Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features

Chemical Carbonylation of Arginine in Peptides and Proteins

The chemoselective incorporation of arginine carbonylation post-translational modification (PTM) within proteins represents an underexplored frontier. This is largely due to the poor nucleophilicity and resistance to chemical oxidation of arginine. Drawing inspiration from the metal catalyzed oxidation (MCO) processes of arginine, we introduce a chemical methodology aimed at generating glutamate-5-semialdehyde from arginine residues within peptides and proteins. This innovative chemical approach capitalizes on the inherent weak nucleophilicity and oxidative properties of arginine. We also demonstrate the application of this strategy to selectively introduce both natural and unnatural post-translational modifications (PTMs) in a targeted manner. Our chemical approach offers a rapid, robust, and highly selective technique, facilitating chemoproteomic profiling of arginine sites prone to forming glutamate-5-semialdehyde PTM within the human proteome. Additionally, this methodology serves as a versatile platform for uncovering microenvironments that are susceptible to undergoing arginine carbonylation PTM, enabling the understanding of the effect of oxidative stress on arginine in proteins and the impact of these PTMs on cellular processes.

dbAMP 3.0: updated resource of antimicrobial activity and structural annotation of peptides in the post-pandemic era

Antimicrobial resistance is one of the most urgent global health threats, especially in the post-pandemic era. Antimicrobial peptides (AMPs) offer a promising alternative to traditional antibiotics, driving growing interest in recent years. dbAMP is a comprehensive database offering extensive annotations on AMPs, including sequence information, functional activity data, physicochemical properties and structural annotations. In this update, dbAMP has curated data from over 5200 publications, encompassing 33,065 AMPs and 2453 antimicrobial proteins from 3534 organisms. Additionally, dbAMP utilizes ESMFold to determine the three-dimensional structures of AMPs, providing over 30,000 structural annotations that facilitate structure-based functional insights for clinical drug development. Furthermore, dbAMP employs molecular docking techniques, providing over 100 docked complexes that contribute useful insights into the potential mechanisms of AMPs. The toxicity and stability of AMPs are critical factors in assessing their potential as clinical drugs. The updated dbAMP introduced an efficient tool for evaluating the hemolytic toxicity and half-life of AMPs, alongside an AMP optimization platform for designing AMPs with high antimicrobial activity, reduced toxicity and increased stability. The updated dbAMP is freely accessible at https://awi.cuhk.edu.cn/dbAMP/. Overall, dbAMP represents a comprehensive and essential resource for AMP analysis and design, poised to advance antimicrobial strategies in the post-pandemic era.

Skin senescence-from basic research to clinical practice

The most recognizable implications of tissue aging manifest themselves on the skin. Skin laxity, roughness, pigmentation disorders, age spots, wrinkles, telangiectasia or hair graying are symptoms of physiological aging. Development of the senescent phenotype depends on the interaction between aging cells and remodeling of the skin's extracellular matrix (ECM) that contains collagen and elastic fiber. Aging changes occur due to the combination of both endogenous (gene mutation, cellular metabolism or hormonal agents) and exogenous factors (ultraviolet light, environmental pollutants, and unsuitable diet). However, overproduction of mitochondrial reactive oxygen species (ROS) is a key factor driving cellular senescence. Aging theories have disclosed a range of diverse molecular mechanisms that are associated with cellular senescence of the body. Theories best supported by evidence include protein glycation, oxidative stress, telomere shortening, cell cycle arrest, and a limited number of cell divisions. Accumulation of the ECM damage is suggested to be a key factor in skin aging. Every cell indicates a functional and morphological change that may be used as a biomarker of senescence. Senescence-associated β-galactosidase (SA-β-gal), cell cycle inhibitors (p16INK4a, p21CIP1, p27, p53), DNA segments with chromatin alterations reinforcing senescence (DNA-SCARS), senescence-associated heterochromatin foci (SAHF), shortening of telomeres or downregulation of lamina B1 constitute just an example of aging biomarkers known so far. Aging may also be assessed non-invasively through measuring the skin fluorescence of advanced glycation end-products (AGEs). This review summarizes the recent knowledge on the pathogenesis and clinical conditions of skin aging as well as biomarkers of skin senescence.

