PTMscape: an open source tool to predict generic post-translational modifications and map modification crosstalk in protein domains and biological processes

Review Insights on Salivary Proteomics Biomarkers in Oral Cancer Detection and Diagnosis

Early detection is crucial for the treatment and prognosis of oral cancer, a potentially lethal condition. Tumor markers are abnormal biological byproducts produced by malignant cells that may be found and analyzed in a variety of bodily fluids, including saliva. Early detection and appropriate treatment can increase cure rates to 80-90% and considerably improve quality of life by reducing the need for costly, incapacitating medicines. Salivary diagnostics has drawn the interest of many researchers and has been proven to be an effective tool for both medication monitoring and the diagnosis of several systemic diseases. Since researchers are now searching for biomarkers in saliva, an accessible bodily fluid, for noninvasive diagnosis of oral cancer, measuring tumor markers in saliva is an interesting alternative to blood testing for early identification, post-treatment monitoring, and monitoring high-risk lesions. New molecular markers for oral cancer detection, treatment, and prognosis have been found as a result of developments in the fields of molecular biology and salivary proteomics. The numerous salivary tumor biomarkers and how they relate to oral cancer and pre-cancer are covered in this article. We are optimistic that salivary protein biomarkers may one day be discovered for the clinical detection of oral cancer because of the rapid advancement of proteomic technology.

Targeting protein modifications in metabolic diseases: molecular mechanisms and targeted therapies

The ever-increasing prevalence of noncommunicable diseases (NCDs) represents a major public health burden worldwide. The most common form of NCD is metabolic diseases, which affect people of all ages and usually manifest their pathobiology through life-threatening cardiovascular complications. A comprehensive understanding of the pathobiology of metabolic diseases will generate novel targets for improved therapies across the common metabolic spectrum. Protein posttranslational modification (PTM) is an important term that refers to biochemical modification of specific amino acid residues in target proteins, which immensely increases the functional diversity of the proteome. The range of PTMs includes phosphorylation, acetylation, methylation, ubiquitination, SUMOylation, neddylation, glycosylation, palmitoylation, myristoylation, prenylation, cholesterylation, glutathionylation, S-nitrosylation, sulfhydration, citrullination, ADP ribosylation, and several novel PTMs. Here, we offer a comprehensive review of PTMs and their roles in common metabolic diseases and pathological consequences, including diabetes, obesity, fatty liver diseases, hyperlipidemia, and atherosclerosis. Building upon this framework, we afford a through description of proteins and pathways involved in metabolic diseases by focusing on PTM-based protein modifications, showcase the pharmaceutical intervention of PTMs in preclinical studies and clinical trials, and offer future perspectives. Fundamental research defining the mechanisms whereby PTMs of proteins regulate metabolic diseases will open new avenues for therapeutic intervention.

Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models

Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, machine learning models largely enriched the prediction of PTMs. However, most machine learning models only rely on protein sequence and little structural information. The lack of the systematic dynamics analysis underlying PTMs largely limits the PTM functional predictions. In this research, we present two dynamics-centric deep learning models, namely, cDL-PAU and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based features to elucidate the molecular basis and underlying functional landscape of PTMs. cDL-PAU achieved satisfactory area under the curve (AUC) scores of 0.804-0.888 for predicting phosphorylation, acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos) sites, displaying reliable improvements. Through a feature selection, the dynamics-based coupling and commute ability show large contributions in discovering PAU sites and FuncPhos sites, suggesting the allosteric propensity for important PTMs. The application of cDL-FuncPhos in three oncoproteins not only corroborates its strong performance in FuncPhos prioritization but also gains insight into the physical basis for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos are available at https://github.com/ComputeSuda/PTM_ML.

GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites

As an important post-translational modification, lysine ubiquitination participates in numerous biological processes and is involved in human diseases, whereas the site specificity of ubiquitination is mainly decided by ubiquitin-protein ligases (E3s). Although numerous ubiquitination predictors have been developed, computational prediction of E3-specific ubiquitination sites is still a great challenge. Here, we carefully reviewed the existing tools for the prediction of general ubiquitination sites. Also, we developed a tool named GPS-Uber for the prediction of general and E3-specific ubiquitination sites. From the literature, we manually collected 1311 experimentally identified site-specific E3-substrate relations, which were classified into different clusters based on corresponding E3s at different levels. To predict general ubiquitination sites, we integrated 10 types of sequence and structure features, as well as three types of algorithms including penalized logistic regression, deep neural network and convolutional neural network. Compared with other existing tools, the general model in GPS-Uber exhibited a highly competitive accuracy, with an area under curve values of 0.7649. Then, transfer learning was adopted for each E3 cluster to construct E3-specific models, and in total 112 individual E3-specific predictors were implemented. Using GPS-Uber, we conducted a systematic prediction of human cancer-associated ubiquitination events, which could be helpful for further experimental consideration. GPS-Uber will be regularly updated, and its online service is free for academic research at http://gpsuber.biocuckoo.cn/.

