to assess reduced muscle mass (RMM) in the Global Leadership Initiative on Malnutrition (GLIM) criteria. We aimed to compare the consistency of different GLIM criteria with Subjective Global Assessment (SGA) and protein-energy wasting (PEW). Methods: In this study, nutritional risk screening 2002 first four questions (NRS-2002-4Q), Nutritional Risk Screening 2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), and Mini-Nutritional Assessment Short-Form (MNA-SF) tools were used as the first step of nutritional risk screening for the GLIM. The RMM is expressed using different metrics. The SGA and PEW were used to diagnose patients and classify them as malnourished and non-malnourished. Kappa (κ) tests were used to compare the concordance between the SGA, PEW, and GLIM of each combination of screening tools. Results: A total of 157 patients were included. Patients with Chronic kidney disease (CKD) stage 1-3 accounted for a large proportion (79.0%). The prevalence rates of malnutrition diagnosed using the SGA and PEW were 18.5% and 19.7%, respectively. The prevalence of GLIM-diagnosed malnutrition ranges from 5.1% to 37.6%, depending on the different screening methods for nutritional risk and the different indicators denoting RMM. The SGA was moderately consistent with the PEW (κ = 0.423, p < 0.001). The consistency among the GLIM, SGA, and PEW was generally low. Using the NRS-2002-4Q to screen for nutritional risk, GLIM had the best agreement with SGA and PEW when skeletal muscle index (SMI), fat-free mass index (FFMI), and hand grip strength (HGS) indicated a reduction in muscle mass (SGA: κ = 0.464, 95% CI 0.28-0.65; PEW: κ = 0.306, 95% CI 0.12-0.49). Conclusion: The concordance between the GLIM criteria and the SGA and PEW depended on the screening tool used in the GLIM process. The inclusion of RMM in the GLIM framework is important. The addition of HGS could further improve the performance of the GLIM standard compared to the use of body composition measurements.

Antibodies targeting the fusion peptide on the HIV envelope provide protection to rhesus macaques against mucosal SHIV challenge.

The fusion peptide (FP) on the HIV-1 envelope (Env) trimer can be targeted by broadly neutralizing antibodies (bNAbs). Here, we evaluated the ability of a human FP-directed bNAb, VRC34.01, along with two vaccine-elicited anti-FP rhesus macaque mAbs, DFPH-a.15 and DF1W-a.01, to protect against simian-HIV (SHIV)BG505 challenge. VRC34.01 neutralized SHIVBG505 with a 50% inhibitory concentration (IC50) of 0.58 µg/ml, whereas DF1W-a.01 and DFPH-a.15 were 4- or 30-fold less potent, respectively. VRC34.01 was infused into four rhesus macaques at a dose of 10 mg/kg and four rhesus macaques at a dose of 2.5 mg/kg. The animals were intrarectally challenged 5 days later with SHIVBG505. In comparison with all 12 control animals that became infected, all four animals infused with VRC34.01 (10 mg/kg) and three out of four animals infused with VRC34.01 (2.5 mg/kg) remained uninfected. Because of the lower potency of DF1W-a.01 and DFPH-a.15 against SHIVBG505, we infused both Abs at a higher dose of 100 mg/kg into four rhesus macaques each, followed by SHIVBG505 challenge 5 days later. Three of four animals that received DF1W-a.01 were protected against infection, whereas all animals that received DFPH-a.15 were protected. Overall, the protective serum neutralization titers observed in these animals were similar to what has been observed for other bNAbs in similar SHIV infection models and in human clinical trials. In conclusion, FP-directed mAbs can thus provide dose-dependent in vivo protection against mucosal SHIV challenges, supporting the development of prophylactic vaccines targeting the HIV-1 Env FP.

CDK11 requires a critical activator SAP30BP to regulate pre-mRNA splicing.

CDK11 is an emerging druggable target for cancer therapy due to its prevalent roles in phosphorylating critical transcription and splicing factors and in facilitating cell cycle progression in cancer cells. Like other cyclin-dependent kinases, CDK11 requires its cognate cyclin, cyclin L1 or cyclin L2, for activation. However, little is known about how CDK11 activities might be modulated by other regulators. In this study, we show that CDK11 forms a tight complex with cyclins L1/L2 and SAP30BP, the latter of which is a poorly characterized factor. Acute degradation of SAP30BP mirrors that of CDK11 in causing widespread and strong defects in pre-mRNA splicing. Furthermore, we demonstrate that SAP30BP facilitates CDK11 kinase activities in vitro and in vivo, through ensuring the stabilities and the assembly of cyclins L1/L2 with CDK11. Together, these findings uncover SAP30BP as a critical CDK11 activator that regulates global pre-mRNA splicing.

