5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a highly heterogeneous malignant disease of the blood cell. The current therapies for AML are unsatisfactory and the molecular mechanisms underlying AML are unclear. 5-methylcytosine (m5C) is an important posttranscriptional modification of mRNA, and is involved in the regulation of mRNA stability, translation, and other aspects of RNA metabolism. However, based on our knowledge of published literature, the role of the m5C regulators has not been explored in AML till date. In this study, we clarified the expression and gene variants of m5C regulators in AML and found that most m5C regulators were differentially expressed and correlated with disease prognosis. We also found that the methylation status of certain m5C regulators (e.g., DNMT3A, DNMT3B) affects the survival of AML patients. Two m5C modification subtypes, and high- and low-risk subgroups identified based on the expression of m5C regulators showed significant differences in the prognosis as well as immune cell infiltration. In addition, most of the m5C regulators were found to be correlated with miRNA expression in AML, as well as IC50 values of many drugs. The miRNA and GSVA analysis were used to identify the different miRNAs and KEGG or hallmark pathways between high- and low-risk subgroups. We also built a prognostic model based on m5C regulators, which was validated by two GSE databases. To verify the reliability of our analysis and conclusions, qPCR was used to identify the expressions of m5C regulators between normal and AML. In summary, we comprehensively explored the molecular characteristics of m5C regulators and built a prognostic model in AML. We proposed new mechanistic insights into the role of m5C in multiple databases and clinical data, which may pave novel ways for the development of therapeutic strategies.

CO2 Enrichment Boosts Highly Selective Infrared-Light-Driven CO2 Conversion to CH4 by UiO-66/Co9S8 Photocatalyst.

Photocatalytic CO2 reduction to high-value chemicals is an attractive approach to mitigate climate change, but it remains a great challenge to produce a specific product selectively by IR light. Hence, UiO-66/Co9S8 composite is designed to couple the advantages of metallic photocatalysts and porous CO2 adsorbers for IR-light-driven CO2-to-CH4 conversion. The metallic nature of Co9S8 endows UiO-66/Co9S8 with exceptional IR light absorption, while UiO-66 dramatically enhances its local CO2 concentration, revealed by finite-element method simulations. As a result, Co9S8 or UiO-66 alone does not show observable IR-light photocatalytic activity, whereas UiO-66/Co9S8 exhibits exceptional activity. The CH4 evolution rate over UiO-66/Co9S8 reaches 25.7 µmol g-1 h-1 with ca.100% selectivity under IR light irradiation, outperforming most reported catalysts under similar reaction conditions. The X-ray absorption fine structure spectroscopy spectra verify the presence of two distinct Co sites and confirm the existence of metallic Co─Co bond in Co9S8. Energy diagrams analysis and transient absorption spectra manifest that CO2 reduction mainly occurs on Co9S8 for UiO-66/Co9S8, while density functional theory calculations demonstrate that high-electron-density Co1 sites are the key active sites, possessing lower energy barriers for further protonation of *CO, leading to the ultra-high selectivity toward CH4.

Predicting Prognosis and Immunotherapy Response in Multiple Cancers Based on the Association of PANoptosis-Related Genes with Tumor Heterogeneity.

