Types Of Post-translational Modifications Research Articles

EdeepSADPr: an extensive deep-learning architecture for prediction of the in situ crosstalks of serine phosphorylation and ADP-ribosylation.

The in situ post-translational modification (PTM) crosstalk refers to the interactions between different types of PTMs that occur on the same residue site of a protein. The crosstalk sites generally have different characteristics from those with the single PTM type. Studies targeting the latter's features have been widely conducted, while studies on the former's characteristics are rare. For example, the characteristics of serine phosphorylation (pS) and serine ADP-ribosylation (SADPr) have been investigated, whereas those of their in situ crosstalks (pSADPr) are unknown. In this study, we collected 3,250 human pSADPr, 7,520 SADPr, 151,227pS and 80,096 unmodified serine sites and explored the features of the pSADPr sites. We found that the characteristics of pSADPr sites are more similar to those of SADPr compared to pS or unmodified serine sites. Moreover, the crosstalk sites are likely to be phosphorylated by some kinase families (e.g., AGC, CAMK, STE and TKL) rather than others (e.g., CK1 and CMGC). Additionally, we constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. We also used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. The best classifiers had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites, respectively. The lowest prediction accuracy was achieved by separating pSADPr and SADPr sites, which is consistent with the observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/. We expect our investigation will promote a comprehensive understanding of crosstalks.

Elucidating the Implications of Norovirus N- and O-Glycosylation, O-GlcNAcylation, and Phosphorylation.

Norovirus is the most common cause of foodborne gastroenteritis, affecting millions of people worldwide annually. Among the ten genotypes (GI-GX) of norovirus, only GI, GII, GIV, GVIII, and GIX infect humans. Some genotypes reportedly exhibit post-translational modifications (PTMs), including N- and O-glycosylation, O-GlcNAcylation, and phosphorylation, in their viral antigens. PTMs have been linked to increased viral genome replication, viral particle release, and virulence. Owing to breakthroughs in mass spectrometry (MS) technologies, more PTMs have been discovered in recent years and have contributed significantly to preventing and treating infectious diseases. However, the mechanisms by which PTMs act on noroviruses remain poorly understood. In this section, we outline the current knowledge of the three common types of PTM and investigate their impact on norovirus pathogenesis. Moreover, we summarize the strategies and techniques for the identification of PTMs.

Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning.

Recently, lysine lactylation (Kla), a novel post-translational modification (PTM), which can be stimulated by lactate, has been found to regulate gene expression and life activities. Therefore, it is imperative to accurately identify Kla sites. Currently, mass spectrometry is the fundamental method for identifying PTM sites. However, it is expensive and time-consuming to achieve this through experiments alone. Herein, we proposed a novel computational model, Auto-Kla, to quickly and accurately predict Kla sites in gastric cancer cells based on automated machine learning (AutoML). With stable and reliable performance, our model outperforms the recently published model in the 10-fold cross-validation. To investigate the generalizability and transferability of our approach, we evaluated the performance of our models trained on two other widely studied types of PTM, including phosphorylation sites in host cells infected with SARS-CoV-2 and lysine crotonylation sites in HeLa cells. The results show that our models achieve comparable or better performance than current outstanding models. We believe that this method will become a useful analytical tool for PTM prediction and provide a reference for the future development of related models. The web server and source code are available at http://tubic.org/Kla and https://github.com/tubic/Auto-Kla, respectively.

Systematic Assessment of Protein C-Termini Mutated in Human Disorders.

All proteins have a carboxyl terminus, and we previously summarized eight mutations in binding and trafficking sequence determinants in the C-terminus that, when disrupted, cause human diseases. These sequence elements for binding and trafficking sites, as well as post-translational modifications (PTMs), are called minimotifs or short linear motifs. We wanted to determine how frequently mutations in minimotifs in the C-terminus cause disease. We searched specifically for PTMs because mutation of a modified amino acid almost always changes the chemistry of the side chain and can be interpreted as loss-of-function. We analyzed data from ClinVar for disease variants, Minimotif Miner and the C-terminome for PTMs, and RefSeq for protein sequences, yielding 20 such potential disease-causing variants. After additional screening, they include six with a previously reported PTM disruption mechanism and nine with new hypotheses for mutated minimotifs in C-termini that may cause disease. These mutations were generally for different genes, with four different PTM types and several different diseases. Our study helps to identify new molecular mechanisms for nine separate variants that cause disease, and this type of analysis could be extended as databases grow and to binding and trafficking motifs. We conclude that mutated motifs in C-termini are an infrequent cause of disease.

