Redox Modification of PKA-Cα Differentially Affects Its Substrate Selection.

The cyclic AMP-dependent protein kinase (PKA) plays an essential role in the regulation of many important cellular processes and is dysregulated in several pervasive diseases, including diabetes, cardiovascular disease, and various neurodegenerative disorders. Previous studies suggest that the alpha isoform of the catalytic subunit of PKA (PKA-Cα) is oxidized on C199, both in vitro and in situ. However, the molecular consequences of these modifications on PKA-Cα's substrate selection remain largely unexplored. C199 is located on the P + 1 loop within PKA-Cα's active site, suggesting that redox modification may affect its kinase activity. Given the proximity of C199 to the substrate binding pocket, we hypothesized that oxidation could differentially alter PKA-Cα's activity toward its substrates. To this end, we examined the effects of diamide- and H2O2-dependent oxidation on PKA-Cα's activity toward select peptide and protein substrates using a combination of biochemical (i.e., trans-phosphorylation assays and steady-state kinetics analysis) and biophysical (i.e., surface plasmon resonance and fluorescence polarization assays) strategies. These studies suggest that redox modification of PKA-Cα differentially affects its activity toward different substrates. For instance, we found that diamide-mediated oxidation caused a marked decrease in PKA-Cα's activity toward some substrates (e.g., Kemptide and CREBtide) while having little effect on others (e.g., Crosstide). In contrast, H2O2-dependent oxidation of PKA-Cα led to an increase in its activity toward each of the substrates at relatively low H2O2 concentrations, with differential effects at higher peroxide concentrations. Together, these studies offer novel insights into crosstalk between redox- and phosphorylation-dependent signaling pathways mediated by PKA. Likewise, since C199 is highly conserved among AGC kinase family members, they also lay the foundation for future studies designed to elucidate the role of redox-dependent modification of kinase substrate selection in physiological and pathological states.

Hydrogen peroxide-dependent oxidation of ERK2 within its D-recruitment site alters its substrate selection.

Extracellular signal-regulated kinases 1 and 2 (ERK1/2) are dysregulated in many pervasive diseases. Recently, we discovered that ERK1/2 is oxidized by signal-generated hydrogen peroxide in various cell types. Since the putative sites of oxidation lie within or near ERK1/2's ligand-binding surfaces, we investigated how oxidation of ERK2 regulates interactions with the model substrates Sub-D and Sub-F. These studies revealed that ERK2 undergoes sulfenylation at C159 on its D-recruitment site surface and that this modification modulates ERK2 activity differentially between substrates. Integrated biochemical, computational, and mutational analyses suggest a plausible mechanism for peroxide-dependent changes in ERK2-substrate interactions. Interestingly, oxidation decreased ERK2's affinity for some D-site ligands while increasing its affinity for others. Finally, oxidation by signal-generated peroxide enhanced ERK1/2's ability to phosphorylate ribosomal S6 kinase A1 (RSK1) in HeLa cells. Together, these studies lay the foundation for examining crosstalk between redox- and phosphorylation-dependent signaling at the level of kinase-substrate selection.

Improving protein succinylation sites prediction using embeddings from protein language model.

Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease.

FEPS: A Tool for Feature Extraction from Protein Sequence.

Machine learning has become one of the most popular choices for developing computational approaches in protein structural bioinformatics. The ability to extract features from protein sequence/structure often becomes one of the crucial steps for the development of machine learning-based approaches. Over the years, various sequence, structural, and physicochemical descriptors have been developed for proteins and these descriptors have been used to predict/solve various bioinformatics problems. Hence, several feature extraction tools have been developed over the years to help researchers to generate numeric features from protein sequences. Most of these tools have some limitations regarding the number of sequences they can handle and the subsequent preprocessing that is required for the generated features before they can be fed to machine learning methods. Here, we present Feature Extraction from Protein Sequences (FEPS), a toolkit for feature extraction. FEPS is a versatile software package for generating various descriptors from protein sequences and can handle several sequences: the number of which is limited only by the computational resources. In addition, the features extracted from FEPS do not require subsequent processing and are ready to be fed to the machine learning techniques as it provides various output formats as well as the ability to concatenate these generated features. FEPS is made freely available via an online web server as well as a stand-alone toolkit. FEPS, a comprehensive toolkit for feature extraction, will help spur the development of machine learning-based models for various bioinformatics problems.

Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs.

Peroxiredoxins (Prxs) are a protein superfamily, present in all organisms, that play a critical role in protecting cellular macromolecules from oxidative damage but also regulate intracellular and intercellular signaling processes involving redox-regulated proteins and pathways. Bioinformatic approaches using computational tools that focus on active site-proximal sequence fragments (known as active site signatures) and iterative clustering and searching methods (referred to as TuLIP and MISST) have recently enabled the recognition of over 38,000 peroxiredoxins, as well as their classification into six functionally relevant groups. With these data providing so many examples of Prxs in each class, machine learning approaches offer an opportunity to extract additional information about features characteristic of these protein groups.In this study, we developed a novel computational method named "RF-Prx" based on a random forest (RF) approach integrated with K-space amino acid pairs (KSAAP) to identify peroxiredoxins and classify them into one of six subgroups. Our process performed in a superior manner compared to other machine learning classifiers. Thus the RF approach integrated with K-space amino acid pairs enabled the detection of class-specific conserved sequences outside the known functional centers and with potential importance. For example, drugs designed to target Prx proteins would likely suffer from cross-reactivity among distinct Prxs if targeted to conserved active sites, but this may be avoidable if remote, class-specific regions could be targeted instead.

Computational Analysis of the Binding Mechanism of GenX and HSA.

One PFOS alternative, ammonium 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy) propanoate, known as GenX, was created to replace one of the original PFAS. This small and tough molecule has been found in surface water, groundwater, drinking water, rainwater, and air emissions in some areas in the United States. Recently, GenX has been shown to have an impact on several disease-related proteins in humans, and just like PFOS, it binds to human protein human serum albumin (HSA). In this paper, we reported four binding sites of GenX on HSA protein via docking and molecular dynamics simulation.

SARS-COV-2, infection, transmission, transcription, translation, proteins, and treatment: A review.

In this review, we describe the key molecular entities involved in the process of infection by SARS-CoV-2, while also detailing how those key entities influence the spread of the disease. We further introduce the molecular mechanisms of preventive and treatment strategies including drugs, antibodies, and vaccines.

DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites.

Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein's primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.

A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites.

Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga.

DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins.

Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine learning strategies, such as support vector machines and random forest, to predict sites of methylation based on a set of "hand-selected" features. As a consequence, the subsequent models may be biased toward one set of features. Moreover, due to the large number of features, model development can often be computationally expensive. In this paper, we propose an alternative approach based on deep learning to predict arginine methylation sites. Our model, which we termed DeepRMethylSite, is computationally less expensive than traditional feature-based methods while eliminating potential biases that can arise through features selection. Based on independent testing on our dataset, DeepRMethylSite achieved efficiency scores of 68%, 82% and 0.51 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Importantly, in side-by-side comparisons with other state-of-the-art methylation site predictors, our method performs on par or better in all scoring metrics tested.

DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction.

BACKGROUND: Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to - 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. RESULTS: Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. CONCLUSION: Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation.

RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.

Malonylation, which has recently emerged as an important lysine modification, regulates diverse biological activities and has been implicated in several pervasive disorders, including cardiovascular disease and cancer. However, conventional global proteomics analysis using tandem mass spectrometry can be time-consuming, expensive and technically challenging. Therefore, to complement and extend existing experimental methods for malonylation site identification, we developed two novel computational methods for malonylation site prediction based on random forest and deep learning machine learning algorithms, RF-MaloSite and DL-MaloSite, respectively. DL-MaloSite requires the primary amino acid sequence as an input and RF-MaloSite utilizes a diverse set of biochemical, physiochemical and sequence-based features. While systematic assessment of performance metrics suggests that both 'RF-MaloSite' and 'DL-MaloSite' perform well in all metrics tested, our methods perform particularly well in the areas of accuracy, sensitivity and overall method performance (assessed by the Matthew's Correlation Coefficient). For instance, RF-MaloSite exhibited MCC scores of 0.42 and 0.40 using 10-fold cross-validation and an independent test set, respectively. Meanwhile, DL-MaloSite was characterized by MCC scores of 0.51 and 0.49 based on 10-fold cross-validation and an independent set, respectively. Importantly, both methods exhibited efficiency scores that were on par or better than those achieved by existing malonylation site prediction methods. The identification of these sites may also provide important insights into the mechanisms of crosstalk between malonylation and other lysine modifications, such as acetylation, glutarylation and succinylation. To facilitate their use, both methods have been made freely available to the research community at https://github.com/dukkakc/DL-MaloSite-and-RF-MaloSite.