Integrated Protein Solubility Shift Assays for Comprehensive Drug Target Identification on a Proteome-Wide Scale.

Target proteins are often stabilized after binding with a ligand and thereby typically become more resistant to denaturation. Based on this phenomenon, several methods without the need to covalently modify the ligand have been developed to identify target proteins for a specific ligand. These methods usually employ complicated workflows with high cost and limited throughput. Here, we develop an iso-pH shift assay (ipHSA) method, a proteome-wide target identification method that detects ligand-induced protein solubility shifts by precipitating proteins with a single concentration of acidic agent followed by protein quantification via data-independent acquisition (DIA). Using a pan-kinase inhibitor, staurosporine, we demonstrated that ipHSA increased throughput compared to the previously developed pH-dependent protein precipitation (pHDPP) method. ipHSA was found to have high complementarity in staurosporine target identification compared with the improved isothermal shift assay (iTSA) and isosolvent shift assay (iSSA) using DIA instead of tandem mass tags (TMTs) for quantification. To further improve target identification sensitivity, we developed an integrated protein solubility shift assay (IPSSA) by pooling the supernatants yielded from ipHSA, iTSA, and iSSA methods. IPSSA exhibited increased sensitivity in screening staurosporine targets by 38, 29, and 38% compared to individual methods. Increasing the number of replicate experiments further enhanced the sensitivity of target identification. Meanwhile, IPSSA also improved the throughput and reduced the cost compared with previous methods. As a fast and efficient tool for drug target identification, IPSSA is expected to have broad applications in the study of the mechanism of action.

Hydrophobic Derivatization Strategy Facilitates Comprehensive Profiling of Protein Methylation.

Protein methylation is receiving more and more attention due to its essential role in diverse biological processes. Large-scale analysis of protein methylation requires the efficient identification of methylated peptides at the proteome level; unfortunately, a significant number of methylated peptides are highly hydrophilic and hardly retained during reversed-phase chromatography, making it difficult to be identified by conventional approaches. Herein, we report the development of a novel strategy by combining hydrophobic derivatization and high pH strong cation exchange enrichment, which significantly expands the identification coverage of the methylproteome. Noteworthily, the total number of identified methylated short peptides was improved by more than 2-fold. By this strategy, we identified 492 methylation sites from NCI-H460 cells compared to only 356 sites identified in native forms. The identification of methylation sites before and after derivatization was highly complementary. Approximately 2-fold the methylation sites were obtained by combining the results identified in both approaches (native and derivatized) as compared with the only analysis in native forms. Therefore, this novel chemical derivatization strategy is a promising approach for the comprehensive identification of protein methylation by improving the identification of methylated short peptides.

Photoreactive Probe-Based Strategy Enables the Specific Identification of the Transient Substrates of Methyltransferase at the Proteome Scale.

To decipher the biological function of protein arginine methyltransferases (PRMTs), the identification of their substrate proteins is crucial. However, this is not a trivial task as the stable and strong interacting proteins always prevail over the weak and transient substrate proteins. Herein, we report the development of a novel photoreactive probe-based strategy to identify the substrate proteins of methyltransferases. By applying it to PRMT1, we demonstrate that this strategy can effectively distinguish substrate proteins from other interacting proteins and allows the identification of highly confident substrate proteins. Noteworthily, we found for the first time that hypomethylation of proteins is a prerequisite for efficient capturing of substrate proteins. This study describes the development of a robust chemical proteomics tool for profiling the transient substrates and can be adapted for broad biomedical applications.

Molecular basis for protein histidine N1-specific methylation of the "His-x-His" motifs by METTL9.

Histidine methylation serves as an intriguing strategy to introduce altered traits of target proteins, including metal ion chelation, histidine-based catalysis, molecular assembly, and translation regulation. As a newly identified histidine methyltransferase, METTL9 catalyzes N1-methylation of protein substrates containing the "His-x-His" motif (HxH, x denotes small side chain residue). Here our structural and biochemical studies revealed that METTL9 specifically methylates the second histidine of the "HxH" motif, while exploiting the first one as a recognition signature. We observed an intimate engagement between METTL9 and a pentapeptide motif, where the small "x" residue is embedded and confined within the substrate pocket. Upon complex formation, the N3 atom of histidine imidazole ring is stabilized by an aspartate residue such that the N1 atom is presented to S-adenosylmethionine for methylation. Moreover, METTL9 displayed a feature in preferred consecutive and "C-to-N" directional methylation of tandem "HxH" repeats that exist in many METTL9 substrates. Collectively, our work illustrates the molecular design of METTL9 in N1-specific methylation of the broadly existing "HxH" motifs, highlighting its importance in histidine methylation biology.