Post‐translational modification and phenotype

Post‐translational modification and phenotype

Enhancements in flavor substances, mouthfeel characteristics and collagen synthesis in the muscle of sub-adult grass carp (Ctenopharyngodon Idella): Application of a dietary lysine nutrition strategy

The growth performance, flavor substances, mouthfeel characteristics and collagen synthesis in the muscle of sub-adult grass carp (659.07 ± 0.74 g) were investigated after 60-day feeding with graded dietary levels of lysine (6.0–17.0 g/kg diet). The results showed that optimal dietary lysine levels boosted the growth of grass carp. Proper dietary lysine levels improved muscle inosine monophosphate, free amino acids, unsaturated fatty acid content, water holding capacity, pH and shear force (hardness). The diets also decreased lactate content, and enhanced collagen content by increasing transcription, translation and post-translational modifications of type I collagen, probably via activating TGF-β1/Smads and PI3K/Akt/p70S6k/la ribonucleoprotein domain family member 6 (LARP6) signaling; this enhanced prolyl 4-hydroxylase (P4H) and lysyl hydroxylase (LOX) content, thereby improving the flavor and mouthfeel of muscle. In summary, this research indicated that dietary lysine could lead to the enhancement of muscle flavor substances, mouthfeel characteristics and type I collagen synthesis in sub-adult grass carp. Based on the PWG and shear force, the dietary requirements of lysine for sub-adult grass carp are 13.43 and 12.96 g/kg, respectively.

Evolutionary Signals in Coronaviral Structural Proteins Suggest Possible Complex Mechanisms of Post-Translational Regulation in SARS-CoV-2 Virus.

Post-translational regulation of proteins has emerged as a central topic of research in the field of functional proteomics. Post-translational modifications (PTMs) dynamically control the activities of proteins and are involved in a wide range of biological processes. Crosstalk between different types of PTMs represents a key mechanism of regulation and signaling. Due to the current pandemic of the novel and dangerous SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) virus, here we present an in silico analysis of different types of PTMs in structural proteins of coronaviruses. A dataset of PTM sites was studied at three levels: conservation analysis, mutational analysis and crosstalk analysis. We identified two sets of PTMs which could have important functional roles in the regulation of the structural proteins of coronaviruses. Additionally, we found seven interesting signals of potential crosstalk events. These results reveal a higher level of complexity in the mechanisms of post-translational regulation of coronaviral proteins and provide new insights into the adaptation process of the SARS-CoV-2 virus.

Posttranslational modifications in pathogenesis of PCOS.

Polycystic ovary syndrome (PCOS) is a lifelong reproductive, metabolic, and psychiatric disorder that affects 5-18% of women, which is associated with a significantly increased lifetime risk of concomitant diseases, including type 2 diabetes, psychiatric disorders, and gynecological cancers. Posttranslational modifications (PTMs) play an important role in changes in protein function and are necessary to maintain cellular viability and biological processes, thus their maladjustment can lead to disease. Growing evidence suggests the association between PCOS and posttranslational modifications. This article mainly reviews the research status of phosphorylation, methylation, acetylation, and ubiquitination, as well as their roles and molecular mechanisms in the development of PCOS. In addition, we briefly summarize research and clinical trials of PCOS therapy to advance our understanding of agents that can be used to target phosphorylated, methylated, acetylated, and ubiquitinated PTM types. It provides not only ideas for future research on the mechanism of PCOS but also ideas for PCOS treatments with therapeutic potential.

Complementary crosstalk between palmitoylation and phosphorylation events in MTIP regulates its role during Plasmodium falciparum invasion

Post-translational modifications (PTMs) including phosphorylation and palmitoylation have emerged as crucial biomolecular events that govern many cellular processes including functioning of motility- and invasion-associated proteins during Plasmodium falciparum invasion. However, no study has ever focused on understanding the possibility of a crosstalk between these two molecular events and its direct impact on preinvasion- and invasion-associated protein–protein interaction (PPI) network-based molecular machinery. Here, we used an integrated in silico analysis to enrich two different catalogues of proteins: (i) the first group defines the cumulative pool of phosphorylated and palmitoylated proteins, and (ii) the second group represents a common set of proteins predicted to have both phosphorylation and palmitoylation. Subsequent PPI analysis identified an important protein cluster comprising myosin A tail interacting protein (MTIP) as one of the hub proteins of the glideosome motor complex in P. falciparum, predicted to have dual modification with the possibility of a crosstalk between the same. Our findings suggested that blocking palmitoylation led to reduced phosphorylation and blocking phosphorylation led to abrogated palmitoylation of MTIP. As a result of the crosstalk between these biomolecular events, MTIP’s interaction with myosin A was found to be abrogated. Next, the crosstalk between phosphorylation and palmitoylation was confirmed at a global proteome level by click chemistry and the phenotypic effect of this crosstalk was observed via synergistic inhibition in P. falciparum invasion using checkerboard assay and isobologram method. Overall, our findings revealed, for the first time, an interdependence between two PTM types, their possible crosstalk, and its direct impact on MTIP-mediated invasion via glideosome assembly protein myosin A in P. falciparum. These insights can be exploited for futuristic drug discovery platforms targeting parasite molecular machinery for developing novel antimalarial therapeutics.