PANoptosis is a newly recognized inflammatory pathway for programmed cell death (PCD). It participates in regulating the internal environment, homeostasis, and disease process in various complex ways and plays a crucial role in tumor development, but its mechanism of action is still unclear. In this study, we comprehensively analyzed the expression of 14 PANoptosis-related genes (PANRGs) in 28 types of tumors. Most PANRGs are upregulated in tumors, including Z-DNA binding protein 1 (ZBP1), nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing 3 (NLRP3), caspase (CASP) 1, CASP6, CASP8, PYCARD, FADD, MAP3K7, RNF31, and RBCK1. PANRGs are highly expressed in GBM, LGG, and PAAD, while their levels in ACC are much lower than those in normal tissues. We found that both the CNV and SNV gene sets in BLCA are closely related to survival performance. Subsequently, we conducted clustering and LASSO analysis on each tumor and found that the inhibitory and the stimulating immune checkpoints positively correlate with ZBP1, NLRP3, CASP1, CASP8, and TNFAIP3. The immune infiltration results indicated that KIRC is associated with most infiltrating immune cells. According to the six tumor dryness indicators, PANRGs in LGG show the strongest tumor dryness but have a negative correlation with RNAss. In KIRC, LIHC, and TGCT, most PANRGs play an important role in tumor heterogeneity. Additionally, we analyzed the linear relationship between PANRGs and miRNA and found that MAP3K7 correlates to many miRNAs in most cancers. Finally, we predicted the possible drugs for targeted therapy of the cancers. These data greatly enhance our understanding of the components of cancer and may lead to the discovery of new biomarkers for predicting immunotherapy response and improving the prognosis of cancer patients.

A novel method for identifying key genes in macroevolution based on deep learning with attention mechanism.

Macroevolution can be regarded as the result of evolutionary changes of synergistically acting genes. Unfortunately, the importance of these genes in macroevolution is difficult to assess and hence the identification of macroevolutionary key genes is a major challenge in evolutionary biology. In this study, we designed various word embedding libraries of natural language processing (NLP) considering the multiple mechanisms of evolutionary genomics. A novel method (IKGM) based on three types of attention mechanisms (domain attention, kmer attention and fused attention) were proposed to calculate the weights of different genes in macroevolution. Taking 34 species of diurnal butterflies and nocturnal moths in Lepidoptera as an example, we identified a few of key genes with high weights, which annotated to the functions of circadian rhythms, sensory organs, as well as behavioral habits etc. This study not only provides a novel method to identify the key genes of macroevolution at the genomic level, but also helps us to understand the microevolution mechanisms of diurnal butterflies and nocturnal moths in Lepidoptera.

Design, evaluation, and immune simulation of potentially universal multi-epitope mpox vaccine candidate: focus on DNA vaccine.

Monkeypox (mpox) is a zoonotic infectious disease caused by the mpox virus. Mpox symptoms are similar to smallpox with less severity and lower mortality. As yet mpox virus is not characterized by as high transmissibility as some severe acute respiratory syndrome 2 (SARS-CoV-2) variants, still, it is spreading, especially among men who have sex with men (MSM). Thus, taking preventive measures, such as vaccination, is highly recommended. While the smallpox vaccine has demonstrated considerable efficacy against the mpox virus due to the antigenic similarities, the development of a universal anti-mpox vaccine remains a necessary pursuit. Recently, nucleic acid vaccines have garnered special attention owing to their numerous advantages compared to traditional vaccines. Importantly, DNA vaccines have certain advantages over mRNA vaccines. In this study, a potentially universal DNA vaccine candidate against mpox based on conserved epitopes was designed and its efficacy was evaluated via an immunoinformatics approach. The vaccine candidate demonstrated potent humoral and cellular immune responses in silico, indicating the potential efficacy in vivo and the need for further research.

A two-step deep learning method for 3DCT-2DUS kidney registration during breathing.

This work proposed KidneyRegNet, a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which comprises a feature network, and a 3D-2D CNN-based registration network. The feature network has handcrafted texture feature layers to reduce the semantic gap. The registration network is an encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with a retrospective dataset cum training data generation strategy and then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite applications. The experiment was performed on 132 U/S sequences, 39 multiple-phase CT and 210 public single-phase CT images, and 25 pairs of CT and U/S sequences. This resulted in a mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. Datasets with small transformations resulted in MCDs of 0.82 and 1.02 mm, respectively. Large transformations resulted in MCDs of 1.10 and 1.28 mm, respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategies.

O-Glycopeptide Truncation Strategy for Heterogeneous O-GalNAc Glycoproteomics Characterization.

