Establishment and clinical application evaluations of a deep mining strategy of plasma proteomics based on nanomaterial protein coronas.

Research on plasma proteomics has received extensive attention, because human plasma is an important sample for disease biomarker research due to its easy clinical accessibility and richness in biological information. Plasma samples contain a large number of leaked proteins from different tissues in the body, immune proteins and communication signal proteins. However, MS signal suppression from high-abundance proteins results in a large number of proteins that are present in low abundance in plasma not being detected by the LC-MS method. This situation makes it more difficult to study neurological diseases, where tissue sampling is difficult and body fluid samples such as plasma or cerebrospinal fluid are both affected by signal suppression. A large number of methods have been developed to deeply mine plasma proteomics information; however, their application limitations remain to some extent. Traditional immuno- or affinity-based depletion, fractionation and subproteome enrichment methods cannot meet the challenges of large clinical cohort applications due to limited time efficiency. In this study, a deep mining strategy of plasma proteomics was established by combing the protein corona formed by deep mining beads (DMB beads, hereafter referred to as magnetic covalent organic frameworks Fe3O4@TpPa-1), DIA-MS detection and the DIA-NN library searching method. By optimizing the enrichment step, mass spectrometry acquisition and data processing, the evaluation results of the deep mining strategy showed the following: depth, the strategy identified and quantified results of 2000+ proteins per plasma sample; stability, more than 87% of the enriched low-abundance proteins had CV < 20%; accuracy, good agreement between measured and theoretical values (1.81/2, 8.68/10, 38.36/50) for the gradient addition of E. coli proteins to a plasma sample; time efficiency, the processing time was reduced from >12h in the traditional method to <5h (incubation 30 min, washing 15 min, reductive/alkylation/digestion/desalting 4 h), and more importantly, 96 samples can be processed simultaneously in combination with the magnetic module of the automated device. The optimal strategy enables greater enrichment of neurological disease-related proteins, including SNCA and BDNF. Finally, the deep mining strategy was applied in a pilot study of multiple system atrophy (MSA) for biomarker discovery. The results showed that a total of 215 proteins were upregulated and 184 proteins were downregulated (p < 0.05) in the MSA group compared with the healthy control group. Eighteen proteins of these differentially expressed proteins were reported to be associated with neurological diseases or expressed specifically in brain tissue, 8 and 4 of which have reference concentrations of µg/L and ng/L, respectively. The alterations of ENPP2 and SLC2A1/Glut1 were reanalyzed by ELISA, further supporting the results of mass spectrometry. In conclusion, the results of the evaluation and application of the deep mining strategy showed promise for clinical research applications.

Molecular evolution in different subtypes of multifocal hepatocellular carcinoma.

BACKGROUND: Multifocal hepatocellular carcinoma (MF-HCC) accounts for > 40% of HCCs, exhibiting a poor prognosis than single primary HCCs. Characterizing molecular features including dynamic changes of mutational signature along with clonal evolution, intrahepatic metastatic timing, and genetic footprint in the preneoplastic stage underlying different subtypes of MF-HCC are important for understanding their molecular evolution and developing a precision management strategy. METHODS: We conducted whole-exome sequencing in 74 tumor samples from spatially distinct regions in 35 resected lesions and adjacent noncancerous tissues from 11 patients, 15 histologically confirmed preneoplastic lesions, and six samples from peripheral blood mononuclear cells. A previously published MF-HCC cohort (n = 9) was included as an independent validation dataset. We combined well-established approaches to investigate tumor heterogeneity, intrahepatic metastatic timing, and molecular footprints in different subtypes of MF-HCCs. RESULTS: We classified MF-HCCs patients into three subtypes, including intrahepatic metastasis, multicentric occurrence, and mixed intrahepatic metastasis and multicentric occurrence. The dynamic changes in mutational signatures between tumor subclonal expansions demonstrated varied etiologies (e.g., aristolochic acid exposure) underlying the clonal progression in different MF-HCC subtypes. Furthermore, the clonal evolution in intrahepatic metastasis exhibited an early metastatic seeding at 10-4-0.01 cm3 in primary tumor volume (below the limits of clinical detection), further validated in an independent cohort. In addition, mutational footprints in the preneoplastic lesions for multicentric occurrence patients revealed common preneoplastic arising clones, evidently being ancestors of different tumor lesions. CONCLUSION: Our study comprehensively characterized the varied tumor clonal evolutionary history underlying different subtypes of MF-HCC and provided important implications for optimizing personalized clinical management for MF-HCC.

