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Cells
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New Insights into Proteomics and Post-translational Modification of Proteins
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New Insights into Proteomics and Post-translational Modification of Proteins

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Methods".

Deadline for manuscript submissions: closed (25 November 2022) | Viewed by 19291

Special Issue Editor

Prof. Dr. Yu Xue

E-Mail Website
Guest Editor
Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
Interests: proteomics; phosphoproteomics; bioinformatics; artificial intelligence biology; deep learning; autophagy; protein kinase
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Proteins are fundamental biological molecules in cells, while post-translational modifications (PTMs) are key mechanisms to dynamically and reversibly regulate the functions of proteins. Systematic and large-scale analysis of protein sequences, structures, variations, expression profiles, PTMs and functional impacts not only enables a much better understanding of life, but also provides high-potential biomarkers and drug targets for biomedical consideration. Recently, advances in cutting-edge technologies, such as high-throughput mass spectrometry, high-content imaging or screening analysis, and protein microarray on the experimental side, as well as artificial intelligence (AI), statistical methods, network modeling, and proteogenomic integration on the computational side, have generated new insights in proteomics and PTMs of proteins and held the concept of proteomics-driven precision medicine (PDPM).

The purpose of this Special Issue is to highlight recent technological innovations in the analysis of proteomics and PTMs of proteins, as well as new biological findings using these new techniques. The topics include but are not limited to high-throughput experimental technology, AI/statistics/network technology, proteomics-oriented findings, and comprehensive data resources on proteins, proteomics and/or PTMs. Interdisciplinary studies are highly encouraged and appreciated. We welcome the submission of both original research articles and reviews.

Prof. Dr. Yu Xue
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cells is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • proteomics
  • post-translational modification
  • phosphorylation
  • phosphoproteomics
  • imaging mass cytometry
  • mass spectrometry
  • high-content screening analysis
  • artificial intelligence
  • machine learning
  • deep learning
  • data resource

Published Papers (7 papers)