Mucin-type O-glycosylation (or O-GalNAcylation) takes place on most membrane and secretory proteins and is vital in regulating protein functions and many biological processes. O-GalNAcylation generally exhibits highly diverse and dense O-glycans linked to carrier proteins, which challenges the analysis of O-GalNAc glycoproteome using conventional methodologies. Here, we report an O-glycopeptide truncation strategy for the characterization of protein O-GalNAcylation in biological samples. The O-glycopeptide truncation strategy utilizes proteases or O-glycopeptidases for targeted cleavage of the enriched tryptic O-glycopeptides. It simplifies the O-glycopeptide backbones, O-glycans, or both, and has been shown to aid the improvement of the analytical coverage of O-GalNAc glycopeptides and glycoproteins. Tryptic O-glycopeptides covered with O-glycan clusters and terminal sialic acids could be well isolated by the hydrophilic-based enrichment approaches. The enriched O-glycopeptides are then enzymatically truncated into shorter or less multiply O-glycosylated peptides, which are more favorable for mass spectrometry detection and database search in general bottom-up glycoproteomics. We also investigate different proteolysis which could be well integrated into the O-glycopeptide truncation strategy. For large-scale analysis, we exploit different truncation schemes and identify nearly 2000 O-glycopeptides corresponding to 391 glycoproteins from 75 µL human serum, achieving the deepest-scale coverage of O-glycoproteins compared to other plasma/serum O-glycoproteomic studies. Together, the O-glycopeptide truncation strategy has great potential to facilitate the in-depth study of O-GalNAc glycoproteomics in biological samples.

Development of a Multi-Epitope Universal mRNA Vaccine Candidate for Monkeypox, Smallpox, and Vaccinia Viruses: Design and In Silico Analyses.

Notwithstanding the presence of a smallpox vaccine that is effective against monkeypox (mpox), developing a universal vaccine candidate against monkeypox virus (MPXV) is highly required as the mpox multi-country outbreak has increased global concern. MPXV, along with variola virus (VARV) and vaccinia virus (VACV), belongs to the Orthopoxvirus genus. Due to the genetic similarity of antigens in this study, we have designed a potentially universal mRNA vaccine based on conserved epitopes that are specific to these three viruses. In order to design a potentially universal mRNA vaccine, antigens A29, A30, A35, B6, and M1 were selected. The conserved sequences among the three viral species-MPXV, VACV, and VARV-were detected, and B and T cell epitopes containing the conserved elements were used for the design of the multi-epitope mRNA construct. Immunoinformatics analyses demonstrated the stability of the vaccine construct and optimal binding to MHC molecules. Humoral and cellular immune responses were induced by immune simulation analyses. Eventually, based on in silico analysis, the universal mRNA multi-epitope vaccine candidate designed in this study may have a potential protection against MPXV, VARV, and VACV that will contribute to the advancement of prevention strategies for unpredictable pandemics.

Designing multi-epitope mRNA construct as a universal influenza vaccine candidate for future epidemic/pandemic preparedness.

Despite the availability of prevention and treatment strategies and advancing immunization approaches, the influenza virus remains a global threat that continues to plague humanity with unpredictable pandemics. Due to the unusual genetic variability and segmented genome, the reassortment between different strains of influenza is facilitated and the viruses continuously evolve and adapt to the host cell's immunity. This underlies the seasonal vaccine mismatches that decrease the vaccine efficacy and increase the risk of outbreaks. Thus, the development of a universal vaccine covering all the influenza A and B strains would reduce the pervasiveness of the influenza virus. In the current study, a potentially universal influenza multi-epitope vaccine was designed based on the experimentally tested conserved T cell and B cell epitopes of hemagglutinin (HA), neuraminidase (NA), nucleoprotein (NP), and matrix-2 proton channel (M2) of the virus. The immune simulation and molecular docking of the vaccine construct with TLR2, TLR3, and TLR4 elicited the favorable immunogenicity of the vaccine and the formation of stable complexes, respectively. Ultimately, based on the immunoinformatics analysis, the universal mRNA multi-epitope vaccine designed in this study might have a protection potential against the various subtypes of influenza A and B.