Using ATCLSTM-Kcr to predict and generate the human lysine crotonylation database.

Lysine crotonylation (Kcr) is an evolutionarily conserved protein post-translational modifications, which plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation, telomere maintenance, inflammation, and cancer. Tandem mass spectrometry (LC-MS/MS) has been used to identify the global Kcr profiling of human, at the same time, many computing methods have been developed to predict Kcr sites without high experiment cost. Deep learning network solves the problem of manual feature design and selection in traditional machine learning (NLP), especially the algorithms in natural language processing which treated peptides as sentences, thus can extract more in-depth information and obtain higher accuracy. In this work, we establish a Kcr prediction model named ATCLSTM-Kcr which use self-attention mechanism combined with NLP method to highlight the important features and further capture the internal correlation of the features, to realize the feature enhancement and noise reduction modules of the model. Independent tests have proved that ATCLSTM-Kcr has better accuracy and robustness than similar prediction tools. Then, we design pipeline to generate MS-based benchmark dataset to avoid the false negatives caused by MS-detectability and improve the sensitivity of Kcr prediction. Finally, we develop a Human Lysine Crotonylation Database (HLCD) which using ATCLSTM-Kcr and the two representative deep learning models to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. HLCD provides an integrated platform for human Kcr sites prediction and screening through multiple prediction scores and conditions, and can be accessed on the website:www.urimarker.com/HLCD/. SIGNIFICANCE: Lysine crotonylation (Kcr) plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation and cancer. To better elucidate the molecular mechanisms of crotonylation and reduce the high experimental cost, we establish a deep learning Kcr prediction model and solve the problem of false negatives caused by the detectability of mass spectrometry (MS). Finally, we develop a Human Lysine Crotonylation Database to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. Our work provides a convenient platform for human Kcr sites prediction and screening through multiple prediction scores and conditions.

Proteomic and phosphoproteomic characteristics of the cortex, hippocampus, thalamus, lung, and kidney in COVID-19-infected female K18-hACE2 mice.

BACKGROUND: Neurological damage caused by coronavirus disease 2019 (COVID-19) has attracted increasing attention. Recently, through autopsies of patients with COVID-19, the direct identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in their central nervous system (CNS) has been reported, indicating that SARS-CoV-2 might directly attack the CNS. The need to prevent COVID-19-induced severe injuries and potential sequelae is urgent, requiring the elucidation of large-scale molecular mechanisms in vivo. METHODS: In this study, we performed liquid chromatography-mass spectrometry-based proteomic and phosphoproteomic analyses of the cortex, hippocampus, thalamus, lungs, and kidneys of SARS-CoV-2-infected K18-hACE2 female mice. We then performed comprehensive bioinformatic analyses, including differential analyses, functional enrichment, and kinase prediction, to identify key molecules involved in COVID-19. FINDINGS: We found that the cortex had higher viral loads than did the lungs, and the kidneys did not have SARS-COV-2. After SARS-CoV-2 infection, RIG-I-associated virus recognition, antigen processing and presentation, and complement and coagulation cascades were activated to different degrees in all five organs, especially the lungs. The infected cortex exhibited disorders of multiple organelles and biological processes, including dysregulated spliceosome, ribosome, peroxisome, proteasome, endosome, and mitochondrial oxidative respiratory chain. The hippocampus and thalamus had fewer disorders than did the cortex; however, hyperphosphorylation of Mapt/Tau, which may contribute to neurodegenerative diseases, such as Alzheimer's disease, was found in all three brain regions. Moreover, SARS-CoV-2-induced elevation of human angiotensin-converting enzyme 2 (hACE2) was observed in the lungs and kidneys, but not in the three brain regions. Although the virus was not detected, the kidneys expressed high levels of hACE2 and exhibited obvious functional dysregulation after infection. This indicates that SARS-CoV-2 can cause tissue infections or damage via complicated routes. Thus, the treatment of COVID-19 requires a multipronged approach. INTERPRETATION: This study provides observations and in vivo datasets for COVID-19-associated proteomic and phosphoproteomic alterations in multiple organs, especially cerebral tissues, of K18-hACE2 mice. In mature drug databases, the differentially expressed proteins and predicted kinases in this study can be used as baits to identify candidate therapeutic drugs for COVID-19. This study can serve as a solid resource for the scientific community. The data in this manuscript will serve as a starting point for future research on COVID-19-associated encephalopathy. FUNDING: This study was supported by grants from the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, the National Natural Science Foundation of China, and the Natural Science Foundation of Beijing.