Pathological Impact of Redox Post-Translational Modifications

Oxidative stress is involved in the development of several pathologies. The different reactive oxygen species (ROS) produced during oxidative stress are at the origin of redox post-translational modifications (PTMs) on proteins and impact nucleic acids and lipids. This review provides an overview of recent data on cysteine and methionine oxidation and protein carbonylation following oxidative stress in a pathological context. Oxidation, like nitration, is a selective process and not all proteins are impacted. It depends on multiple factors, including amino acid environment, accessibility, and physical and chemical properties, as well as protein structures. Thiols can undergo reversible oxidations and others that are irreversible. On the contrary, carbonylation represents irreversible PTM. To date, hundreds of proteins were shown to be modified by ROS and reactive nitrogen species (RNS). We reviewed recent advances in the impact of redox-induced PTMs on protein functions and activity, as well as its involvement in disease development or treatment. These data show a complex situation of the involvement of redox PTM on the function of targeted proteins. Many proteins can have their activity decreased by the oxidation of cysteine thiols or methionine S-methyl thioethers, while for other proteins, this oxidation will be activating. This complexity of redox PTM regulation suggests that a global antioxidant therapeutic approach, as often proposed, is unlikely to be effective. However, the specificity of the effect obtained by targeting a cysteine or methionine residue to be able to inactivate or activate a particular protein represents a major interest if it is possible to consider this targeting from a therapeutic point of view with our current pharmacological tools. Antioxid. Redox Signal. 41, 152-180.

CarSitePred: an integrated algorithm for identifying carbonylated sites based on KNDUA-LNDOT resampling technique

Carbonylated sites are the determining factors for functional changes or deletions in carbonylated proteins, so identifying carbonylated sites is essential for understanding the process of protein carbonylated and exploring the pathogenesis of related diseases. The current wet experimental methods for predicting carbonylated modification sites ae not only expensive and time-consuming, but also have limited protein processing capabilities and cannot meet the needs of researchers. The identification of carbonylated sites using computational methods not only improves the functional characterization of proteins, but also provides researchers with free tools for predicting carbonylated sites. Therefore, it is essential to establish a model using computational methods that can accurately predict protein carbonylated sites. In this study, a prediction model, CarSitePred, is proposed to identify carbonylation sites. In CarSitePred, specific location amino acid hydrophobic hydrophilic, one-to-one numerical conversion of amino acids, and AlexNet convolutional neural networks convert preprocessed carbonylated sequences into valid numerical features. The K-means Normal Distribution-based Undersampling Algorithm (KNDUA) and Localized Normal Distribution Oversampling Technology (LNDOT) were firstly proposed and employed to balance the K, P, R and T carbonylation training dataset. And for the first time, carbonylation modification sites were transformed into the form of images and directly inputted into AlexNet convolutional neural network to extract features for fitting SVM classifiers. The 10-fold cross-validation and independent testing results show that CarSitePred achieves better prediction performance than the best currently available prediction models. Availability: https://github.com/zuoyun123/CarSitePred.

Divalent ions as mediators of carbonylation in cardiac myosin binding protein C

The dosing and efficacy of chemotherapeutic drugs can be limited by toxicity caused by off-pathway reactions. One hypothesis for how such toxicity arises is via metal-catalyzed oxidative damage of cardiac myosin binding protein C (cMyBP-C) found in cardiac tissue. Previous research indicates that metal ion mediated reactive oxygen species induce high levels of protein carbonylation, changing the structure and function of this protein. In this work, we use long timescale all-atom molecular dynamics simulations to investigate the ion environment surrounding the C0 and C1 subunits of cMyBP-C responsible for actin binding. We show that divalent cations are co-localized with protein carbonylation-prone amino acid residues and that carbonylation of these residues can lead to site-specific interruption to the actin-cMyBP-C binding.

Insight on the meat quality and carbonylation profile of breast muscle of broilers in response to chronic heat stress: A proteomic research

This study was conducted to explore the influences of carbonyl modification on proteins within the breast muscle of heat-stressed broilers and their correlations to decreased meat quality. The results showed that birds that suffered from heat stress had higher lightness, drip loss, shear force value, and hardness, and lower redness and springiness of breast meat than those under normal control and pair fed treatments. Proteomic analysis identified a total of 921 differentially carbonylated sites, which were allocated to 419 proteins. The modified sites included Lys, Pro, Arg, Trp, Cys, His, and Met. Seven motif sequences were detected, where five motifs neighbored Lys and two neighbored Pro. The differentially carbonylated proteins in heat-stressed birds mainly participated in the glycolytic process, collagen fibril organization, calcium homeostasis, and apoptosis. This study provided a unique landscape of the muscular carbonyl modification rule and unraveled the potential impact of carbonylated protein on meat quality.