Efficient four-wave mixing wavelength conversion in a hybrid silicon slot and polymer microring resonator.

We present a silicon slot microring resonator for efficient frequency conversion via four-wave mixing (FWM). The slot consists of a narrow silicon waveguide pair with a gap of 80 nm, which is filled with a nonlinear optical polymer. The group velocity dispersion for the microring is controlled by engineering the geometry of the slot structure. Because of the large buildup factor of the slot microring, an FWM conversion efficiency of -27.4 dB is achieved with an optical pump power of less than 1.0 mW. From the measured power dependence of FWM generation, a nonlinear refractive index coefficient of 1.31 × 10-17 m2 W-1 is obtained at a wavelength of 1562 nm. This work presents a hybrid silicon slot and polymer microring as a potential nonlinear device for applications in integrated photonic devices.

Pseudo-labeling generative adversarial networks for medical image classification.

Semi-supervised learning has become a popular technology in recent years. In this paper, we propose a novel semi-supervised medical image classification algorithm, called Pseudo-Labeling Generative Adversarial Networks (PLGAN), which only uses a small number of real images with few labels to generate fake images or mask images to enlarge the sample size of the labeled training set. First, we combine MixMatch to generate pseudo labels for the fake and unlabeled images to do the classification. Second, contrastive learning and self-attention mechanisms are introduced into PLGAN to exclude the influence of unimportant details. Third, the problem of mode collapse in contrastive learning is well addressed by cyclic consistency loss. Finally, we design global and local classifiers to complement each other with the key information needed for classification. The experimental results on four medical image datasets show that PLGAN can obtain relatively high learning performance by using few labeled and unlabeled data. For example, the classification accuracy of PLGAN is 11% higher than that of MixMatch with 100 labeled images and 1000 unlabeled images on the OCT dataset. In addition, we also conduct other experiments to verify the effectiveness of our algorithm.

Applications of machine learning in pine nuts classification.

Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana, Pinus yunnanensis, Pinus thunbergii, Pinus armandii, Pinus massoniana, Pinus elliottii and Pinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951-957 nm, 1,147-1,154 nm, 1,907-1,927 nm, 2,227-2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.

Glyco-Decipher enables glycan database-independent peptide matching and in-depth characterization of site-specific N-glycosylation.

Glycopeptides with unusual glycans or poor peptide backbone fragmentation in tandem mass spectrometry are unaccounted for in typical site-specific glycoproteomics analysis and thus remain unidentified. Here, we develop a glycoproteomics tool, Glyco-Decipher, to address these issues. Glyco-Decipher conducts glycan database-independent peptide matching and exploits the fragmentation pattern of shared peptide backbones in glycopeptides to improve the spectrum interpretation. We benchmark Glyco-Decipher on several large-scale datasets, demonstrating that it identifies more peptide-spectrum matches than Byonic, MSFragger-Glyco, StrucGP and pGlyco 3.0, with a 33.5%-178.5% increase in the number of identified glycopeptide spectra. The database-independent and unbiased profiling of attached glycans enables the discovery of 164 modified glycans in mouse tissues, including glycans with chemical or biological modifications. By enabling in-depth characterization of site-specific protein glycosylation, Glyco-Decipher is a promising tool for advancing glycoproteomics analysis in biological research.

A Tyrosine Phosphoproteome Analysis Approach Enabled by Selective Dephosphorylation with Protein Tyrosine Phosphatase.

