Protein post-translational modifications: In silico prediction tools and molecular modeling

Post-translational modifications of vertebrate striated muscle myosin heavy chains

Post-translational modifications (PTMs) play a crucial role in regulating the function of many sarcomeric proteins, including myosin. Myosins comprise a family of motor proteins that play fundamental roles in cell motility in general and muscle contraction in particular. A myosin molecule consists of two myosin heavy chains (MyHCs) and two pairs of myosin light chains (MLCs); two MLCs are associated with the neck region of each MyHC's N-terminal head domain, while the two MyHC C-terminal tails form a coiled-coil that polymerizes with other MyHCs to form the thick filament backbone. Myosin undergoes extensive PTMs, and dysregulation of these PTMs may lead to abnormal muscle function and contribute to the development of myopathies and cardiovascular disorders. Recent studies have uncovered the significance of PTMs in regulating MyHC function and showed how these PTMs may provide additional modulation of contractile processes. Here, we discuss MyHC PTMs that have been biochemically and/or functionally studied in mammals' and rodents' striated muscle. We have identified hotspots or specific regions in three isoforms of myosin (MYH2, MYH6, and MYH7) where the prevalence of PTMs is more frequent and could potentially play a significant role in fine-tuning the activity of these proteins.

SRT1720 inhibits bladder cancer cell progression by impairing autophagic flux

Bladder cancer (BC) is the most common cancer of the urinary tract, with poor survival, high recurrence rates, and lacking of targeted drugs. In this study, we constructed a library to screen compounds inhibiting bladder cancer cells growth. Among them, SRT1720 was identified to inhibit bladder cancer cell proliferation in vitro and in vivo. SRT1720 treatment also suppressed bladder cancer cells migration, invasion and induced apoptosis. Mechanism studies shown that SRT1720 promoted autophagosomes accumulation by inducing early-stage autophagy but disturbed the late-stage of autophagy by blocking fusion of autophagosomes and lysosomes. SRT1720 appears to induce autophagy related proteins expression and alter autophagy-related proteins acetylation to impede the autophagy flux. LAMP2, an important lysosomal associated membrane protein, may mediate SRT1720-inhibited autophagy flux as SRT1720 treatment significantly deacetylated LAMP2 which may influence its activity. Taken together, our results demonstrated that SRT1720 mediated apoptosis and autophagy flux inhibition may be a novel therapeutic strategy for bladder cancer treatment.

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network

As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. Networks of sequence similarity and Gaussian interaction profile kernel similarity were constructed for phosphorylation sites, as well as networks of disease semantic similarity, disease symptom similarity and Gaussian interaction profile kernel similarity were constructed for diseases. To effectively combine different phosphorylation sites and disease similarity information, random walk with restart algorithm was used to obtain the topology information of the network. Then, the diffusion component analysis method was utilized to obtain the comprehensive phosphorylation site similarity and disease similarity. Meanwhile, the reliable negative samples were screened based on the Euclidean distance method. Finally, a convolutional neural network (CNN) model was constructed to identify potential associations between phosphorylation sites and diseases. Based on tenfold cross-validation, the evaluation indicators were obtained including accuracy of 93.48%, specificity of 96.82%, sensitivity of 90.15%, precision of 96.62%, Matthew's correlation coefficient of 0.8719, area under the receiver operating characteristic curve of 0.9786 and area under the precision-recall curve of 0.9836. Additionally, most of the top 20 predicted disease-related phosphorylation sites (19/20 for Alzheimer's disease; 20/16 for neuroblastoma) were verified by literatures and databases. These results show that the proposed method has an outstanding prediction performance and a high practical value.

Trioxidized cysteine in the aging proteome mimics the structural dynamics and interactome of phosphorylated serine

Aging is the primary risk factor for the development of numerous human chronic diseases. On a molecular level, it significantly impacts the regulation of protein modifications, leading to the accumulation of degenerative protein modifications (DPMs) such as aberrant serine phosphorylation (p-Ser) and trioxidized cysteine (t-Cys) within the proteome. The altered p-Ser is linked to abnormal cell signaling, while the accumulation of t-Cys is associated with chronic diseases induced by oxidative stress. Despite this, the potential cross-effects and functional interplay between these two critical molecular factors of aging remain undisclosed. This study analyzes the aging proteome of wild-type C57BL/6NTac mice over 2 years using advanced proteomics and bioinformatics. Our objective is to provide a comprehensive analysis of how t-Cys affects cell signaling and protein structure in the aging process. The results obtained indicate that t-Cys residues accumulate in the aging proteome, interact with p-Ser interacting enzymes, as validated in vitro, and alter their structures similarly to p-Ser. These findings have significant implications for understanding the interplay of oxidative stress and phosphorylation in the aging process. Additionally, they open new venues for further research on the role(s) of these protein modifications in various human chronic diseases and aging, wherein exacerbated oxidation and aberrant phosphorylation are implicated.

Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences

The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety of biological functions, such as cellular regulation and pathogenesis. This modification plays a crucial role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life processes. However, accurately identifying malonylation sites is crucial to understand the molecular mechanism of malonylation, and the experimental identification can be a challenging and costly task. Recently, approaches based on machine learning (ML) have been suggested to address this issue. It has been demonstrated that these procedures improve accuracy while lowering costs and time constraints. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features and inefficient underlying classifiers. As a result, there is an urgent need for effective predictors and calculation methods. In this study, we provide a comprehensive analysis and review of existing prediction models, tools and benchmark datasets for predicting malonylation sites in protein sequences followed by a comparison study. The review consists of the specifications of benchmark datasets, explanation of features and encoding methods, descriptions of the predictions approaches and their embedding ML or deep learning models and the description and comparison of the existing tools in this domain. To evaluate and compare the prediction capability of the tools, a new bunch of data has been extracted based on the most updated database and the tools have been assessed based on the extracted data. Finally, a hybrid architecture consisting of several classifiers including classical ML models and a deep learning model has been proposed to ensemble the prediction results. This approach demonstrates the better performance in comparison with all prediction tools included in this study (the source codes of the models presented in this manuscript are available in https://github.com/Malonylation). Database URL: https://github.com/A-Golshan/Malonylation.

Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.

Analysis of Structural Changes in the Protein near the Phosphorylation Site

Modification of the protein after synthesis (PTM) often affects protein function as supported by numerous studies. However, there is no consensus about the degree of structural protein changes after modification. For phosphorylation of serine, threonine, and tyrosine, which is a common PTM in the biology of living organisms, we consider topical issues related to changes in the geometric parameters of a protein (Rg, RMSD, Cα displacement, SASA). The effect of phosphorylation on protein geometry was studied both for the whole protein and at the local level (i.e., in different neighborhoods of the modification site). Heterogeneity in the degree of protein structural changes after phosphorylation was revealed, which allowed for us to isolate a group of proteins having pronounced local structural changes in the neighborhoods of up to 15 amino acid residues from the modification site. This is a comparative study of protein structural changes in neighborhoods of 3-15 amino acid residues from the modified site. Amino acid phosphorylation in proteins with pronounced local changes caused switching from the inactive functional state to the active one.

HPV16 Impacts NHERF2 Expression in Oropharyngeal Cancers

Infection with human papillomaviruses (HPVs), in particular with HPV type 16, is now considered to be a key risk factor for the development of a subset of oropharyngeal squamous cell carcinomas (OPSCC) that show different epidemiological, clinical, and prognostic characteristics from HPV-negative (HPV-) OPSCCs. So far, extensive research efforts aiming to distinguish these two distinct entities have not identified specific biomarkers, nor led to different therapies. Previous research has shown that HPV16 E6 oncoprotein binds NHERF2, inducing its proteasomal degradation, and consequently increasing cell proliferation; we therefore aimed to investigate how this might be reflected in human histological samples. We analyzed NHERF2 expression patterns in HPV16-positive (HPV16+) and HPV- OPSCC samples, to investigate any potential differences in NHERF2 pattern. Interestingly, we observed a statistically significant decrease in NHERF2 levels in HPV16+ and poorly differentiated HPV- OPSCCs, compared with healthy tissue. Furthermore, we observed a significant reduction in the percentage of NHERF2 immunoreactive cancer cells in HPV16+ tumors, compared with well and moderately differentiated HPV- OPSCCs, suggesting the importance of 16E6's targeting of NHERF2 in HPV-driven oncogenesis in the head and neck area.

A small protein coded within the mitochondrial canonical gene nd4 regulates mitochondrial bioenergetics

BACKGROUND

Mitochondria have a central role in cellular functions, aging, and in certain diseases. They possess their own genome, a vestige of their bacterial ancestor. Over the course of evolution, most of the genes of the ancestor have been lost or transferred to the nucleus. In humans, the mtDNA is a very small circular molecule with a functional repertoire limited to only 37 genes. Its extremely compact nature with genes arranged one after the other and separated by short non-coding regions suggests that there is little room for evolutionary novelties. This is radically different from bacterial genomes, which are also circular but much larger, and in which we can find genes inside other genes. These sequences, different from the reference coding sequences, are called alternatives open reading frames or altORFs, and they are involved in key biological functions. However, whether altORFs exist in mitochondrial protein-coding genes or elsewhere in the human mitogenome has not been fully addressed.

RESULTS

We found a downstream alternative ATG initiation codon in the + 3 reading frame of the human mitochondrial nd4 gene. This newly characterized altORF encodes a 99-amino-acid-long polypeptide, MTALTND4, which is conserved in primates. Our custom antibody, but not the pre-immune serum, was able to immunoprecipitate MTALTND4 from HeLa cell lysates, confirming the existence of an endogenous MTALTND4 peptide. The protein is localized in mitochondria and cytoplasm and is also found in the plasma, and it impacts cell and mitochondrial physiology.

CONCLUSIONS

Many human mitochondrial translated ORFs might have so far gone unnoticed. By ignoring mtaltORFs, we have underestimated the coding potential of the mitogenome. Alternative mitochondrial peptides such as MTALTND4 may offer a new framework for the investigation of mitochondrial functions and diseases.

c-Jun N-terminal kinase 1 (JNK1) phosphorylates OTX2 transcription factor that regulates early retinal development

BACKGROUND

The transcription factor orthodenticle homeobox 2 (OTX2) has critical functions in brain and eye development, and its mutations in humans are related to retinal diseases, such as ocular coloboma and microphthalmia. However, the regulatory mechanisms of OTX2 are poorly identified.

OBJECTIVE

The identification of JNK1 as an OTX2 regulatory protein through the protein interaction and phosphorylation.

METHODS

To identify the binding partner of OTX2, we performed co-immunoprecipitation and detected with a pooled antibody that targeted effective kinases. The protein interaction between JNK1 and OTX2 was identified with the co-immunoprecipitation and immunocytochemistry. In vivo and in vitro kinase assay of JNK1 was performed to detect the phosphorylation of OTX2 by JNK1.

RESULTS

JNK1 directly interacted with OTX2 through the transactivation domain at the c-terminal region. The protein-protein interaction and co-localization between JNK1 and OTX2 were further validated in the developing P0 mouse retina. In addition, we confirmed that the inactivation of JNK1 K55N mutant significantly reduced the JNK1-mediated phosphorylation of OTX2 by performing an immune complex protein kinase assay.