Effects of Packaging Materials on Structural and Simulated Digestive Characteristics of Walnut Protein during Accelerated Storage

Walnuts are rich in fat and proteins that become oxidized during the processing and storage conditions of their kernels. In this study, the effect of three packaging materials (e.g., polyethylene sealed packaging, polyamide/polyethylene vacuum packaging, and polyethylene terephthalate/aluminum foil/polyethylene vacuum packaging) were investigated on the oxidation, structural and digestive properties of walnut kernel proteins. Results showed that the amino acid content gradually decreased and carbonyl derivatives and dityrosine were formed during storage. The protein molecule structure became disordered as the α-helix decreased and the random coil increased. The endogenous fluorescence intensity decreased and the maximum fluorescence value was blue-shifted. After 15 days of storage, surface hydrophobicity decreased, while SDS-PAGE and HPLC indicated the formation of large protein aggregates, leading to a reduction in solubility. By simulating gastrointestinal digestion, we found that oxidation adversely affected the digestive properties of walnut protein isolate and protein digestibility was best for polyethylene terephthalate/aluminum foil/polyethylene vacuum packaging. The degree of protein oxidation in walnuts increased during storage, which showed that except for fat oxidation, the effect of protein oxidation on quality should be considered. The results of the study provided new ideas and methods for walnut quality control.

Behavioral, histopathological, genetic, and organism-wide responses to phenanthrene-induced oxidative stress in Eisenia fetida earthworms in natural soil microcosms

Phenanthrene (PHE) contamination not only changes the quality of soil environment but also threatens to the soil organisms. There is lack of focus on the eco-toxicity potential of this contaminant in real soil in the current investigation. Here, we assessed the toxic effects of PHE on earthworms (Eisenia fetida) in natural soil matrix. PHE exhibited a relatively high toxicity to E. fetida in natural soil, with the LC₅₀ determined to be 56.68 mg kg^-1 after a 14-day exposure. Excessive ROS induced by PHE, leading to oxidative damage to biomacromolecules in E. fetida, including lipid peroxidation, protein carbonylation, and DNA damage. The antioxidant defense system (total antioxidant capacity, glutathione S-transferase, peroxidase, catalase, carboxylesterase, and superoxide dismutase) in E. fetida responded quickly to scavenge excess ROS and free radicals. Exposure to PHE resulted in earthworm avoidance responses (2.5 mg kg^-1) and habitat function loss (10 mg kg^-1). Histological observations indicated that the intestine, body wall, and seminal vesicle in E. fetida were severely damaged after exposure to high-dose PHE. Moreover, earthworm growth (weight change) and reproduction (cocoon production and the number of juvenile) were also inhibited after exposure to this pollutant. Furthermore, the integrated toxicity of PHE toward E. fetida at different doses and exposure times was assessed by the integrated biomarker response (IBR), which confirmed that PHE is more toxic to earthworms in the high-dose and long-term exposure groups. Our results showed that PHE exposure induced oxidative stress, disturbed antioxidant defense system, and caused oxidative damage in E. fetida. These effects can trigger behavior changes and damage histological structure, finally cause growth inhibition, genotoxicity, and reproductive toxicity in earthworms. The strength of this study is the comprehensive toxicity evaluation of PHE to earthworms and highlights the need to investigate the eco-toxicity potential of exogenous environmental pollutants in a real soil environment.

Protein Carbonylation: Emerging Roles in Plant Redox Biology and Future Prospects

Plants are sessile in nature and they perceive and react to environmental stresses such as abiotic and biotic factors. These induce a change in the cellular homeostasis of reactive oxygen species (ROS). ROS are known to react with cellular components, including DNA, lipids, and proteins, and to interfere with hormone signaling via several post-translational modifications (PTMs). Protein carbonylation (PC) is a non-enzymatic and irreversible PTM induced by ROS. The non-enzymatic feature of the carbonylation reaction has slowed the efforts to identify functions regulated by PC in plants. Yet, in prokaryotic and animal cells, studies have shown the relevance of protein carbonylation as a signal transduction mechanism in physiological processes including hydrogen peroxide sensing, cell proliferation and survival, ferroptosis, and antioxidant response. In this review, we provide a detailed update on the most recent findings pertaining to the role of PC and its implications in various physiological processes in plants. By leveraging the progress made in bacteria and animals, we highlight the main challenges in studying the impacts of carbonylation on protein functions in vivo and the knowledge gap in plants. Inspired by the success stories in animal sciences, we then suggest a few approaches that could be undertaken to overcome these challenges in plant research. Overall, this review describes the state of protein carbonylation research in plants and proposes new research avenues on the link between protein carbonylation and plant redox biology.