Protein tyrosine phosphorylation (pTyr) plays a prominent role in signal transduction and regulation in all eukaryotic cells. In conventional immunoaffinity purification (IP) methods, phosphotyrosine peptides are isolated from the digest of cellular protein extracts with a phosphotyrosine-specific antibody and are identified by tandem mass spectrometry. However, low sensitivity, poor reproducibility, and high cost are universal concerns for IP approaches. In this study, we presented an antibody-free approach to identify phosphotyrosine peptides by using protein tyrosine phosphatase (PTP). It was found that most of the PTPs including PTP1B, TCPTP, and SHP1 can efficiently and selectively dephosphorylate phosphotyrosine peptides. We then designed a workflow by combining two Ti4+-IMAC-based phosphopeptide enrichment steps with PTP-catalyzed dephosphorylation for tyrosine phosphoproteomics analysis. This workflow was first validated by selective detection of phosphotyrosine peptides from semicomplex samples and then applied to analyze the tyrosine phosphoproteome of Jurkat T cells. Around 1000 putative former phosphotyrosine peptides were identified from less than 500 µg of cell lysate. The tyrosine phosphosites on the majority of these peptides could be unambiguously determined for over 70% of them possessing only one tyrosine residue. It was also found that the tyrosine sites identified by this method were highly complementary to those identified by the SH2 superbinder-based method. Therefore, the combination of Ti4+-IMAC enrichment with PTP dephosphorylation provides an alternative and cost-effective approach for tyrosine phosphoproteomics analysis.

Efficient silicon and side-cladding waveguide modulator with electro-optic polymer.

Efficient electro-optic (EO) modulation can be generated in the hybrid silicon modulator with EO polymer in the form of an in-plane coplanar waveguide and electrode structure. Strong confinement of the optical field in the hybrid structure is critical to performing efficient electric poling and modulation of the EO polymer. The waveguide consists of silica-based side claddings and an EO core for increasing the integral of the optical field and the overlap interaction between the optical field and the modulated electric field within the EO polymer. We discuss in detail the volume resistivity dependence of the efficiency of electric poling and modulation for various side-cladding materials. In a Mach-Zehnder interferometer modulator, the measured half-wave-voltage length product (VπL) is 1.9 V·cm at an optical communication wavelength of 1,550 nm under the TE optical mode operation. The high-speed signaling of the device is demonstrated by generating on-off-keying transmission at signal rates up to 52 Gbit/s with a Q factor of 6.1 at a drive voltage of 2.0 Vpp.

MS-Decipher: a user-friendly proteome database search software with an emphasis on deciphering the spectra of O-linked glycopeptides.

MOTIVATION: The interpretation of mass spectrometry (MS) data is a crucial step in proteomics analysis, and the identification of post-translational modifications (PTMs) is vital for the understanding of the regulation mechanism of the living system. Among various PTMs, glycosylation is one of the most diverse ones. Though many search engines have been developed to decipher proteomic data, some of them are difficult to operate and have poor performance on glycoproteomic datasets compared to advanced glycoproteomic software. RESULTS: To simplify the analysis of proteomic datasets, especially O-glycoproteomic datasets, here, we present a user-friendly proteomic database search platform, MS-Decipher, for the identification of peptides from MS data. Two scoring schemes can be chosen for peptide-spectra matching. It was found that MS-Decipher had the same sensitivity and confidence in peptide identification compared to traditional database searching software. In addition, a special search mode, O-Search, is integrated into MS-Decipher to identify O-glycopeptides for O-glycoproteomic analysis. Compared with Mascot, MetaMorpheus and MSFragger, MS-Decipher can obtain about 139.9%, 48.8% and 6.9% more O-glycopeptide-spectrum matches. A useful tool is provided in MS-Decipher for the visualization of O-glycopeptide-spectra matches. MS-Decipher has a user-friendly graphical user interface, making it easier to operate. Several file formats are available in the searching and validation steps. MS-Decipher is implemented with Java, and can be used cross-platform. AVAILABILITY AND IMPLEMENTATION: MS-Decipher is freely available at https://github.com/DICP-1809/MS-Decipher for academic use. For detailed implementation steps, please see the user guide. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

End to End Unsupervised Rigid Medical Image Registration by Using Convolutional Neural Networks.

In this paper, we focus on the issue of rigid medical image registration using deep learning. Under ultrasound, the moving of some organs, e.g., liver and kidney, can be modeled as rigid motion. Therefore, when the ultrasound probe keeps stationary, the registration between frames can be modeled as rigid registration. We propose an unsupervised method with Convolutional Neural Networks. The network estimates from the input image pair the transform parameters first then the moving image is wrapped using the parameters. The loss is calculated between the registered image and the fixed image. Experiments on ultrasound data of kidney and liver verified that the method is capable of achieve higher accuracy compared with traditional methods and is much faster.