CONCLUSION

c-Jun N-terminal kinase 1 (JNK1) phosphorylates OTX2 transcription factor through the protein-protein interaction.

Proteomics strategies for urothelial bladder cancer diagnosis, prognosis and treatment: Trends for tumor biomarker sources

Urothelial bladder cancer (UBC) is a heterogeneous multifactorial malignancy with a high recurrence rate. Current procedures for UBC diagnosis suffering from the lack of clinical sensitivity and specificity screening tests. Therefore, biomarkers have promising values to predict pathological conditions and can be considered as effective targets for early diagnosis, prognosis and antitumor immunotherapy. Recently, researchers have been interested for tumor proteins as biomarkers for different diseases. At present, proteomics methods have rapidly progressive that has potential identified biomarkers of UBC. Specifically, there has been several studies on the potential application of proteomics for the identification, quantification, and profiling of proteins for UBC in different sources. Based on these studies, using the panel of biomarkers as proteomic patterns may achieve higher sensitivity and specificity than single proteins in the diagnosis of UBC. In the present review, we evaluate recent literature related to the UBC proteome focusing especially on new proteomics techniques. Moreover, we classify UBC tumor biomarkers as diagnostic, prognostic, and therapeutic targets based on their sources (urine, serum/plasm, cell line, and tumor tissue) and we also discuss the advantages and limitations of each source. In this manner, this review article provides a critical assessment presentation of the advances in proteomics for all aspects of UBC diagnosis, prognosis, and treatment based on sources.

MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases

We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enable simultaneous prediction of multiple PTMs with high performance and computation efficiency. MIND-S also features an interpretation module, which provides the relevance of each amino acid for making the predictions and is validated with known motifs. The interpretation module also captures PTM patterns without any supervision. Furthermore, MIND-S enables examination of mutation effects on PTMs. We document a workflow, its applications to 26 types of PTMs of two datasets consisting of ∼50,000 proteins, and an example of MIND-S identifying a PTM-interrupting SNP with validation from biological data. We also include use case analyses of targeted proteins. Taken together, we have demonstrated that MIND-S is accurate, interpretable, and efficient to elucidate PTM-relevant biological processes in health and diseases.

Emerging Roles of SIRT5 in Metabolism, Cancer, and SARS-CoV-2 Infection

Sirtuin 5 (SIRT5) is a predominantly mitochondrial enzyme catalyzing the removal of glutaryl, succinyl, malonyl, and acetyl groups from lysine residues through a NAD+-dependent deacylase mechanism. SIRT5 is an important regulator of cellular homeostasis and modulates the activity of proteins involved in different metabolic pathways such as glycolysis, tricarboxylic acid (TCA) cycle, fatty acid oxidation, electron transport chain, generation of ketone bodies, nitrogenous waste management, and reactive oxygen species (ROS) detoxification. SIRT5 controls a wide range of aspects of myocardial energy metabolism and plays critical roles in heart physiology and stress responses. Moreover, SIRT5 has a protective function in the context of neurodegenerative diseases, while it acts as a context-dependent tumor promoter or suppressor. In addition, current research has demonstrated that SIRT5 is implicated in the SARS-CoV-2 infection, although opposing conclusions have been drawn in different studies. Here, we review the current knowledge on SIRT5 molecular actions under both healthy and diseased settings, as well as its functional effects on metabolic targets. Finally, we revise the potential of SIRT5 as a therapeutic target and provide an overview of the currently reported SIRT5 modulators, which include both activators and inhibitors.

EhSir2c, a Sir2 homolog from the human pathogen Entamoeba histolytica interacts with a DNA repair protein, EhRAD23: Protein-protein interaction, docking and functional study

Chromosome segregation is a crucial phenomenon in the cell cycle and defects in genome segregation result in an abnormality in various cellular events. Unlike higher eukaryotes, chromosome segregation and a number of cell cycle events are unusual in the protozoan parasite Entamoeba histolytica (E. histolytica). Characterization of Sir2 proteins from E. histolytica may reveal its unique cellular events as they play role in diverse cellular processes including chromosome segregation. E. histolytica has four homologs of Sir2 proteins. EhSir2a and EhSir2b show sequence similarity towards eukaryotic Sir2 homologs, whereas EhSir2c and EhSir2d are more like prokaryotic sirtuins. Using both computational and experimental methods, EhSir2c has been characterized in this study. The three-dimensional structure of EhSir2c is predicted by homology modelling. The protein interactors of EhSir2c have been identified by yeast-two-hybrid screening against the cDNA library of E. histolytica. We have identified a novel interactor, EhRAD23 which is a homolog of UV excision repair protein RAD23. The interaction of EhSir2c and EhRAD23 was validated by pull-down assay. UV-C irradiation up-regulates the relative expression of EhSir2c, suggesting the necessity of EhSir2c in UV-induced stress in this parasite.Communicated by Ramaswamy H. Sarma.

Defining the S-Glutathionylation Proteome by Biochemical and Mass Spectrometric Approaches

Protein S-glutathionylation (SSG) is a reversible post-translational modification (PTM) featuring the conjugation of glutathione to a protein cysteine thiol. SSG can alter protein structure, activity, subcellular localization, and interaction with small molecules and other proteins. Thus, it plays a critical role in redox signaling and regulation in various physiological activities and pathological events. In this review, we summarize current biochemical and analytical approaches for characterizing SSG at both the proteome level and at individual protein levels. To illustrate the mechanism underlying SSG-mediated redox regulation, we highlight recent examples of functional and structural consequences of SSG modifications. Finally, we discuss the analytical challenges in characterizing SSG and the thiol PTM landscape, future directions for understanding of the role of SSG in redox signaling and regulation and its interplay with other PTMs, and the potential role of computational approaches to accelerate functional discovery.