The risk of acute and early HIV (AEH) infection among MSM with different behaviour trajectories: an open cohort study in Tianjin, China, 2011-2019.

BACKGROUND: Acute and early HIV (AEH) infection is characterized by a high viral load and infectivity. Approximately 50% of cases of HIV-1 transmission occur during AEH. Understanding sexual behaviour trajectories would be useful for predicting changes in the risk of HIV acquisition. However, few studies have investigated sexual behaviour trajectories and their association with AEH acquisition. This study identified behaviour trajectories among men who have sex with men (MSM), determined the risk of AEH infection, and compared risk factors between different behaviour trajectories. METHODS: The study was based on an ongoing prospective open cohort of voluntary HIV counselling and testing (VHCT) among MSM in Tianjin, China. From 2011 to 2019, 1974 MSM were recruited. Group-based trajectory modelling (GBTM) was used to identify behaviour trajectories by constructing a sexual risk behaviour score. Logistic regression and generalized estimating equation (GEE) were used to compare the risk of AEH infection and risk factors for different behaviour trajectories. All data analyses were performed using SAS 9.4. RESULTS: The incidence of AEH infection was 1.76/100 person-years, with 64 AEH infections documented in 3633 person-years of follow-up. Three sexual behaviour trajectories were identified: CL (consistently low risk, 35.46%), CH (consistently high risk, 42.71%) and HTL (high to low risk, 21.83%). MSM in the HTL and CH groups had higher AEH infection rates than MSM in the CL group (6.73%, 3.08% and 1.28%, respectively), with ORs of 5.54 (2.60, 11.82) and 2.44 (1.14, 5.25), respectively. MSM aged 30-50 years old and MSM who underwent HIV testing in the last year were more likely to be in the CH group and HTL group. In addition, the HTL group was characterized by a lower likelihood of local registration and a higher likelihood of working as a MSW. CONCLUSION: MSM in the CH group and the HTL group had a higher risk of AEH infection. In the future, VHCT should be performed more often among younger MSM, and HIV counselling should be given the same priority as HIV testing. In addition, VHCT combined with PrEP may have a better preventive impact on MSM with a high risk of AEH infection.

An antibody-free enrichment approach enabled by reductive glutaraldehydation for monomethyllysine proteome analysis.

Protein lysine monomethylation is an important post-translational modification participated in regulating many biological processes. There is growing interest in identifying these methylation events. However, the introduction of one methyl group on lysine residues has negligible effect on changing the physical and chemical properties of proteins or peptides, making enriching and identifying monomethylated lysine (Kme1) proteins or peptides extraordinarily challenging. In this study, we proposed an antibody-free chemical proteomics approach to capture Kme1 peptides from complex protein digest. By exploiting reductive glutaraldehydation, 5-aldehyde-pentanyl modified Kme1 residues and piperidine modified primary amines were generated at the same time. The peptides with aldehyde modified Kme1 residues were then enriched by solid-phase hydrazide chemistry. This chemical proteomics approach was validated by using several synthetic peptides. It was demonstrated that it can enrich and detect Kme1 peptide from peptide mixture containing 5000-fold more bovine serum albumin tryptic digest. Besides, we extended our approach to profile Kme1 using heavy methyl stable isotope labeling by amino acids in cell culture (hmSILAC) labeled Jurkat T cells and Hela cells. Totally, 29 Kme1 sites on 25 proteins were identified with high confidence and 11 Kme1 sites were identified in both two types cells. This is the first antibody-free chemical proteomics approach to enrich Kme1 peptides from complex protein digest, and it provides a potential avenue for the analysis of methylome.

Prostate Cancer Risk Prediction and Online Calculation Based on Machine Learning Algorithm.