[Precision oncology from a proteogenomics perspective].

Cancer is a heterogeneous disease with complex mechanisms that requires targeted precision medicine strategies. The growth of precision medicine is indispensable from the rapid development of genomics. However, genomics has certain limitations in molecular phenotype analysis, proteogenomics thus arose at the right time. Proteogenomics is the merging of proteomics and genomics. This review describes the limitations of genomic analysis and highlights the importance of proteogenomics to re-understand precision oncology from a proteogenomic perspective. In addition, the application of proteogenomics in precision oncology is briefly introduced, the related public data projects are described, and finally, the challenges that need to be addressed at this stage are proposed.

Longitudinal Serum Proteome Characterization of COVID-19 Patients With Different Severities Revealed Potential Therapeutic Strategies.

The COVID-19 pandemic caused by SARS-CoV-2 is exerting huge pressure on global healthcare. Understanding of the molecular pathophysiological alterations in COVID-19 patients with different severities during disease is important for effective treatment. In this study, we performed proteomic profiling of 181 serum samples collected at multiple time points from 79 COVID-19 patients with different severity levels (asymptomatic, mild, moderate, and severe/critical) and 27 serum samples from non-COVID-19 control individuals. Dysregulation of immune response and metabolic reprogramming was found in severe/critical COVID-19 patients compared with non-severe/critical patients, whereas asymptomatic patients presented an effective immune response compared with symptomatic COVID-19 patients. Interestingly, the moderate COVID-19 patients were mainly grouped into two distinct clusters using hierarchical cluster analysis, which demonstrates the molecular pathophysiological heterogeneity in COVID-19 patients. Analysis of protein-level alterations during disease progression revealed that proteins involved in complement activation, the coagulation cascade and cholesterol metabolism were restored at the convalescence stage, but the levels of some proteins, such as anti-angiogenesis protein PLGLB1, would not recovered. The higher serum level of PLGLB1 in COVID-19 patients than in control groups was further confirmed by parallel reaction monitoring (PRM). These findings expand our understanding of the pathogenesis and progression of COVID-19 and provide insight into the discovery of potential therapeutic targets and serum biomarkers worth further validation.

Phosphoproteome profiling of mouse liver during normal aging.

BACKGROUND: Aging is a complex biological process accompanied by a time-dependent functional decline that affects most living organisms. Omics studies help to comprehensively understand the mechanism of aging and discover potential intervention methods. Old mice are frequently obese with a fatty liver. METHODS: We applied mass spectrometry-based phosphoproteomics to obtain a global phosphorylation profile of the liver in mice aged 2 or 18 months. MaxQuant was used for quantitative analysis and PCA was used for unsupervised clustering. RESULTS: Through phosphoproteome analysis, a total of 5,685 phosphosites in 2,335 proteins were filtered for quantitative analysis. PCA analysis of both the phosphoproteome and transcriptome data could distinguish young and old mice. However, from kinase prediction, kinase-substrate interaction analysis, and KEGG functional enrichment analysis done with phosphoproteome data, we observed high phosphorylation of fatty acid biosynthesis, ß-oxidation, and potential secretory processes, together with low phosphorylation of the Egfr-Sos1-Araf/Braf-Map2k1-Mapk1 pathway and Ctnnb1 during aging. Proteins with differentially expressed phosphosites seemed more directly related to the aging-associated fatty liver phenotype than the differentially expressed transcripts. The phosphoproteome may reveal distinctive biological functions that are lost in the transcriptome. CONCLUSIONS: In summary, we constructed a phosphorylation-associated network in the mouse liver during normal aging, which may help to discover novel antiaging strategies.