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experimentally verified bioactive peptides has increased significantly, various of in silico approaches are imperative for investigating the characteristics of ACPs. However, the lack of methods for investigating the differences in physicochemical properties of ACPs. In this study, we compared the N- and C-terminal amino acid composition for each peptide, there are three major subtypes of ACPs that are defined based on the distribution of positively charged residues. For the first time, we were motivated to develop a two-step machine learning model for identification of the subtypes of ACPs, which classify the input data into the corresponding group before applying the classifier. Further, to improve the predictive power, the hybrid feature sets were considered for prediction. Evaluation by five-fold cross-validation showed that the two-step model trained with sequence-based features and physicochemical properties was most effective in discriminating between ACPs and non-ACPs. The two-step model trained with the hybrid features performed well, with a sensitivity of 86.75%, a specificity of 85.75%, an accuracy of 86.08%, and a Matthews Correlation Coefficient value of 0.703. Furthermore, the model also consistently provides the effective performance in independent testing set, with sensitivity of 77.6%, specificity of 94.74%, accuracy of 88.99% and the MCC value reached 0.75. Finally, the two-step model has been implemented as a web-based tool, namely iDACP, which is now freely available at http://mer.hc.mmh.org.tw/iDACP/ .

iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features

MOTIVATION

Protein carbonylation is one of the most important oxidative stress-induced post-translational modifications, which is generally characterized as stability, irreversibility and relative early formation. It plays a significant role in orchestrating various biological processes and has been already demonstrated to be related to many diseases. However, the experimental technologies for carbonylation sites identification are not only costly and time consuming, but also unable of processing a large number of proteins at a time. Thus, rapidly and effectively identifying carbonylation sites by computational methods will provide key clues for the analysis of occurrence and development of diseases.

RESULTS

In this study, we developed a predictor called iCarPS to identify carbonylation sites based on sequence information. A novel feature encoding scheme called residues conical coordinates combined with their physicochemical properties was proposed to formulate carbonylated protein and non-carbonylated protein samples. To remove potential redundant features and improve the prediction performance, a feature selection technique was used. The accuracy and robustness of iCarPS were proved by experiments on training and independent datasets. Comparison with other published methods demonstrated that the proposed method is powerful and could provide powerful performance for carbonylation sites identification.

AVAILABILITY AND IMPLEMENTATION

Based on the proposed model, a user-friendly webserver and a software package were constructed, which can be freely accessed at http://lin-group.cn/server/iCarPS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques

BACKGROUND

Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington's disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins.

RESULTS

In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses "support vector machine" subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew's correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools.

CONCLUSION

The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/.

Identification and characterization of carbonylation sites in trastuzumab biosimilars

Detection of metal catalyzed carbonylation in proteins is traditionally based on derivatization followed by detection and quantification via spectroscopy or immunodetection. However, these measure only cumulative carbonylation and do not identify the specific sites of modification within the protein. Recently, fluorescein thiosemicarbazide (FTC) based semi-microplate method was adapted for high throughput monitoring of carbonyl content during mAb process development, using size-exclusion chromatography followed by ultraviolet and fluorescence detection. Here, we have examined carbonylation in originators and 4 biosimilars of an IgG1 therapeutic monoclonal antibody, trastuzumab, a first line of therapy for HER2 positive breast cancer. The hyphenated RP-ESI-MS/MS detection was able to identify the location of each of the carbonylated amino acids for all products. The result is a comprehensive map of a total of 27 unique identified carbonylation sites of trastuzumab found across multiple batches of originator as well as marketed biosimilars. Our results demonstrate that although the different carbonylation sites are spread across different domains throughout the mAb sequence, the complementarity determining regions (CDRs) are free of carbonylation and all identified sites lie within the framework region of the variable domain. Moreover, the constant- heavy domain 3 (CH₃) region seems to be particularly resistant to process induced carbonylation.

iDPGK: characterization and identification of lysine phosphoglycerylation sites based on sequence-based features

Background

Protein phosphoglycerylation, the addition of a 1,3-bisphosphoglyceric acid (1,3-BPG) to a lysine residue of a protein and thus to form a 3-phosphoglyceryl-lysine, is a reversible and non-enzymatic post-translational modification (PTM) and plays a regulatory role in glucose metabolism and glycolytic process. As the number of experimentally verified phosphoglycerylated sites has increased significantly, statistical or machine learning methods are imperative for investigating the characteristics of phosphoglycerylation sites. Currently, research into phosphoglycerylation is very limited, and only a few resources are available for the computational identification of phosphoglycerylation sites.