Minor Kinases with Major Roles in Cytokinesis Regulation

Cytokinesis, the conclusive act of cell division, allows cytoplasmic organelles and chromosomes to be faithfully partitioned between two daughter cells. In animal organisms, its accurate regulation is a fundamental task for normal development and for preventing aneuploidy. Cytokinesis failures produce genetically unstable tetraploid cells and ultimately result in chromosome instability, a hallmark of cancer cells. In animal cells, the assembly and constriction of an actomyosin ring drive cleavage furrow ingression, resulting in the formation of a cytoplasmic intercellular bridge, which is severed during abscission, the final event of cytokinesis. Kinase-mediated phosphorylation is a crucial process to orchestrate the spatio-temporal regulation of the different stages of cytokinesis. Several kinases have been described in the literature, such as cyclin-dependent kinase, polo-like kinase 1, and Aurora B, regulating both furrow ingression and/or abscission. However, others exist, with well-established roles in cell-cycle progression but whose specific role in cytokinesis has been poorly investigated, leading to considering these kinases as "minor" actors in this process. Yet, they deserve additional attention, as they might disclose unexpected routes of cell division regulation. Here, we summarize the role of multifunctional kinases in cytokinesis with a special focus on those with a still scarcely defined function during cell cleavage. Moreover, we discuss their implication in cancer.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

Casein kinase CK2 structure and activities in plants

Casein kinase CK2 is a highly conserved serine/threonine protein kinase and exists in all eukaryotes. It has been demonstrated to be widely involved in the biological processes of plants. The CK2 holoenzyme is a heterotetramer consisting of two catalytic subunits (α and/or α') and two regulatory subunits (β). CK2 in plants is generally encoded by multiple genes, with monomeric and oligomeric forms present in the tissue. Various subunit genes of CK2 have been cloned and characterized from Arabidopsis thaliana, tobacco, maize, wheat, tomato, and other plants. This paper reviews the structural features of CK2, provides a clear classification of its physiological functions and mechanisms of action, and elaborates on the regulation of CK2 activity to provide a knowledge base for subsequent studies of CK2 in plants.

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection

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Endothelial dysfunction misleads blood marker discovery by differential expression.

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Blood-derived surrogate transcriptome of target-tissue avoids the false discovery.

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ITGA5 implies polymicrobial infection of maternal-fetal interface in preeclampsia.

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ITGA5 and IRF6 implies viral co-infection in early-onset preeclampsia.

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ITGA5, IRF6, and P2RX7 differ imminent preeclampsia from COVID-19 infection.

Background

A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection.

Methods

The surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information.

Results

A prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5.

Conclusions

In a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.

Advances in enrichment methods for mass spectrometry-based proteomics analysis of post-translational modifications

Post-translational modifications (PTMs) occur during or after protein biosynthesis and increase the functional diversity of proteome. They comprise phosphorylation, acetylation, methylation, glycosylation, ubiquitination, sumoylation (among many other modifications), and influence all aspects of cell biology. Mass-spectrometry (MS)-based proteomics is the most powerful approach for PTM analysis. Despite this, it is challenging due to low abundance and labile nature of many PTMs. Hence, enrichment of modified peptides is required for MS analysis. This review provides an overview of most common PTMs and a discussion of current enrichment methods for MS-based proteomics analysis. The traditional affinity strategies, including immunoenrichment, chromatography and protein pull-down, are outlined together with their strengths and shortcomings. Moreover, a special attention is paid to chemical enrichment strategies, such as capture by chemoselective probes, metabolic and chemoenzymatic labelling, which are discussed with an emphasis on their recent progress. Finally, the challenges and future trends in the field are discussed.

Thiol-based Oxidative Posttranslational Modifications (OxiPTMs) of Plant Proteins

The thiol group of cysteine (Cys) residues, often present in the active center of the protein, is of particular importance to protein function, which is significantly determined by the redox state of a protein's environment. Our knowledge of different thiol-based oxidative posttranslational modifications (oxiPTMs), which compete for specific protein thiol groups, has increased over the last 10 years. The principal oxiPTMs include S-sulfenylation, S-glutathionylation, S-nitrosation, persulfidation, S-cyanylation and S-acylation. The role of each oxiPTM depends on the redox cellular state, which in turn depends on cellular homeostasis under either optimal or stressful conditions. Under such conditions, the metabolism of molecules such as glutathione, NADPH (reduced nicotinamide adenine dinucleotide phosphate), nitric oxide, hydrogen sulfide and hydrogen peroxide can be altered, exacerbated and, consequently, outside the cell's control. This review provides a broad overview of these oxiPTMs under physiological and unfavorable conditions, which can regulate the function of target proteins.

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.

Genetic Analysis of ABCB4 Mutations and Variants Related to the Pathogenesis and Pathophysiology of Low Phospholipid-Associated Cholelithiasis

Clinical studies have revealed that the ABCB4 gene encodes the phospholipid transporter on the canalicular membrane of hepatocytes, and its mutations and variants are the genetic basis of low phospholipid-associated cholelithiasis (LPAC), a rare type of gallstone disease caused by a single-gene mutation or variation. The main features of LPAC include a reduction or deficiency of phospholipids in bile, symptomatic cholelithiasis at <40 years of age, intrahepatic sludge and microlithiasis, mild chronic cholestasis, a high cholesterol/phospholipid ratio in bile, and recurrence of biliary symptoms after cholecystectomy. Needle-like cholesterol crystals, putatively “anhydrous” cholesterol crystallization at low phospholipid concentrations in model and native bile, are characterized in ABCB4 knockout mice, a unique animal model for LPAC. Gallbladder bile with only trace amounts of phospholipids in these mice is supersaturated with cholesterol, with lipid composition plotting in the left two-phase zone of the ternary phase diagram, consistent with “anhydrous” cholesterol crystallization. In this review, we summarize the molecular biology and physiological functions of ABCB4 and comprehensively discuss the latest advances in the genetic analysis of ABCB4 mutations and variations and their roles in the pathogenesis and pathophysiology of LPAC in humans, based on the results from clinical studies and mouse experiments. To date, approximately 158 distinct LPAC-causing ABCB4 mutations and variants in humans have been reported in the literature, indicating that it is a monogenic risk factor for LPAC. The elucidation of the ABCB4 function in the liver, the identification of ABCB4 mutations and variants in LPAC patients, and the exploration of gene therapy for ABCB4 deficiency in animal models can help us to better understand the cellular, molecular, and genetic mechanisms underlying the onset of the disease, and will pave the way for early diagnosis and prevention of susceptible subjects and effective intervention for LPAC in patients.