Global profiling of lysine succinylation in human lungs.

The lysine succinylation (Ksucc) is involved in many core energy metabolism pathways and affects the metabolic process in mitochondria, making this modification highly valuable for studying diseases related to mitochondrial disorders. In this paper, we used liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform the first global profiling of succinylation in human lungs under normal physiological conditions. Using an MS-based platform, we identified 1485 Ksucc sites in 568 proteins. We then compared these sites with those previously identified in human succinylome studies to investigate specific succinylated proteins and identify their possible functions in the lung and to explore the substrate preferences of succinylation modifiers in different cell lines and at different subcellular localizations. Our work expands the succinylation database and supplementary materials on the human succinylome and will thus help in further study of the function of Ksucc and regulation under related physiological and pathological conditions.

PSL-LCCL: a resource for subcellular protein localization in liver cancer cell line SK_HEP1.

The characterization of subcellular protein localization provides a basis for further understanding cellular behaviors. A delineation of subcellular localization of proteins on cytosolic membrane-bound organelles in human liver cancer cell lines (hLCCLs) has yet to be performed. To obtain its proteome-wide view, we isolated and enriched six cytosolic membrane-bound organelles in one of the hLCCLs (SK_HEP1) and quantified their proteins using mass spectrometry. The vigorous selection of marker proteins and a machine-learning-based algorithm were implemented to localize proteins at cluster and neighborhood levels. We validated the performance of the proposed method by comparing the predicted subcellular protein localization with publicly available resources. The profiles enabled investigating the correlation of protein domains with their subcellular localization and colocalization of protein complex members. A subcellular proteome database for SK_HEP1, including (i) the subcellular protein localization and (ii) the subcellular locations of protein complex members and their interactions, was constructed. Our research provides resources for further research on hLCCLs proteomics. Database URL:  http://www.igenetics.org.cn/project/PSL-LCCL/.

iProX in 2021: connecting proteomics data sharing with big data.

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics.

Global Lysine Crotonylation Profiling of Mouse Liver.

Lysine crotonylation (Kcr) is a recently discovered post-translational modification that potentially regulates multiple biological processes. With an objective to expand the available crotonylation datasets, LC-MS/MS is performed using mouse liver samples under normal physiological conditions to obtain in vivo crotonylome. A label-free strategy is used and 10 034 Class I (localization probabilities > 0.75) crotonylated sites are identified in 2245 proteins. The KcrE, KcrD, and EKcr motifs are significantly enriched in the crotonylated peptides. The identified crotonylated proteins are mostly enzymes and primarily located in the cytoplasm and nucleus. Functional enrichment analysis based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes shows that the crotonylated proteins are closely related to the purine-containing compound metabolic process, ribose phosphate metabolic process, carbon metabolism pathway, ribosome pathway, and a series of metabolism-associated biological processes. To the best of the authors' knowledge, this research provides the first report on the mouse liver crotonylome. Furthermore, it offers additional evidence that crotonylation exists in non-histone proteins, and is likely involved in various biological processes. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium with the dataset identifiers PXD019145.

iProX: an integrated proteome resource.

Sharing of research data in public repositories has become best practice in academia. With the accumulation of massive data, network bandwidth and storage requirements are rapidly increasing. The ProteomeXchange (PX) consortium implements a mode of centralized metadata and distributed raw data management, which promotes effective data sharing. To facilitate open access of proteome data worldwide, we have developed the integrated proteome resource iProX (http://www.iprox.org) as a public platform for collecting and sharing raw data, analysis results and metadata obtained from proteomics experiments. The iProX repository employs a web-based proteome data submission process and open sharing of mass spectrometry-based proteomics datasets. Also, it deploys extensive controlled vocabularies and ontologies to annotate proteomics datasets. Users can use a GUI to provide and access data through a fast Aspera-based transfer tool. iProX is a full member of the PX consortium; all released datasets are freely accessible to the public. iProX is based on a high availability architecture and has been deployed as part of the proteomics infrastructure of China, ensuring long-term and stable resource support. iProX will facilitate worldwide data analysis and sharing of proteomics experiments.