Unique Posttranslational Modification Sites of Acetylation, Citrullination, Glutarylation, and Phosphorylation Are Found to Be Specific to the Proteins Partitioned in the Triton X-114 Fractions of Leptospira.

Posttranslational modifications (PTMs) are decisive factors in the structure, function, and localization of proteins in prokaryotic and eukaryotic organisms. However, prokaryotic organisms lack subcellular organelles, and protein localization based on subcellular locations like cytoplasm, inner membrane, periplasm, and outer membrane can be accounted for functional characterization. We have identified 131 acetylated, 1182 citrullinated, 72 glutarylated, 5 palmitoylated, and 139 phosphorylated proteins from Triton X-114 fractionated proteins of Leptospira, the pathogen of re-emerging zoonotic disease leptospirosis. In total, 74.7% of proteins were found exclusively in different Triton X-114 fractions. Additionally, 21.9% of proteins in multiple fractions had one or more PTM specific to different Triton X-114 fractions. Altogether, 96.6% of proteins showed exclusiveness to different Triton X-114 fractions either due to the presence of the entire protein or with a specific PTM type or position. Further, the PTM distribution within Triton X-114 fractions showed higher acetylation in aqueous, glutarylation in detergent, phosphorylation in pellet, and citrullination in wash fractions representing cytoplasmic, outer membrane, inner membrane, and extracellular locations, respectively. Identification of PTMs in proteins with respect to the subcellular localization will help to characterize candidate proteins before developing novel drugs and vaccines rationally to combat leptospirosis.

The structural context of posttranslational modifications at a proteome-wide scale.

The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of posttranslational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, disordered regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis, which revealed examples of spatial coregulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing.

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues.

Mass spectrometry can provide deep coverage of post-translational modifications (PTMs), although enrichment of these modifications from complex biological matrices is often necessary due to their low stoichiometry in comparison to non-modified analytes. Most enrichment workflows of PTMs on peptides in bottom-up proteomics workflows, where proteins are enzymatically digested before the resulting peptides are analyzed, only enrich one type of modification. It is the entire complement of PTMs, however, that leads to biological functions, and enrichment of a single type of PTM may miss such crosstalk of PTMs. PTM crosstalk has been observed between protein glycosylation and phosphorylation, the two most common PTMs in human proteins and also the two most studied PTMs using mass spectrometry workflows. Using the simultaneous enrichment strategy described herein, both PTMs are enriched from post-mortem human pancreatic tissue, a complex biological matrix. Dual-functional Ti(IV)-immobilized metal affinity chromatography is used to separate various forms of glycosylation and phosphorylation simultaneously in multiple fractions in a convenient spin tip-based method, allowing downstream analyses of potential PTM crosstalk interactions. This enrichment workflow for glyco- and phosphopeptides can be applied to various sample types to achieve deep profiling of multiple PTMs and identify potential target molecules for future studies.

Histone post-translational modifications - cause and consequence of genome function.

Much has been learned since the early 1960s about histone post-translational modifications (PTMs) and how they affect DNA-templated processes at the molecular level. This understanding has been bolstered in the past decade by the identification of new types of histone PTM, the advent of new genome-wide mapping approaches and methods to deposit or remove PTMs in a locally and temporally controlled manner. Now, with the availability of vast amounts of data across various biological systems, the functional role of PTMs in important processes (such as transcription, recombination, replication, DNA repair and the modulation of genomic architecture) is slowly emerging. This Review explores the contribution of histone PTMs to the regulation of genome function by discussing when these modifications play a causative (or instructive) role in DNA-templated processes and when they are deposited as a consequence of such processes, to reinforce and record the event. Important advances in the field showing that histone PTMs can exert both direct and indirect effects on genome function are also presented.

Post-Translational Modifications of G Protein-Coupled Receptors Revealed by Proteomics and Structural Biology.