Objective To build a prostate cancer (PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelligence (AI) technology under healthcare data platforms. Methods After preprocessing of the data from Population Health Data Archive, smuothly clipped absolute deviation (SCAD) was used to select features. Random forest (RF), support vector machine (SVM), back propagation neural network (BP), and convolutional neural network (CNN) were used to predict the risk of PCa, among which BP and CNN were used on the enhanced data by SMOTE. The performances of models were compared using area under the curve (AUC) of the receiving operating characteristic curve. After the optimal model was selected, we used the Shiny to develop an online calculator for PCa risk prediction based on predictive indicators. Results Inorganic phosphorus, triglycerides, and calcium were closely related to PCa in addition to the volume of fragmented tissue and free prostate-specific antigen (PSA). Among the four models, RF had the best performance in predicting PCa (accuracy: 96.80%; AUC: 0.975, 95% CI: 0.964-0.986). Followed by BP (accuracy: 85.36%; AUC: 0.892, 95% CI: 0.849-0.934) and SVM (accuracy: 82.67%; AUC: 0.824, 95% CI: 0.805-0.844). CNN performed worse (accuracy: 72.37%; AUC: 0.724, 95% CI: 0.670-0.779). An online platform for PCa risk prediction was developed based on the RF model and the predictive indicators. Conclusions This study revealed the application value of traditional machine learning and deep learning models in disease risk prediction under healthcare data platform, proposed new ideas for PCa risk prediction in patients suspected for PCa and had undergone core needle biopsy. Besides, the online calculation may enhance the practicability of AI prediction technology and facilitate medical diagnosis.

Highly effective identification of drug targets at the proteome level by pH-dependent protein precipitation.

Fully understanding the target spaces of drugs is essential for investigating the mechanism of drug action and side effects, as well as for drug discovery and repurposing. In this study, we present an energetics-based approach, termed pH-dependent protein precipitation (pHDPP), to probe the ligand-induced protein stability shift for proteome-wide drug target identification. We demonstrate that pHDPP works for a diverse array of ligands, including a folate derivative, an ATP analog, a CDK inhibitor and an immunosuppressant, enabling highly specific identification of target proteins from total cell lysates. This approach is compared to thermal and solvent-induced denaturation approaches with a pan-kinase inhibitor as the model drug, demonstrating its high sensitivity and high complementarity to other approaches. Dihydroartemisinin (DHA), a dominant derivative of artemisinin to treat malaria, is known to have an extraordinary effect on the treatment of various cancers. However, the anti-tumor mechanisms remain unknown. pHDPP was applied to reveal the target space of DHA and 45 potential target proteins were identified. Pathway analysis indicated that these target proteins were mainly involved in metabolism and apoptosis pathways. Two cancer-related target proteins, ALDH7A1 and HMGB1, were validated by structural simulation and AI-based target prediction methods. And they were further validated to have strong affinity to DHA by using cellular thermal shift assay (CETSA). In summary, pHDPP is a powerful tool to construct the target protein space to reveal the mechanism of drug action and would have broad application in drug discovery studies.

Effects of daily mean temperature and other meteorological variables on bacillary dysentery in Beijing-Tianjin-Hebei region, China.

BACKGROUND: Although previous studies have shown that meteorological factors such as temperature are related to the incidence of bacillary dysentery (BD), researches about the non-linear and interaction effect among meteorological variables remain limited. The objective of this study was to analyze the effects of temperature and other meteorological variables on BD in Beijing-Tianjin-Hebei region, which is a high-risk area for BD distribution. METHODS: Our study was based on the daily-scale data of BD cases and meteorological variables from 2014 to 2019, using generalized additive model (GAM) to explore the relationship between meteorological variables and BD cases and distributed lag non-linear model (DLNM) to analyze the lag and cumulative effects. The interaction effects and stratified analysis were developed by the GAM. RESULTS: A total of 147,001 cases were reported from 2014 to 2019. The relationship between temperature and BD was approximately liner above 0 °C, but the turning point of total temperature effect was 10 °C. Results of DLNM indicated that the effect of high temperature was significant on lag 5d and lag 6d, and the lag effect showed that each 5 °C rise caused a 3% [Relative risk (RR) = 1.03, 95% Confidence interval (CI): 1.02-1.05] increase in BD cases. The cumulative BD cases delayed by 7 days increased by 31% for each 5 °C rise in temperature above 10 °C (RR = 1.31, 95% CI: 1.30-1.33). The interaction effects and stratified analysis manifested that the incidence of BD was highest in hot and humid climates. CONCLUSIONS: This study suggests that temperature can significantly affect the incidence of BD, and its effect can be enhanced by humidity and precipitation, which means that the hot and humid environment positively increases the incidence of BD.

Potent anti-viral activity of a trispecific HIV neutralizing antibody in SHIV-infected monkeys.