LFAQ: Toward Unbiased Label-Free Absolute Protein Quantification by Predicting Peptide Quantitative Factors.

Mass spectrometry (MS) has become a predominant choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose an algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on data sets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins. The advantages of LFAQ were further confirmed using the data from published papers.

Validation of a multi-omics strategy for prioritizing personalized candidate driver genes

Significant heterogeneity between different tumors prevents the discovery of cancer driver genes, especially in a patient-specific manner. We previously prioritized five personalized candidate mutation-driver genes in a hyper-mutated hepatocellular carcinoma patient using a multi-omics strategy. However, the roles of the prioritized driver genes and patient-specific mutations in hepatocarcinogenesis are unclear. We investigated the impact of the tumor-mutated allele on structure-function relationship of the encoded protein and assessed both loss- and gain-of-function of these genes and mutations on hepatoma cell behaviors in vitro. The prioritized mutation-driver genes act as tumor suppressor genes and inhibit cell proliferation and migration. In addition, the loss-of-function effect of the patient-specific mutations promoted cell proliferation and migration. Of note, the HNF1A S247T mutation significantly reduced the HNF1A transcriptional activity for hepatocyte nuclear factor 4 alpha (HNF4A) but did not disrupt nuclear localization of HNF1A. The results provide evidence for supporting the validity of our proposed multi-omics strategy, which supplies a new avenue for prioritizing mutation-drivers towards personalized cancer therapy.

Human fallopian tube proteome shows high coverage of mesenchymal stem cells associated proteins.

The object of this research was to report a draft proteome of human fallopian tube (hFT) comprises 5416 identified proteins, which could be considered as a physiological reference to complement Human Proteome Draft. The proteomic raw data and metadata were stored in an integrated proteome resources centre iProX (IPX00034300). This hFT proteome contains many hFT markers newly identified by mass spectrum. This hFT proteome comprises 660 high-, 3605 medium- and 1181 low-abundant proteins. Ribosome, cytoskeleton, vesicle and protein folding associated proteins showed obvious tendency to be higher abundance in hFT. The extraordinary high coverage of mesenchymal stem cells (MSCs)-associated proteins were identified in this hFT proteome, which highly supported that hFT should contain a plenty of MSCs.

Leveraging a Multi-Omics Strategy for Prioritizing Personalized Candidate Mutation-Driver Genes: A Proof-of-Concept Study.

The expression of mutant forms of proteins (e.g., oncogenes and tumor suppressors) has implications in cancer biology and clinical practice. Initial efforts have been made to characterize the transcription of tumor-mutated alleles; however, few studies have been reported to link tumor-mutated alleles to proteomics. We aimed to characterize the transcriptional and translational patterns of tumor-mutated alleles. We performed whole-exome sequencing, RNA-seq, and proteome profiling in a hyper-mutated patient of hepatocellular carcinoma. Using the patient as a model, we show that only a small proportion of tumor-mutated alleles were expressed. In this case, 42% and 3.5% of the tumor-mutated alleles were identified to be transcribed and translated, respectively. Compared with genes with germline variations or without mutations, somatic mutations significantly reduced protein expression abundance. Using the transcriptional and translational patterns of tumor-mutated alleles, we classified the mutations into four types, and only one type may be associated with the liver cancer and lead to hepatocarcinogenesis in the patient. Our results demonstrate how tumor-mutated alleles are transcribed and translated, and how the expression enables the classification of somatic mutations that cause cancer. Leveraging multiple 'omics' datasets provides a new avenue for understanding patient-specific mutations that underlie carcinogenesis.

Evolutionary Characteristics of Missing Proteins: Insights into the Evolution of Human Chromosomes Related to Missing-Protein-Encoding Genes.