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Research

16 pages, 2274 KiB  
Article
PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions
by Xiaoxiu Tan, Linfeng Xu, Xingxing Jian, Jian Ouyang, Bo Hu, Xinrong Yang, Tao Wang and Lu Xie
Cells 2023, 12(5), 782; https://doi.org/10.3390/cells12050782 - 01 Mar 2023
Cited by 2 | Viewed by 2506
Abstract
The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived [...] Read more.
The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI. Full article
(This article belongs to the Special Issue New Insights into Proteomics and Post-translational Modification of Proteins)
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31 pages, 5002 KiB  
Article
A Practical and Analytical Comparative Study of Gel-Based Top-Down and Gel-Free Bottom-Up Proteomics Including Unbiased Proteoform Detection
by Huriye Ercan, Ulrike Resch, Felicia Hsu, Goran Mitulovic, Andrea Bileck, Christopher Gerner, Jae-Won Yang, Margarethe Geiger, Ingrid Miller and Maria Zellner
Cells 2023, 12(5), 747; https://doi.org/10.3390/cells12050747 - 26 Feb 2023
Cited by 8 | Viewed by 2521
Abstract
Proteomics is an indispensable analytical technique to study the dynamic functioning of biological systems via different proteins and their proteoforms. In recent years, bottom-up shotgun has become more popular than gel-based top-down proteomics. The current study examined the qualitative and quantitative performance of [...] Read more.
Proteomics is an indispensable analytical technique to study the dynamic functioning of biological systems via different proteins and their proteoforms. In recent years, bottom-up shotgun has become more popular than gel-based top-down proteomics. The current study examined the qualitative and quantitative performance of these two fundamentally different methodologies by the parallel measurement of six technical and three biological replicates of the human prostate carcinoma cell line DU145 using its two most common standard techniques, label-free shotgun and two-dimensional differential gel electrophoresis (2D-DIGE). The analytical strengths and limitations were explored, finally focusing on the unbiased detection of proteoforms, exemplified by discovering a prostate cancer-related cleavage product of pyruvate kinase M2. Label-free shotgun proteomics quickly yields an annotated proteome but with reduced robustness, as determined by three times higher technical variation compared to 2D-DIGE. At a glance, only 2D-DIGE top-down analysis provided valuable, direct stoichiometric qualitative and quantitative information from proteins to their proteoforms, even with unexpected post-translational modifications, such as proteolytic cleavage and phosphorylation. However, the 2D-DIGE technology required almost 20 times as much time per protein/proteoform characterization with more manual work. Ultimately, this work should expose both techniques’ orthogonality with their different contents of data output to elucidate biological questions. Full article
(This article belongs to the Special Issue New Insights into Proteomics and Post-translational Modification of Proteins)
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17 pages, 5246 KiB  
Article
Proteomic and Phosphoproteomic Profiling Reveals the Oncogenic Role of Protein Kinase D Family Kinases in Cholangiocarcinoma
by Yun Lu, Xiangyu Li, Kai Zhao, Yuanxin Shi, Zhengdong Deng, Wei Yao and Jianming Wang
Cells 2022, 11(19), 3088; https://doi.org/10.3390/cells11193088 - 30 Sep 2022
Cited by 5 | Viewed by 1994
Abstract
Cholangiocarcinoma (CCA) is a lethal malignancy in the hepatobiliary system, with dysregulated protein expression and phosphorylation signaling. However, the protein and phosphorylation signatures of CCAs are little-known. Here, we performed the proteomic and phosphoproteomic profiling of tumors and normal adjacent tissues (NATs) from [...] Read more.
Cholangiocarcinoma (CCA) is a lethal malignancy in the hepatobiliary system, with dysregulated protein expression and phosphorylation signaling. However, the protein and phosphorylation signatures of CCAs are little-known. Here, we performed the proteomic and phosphoproteomic profiling of tumors and normal adjacent tissues (NATs) from patients with CCA and predicted eleven PKs high-potentially related to CCA with a comprehensive inference of the functional protein kinases (PKs) (CifPK) pipeline. Besides the two known CCA-associated PKs, we screened the remaining candidates and uncovered five PKs as novel regulators in CCA. Specifically, the protein kinase D (PKD) family members, including PRKD1, PRKD2, and PRKD3, were identified as critical regulators in CCA. Moreover, the pan-inhibitor of the PKD family, 1-naphthyl PP1 (1-NA-PP1), was validated as a potent agent for inhibiting the proliferation, migration, and invasion ability of CCA cells. This study reveals new PKs associated with CCA and suggests PRKD kinases as novel treatment targets for CCA. Full article
(This article belongs to the Special Issue New Insights into Proteomics and Post-translational Modification of Proteins)
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16 pages, 3108 KiB  
Article
Integration of Human Protein Sequence and Protein-Protein Interaction Data by Graph Autoencoder to Identify Novel Protein-Abnormal Phenotype Associations
by Yuan Liu, Ruirui He, Yingjie Qu, Yuan Zhu, Dianke Li, Xinping Ling, Simin Xia, Zhenqiu Li and Dong Li
Cells 2022, 11(16), 2485; https://doi.org/10.3390/cells11162485 - 10 Aug 2022
Cited by 4 | Viewed by 2245
Abstract
Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far [...] Read more.
Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein–protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets. Full article
(This article belongs to the Special Issue New Insights into Proteomics and Post-translational Modification of Proteins)
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11 pages, 397 KiB  
Article
ProB-Site: Protein Binding Site Prediction Using Local Features
by Sharzil Haris Khan, Hilal Tayara and Kil To Chong
Cells 2022, 11(13), 2117; https://doi.org/10.3390/cells11132117 - 05 Jul 2022
Cited by 5 | Viewed by 4259
Abstract
Protein–protein interactions (PPIs) are responsible for various essential biological processes. This information can help develop a new drug against diseases. Various experimental methods have been employed for this purpose; however, their application is limited by their cost and time consumption. Alternatively, computational methods [...] Read more.