Novel Post-translational Modifications in Human Serum Albumin

Aim:

This study aims to identify novel post-translational modifications in human serum albumin by mass spectrometry.

Background:

Serum albumin is the most abundant protein in plasma, has many physiological functions, and is in contact with most of the cells and tissues of the human body. Post-translational modifications (PTMs) may affect functions, stability, and localization of albumin.

Objective:

Identify novel PTMs in human serum albumin by mass spectrometry.

Methods:

Human serum albumin (HSA) was used for tryptic digestion in-solution or in-gel. Mass spectrometry was applied to identify PTMs in HSA. 3-dimensional modeling was applied to explore the potential impact of PTMs on known functions of albumin.

Results:

Here, we report the identification of 61 novel PTMs of human serum albumin. Phosphorylation, glycosylation, nitrosylation, deamidation, methylation, acetylation, palmitoylation, geranylation, and farnesylation are some examples of the identified PTMs. Mass spectrometry was used for the identification of PTMs in a purified HSA and HSA from the human plasma. Threedimensional modeling of albumin with selected PTMs showed the location of these PTMs in the regions involved in albumin interactions with drugs, metals, and fatty acids. The location of PTMs in these regions may modify the binding capacity of albumin.

Conclusion:

This report adds 61 novel PTMs to the catalog of human albumin.

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction.

Dissecting Plasmodium yoelii Pathobiology: Proteomic Approaches for Decoding Novel Translational and Post-Translational Modifications

Malaria is a vector-borne disease. It is caused by Plasmodium parasites. Plasmodium yoelii is a rodent model parasite, primarily used for studying parasite development in liver cells and vectors. To better understand parasite biology, we carried out a high-throughput-based proteomic analysis of P. yoelii. From the same mass spectrometry (MS)/MS data set, we also captured several post-translational modified peptides by following a bioinformatics analysis without any prior enrichment. Further, we carried out a proteogenomic analysis, which resulted in improvements to some of the existing gene models along with the identification of several novel genes. Analysis of proteome and post-translational modifications (PTMs) together resulted in the identification of 3124 proteins. The identified PTMs were found to be enriched in mitochondrial metabolic pathways. Subsequent bioinformatics analysis provided an insight into proteins associated with metabolic regulatory mechanisms. Among these, the tricarboxylic acid (TCA) cycle and the isoprenoid synthesis pathway are found to be essential for parasite survival and drug resistance. The proteogenomic analysis discovered 43 novel protein-coding genes. The availability of an in-depth proteomic landscape of a malaria pathogen model will likely facilitate further molecular-level investigations on pre-erythrocytic stages of malaria.

Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network

Phosphorylation of proteins is one of the most significant post-translational modifications (PTMs) and plays a crucial role in plant functionality due to its impact on signaling, gene expression, enzyme kinetics, protein stability and interactions. Accurate prediction of plant phosphorylation sites (p-sites) is vital as abnormal regulation of phosphorylation usually leads to plant diseases. However, current experimental methods for PTM prediction suffers from high-computational cost and are error-prone. The present study develops machine learning-based prediction techniques, including a high-performance interpretable deep tabular learning network (TabNet) to improve the prediction of protein p-sites in soybean. Moreover, we use a hybrid feature set of sequential-based features, physicochemical properties and position-specific scoring matrices to predict serine (Ser/S), threonine (Thr/T) and tyrosine (Tyr/Y) p-sites in soybean for the first time. The experimentally verified p-sites data of soybean proteins are collected from the eukaryotic phosphorylation sites database and database post-translational modification. We then remove the redundant set of positive and negative samples by dropping protein sequences with >40% similarity. It is found that the developed techniques perform >70% in terms of accuracy. The results demonstrate that the TabNet model is the best performing classifier using hybrid features and with window size of 13, resulted in 78.96 and 77.24% sensitivity and specificity, respectively. The results indicate that the TabNet method has advantages in terms of high-performance and interpretability. The proposed technique can automatically analyze the data without any measurement errors and any human intervention. Furthermore, it can be used to predict putative protein p-sites in plants effectively. The collected dataset and source code are publicly deposited at https://github.com/Elham-khalili/Soybean-P-sites-Prediction.

Histological Grade and Tumor Stage Are Correlated with Expression of Receptor Activator of Nuclear Factor Kappa b (Rank) in Epithelial Ovarian Cancers

The receptor activator of nuclear factor kappa B (RANK) is becoming recognized as a master regulator of tumorigenesis, yet its role in gynecological cancers remains mostly unexplored. We investigated whether there is a gradation of RANK protein and mRNA expression in epithelial ovarian cancer (EOC) according to malignancy and tumor staging. Immunohistochemical expression of RANK was examined in a cohort of 135 (benign n = 29, borderline n= 23 and malignant n = 83) EOCs. Wild type and truncated RANK mRNA isoform quantification was performed in a cohort of 168 (benign n = 26, borderline n = 13 and malignant n = 129) EOCs. RANK protein and mRNA values were increased in malignant vs. benign or borderline conditions across serous, mucinous and endometrioid cancer subtypes. Additionally, a trend of increased RANK values with staging was observed for the mucinous and serous histotype. Thus, increased expression of RANK appears associated with the evolution of disease to the onset of malignancy in EOC. Moreover, in some EOC histotypes, RANK expression is additionally associated with clinicopathological markers of tumor aggressiveness, suggesting a role in further progression of tumor activity.