RF-GlutarySite: a random forest based predictor for glutarylation sites.

Glutarylation, which is a newly identified posttranslational modification that occurs on lysine residues, has recently emerged as an important regulator of several metabolic and mitochondrial processes. However, the specific sites of modification on individual proteins, as well as the extent of glutarylation throughout the proteome, remain largely uncharacterized. Though informative, proteomic approaches based on mass spectrometry can be expensive, technically challenging and time-consuming. Therefore, the ability to predict glutarylation sites from protein primary sequences can complement proteomics analyses and help researchers study the characteristics and functional consequences of glutarylation. To this end, we used Random Forest (RF) machine learning strategies to identify the physiochemical and sequence-based features that correlated most substantially with glutarylation. We then used these features to develop a novel method to predict glutarylation sites from primary amino acid sequences using RF. Based on 10-fold cross-validation, the resulting algorithm, termed 'RF-GlutarySite', achieved efficiency scores of 75%, 81%, 68% and 0.50 with respect to accuracy (ACC), sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Likewise, using an independent test set, RF-GlutarySite exhibited ACC, SN, SP and MCC scores of 72%, 73%, 70% and 0.43, respectively. Results using both 10-fold cross validation and an independent test set were on par with or better than those achieved by existing glutarylation site predictors. Notably, RF-GlutarySite achieved the highest SN score among available glutarylation site prediction tools. Consequently, our method has the potential to uncover new glutarylation sites and to facilitate the discovery of relationships between glutarylation and well-known lysine modifications, such as acetylation, methylation and SUMOylation, as well as a number of recently identified lysine modifications, such as malonylation and succinylation.

Inflammation and Kidney Injury in Diabetic African American Men.

African Americans are disproportionately burdened by diabetic kidney disease (DKD). However, little is known about the cellular and molecular mechanisms underlying the onset and progression of DKD in this population. The goal of the current study was to determine the association between specific inflammation markers and kidney injury in diabetic African American men. To this end, we recruited diabetic patients either with (n = 20) or without (n = 87) diagnosed kidney disease along with age-matched nondiabetic controls (n = 81). Urinary albumin-to-creatinine ratios (UACRs) and estimated glomerular filtration rates (eGFR) were used for biochemical assessment of kidney function. We then measured plasma and urinary levels of seven inflammatory markers, including adiponectin, C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), TNF receptor 1 (TNFR1), TNF receptor 2 (TNFR2), interleukin-6 (IL-6), and intercellular cell adhesion molecule-1 (ICAM-1). Plasma levels of TNF-α, TNFR1, and TNFR2 were significantly higher in diabetics with macroalbuminuria compared to nondiabetic controls and diabetics with normoalbuminuria or microalbuminuria. Likewise, urinary levels of ICAM-1 were higher in diabetics with macroalbuminuria compared to the other groups. Indeed, urinary ICAM-1, plasma TNF-α, and adiponectin had moderate positive correlations with UACR while plasma TNFR1 and TNFR2 levels were strongly correlated with kidney injury, indicated by multiple biomarkers of kidney injury. In contrast, though plasma CRP was elevated in diabetic subjects relative to nondiabetic controls, its levels did not correlate with kidney injury. Together, these data suggest that inflammation, particularly that mediated by the TNF-α/NF-κB signaling axis, may play a role in the pathogenesis of DKD in African American men.

SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites.