G protein–coupled receptors (GPCRs) are a protein superfamily comprising >800 members that regulate numerous cellular and physiologic responses. GPCRs represent the largest class of therapeutic targets with implications in various diseases. Although advances in GPCR structural and pharmacological research have significantly improved our knowledge of GPCR signaling mechanisms, mapping diverse post-translational modifications (PTMs) of GPCR proteins and understanding their regulatory roles have received much less attention. Mass spectrometry-based proteomics has become the most popular technology for profiling protein PTMs in a systematic manner. Herein we provide an overview of PTM types, locations, crosstalk and dynamic regulation for different GPCRs that are characterized using proteomic and/or biochemical approaches. Our main focus is on glycosylation, phosphorylation, ubiquitination and palmitoylation that are known to modulate receptor folding, biosynthesis, trafficking, dimerization and signaling. Furthermore, we discuss the locations of specific PTM sites in the structure of a given GPCR and its signaling complex to highlight the importance of PTM regulation in the molecular basis of GPCRs, which may shed new light on structure-based drug discovery.

SAMDC3 enhances resistance to Barley stripe mosaic virus by promoting the ubiquitination and proteasomal degradation of viral γb protein

Posttranslational modifications (PTMs) play important roles in virus-host interplay. We previously demonstrated that Barley stripe mosaic virus (BSMV) γb protein is phosphorylated by different host kinases to support or impede viral infection. However, whether and how other types of PTMs participate in BSMV infection remains to be explored. Here, we report that S-adenosylmethionine decarboxylase 3 (SAMDC3) from Nicotiana benthamiana or wheat (Triticum aestivum) interacts with γb. BSMV infection induced SAMDC3 expression. Overexpression of SAMDC3 led to the destabilization of γb and reduction in viral infectivity, whereas knocking out NbSAMDC3 increased susceptibility to BSMV. NbSAMDC3 positively regulated the 26S proteasome-mediated degradation of γb via its PEST domain. Further mechanistic studies revealed that γb can be ubiquitinated in planta and that NbSAMDC3 promotes the proteasomal degradation of γb by increasing γb ubiquitination. We also found evidence that ubiquitination occurs at nonlysine residues (Ser-133 and Cys-144) within γb. Together, our results provide a function for SAMDC3 in defence against BSMV infection through targeting of γb abundance, which contributes to our understanding of how a plant host deploys the ubiquitin-proteasome system to mount defences against viral infections.

Current status of PTMs structural databases: applications, limitations and prospects.

Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure-function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.

Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT

MotivationProtein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.MethodWe use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.Results and conclusionThe PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter approx 5700 (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts.

Analysis of Posttranslational Modifications in Arabidopsis Proteins and Metabolic Pathways Using the FAT-PTM Database.

Posttranslational modifications (PTMs) are critical regulators of protein behavior, and over 200 different types of PTMs have been identified. Recent developments in mass spectrometry technology and sample enrichment approaches have led to a massive expansion in the number of identified PTM types and sites within eukaryotic proteins. As these types of data become increasingly available, it is important to develop additional analysis tools and data repositories to investigate PTM cross talk and larger networks of PTMs. Recently, we developed the Functional Analysis Tools for Post-translational Modifications (FAT-PTM) database, which supports data from publicly available proteomic analyses encompassing eight different types of PTMs and over 49,000 PTM sites. In this chapter, we describe the utility of FAT-PTM for analysis of posttranslationally modified proteins in three different contexts. First, a simple protein search tool is available that allows users to investigate proteins in the Arabidopsis proteome to identify types of PTMs that are associated with the query protein as well as quantitative phosphorylation site changes associated with ten different experimental conditions. Second, FAT-PTM contains a metabolic pathway analysis tool to investigate PTMs in the broader context of over 600 different metabolic pathways compiled from the Plant Metabolic Network. Finally, FAT-PTM contains a comodification tool that can be used to identify groups of proteins that are subject to two or more user-defined PTMs. Overall, FAT-PTM provides a user-friendly platform to visualize posttranslationally modified proteins at the individual, metabolic pathway, and PTM cross-talk levels.

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.

Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.

Exploration of Protein Posttranslational Modification Landscape and Cross Talk with CrossTalkMapper.

Post-translational modifications (PTMs) of proteins play crucial roles in defining protein function. They often do not occur alone, leading to a large variety of proteoforms that correspond to different combinations of multiple PTMs simultaneously decorating a protein. Changes of these proteoforms can be quantified via middle-down and top-down mass spectrometry experiments where the simultaneous PTM settings are obtained by measuring long peptides or entire proteins. Data from such experiments poses big challenges in identifying relevant features of biological and clinical importance. Generally, multiple data layers need to be considered such as proteoforms, individual PTMs, and PTM types. Therein, visualization methods are a crucial part of data analysis as they provide, if applied correctly, insights into both general behaviors as well as a deep view into fine-grained behavior. Here, we present a workflow to visualize histone proteins and their myriad of PTMs based on different R visualization modules applied to data from quantitative middle-down experiments. The procedure can be adapted to diverse experimental designs and is applicable to different proteins and PTMs.