Plant Proteoforms Under Environmental Stress: Functional Proteins Arising From a Single Gene

Proteins are directly involved in plant phenotypic response to ever changing environmental conditions. The ability to produce multiple mature functional proteins, i.e., proteoforms, from a single gene sequence represents an efficient tool ensuring the diversification of protein biological functions underlying the diversity of plant phenotypic responses to environmental stresses. Basically, two major kinds of proteoforms can be distinguished: protein isoforms, i.e., alterations at protein sequence level arising from posttranscriptional modifications of a single pre-mRNA by alternative splicing or editing, and protein posttranslational modifications (PTMs), i.e., enzymatically catalyzed or spontaneous modifications of certain amino acid residues resulting in altered biological functions (or loss of biological functions, such as in non-functional proteins that raised as a product of spontaneous protein modification by reactive molecular species, RMS). Modulation of protein final sequences resulting in different protein isoforms as well as modulation of chemical properties of key amino acid residues by different PTMs (such as phosphorylation, N- and O-glycosylation, methylation, acylation, S-glutathionylation, ubiquitinylation, sumoylation, and modifications by RMS), thus, represents an efficient means to ensure the flexible modulation of protein biological functions in response to ever changing environmental conditions. The aim of this review is to provide a basic overview of the structural and functional diversity of proteoforms derived from a single gene in the context of plant evolutional adaptations underlying plant responses to the variability of environmental stresses, i.e., adverse cues mobilizing plant adaptive mechanisms to diminish their harmful effects.

ADPriboDB 2.0: an updated database of ADP-ribosylated proteins

ADP-ribosylation is a protein modification responsible for biological processes such as DNA repair, RNA regulation, cell cycle and biomolecular condensate formation. Dysregulation of ADP-ribosylation is implicated in cancer, neurodegeneration and viral infection. We developed ADPriboDB (adpribodb.leunglab.org) to facilitate studies in uncovering insights into the mechanisms and biological significance of ADP-ribosylation. ADPriboDB 2.0 serves as a one-stop repository comprising 48 346 entries and 9097 ADP-ribosylated proteins, of which 6708 were newly identified since the original database release. In this updated version, we provide information regarding the sites of ADP-ribosylation in 32 946 entries. The wealth of information allows us to interrogate existing databases or newly available data. For example, we found that ADP-ribosylated substrates are significantly associated with the recently identified human protein interaction networks associated with SARS-CoV-2, which encodes a conserved protein domain called macrodomain that binds and removes ADP-ribosylation. In addition, we create a new interactive tool to visualize the local context of ADP-ribosylation, such as structural and functional features as well as other post-translational modifications (e.g. phosphorylation, methylation and ubiquitination). This information provides opportunities to explore the biology of ADP-ribosylation and generate new hypotheses for experimental testing.

ADPriboDB v2.0: An Updated Database of ADP-ribosylated Proteins

ADP-ribosylation is a protein modification responsible for biological processes such as DNA repair, RNA regulation, cell cycle, and biomolecular condensate formation. Dysregulation of ADP-ribosylation is implicated in cancer, neurodegeneration, and viral infection. We developed ADPriboDB (adpribodb.leunglab.org) to facilitate studies in uncovering insights into the mechanisms and biological significance of ADP-ribosylation. ADPriboDB 2.0 serves as a one-stop repository comprising 48,346 entries and 9,097 ADP-ribosylated proteins, of which 6,708 were newly identified since the original database release. In this updated version, we provide information regarding the sites of ADP-ribosylation in 32,946 entries. The wealth of information allows us to interrogate existing databases or newly available data. For example, we found that ADP-ribosylated substrates are significantly associated with the recently identified human protein interaction networks associated with SARS-CoV-2, which encodes a conserved protein domain called macrodomain that binds and removes ADP-ribosylation. In addition, we create a new interactive tool to visualize the local context of ADP-ribosylation, such as structural and functional features as well as other post-translational modifications (e.g., phosphorylation, methylation and ubiquitination). This information provides opportunities to explore the biology of ADP-ribosylation and generate new hypotheses for experimental testing.

Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features

Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.