Positioning of an unprecedented 1,5-oxaza spiroquinone scaffold into SMYD2 inhibitors in epigenetic space

Lysine methyltransferases are important regulators of epigenetic signaling and are emerging as a novel drug target for drug discovery. This work demonstrates the positioning of novel 1,5-oxaza spiroquinone scaffold into selective SET and MYND domain-containing proteins 2 methyltransferases inhibitors. Selectivity of the scaffold was identified by epigenetic target screening followed by SAR study for the scaffold. The optimization was performed iteratively by two-step optimization consisting of iterative synthesis and computational studies (docking, metadynamics simulations). Computational binding studies guided the important interactions of the spiro[5.5]undeca scaffold in pocket 1 and Lysine channel and suggested extension of tail length for the improvement of potency (IC₅₀: up to 399 nM). The effective performance of cell proliferation assay for chosen compounds (IC₅₀: up to 11.9 nM) led to further evaluation in xenograft assay. The potent compound 24 demonstrated desirable in vivo efficacy with growth inhibition rate of 77.7% (4 fold decrease of tumor weight and 3 fold decrease of tumor volume). Moreover, mirosomal assay and pharmacokinetic profile suggested further developability of this scaffold through the identification of major metabolites (dealkylation at silyl group, reversible hydration product, the absence of toxic quinone fragments) and enough exposure of the testing compound 24 in plasma. Such spiro[5.5]undeca framework or ring system was neither been reported nor suggested as a modulator of methyltransferases. The chemo-centric target positioning and structural novelty can lead to potential pharmacological benefit.

Capillary Electrophoresis-Mass Spectrometry (CE-MS) by Sheath-Flow Nanospray Interface and Its Use in Biopharmaceutical Applications

Both capillary electrophoresis (CE) and mass spectrometry (MS) technologies are powerful analytical tools that have been used extensively in the characterization of biologics in the biopharmaceutical industry. The direct coupling of CE with MS is an attractive approach, in that the high separation capability of CE and the ultrasensitive detection and accurate identification performance of MS can be combined to provide a powerful system for the analysis of complex analytes. In this chapter, we discuss the detailed procedure of carrying out CE-MS analysis using a nano sheath-flow interface and its applications including intact mass analysis of monoclonal antibodies and fusion proteins, and a biotransformation study of two Fc-FGF21 molecules in a single-dose pharmacokinetic mice study. Optimization processes, including the finetuning of CE conditions and MS parameters, are illustrated in this chapter, with focuses on method robustness and assay reproducibility.

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

Structural property, molecular regulation, and functional diversity of glutamine synthetase in higher plants: a data-mining bioinformatics approach

Glutamine synthetase (GS; E.C.6.3.1.2) is a key enzyme in higher plants with two isozymes, cytosolic GS1 and plastidic GS2, and involves in the assimilation and recycling of NH₄⁺ ions and maintenance of complex traits such as crop nitrogen-use efficiency and yield. Our present understanding of crop nitrogen-use efficiency and its correlation with the functional role of the GS family genes is inadequate, which delays harnessing the benefit of this key enzyme in crop improvement. In this report, we performed a comprehensive investigation on the phylogenetic relationship, structural properties, complex multilevel gene regulation, and expression patterns of the GS genes to enrich present understanding about the enzyme. Our Gene Ontology and protein-protein interactions analysis revealed the functional aspects of GS isozymes in stress mitigation, aging, nucleotide biosynthesis/transport, DNA repair and response to metals. The insight gained here contributes to the future research strategies in developing climate-smart crops for global sustainability.

Understanding the impact of missense mutations on the structure and function of the EDA gene in X-linked hypohidrotic ectodermal dysplasia: A bioinformatics approach

X-linked hypohidrotic dysplasia (XLHED), caused by mutations in the EDA gene, is a rare genetic disease that affects the development and function of the teeth, hair, nails, and sweat glands. The structural and functional consequences of caused by an ectodysplasin-A (EDA) mutations on protein phenotype, stability, and posttranslational modifications (PTMs) have not been well investigated. The present investigation involves five missense mutations that cause XLHED (L56P, R155C, P220L, V251M, and V322A) in different domains of EDA (TM, furin, collagen, and tumor necrosis factor [TNF]) from previously published papers. The deleterious nature of EDA mutant variants was identified using several computational algorithm tools. The point mutations induce major drifts in the structural flexibility of EDA mutant variants and have a negative impact on their stability, according to the 3D protein modeling tool assay. Using the molecular docking technique, EDA/EDA variants were docked to 10 EDA interacting partners, retrieved from the STRING database. We found a novel biomarker CD68 by molecular docking analysis, suggesting all five EDA variants had lower affinity for EDAR, EDA2R, and CD68, implying that they would affect embryonic signaling between the ectodermal and mesodermal cell layers. In silico research such as gene ontology, subcellular localization, protein-protein interaction, and PTMs investigations indicates major functional alterations would occur in EDA variants. According to molecular simulations, EDA variants influence the structural conformation, compactness, stiffness, and function of the EDA protein. Further studies on cell line and animal models might be useful in determining their specific roles in functional annotations.