Broadly neutralizing antibodies (bNAbs) represent an alternative to drug therapy for the treatment of HIV-1 infection. Immunotherapy with single bNAbs often leads to emergence of escape variants, suggesting a potential benefit of combination bNAb therapy. Here, a trispecific bNAb reduces viremia 100- to 1000-fold in viremic SHIV-infected macaques. After treatment discontinuation, viremia rebounds transiently and returns to low levels, through CD8-mediated immune control. These viruses remain sensitive to the trispecific antibody, despite loss of sensitivity to one of the parental bNAbs. Similarly, the trispecific bNAb suppresses the emergence of resistance in viruses derived from HIV-1-infected subjects, in contrast to parental bNAbs. Trispecific HIV-1 neutralizing antibodies, therefore, mediate potent antiviral activity in vivo and may minimize the potential for immune escape.

Starch branching enzymes as putative determinants of postharvest quality in horticultural crops.

Starch branching enzymes (SBEs) are key determinants of the structure and amount of the starch in plant organs, and as such, they have the capacity to influence plant growth, developmental, and fitness processes, and in addition, the industrial end-use of starch. However, little is known about the role of SBEs in determining starch structure-function relations in economically important horticultural crops such as fruit and leafy greens, many of which accumulate starch transiently. Further, a full understanding of the biological function of these types of starches is lacking. Because of this gap in knowledge, this minireview aims to provide an overview of SBEs in horticultural crops, to investigate the potential role of starch in determining postharvest quality. A systematic examination of SBE sequences in 43 diverse horticultural species, identified SBE1, 2 and 3 isoforms in all species examined except apple, olive, and Brassicaceae, which lacked SBE1, but had a duplicated SBE2. Among our findings after a comprehensive and critical review of published data, was that as apple, banana, and tomato fruits ripens, the ratio of the highly digestible amylopectin component of starch increases relative to the more digestion-resistant amylose fraction, with parallel increases in SBE2 transcription, fruit sugar content, and decreases in starch. It is tempting to speculate that during the ripening of these fruit when starch degradation occurs, there are rearrangements made to the structure of starch possibly via branching enzymes to increase starch digestibility to sugars. We propose that based on the known action of SBEs, and these observations, SBEs may affect produce quality, and shelf-life directly through starch accumulation, and indirectly, by altering sugar availability. Further studies where SBE activity is fine-tuned in these crops, can enrich our understanding of the role of starch across species and may improve horticulture postharvest quality.

An efficient approach based on basic strong cation exchange chromatography for enriching methylated peptides with high specificity for methylproteomics analysis.

Protein methylation as one of the most important post-translational modifications has been under the spotlight due to its essential role in many biological processes. Development of methods for large-scale analysis of protein methylation greatly accelerates the related researches. To date, antibody-based enrichment strategy is the most common approach for methylproteomics analysis. However, it is still lacking of a pan-specific antibody to enrich peptides or proteins carrying all kinds of lysine and arginine methylation forms. Herein, an online basic strong cation exchange chromatography was developed to enrich methylated peptides from protein digests prepared by two complementary methods, including direct multiple enzymes digestion and carboxylic amidation followed by multiple enzymes digestion. After enrichment, the majority of identifications were obtained from direct multiple enzymes digested sample. The enrichment specificity of methylated peptides was up to 28.5%, and 445 methylation forms corresponding to 376 methylation sites were identified on 194 proteins in one LC-MS/MS run using only 100 µg of digests. This method has great potential in studying protein methylation mediated biological processes.

Chemical Depletion of Histidine-Containing Peptides Allows Identification of More Low-Abundance Methylation Sites from Proteome Samples.

Protein methylation, especially that occurs on arginine and lysine residues, is one of the most important post-translational modifications involved in various cellular processes including RNA splicing, DNA repair, and so forth. Systematic analysis of protein methylation would facilitate the understanding of its regulatory mechanisms. Strong cation chromatography has been used to globally analyze arginine/lysine methylation at the proteome scale with good performance. However, the co-enriched histidine-containing peptides severely interfere with the detection of low-abundance methylpeptides. Here, we developed a novel chemical strategy which enabled almost complete depletion of histidine-containing peptides in the protein digest, thereby resulting in the identification of more low-abundance arginine/lysine methylpeptides. Totally, 333 arginine and lysine methylation forms from 207 proteins were identified in this study. Overall, the number of methylation identifications increased about 50% by using our new method. Data are available via ProteomeXchange with the identifier PXD023845.