Protein–protein interactions (PPIs) are responsible for various essential biological processes. This information can help develop a new drug against diseases. Various experimental methods have been employed for this purpose; however, their application is limited by their cost and time consumption. Alternatively, computational methods are considered viable means to achieve this crucial task. Various techniques have been explored in the literature using the sequential information of amino acids in a protein sequence, including machine learning and deep learning techniques. The current efficiency of interaction-site prediction still has growth potential. Hence, a deep neural network-based model, ProB-site, is proposed. ProB-site utilizes sequential information of a protein to predict its binding sites. The proposed model uses evolutionary information and predicted structural information extracted from sequential information of proteins, generating three unique feature sets for every amino acid in a protein sequence. Then, these feature sets are fed to their respective sub-CNN architecture to acquire complex features. Finally, the acquired features are concatenated and classified using fully connected layers. This methodology performed better than state-of-the-art techniques because of the selection of the best features and contemplation of local information of each amino acid. Full article
(This article belongs to the Special Issue New Insights into Proteomics and Post-translational Modification of Proteins)
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27 pages, 3262 KiB  
Article
Comprehensive Evaluation of Different TiO2-Based Phosphopeptide Enrichment and Fractionation Methods for Phosphoproteomics
by Jiaran Li, Jifeng Wang, Yumeng Yan, Na Li, Xiaoqing Qing, Ailikemu Tuerxun, Xiaojing Guo, Xiulan Chen and Fuquan Yang
Cells 2022, 11(13), 2047; https://doi.org/10.3390/cells11132047 - 28 Jun 2022
Cited by 6 | Viewed by 2501
Abstract
Protein phosphorylation is an essential post-translational modification that regulates multiple cellular processes. Due to their low stoichiometry and ionization efficiency, it is critical to efficiently enrich phosphopeptides for phosphoproteomics. Several phosphopeptide enrichment methods have been reported; however, few studies have comprehensively compared different [...] Read more.
Protein phosphorylation is an essential post-translational modification that regulates multiple cellular processes. Due to their low stoichiometry and ionization efficiency, it is critical to efficiently enrich phosphopeptides for phosphoproteomics. Several phosphopeptide enrichment methods have been reported; however, few studies have comprehensively compared different TiO2-based phosphopeptide enrichment methods using complex proteomic samples. Here, we compared four TiO2-based phosphopeptide enrichment methods that used four non-phosphopeptide excluders (glutamic acid, lactic acid, glycolic acid, and DHB). We found that these four TiO2-based phosphopeptide enrichment methods had different enrichment specificities and that phosphopeptides enriched by the four methods had different physicochemical characteristics. More importantly, we discovered that phosphopeptides had a higher deamidation ratio than peptides from cell lysate and that phosphopeptides enriched using the glutamic acid method had a higher deamidation ratio than the other three methods. We then compared two phosphopeptide fractionation methods: ammonia- or TEA-based high pH reversed-phase (HpH-RP). We found that fewer phosphopeptides, especially multi-phosphorylated peptides, were identified using the ammonia-based method than using the TEA-based method. Therefore, the TEA-based HpH-RP fractionation method performed better than the ammonia method. In conclusion, we comprehensively evaluated different TiO2-based phosphopeptide enrichment and fractionation methods, providing a basis for selecting the proper protocols for comprehensive phosphoproteomics. Full article
(This article belongs to the Special Issue New Insights into Proteomics and Post-translational Modification of Proteins)
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18 pages, 1856 KiB  
Article
iPCD: A Comprehensive Data Resource of Regulatory Proteins in Programmed Cell Death
by Dachao Tang, Cheng Han, Shaofeng Lin, Xiaodan Tan, Weizhi Zhang, Di Peng, Chenwei Wang and Yu Xue
Cells 2022, 11(13), 2018; https://doi.org/10.3390/cells11132018 - 24 Jun 2022
Viewed by 2154
Abstract
Programmed cell death (PCD) is an essential biological process involved in many human pathologies. According to the continuous discovery of new PCD forms, a large number of proteins have been found to regulate PCD. Notably, post-translational modifications play critical roles in PCD process [...] Read more.
Programmed cell death (PCD) is an essential biological process involved in many human pathologies. According to the continuous discovery of new PCD forms, a large number of proteins have been found to regulate PCD. Notably, post-translational modifications play critical roles in PCD process and the rapid advances in proteomics have facilitated the discovery of new PCD proteins. However, an integrative resource has yet to be established for maintaining these regulatory proteins. Here, we briefly summarize the mainstream PCD forms, as well as the current progress in the development of public databases to collect, curate and annotate PCD proteins. Further, we developed a comprehensive database, with integrated annotations for programmed cell death (iPCD), which contained 1,091,014 regulatory proteins involved in 30 PCD forms across 562 eukaryotic species. From the scientific literature, we manually collected 6493 experimentally identified PCD proteins, and an orthologous search was then conducted to computationally identify more potential PCD proteins. Additionally, we provided an in-depth annotation of PCD proteins in eight model organisms, by integrating the knowledge from 102 additional resources that covered 16 aspects, including post-translational modification, protein expression/proteomics, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein–protein interaction, drug–target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, subcellular localization and DNA and RNA element. With a data volume of 125 GB, we anticipate that iPCD can serve as a highly useful resource for further analysis of PCD in eukaryotes. Full article
(This article belongs to the Special Issue New Insights into Proteomics and Post-translational Modification of Proteins)
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