Development and Validation of AMBER-FB15-Compatible Force Field Parameters for Phosphorylated Amino Acids

Phosphorylation of select amino acid residues is one of the most common biological mechanisms for regulating protein structures and functions. While computational modeling can be used to explore the detailed structural changes associated with phosphorylation, most molecular mechanics force fields developed for the simulation of phosphoproteins have been noted to be inconsistent with experimental data. In this work, we parameterize force fields for the phosphorylated forms of the amino acids serine, threonine, and tyrosine using the ForceBalance software package with the goal of improving agreement with experiments for these residues. Our optimized force field, denoted as FB18, is parameterized using high-quality ab initio potential energy scans and is designed to be fully compatible with the AMBER-FB15 protein force field. When utilized in MD simulations together with the TIP3P-FB water model, we find that FB18 consistently enhances the prediction of experimental quantities such as ³J NMR couplings and intramolecular hydrogen-bonding propensities in comparison to previously published models. As was reported with AMBER-FB15, we also see improved agreement with the reference QM calculations in regions at and away from local minima. We thus believe that the FB18 parameter set provides a promising route for the further investigation of the varied effects of protein phosphorylation.

Post-translational modifications of proteins associated with yeast peroxisome membrane: An essential mode of regulatory mechanism

Peroxisomes are single membrane‐bound organelles important for the optimum functioning of eukaryotic cells. Seminal discoveries in the field of peroxisomes are made using yeast as a model. Several proteins required for the biogenesis and function of peroxisomes are identified to date. As with proteins involved in other major cellular pathways, peroxisomal proteins are also subjected to regulatory post‐translational modifications. Identification, characterization and mapping of these modifications to specific amino acid residues on proteins are critical toward understanding their functional significance. Several studies have tried to identify post‐translational modifications of peroxisomal proteins and determine their impact on peroxisome structure and function. In this manuscript, we provide an overview of the various post‐translational modifications that govern the peroxisome dynamics in yeast.

Global Lysine Acetylome Analysis of LPS-Stimulated HepG2 Cells Identified Hyperacetylation of PKM2 as a Metabolic Regulator in Sepsis

Sepsis-induced liver dysfunction (SILD) is a common event and is strongly associated with mortality. Establishing a causative link between protein post-translational modification and diseases is challenging. We studied the relationship among lysine acetylation (Kac), sirtuin (SIRTs), and the factors involved in SILD, which was induced in LPS-stimulated HepG2 cells. Protein hyperacetylation was observed according to SIRTs reduction after LPS treatment for 24 h. We identified 1449 Kac sites based on comparative acetylome analysis and quantified 1086 Kac sites on 410 proteins for acetylation. Interestingly, the upregulated Kac proteins are enriched in glycolysis/gluconeogenesis pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) category. Among the proteins in the glycolysis pathway, hyperacetylation, a key regulator of lactate level in sepsis, was observed at three pyruvate kinase M2 (PKM2) sites. Hyperacetylation of PKM2 induced an increase in its activity, consequently increasing the lactate concentration. In conclusion, this study is the first to conduct global profiling of Kac, suggesting that the Kac mechanism of PKM2 in glycolysis is associated with sepsis. Moreover, it helps to further understand the systematic information regarding hyperacetylation during the sepsis process.

Recent advances in proteomics and its implications in pituitary endocrine disorders

Pituitary adenoma is considered as one of the most frequent intracranial tumors having salient impact on human health such as mass effects, hypopituitarism and visual defects etc. During the past few decades, there has been enormous advancement in mass spectrometry (MS)-based proteomics. However, very little is known about the molecular pathogenesis of pituitary adenomas in the context of proteomics. In this review article, we have focused on the provenance of pituitary tumors and their pathogenesis with the help of MS-based proteomics approaches. Recent advancements in quantitative proteomic approaches are outlined here that would be useful in the near pituitary adenoma proteomics research. This review discusses the enormous potential of pituitary adenomas research through proteomics with a common aim of deciphering disease pathobiology and identifying the work done in studying pituitary tumors during past decade.

A systematic assessment of structural heterogeneity and IgG/IgE-binding of ovalbumin

Ovalbumin (OVA), one of the major allergens in hen egg, exhibits extensive structural heterogeneity due to a range of post-translational modifications (PTMs). However, analyzing the structural heterogeneity of native OVA is challenging, and the relationship between heterogeneity and IgG/IgE-binding of OVA remains unclear. In this work, ion exchange chromatography (IXC) with salt gradient elution and on-line detection by native electrospray ionization mass spectrometry (ESI MS) was used to assess the structural heterogeneity of OVA, while inhibition-ELISA was used to assess the IgG/IgE binding characteristics of OVA. Over 130 different OVA proteoforms (including glycan-free species and 32 pairs of isobaric species) were identified. Proteoforms with acetylation, phosphorylation, oxidation and succinimide modifications had reduced IgG/IgE binding capacities, whereas those with few structural modifications had higher IgG/IgE binding capacities. OVA isoforms with a sialic acid-containing glycan modification had the highest IgG/IgE binding capacity. Our results demonstrate that on-line native IXC/MS with salt gradient elution can be used for rapid assessment of the structural heterogeneity of proteins. An improved understanding of the relationship between IgG/IgE binding capacity and OVA structure provides a basis for developing biotechnology or food processing methods for reducing protein allergenicity reduction.