Although the "missing protein" is a temporary concept in C-HPP, the biological information for their "missing" could be an important clue in evolutionary studies. Here we classified missing-protein-encoding genes into two groups, the genes encoding PE2 proteins (with transcript evidence) and the genes encoding PE3/4 proteins (with no transcript evidence). These missing-protein-encoding genes distribute unevenly among different chromosomes, chromosomal regions, or gene clusters. In the view of evolutionary features, PE3/4 genes tend to be young, spreading at the nonhomology chromosomal regions and evolving at higher rates. Interestingly, there is a higher proportion of singletons in PE3/4 genes than the proportion of singletons in all genes (background) and OTCSGs (organ, tissue, cell type-specific genes). More importantly, most of the paralogous PE3/4 genes belong to the newly duplicated members of the paralogous gene groups, which mainly contribute to special biological functions, such as "smell perception". These functions are heavily restricted into specific type of cells, tissues, or specific developmental stages, acting as the new functional requirements that facilitated the emergence of the missing-protein-encoding genes during evolution. In addition, the criteria for the extremely special physical-chemical proteins were first set up based on the properties of PE2 proteins, and the evolutionary characteristics of those proteins were explored. Overall, the evolutionary analyses of missing-protein-encoding genes are expected to be highly instructive for proteomics and functional studies in the future.

Tissue-Based Proteogenomics Reveals that Human Testis Endows Plentiful Missing Proteins.

Investigations of missing proteins (MPs) are being endorsed by many bioanalytical strategies. We proposed that proteogenomics of testis tissue was a feasible approach to identify more MPs because testis tissues have higher gene expression levels. Here we combined proteomics and transcriptomics to survey gene expression in human testis tissues from three post-mortem individuals. Proteins were extracted and separated with glycine- and tricine-SDS-PAGE. A total of 9597 protein groups were identified; of these, 166 protein groups were listed as MPs, including 138 groups (83.1%) with transcriptional evidence. A total of 2948 proteins are designated as MPs, and 5.6% of these were identified in this study. The high incidence of MPs in testis tissue indicates that this is a rich resource for MPs. Functional category analysis revealed that the biological processes that testis MPs are mainly involved in are sexual reproduction and spermatogenesis. Some of the MPs are potentially involved in tumorgenesis in other tissues. Therefore, this proteogenomics analysis of individual testis tissues provides convincing evidence of the discovery of MPs. All mass spectrometry data from this study have been deposited in the ProteomeXchange (data set identifier PXD002179).

Special Enrichment Strategies Greatly Increase the Efficiency of Missing Proteins Identification from Regular Proteome Samples.

As part of the Chromosome-Centric Human Proteome Project (C-HPP) mission, laboratories all over the world have tried to map the entire missing proteins (MPs) since 2012. On the basis of the first and second Chinese Chromosome Proteome Database (CCPD 1.0 and 2.0) studies, we developed systematic enrichment strategies to identify MPs that fell into four classes: (1) low molecular weight (LMW) proteins, (2) membrane proteins, (3) proteins that contained various post-translational modifications (PTMs), and (4) nucleic acid-associated proteins. Of 8845 proteins identified in 7 data sets, 79 proteins were classified as MPs. Among data sets derived from different enrichment strategies, data sets for LMW and PTM yielded the most novel MPs. In addition, we found that some MPs were identified in multiple-data sets, which implied that tandem enrichments methods might improve the ability to identify MPs. Moreover, low expression at the transcription level was the major cause of the "missing" of these MPs; however, MPs with higher expression level also evaded identification, most likely due to other characteristics such as LMW, high hydrophobicity and PTM. By combining a stringent manual check of the MS2 spectra with peptides synthesis verification, we confirmed 30 MPs (neXtProt PE2 ∼ PE4) and 6 potential MPs (neXtProt PE5) with authentic MS evidence. By integrating our large-scale data sets of CCPD 2.0, the number of identified proteins has increased considerably beyond simulation saturation. Here, we show that special enrichment strategies can break through the data saturation bottleneck, which could increase the efficiency of MP identification in future C-HPP studies. All 7 data sets have been uploaded to ProteomeXchange with the identifier PXD002255.