Result

We present a bioinformatics investigation of phosphoglycerylation sites based on sequence-based features. The TwoSampleLogo analysis reveals that the regions surrounding the phosphoglycerylation sites contain a high relatively of positively charged amino acids, especially in the upstream flanking region. Additionally, the non-polar and aliphatic amino acids are more abundant surrounding phosphoglycerylated lysine following the results of PTM-Logo, which may play a functional role in discriminating between phosphoglycerylation and non-phosphoglycerylation sites. Many types of features were adopted to build the prediction model on the training dataset, including amino acid composition, amino acid pair composition, positional weighted matrix and position-specific scoring matrix. Further, to improve the predictive power, numerous top features ranked by F-score were considered as the final combination for classification, and thus the predictive models were trained using DT, RF and SVM classifiers. Evaluation by five-fold cross-validation showed that the selected features was most effective in discriminating between phosphoglycerylated and non-phosphoglycerylated sites.

Conclusion

The SVM model trained with the selected sequence-based features performed well, with a sensitivity of 77.5%, a specificity of 73.6%, an accuracy of 74.9%, and a Matthews Correlation Coefficient value of 0.49. Furthermore, the model also consistently provides the effective performance in independent testing set, yielding sensitivity of 75.7% and specificity of 64.9%. Finally, the model has been implemented as a web-based system, namely iDPGK, which is now freely available at http://mer.hc.mmh.org.tw/iDPGK/.

Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage

Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS susceptibility essential. The radiation‐resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ‐irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery, and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein‐intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties.

Redox Post-translational Modifications of Protein Thiols in Brain Aging and Neurodegenerative Conditions-Focus on S-Nitrosation

Reactive oxygen species and reactive nitrogen species (RONS) are by-products of aerobic metabolism. RONS trigger a signaling cascade that can be transduced through oxidation-reduction (redox)-based post-translational modifications (redox PTMs) of protein thiols. This redox signaling is essential for normal cellular physiology and coordinately regulates the function of redox-sensitive proteins. It plays a particularly important role in the brain, which is a major producer of RONS. Aberrant redox PTMs of protein thiols can impair protein function and are associated with several diseases. This mini review article aims to evaluate the role of redox PTMs of protein thiols, in particular S-nitrosation, in brain aging, and in neurodegenerative diseases. It also discusses the potential of using redox-based therapeutic approaches for neurodegenerative conditions.

When It Comes to an End: Oxidative Stress Crosstalk with Protein Aggregation and Neuroinflammation Induce Neurodegeneration

Neurodegenerative diseases are characterized by a progressive loss of neurons in the brain or spinal cord that leads to a loss of function of the affected areas. The lack of effective treatments and the ever-increasing life expectancy is raising the number of individuals affected, having a tremendous social and economic impact. The brain is particularly vulnerable to oxidative damage given the high energy demand, low levels of antioxidant defenses, and high levels of metal ions. Driven by age-related changes, neurodegeneration is characterized by increased oxidative stress leading to irreversible neuronal damage, followed by cell death. Nevertheless, neurodegenerative diseases are known as complex pathologies where several mechanisms drive neuronal death. Herein we discuss the interplay among oxidative stress, proteinopathy, and neuroinflammation at the early stages of neurodegenerative diseases. Finally, we discuss the use of the Nrf2-ARE pathway as a potential therapeutic strategy based on these molecular mechanisms to develop transformative medicines.