Detection of single peptide with only one amino acid modification via electronic fingerprinting using reengineered durable channel of Phi29 DNA packaging motor

Protein post-translational modification (PTM) is crucial to modulate protein interactions and activity in various biological processes. Emerging evidence has revealed PTM patterns participate in the pathology onset and progression of various diseases. Current PTM identification relies mainly on mass spectrometry-based approaches that limit the assessment to the entire protein population in question. Here we report a label-free method for the detection of the single peptide with only one amino acid modification via electronic fingerprinting using reengineered durable channel of phi29 DNA packaging motor, which bears the deletion of 25-amino acids (AA) at the C-terminus or 17-AA at the internal loop of the channel. The mutant channels were used to detect propionylation modification via single-molecule fingerprinting in either the traditional patch-clamp or the portable MinION™ platform of Oxford Nanopore Technologies. Up to 2000 channels are available in the MinION™ Flow Cells. The current signatures and dwell time of individual channels were identified. Peptides with only one propionylation were differentiated. Excitingly, identification of single or multiple modifications on the MinION™ system was achieved. The successful application of PTM differentiation on the MinION™ system represents a significant advance towards developing a label-free and high-throughput detection platform utilizing nanopores for clinical diagnosis based on PTM.

Predicted N-terminal N-linked glycosylation sites may underlie membrane protein expression patterns in Saccharomyces cerevisiae

N-linked glycosylation is one type of posttranslational modification that proteins undergo during expression. The following describes the effects of N-linked glycosylation on high-level membrane protein expression in yeast with an emphasis on Saccharomyces cerevisiae. N-linked glycosylation is highlighted here as an important consideration when preparing membrane protein gene constructs for expression in S. cerevisiae, which continues to be used as a workhorse in both research and industrial applications. Non-native N-linked glycosylation commonly occurs during the heterologous expression of mammalian proteins in many yeast species which can have important immunological consequences when used in the production of biotherapeutic proteins or peptides. Further, non-native N-linked glycosylation can lead to improper protein folding and premature degradation, which can impede high-level expression yields and hinder downstream analysis. Multiple strategies are presented in this article, which suggest different methods that can be implemented to circumvent the unwanted consequences of N-linked glycosylation during the expression process. These considerations may have long-term benefits for high-level protein production in S. cerevisiae across a broad spectrum of expression targets with special emphasis placed on G-protein coupled receptors, one of the largest families of membrane proteins.

The consequences of a high-calorie diet background before calorie restriction on skeletal muscles in a mouse model

The beneficial effects of calorie restriction (CR) are numerous. However, there is no scientific evidence about how a high-calorie diet (HCD) background influences the mechanisms underlying CR on skeletal muscles in an experimental mouse model. Herein we present empirical evidence showing significant interactions between HCD (4 months) and CR (3 months). Pectoralis major and quadriceps femoris vastus medialis, in the experimental and control groups, displayed metabolic and physiologic heterogeneity and remarkable plasticity, according to the dietary interventions. HCD-CR not only altered genetic activation patterns of satellite SC markers but also boosted the expression of myogenic regulatory factors and key activators of mitochondrial biogenesis, which in turn were also associated with metabolic fiber transition. Our data prompt us to theorize that the effects of CR may vary according to the physiologic, metabolic, and genetic peculiarities of the skeletal muscle described here and that INTM/IM lipid infiltration and tissue-specific fuel-energy status (demand/supply) both hold dependent-interacting roles with other key anti-aging mechanisms triggered by CR. Systematic integration of an HCD with CR appears to bring potential benefits for skeletal muscle function and energy metabolism. However, at this stage of our research, an optimal balance between the two dietary conditions, where anti-aging effects can be accomplished, is under intensive investigation in combination with other tissues and organs at different levels of organization within the organ system.

CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

Motivation

Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies, are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme.

Results

We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software.

Availability and implementation

www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe.

Supplementary information

Supplementary data are available at Bioinformatics online.

The main post-translational modifications and related regulatory pathways in the malaria parasite Plasmodium falciparum: An update

There are important challenges when investigating individual post-translational modifications (PTMs) or protein interaction network and delineating if PTMs or their changes and cross-talks are involved during infection, disease initiation or as a result of disease progression. Proteomics and in silico approaches now offer the possibility to complement each other to further understand the regulatory involvement of these modifications in parasites and infection biology. Accordingly, the current review highlights key expressed or altered proteins and PTMs are invisible switches that turn on and off the function of most of the proteins. PTMs include phosphorylation, glycosylation, ubiquitylation, palmitoylation, myristoylation, prenylation, acetylation, methylation, and epigenetic PTMs in P. falciparum which have been recently identified. But also other low-abundant or overlooked PTMs that might be important for the parasite's survival, infectivity, antigenicity, immunomodulation and pathogenesis. We here emphasize the PTMs as regulatory pathways playing major roles in the biology, pathogenicity, metabolic pathways, survival, host-parasite interactions and the life cycle of P. falciparum. Further validations and functional characterizations of such proteins might confirm the discovery of therapeutic targets and might most likely provide valuable data for the treatment of P. falciparum, the main cause of severe malaria in human.

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

BACKGROUND

Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases-most commonly neurological disorders, Alzheimer's disease, and Parkinson's disease-thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features.

METHODS

In the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models.

RESULTS

The RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation.

CONCLUSIONS

The results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.

The search for inhibitors of macrodomains for targeting the readers and erasers of mono-ADP-ribosylation

Macrodomains are evolutionarily conserved structural elements. Many macrodomains feature as binding modules of ADP-ribose, thus participating in the recognition and removal of mono- and poly-ADP-ribosylation. Macrodomains are involved in the regulation of a variety of physiological processes and represent valuable therapeutic targets. Moreover, as part of the nonstructural proteins of certain viruses, macrodomains are also pivotal for viral replication and pathogenesis. Thus, targeting viral macrodomains with inhibitors is considered to be a promising antiviral intervention. In this review, we summarize our current understanding of human and viral macrodomains that are related to mono-ADP-ribosylation, with emphasis on the search for inhibitors. The advances summarized here will be helpful for the design of macrodomain-specific agents for therapeutic and diagnostic applications.