Rapid Enzyme-Mediated Biotinylation for Cell Surface Proteome Profiling.

Cell surface is the primary site for sensing extracellular stimuli. The knowledge of the transient changes on the surfaceome upon a perturbation is very important as the initial changed proteins could be driving molecules for some phenotype. In this study, we report a fast cell surface labeling strategy based on peroxidase-mediated oxidative tyrosine coupling strategy, enabling efficient and selective cell surface labeling within seconds. With a labeling time of 1 min, 2684 proteins, including 1370 (51%) cell surface-annotated proteins (cell surface/plasma membrane/extracellular), 732 transmembrane proteins, and 81 cluster of differentiation antigens, were identified from HeLa cells. By comparison with the negative control experiment using quantitative proteomics, 500 (68%) out of the 731 significantly enriched proteins (p-value < 0.05, ≥2-fold) in positive experimental samples were cell surface-annotated proteins. Finally, this technology was applied to track the dynamic changes of the surfaceome upon insulin stimulation at two time points (5 min and 2 h) in HepG2 cells. Thirty-two proteins, including INSR, CTNNB1, TFRC, IGF2R, and SORT1, were found to be significantly regulated (p-value < 0.01, ≥1.5-fold) after insulin exposure by different mechanisms. We envision that this technique could be a powerful tool to analyze the transient changes of the surfaceome with a good time resolution and to delineate the temporal and spatial regulation of cellular signaling.

Efficient Infrared-Light-Driven CO2 Reduction Over Ultrathin Metallic Ni-doped CoS2 Nanosheets.

Converting CO2 and H2 O into carbon-based fuel by IR light is a tough task. Herein, compared with other single-component photocatalysts, the most efficient IR-light-driven CO2 reduction is achieved by an element-doped ultrathin metallic photocatalyst-Ni-doped CoS2 nanosheets (Ni-CoS2 ). The evolution rate of CH4 over Ni-CoS2 is up to 101.8 µmol g-1 h-1 . The metallic and ultrathin nature endow Ni-CoS2 with excellent IR light absorption ability. The PL spectra and Arrhenius plots indicate that Ni atoms could facilitate the separation of photogenerated carriers and the decrease of the activation energy. Moreover, in situ FTIR, DFT calculations, and CH4 -TPD reveal that the doped Ni atoms in CoS2 could effectively depress the formation energy of the *COOH, *CHO and desorption energy of CH4 . This work manifests that element doping in atomic level is a powerful way to control the reaction intermediates, providing possibilities to realize high-efficiency IR-light-driven CO2 reduction.

Selective enrichment of N-terminal proline peptides via hydrazide chemistry for proteomics analysis.

A challenge for shotgun proteomics is the identification of low abundance proteins, which is always hampered owing to the extreme complexity of protein digests and highly dynamic concentration range of proteins. To reduce the complexity of the peptide mixture, we developed a novel method to selectively enrich N-terminal proline peptides via hydrazide chemistry. This method consisted of ortho-phthalaldehyde (OPA) blocking of primary amines in peptides, reductive glutaraldehydation of N-terminal proline and solid phase hydrazide chemistry enrichment of aldehyde-modified N-terminal proline peptide. After enrichment, the number of detected peptides containing N-terminal proline increased from 1304 to 4039 and the ratio of N-terminal proline peptides jumped from 4.4% to 93.7%, showing good enrichment specificity towards N-terminal proline peptides. Besides, the ratio of identified peptides to proteins was decreased from 7.8 (29751/3811) to 1.5 (4347/2821), indicating that sample complexity was drastically reduced through this method. As a result, this novel approach for enriching N-terminal proline peptides is effective in identification of low abundance protein owing to the reduction of sample complexity.