Acute total body ionizing gamma radiation induces long-term adverse effects and immediate changes in cardiac protein oxidative carbonylation in the rat

Radiation-induced heart disease presents a significant challenge in the event of an accidental radiation exposure as well as to cancer patients who receive acute doses of irradiation as part of radiation therapy. We utilized the spontaneously hypertensive Wistar-Kyoto rat model, previously shown to demonstrate drug-induced cardiomyopathy, to evaluate the acute and long-term effects of sub-lethal total body gamma irradiation at two, four, and fifty-two weeks. We further examined irreversible oxidative protein carbonylation in the heart immediately following irradiation in the normotensive Wistar-Kyoto rat. Both males and females sustained weight loss and anemic conditions compared to untreated controls over a one-year period as reflected by reduced body weight and low red blood cell count. Increased inflammation was detected by elevated IL-6 serum levels selectively in males at four weeks. Serum cardiac troponin T and I analyses revealed signs of cardiomyopathy at earlier timepoints, but high variability was observed, especially at one year. Echocardiography at two weeks following 5.0Gy treatment revealed a significant decrease in cardiac output in females and a significant decrease in both diastolic and systolic volumes in males. Following 10.0Gy irradiation in the normotensive Wistar-Kyoto rat, the heart tissue showed an increase in total protein oxidative carbonylation accompanied by DNA damage indicated by an increase in γ-H2AX. Using proteomic analyses, we identified several novel proteins which showed a marked difference in carbonylation including those of mitochondrial origin and most notably, cardiac troponin T, one of the key proteins involved in cardiomyocyte contractility. Overall, we present findings of acute oxidative protein damage, DNA damage, cardiac troponin T carbonylation, and long-term cardiomyopathy in the irradiated animals.

dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data

Antimicrobial peptides (AMPs), naturally encoded from genes and generally contained 10–100 amino acids, are crucial components of the innate immune system and can protect the host from various pathogenic bacteria, as well as viruses. In recent years, the widespread use of antibiotics has inspired the rapid growth of antibiotic-resistant microorganisms that usually induce critical infection and pathogenesis. An increasing interest therefore was motivated to explore natural AMPs that enable the development of new antibiotics. With the potential of AMPs being as new drugs for multidrug-resistant pathogens, we were thus motivated to develop a database (dbAMP, http://csb.cse.yzu.edu.tw/dbAMP/) by accumulating comprehensive AMPs from public domain and manually curating literature. Currently in dbAMP there are 12 389 unique entries, including 4271 experimentally verified AMPs and 8118 putative AMPs along with their functional activities, supported by 1924 research articles. The advent of high-throughput biotechnologies, such as mass spectrometry and next-generation sequencing, has led us to further expand dbAMP as a database-assisted platform for providing comprehensively functional and physicochemical analyses for AMPs based on the large-scale transcriptome and proteome data. Significant improvements available in dbAMP include the information of AMP–protein interactions, antimicrobial potency analysis for ‘cryptic’ region detection, annotations of AMP target species, as well as AMP detection on transcriptome and proteome datasets. Additionally, a Docker container has been developed as a downloadable package for discovering known and novel AMPs on high-throughput omics data. The user-friendly visualization interfaces have been created to facilitate peptide searching, browsing, and sequence alignment against dbAMP entries. All the facilities integrated into dbAMP can promote the functional analyses of AMPs and the discovery of new antimicrobial drugs.

Protective effect of catechin on pentachlorophenol-induced cytotoxicity and genotoxicity in isolated human blood cells

Pentachlorophenol (PCP) is an organochlorine compound that is used as pesticide, biocide, and wood preservative. PCP is highly toxic and carcinogenic. It has been detected in food and several consumable products. The toxicity of PCP is thought to be due to generation of oxidative stress in cells. We examined whether the dietary antioxidant catechin can attenuate or protect human erythrocytes and lymphocytes against PCP-induced cytotoxicity and genotoxicity, respectively. Human erythrocytes were treated with increasing concentrations of catechin (0.05-2.5 mM) for 30 min followed by addition of 0.75 mM PCP and further incubation for 4 h at 37 °C. Hemolysates were prepared and assayed for various biochemical parameters. Treatment with PCP alone increased the generation of reactive oxygen and nitrogen species, lipid and protein oxidation, and damaged the plasma membrane, when compared to PCP untreated (control) cells. It significantly decreased glutathione level, total sulfhydryl content, and cellular antioxidant power. PCP treatment lowered the activity of antioxidant enzymes and inhibited enzymes of glucose metabolism. However, prior incubation with catechin attenuated the PCP-induced changes in all these parameters in a catechin concentration-dependent manner. Scanning electron microscopy of erythrocytes confirmed these biochemical results. PCP treatment converted the normal discoidal erythrocytes to irregularly contracted cells, acanthocytes, and echinocytes but the presence of catechin inhibited these morphological changes and erythrocytes retained their biconcave shape to a large extent. Genotoxicity was studied in human lymphocytes by single-cell gel electrophoresis (comet assay). It showed strand breaks and longer comet tail length in PCP alone treated cells. The comet tail length was reduced in the catechin +PCP-treated lymphocytes showing that catechin protected cells from PCP-induced DNA damage. These results show that catechin protects human blood cells against PCP-induced oxidative damage.