What Is New in (Plant) Proteomics Methods and Protocols: The 2015-2019 Quinquennium

The third edition of "Plant Proteomics Methods and Protocols," with the title "Advances in Proteomics Techniques, Data Validation, and Integration with Other Classic and -Omics Approaches in the Systems Biology Direction," was conceived as being based on the success of the previous editions, and the continuous advances and improvements in proteomic techniques, equipment, and bioinformatics tools, and their uses in basic and translational plant biology research that has occurred in the past 5 years (in round figures, of around 22,000 publications referenced in WoS, 2000 were devoted to plants).The monograph contains 29 chapters with detailed proteomics protocols commonly employed in plant biology research. They present recent advances at all workflow stages, starting from the laboratory (tissue and cell fractionation, protein extraction, depletion, purification, separation, MS analysis, quantification) and ending on the computer (algorithms for protein identification and quantification, bioinformatics tools for data analysis, databases and repositories). At the end of each chapter there are enough explanatory notes and comments to make the protocols easily applicable to other biological systems and/or studies, discussing limitations, artifacts, or pitfalls. For that reason, as with the previous editions, it would be especially useful for beginners or novices.Out of the 29 chapters, six are devoted to descriptive proteomics, with a special emphasis on subcellular protein profiling (Chapters 5 - 10 ), six to PTMs (Chapters 11 , and 14 - 18 ), three to protein interactions (Chapters 19 - 21 ), and two to specific proteins, peroxidases (Chapter 24 ) and proteases and protease inhibitors (Chapter 26 ). The book reflects the new trajectory in MS-based protein identification and quantification, moving from the classic gel-based approaches to the most recent labeling (Chapters 10 , 11 , 29 ), shotgun (Chapters 5 , 7 , 12 , 15 ), parallel reaction monitoring (Chapter 16 ), and targeted data acquisition (Chapter 13 ). MS imaging (Chapter 25 ), the only in vivo MS-based proteomics strategy, is far from being fully optimized and exploited in plant biology research. A confident protein identification and quantitation, especially in orphan species, of low-abundance proteins, is still a challenging task (Chapters 4 , 28 ).What is really new is the use of different techniques for proteomics data validation and their integration into other classic and -omics approaches in the systems biology direction. Chapter 2 reports on multiple extractions in a single experiment of the different biomolecules, nucleic acids, proteins, and metabolites. Chapter 27 describes how metabolic pathways can be reconstructed from multiple -omics data, and Chapter 3 network building. Finally, Chapters 22 and 23 deal with, respectively, the search for allele-specific proteins and proteogenomics.Around 200 groups were, almost 1 year ago, invited to take part in this edition. Unfortunately, only 10% of them kindly accepted. My gratitude to those who accepted our invitation but also to those who did not, as all of them have contributed to the plant proteomics field. I will enlist, in this introductory chapter, following my own judgment, some of the relevant papers published in the past 5 years, those that have shown us how to enhance and exploit the potential of proteomics in plant biology research, without aiming at giving a too exhaustive list.

Protein Post-Translational Modification Crosstalk in Acute Myeloid Leukemia Calls for Action

BACKGROUND

Post-translational modification (PTM) crosstalk is a young research field. However, there is now evidence of the extraordinary characterization of the different proteoforms and their interactions in a biological environment that PTM crosstalk studies can describe. Besides gene expression and phosphorylation profiling of acute myeloid leukemia (AML) samples, the functional combination of several PTMs that might contribute to a better understanding of the complexity of the AML proteome remains to be discovered.

OBJECTIVE

By reviewing current workflows for the simultaneous enrichment of several PTMs and bioinformatics tools to analyze mass spectrometry (MS)-based data, our major objective is to introduce the PTM crosstalk field to the AML research community.

RESULTS

After an introduction to PTMs and PTM crosstalk, this review introduces several protocols for the simultaneous enrichment of PTMs. Two of them allow a simultaneous enrichment of at least three PTMs when using 0.5-2 mg of cell lysate. We have reviewed many of the bioinformatics tools used for PTM crosstalk discovery as its complex data analysis, mainly generated from MS, becomes challenging for most AML researchers. We have presented several non-AML PTM crosstalk studies throughout the review in order to show how important the characterization of PTM crosstalk becomes for the selection of disease biomarkers and therapeutic targets.

CONCLUSION

Herein, we have reviewed the advances and pitfalls of the emerging PTM crosstalk field and its potential contribution to unravel the heterogeneity of AML. The complexity of sample preparation and bioinformatics workflows demands a good interaction between experts of several areas.

PTMselect: optimization of protein modifications discovery by mass spectrometry

Discovery of protein modification sites relies on protein digestion by proteases and mass spectrometry (MS) identification of the modified peptides. Depending on proteases used and target protein sequence, this method yields highly variable coverage of modification sites. We introduce PTMselect, a digestion-simulating software which tailors the optimal set of proteases for discovery of global or targeted modification from any single or multiple proteins.