Identification of oxidant susceptible proteins in Salmonella Typhimurium

The human gut pathogen, Salmonella Typhimurium (S. Typhimurium) not only survives but also replicates inside the phagocytic cells. Bacterial proteins are the primary targets of phagocyte generated oxidants. Because of the different amino acid composition, some proteins are more prone to oxidation than others. Many oxidant induced modifications to amino acids have been described. Introduction of carbonyl group is one of such modifications, which takes place quite early following exposure of proteins to oxidants and is quite stable. Therefore, carbonyl groups can be exploited to identify oxidant susceptible proteins. Hypochlorous acid (HOCl) is one of the most potent oxidants produced by phagocytes. Incubation of S. Typhimurium with 3 mM HOCl resulted in more than 150 folds loss of bacterial viability. Proteins extracted from HOCl exposed S. Typhimurium cells showed about 60 folds (p < 0.001) more carbonyl levels as compared to unexposed cells. Similarly, 2, 4-Dinitrophenylhydrazine (2, 4-DNPH) derivatized proteins of HOCl treated S. Typhimurium cultures reacted strongly with anti-DNP antibodies as compared to buffer treated counterpart. Next, we have derivatized carbonyl groups on the proteins with biotin hydrazide. The derivatized proteins were then isolated by avidin affinity chromatography. Mass spectrometry based analysis revealed the presence of 204 proteins.

Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method

Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods. Hence, this work aimed to carry out the succinylation site prediction based on a deep learning model. The abundance of MS-verified succinylated peptides enabled the investigation of substrate site specificity of succinylation sites through sequence-based attributes, such as position-specific amino acid composition, the composition of k-spaced amino acid pairs (CKSAAP), and position-specific scoring matrix (PSSM). Additionally, the maximal dependence decomposition (MDD) was adopted to detect the substrate signatures of lysine succinylation sites by dividing all succinylated sequences into several groups with conserved substrate motifs. According to the results of ten-fold cross-validation, the deep learning model trained using PSSM and informative CKSAAP attributes can reach the best predictive performance and also perform better than traditional machine-learning methods. Moreover, an independent testing dataset that truly did not exist in the training dataset was used to compare the proposed method with six existing prediction tools. The testing dataset comprised of 218 positive and 2621 negative instances, and the proposed model could yield a promising performance with 84.40% sensitivity, 86.99% specificity, 86.79% accuracy, and an MCC value of 0.489. Finally, the proposed method has been implemented as a web-based prediction tool (CNN-SuccSite), which is now freely accessible at http://csb.cse.yzu.edu.tw/CNN-SuccSite/.

CarbonylDB: a curated data-resource of protein carbonylation sites

Motivation

Oxidative stress and protein damage have been associated with over 200 human ailments including cancer, stroke, neuro-degenerative diseases and aging. Protein carbonylation, a chemically diverse oxidative post-translational modification, is widely considered as the biomarker for oxidative stress and protein damage. Despite their importance and extensive studies, no database/resource on carbonylated proteins/sites exists. As such information is very useful to research in biology/medicine, we have manually curated a data-resource (CarbonylDB) of experimentally-confirmed carbonylated proteins/sites.

Results

The CarbonylDB currently contains 1495 carbonylated proteins and 3781 sites from 21 species, with human, rat and yeast as the top three species. We have made further analyses of these carbonylated proteins/sites and presented their occurrence and occupancy patterns. Carbonylation site data on serum albumin, in particular, provides a fine model system to understand the dynamics of oxidative protein modifications/damage.

Availability and implementation

The CarbonylDB is available as a web-resource and for download at http://digbio.missouri.edu/CarbonylDB/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Causative Links between Protein Aggregation and Oxidative Stress: A Review

Compelling evidence supports a tight link between oxidative stress and protein aggregation processes, which are noticeably involved in the development of proteinopathies, such as Alzheimer’s disease, Parkinson’s disease, and prion disease. The literature is tremendously rich in studies that establish a functional link between both processes, revealing that oxidative stress can be either causative, or consecutive, to protein aggregation. Because oxidative stress monitoring is highly challenging and may often lead to artefactual results, cutting-edge technical tools have been developed recently in the redox field, improving the ability to measure oxidative perturbations in biological systems. This review aims at providing an update of the previously known functional links between oxidative stress and protein aggregation, thereby revisiting the long-established relationship between both processes.

In silico analysis of the V66M variant of human BDNF in psychiatric disorders: An approach to precision medicine

Brain-derived neurotrophic factor (BDNF) plays an important role in neurogenesis and synapse formation. The V66M is the most prevalent BDNF mutation in humans and impairs the function and distribution of BDNF. This mutation is related to several psychiatric disorders. The pro-region of BDNF, particularly position 66 and its adjacent residues, are determinant for the intracellular sorting and activity-dependent secretion of BDNF. However, it has not yet been fully elucidated. The present study aims to analyze the effects of the V66M mutation on BDNF structure and function. Here, we applied nine algorithms, including SIFT and PolyPhen-2, for functional and stability prediction of the V66M mutation. The complete theoretical model of BNDF was generated by Rosetta and validated by PROCHECK, RAMPAGE, ProSa, QMEAN and Verify-3D algorithms. Structural alignment was performed using TM-align. Phylogenetic analysis was performed using the ConSurf server. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS 2018.2 package. The V66M mutation was predicted as deleterious by PolyPhen-2 and SIFT in addition to being predicted as destabilizing by I-Mutant. According to SNPeffect, the V66M mutation does not affect protein aggregation, amyloid propensity, and chaperone binding. The complete theoretical structure of BDNF proved to be a reliable model. Phylogenetic analysis indicated that the V66M mutation of BDNF occurs at a non-conserved position of the protein. MD analyses indicated that the V66M mutation does not affect the BDNF flexibility and surface-to-volume ratio, but affects the BDNF essential motions, hydrogen-bonding and secondary structure particularly at its pre and pro-domain, which are crucial for its activity and distribution. Thus, considering that these parameters are determinant for protein interactions and, consequently, protein function; the alterations observed throughout the MD analyses may be related to the functional impairment of BDNF upon V66M mutation, as well as its involvement in psychiatric disorders.

Redox Signaling by Reactive Electrophiles and Oxidants

The concept of cell signaling in the context of nonenzyme-assisted protein modifications by reactive electrophilic and oxidative species, broadly known as redox signaling, is a uniquely complex topic that has been approached from numerous different and multidisciplinary angles. Our Review reflects on five aspects critical for understanding how nature harnesses these noncanonical post-translational modifications to coordinate distinct cellular activities: (1) specific players and their generation, (2) physicochemical properties, (3) mechanisms of action, (4) methods of interrogation, and (5) functional roles in health and disease. Emphasis is primarily placed on the latest progress in the field, but several aspects of classical work likely forgotten/lost are also recollected. For researchers with interests in getting into the field, our Review is anticipated to function as a primer. For the expert, we aim to stimulate thought and discussion about fundamentals of redox signaling mechanisms and nuances of specificity/selectivity and timing in this sophisticated yet fascinating arena at the crossroads of chemistry and biology.

PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach

Successful spermatogenesis and oogenesis are the two genetically independent processes preceding embryo development. To date, several fertility-related proteins have been described in mammalian species. Nevertheless, further studies are required to discover more proteins associated with the development of germ cells and embryogenesis in order to shed more light on the processes. This work builds on our previous software (OOgenesis_Pred), mainly focusing on algorithms beyond what was previously done, in particular new fertility-related proteins and their classes (embryogenesis, spermatogenesis and oogenesis) based on the support vector machine according to the concept of Chou's pseudo-amino acid composition features. The results of five-fold cross validation, as well as the independent test demonstrated that this method is capable of predicting the fertility-related proteins and their classes with accuracy of more than 80%. Moreover, by using feature selection methods, important properties of fertility-related proteins were identified that allowed for their accurate classification. Based on the proposed method, a two-layer classifier software, named as "PrESOgenesis" ( https://github.com/mrb20045/PrESOgenesis ) was developed. The tool identified a query sequence (protein or transcript) as fertility or non-fertility-related protein at the first layer and then classified the predicted fertility-related protein into different classes of embryogenesis, spermatogenesis or oogenesis at the second layer.

MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs

Background

Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson’s disease, and Alzheimer’s disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures.

Results

By manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing.

Conclusion

This study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/) and are also anticipated to facilitate the study of large-scale carbonylated proteomes.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0511-4) contains supplementary material, which is available to authorized users.