Protein S-sulfenylation, which results from oxidation of free thiols on cysteine residues, has recently emerged as an important post-translational modification that regulates the structure and function of proteins involved in a variety of physiological and pathological processes. By altering the size and physiochemical properties of modified cysteine residues, sulfenylation can impact the cellular function of proteins in several different ways. Thus, the ability to rapidly and accurately identify putative sulfenylation sites in proteins will provide important insights into redox-dependent regulation of protein function in a variety of cellular contexts. Though bottom-up proteomic approaches, such as tandem mass spectrometry (MS/MS), provide a wealth of information about global changes in the sulfenylation state of proteins, MS/MS-based experiments are often labor-intensive, costly and technically challenging. Therefore, to complement existing proteomic approaches, researchers have developed a series of computational tools to identify putative sulfenylation sites on proteins. However, existing methods often suffer from low accuracy, specificity, and/or sensitivity. In this study, we developed SVM-SulfoSite, a novel sulfenylation prediction tool that uses support vector machines (SVM) to identify key determinants of sulfenylation among five feature classes: binary code, physiochemical properties, k-space amino acid pairs, amino acid composition and high-quality physiochemical indices. Using 10-fold cross-validation, SVM-SulfoSite achieved 95% sensitivity and 83% specificity, with an overall accuracy of 89% and Matthew's correlation coefficient (MCC) of 0.79. Likewise, using an independent test set of experimentally identified sulfenylation sites, our method achieved scores of 74%, 62%, 80% and 0.42 for accuracy, sensitivity, specificity and MCC, with an area under the receiver operator characteristic (ROC) curve of 0.81. Moreover, in side-by-side comparisons, SVM-SulfoSite performed as well as or better than existing sulfenylation prediction tools. Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein S-sulfenylation.

Undiagnosed Kidney Injury in Uninsured and Underinsured Diabetic African American Men and Putative Role of Meprin Metalloproteases in Diabetic Nephropathy.

Diabetes is the leading cause of chronic kidney disease. African Americans are disproportionately burdened by diabetic kidney disease (DKD) and end stage renal disease (ESRD). Disparities in DKD have genetic and socioeconomic components, yet its prevalence in African Americans is not adequately studied. The current study used multiple biomarkers of DKD to evaluate undiagnosed DKD in uninsured and underinsured African American men in Greensboro, North Carolina. Participants consisted of three groups: nondiabetic controls, diabetic patients without known kidney disease, and diabetic patients with diagnosed DKD. Our data reveal undiagnosed kidney injury in a significant proportion of the diabetic patients, based on levels of both plasma and urinary biomarkers of kidney injury, namely, urinary albumin to creatinine ratio, kidney injury molecule-1, cystatin C, and neutrophil gelatinase-associated lipocalin. We also found that the urinary levels of meprin A, meprin B, and two kidney meprin targets (nidogen-1 and monocytes chemoattractant protein-1) increased with severity of kidney injury, suggesting a potential role for meprin metalloproteases in the pathophysiology of DKD in this subpopulation. The study also demonstrates a need for more aggressive tests to assess kidney injury in uninsured diabetic patients to facilitate early diagnosis and targeted interventions that could slow progression to ESRD.

Integrated Strategies to Gain a Systems-Level View of Dynamic Signaling Networks.

In order to survive and function properly in the face of an ever changing environment, cells must be able to sense changes in their surroundings and respond accordingly. Cells process information about their environment through complex signaling networks composed of many discrete signaling molecules. Individual pathways within these networks are often tightly integrated and highly dynamic, allowing cells to respond to a given stimulus (or, as is typically the case under physiological conditions, a combination of stimuli) in a specific and appropriate manner. However, due to the size and complexity of many cellular signaling networks, it is often difficult to predict how cellular signaling networks will respond under a particular set of conditions. Indeed, crosstalk between individual signaling pathways may lead to responses that are nonintuitive (or even counterintuitive) based on examination of the individual pathways in isolation. Therefore, to gain a more comprehensive view of cell signaling processes, it is important to understand how signaling networks behave at the systems level. This requires integrated strategies that combine quantitative experimental data with computational models. In this chapter, we first examine some of the progress that has recently been made toward understanding the systems-level regulation of cellular signaling networks, with a particular emphasis on phosphorylation-dependent signaling networks. We then discuss how genetically targetable fluorescent biosensors are being used together with computational models to gain unique insights into the spatiotemporal regulation of signaling networks within single, living cells.

RF-Hydroxysite: a random forest based predictor for hydroxylation sites.

Protein hydroxylation is an emerging posttranslational modification involved in both normal cellular processes and a growing number of pathological states, including several cancers. Protein hydroxylation is mediated by members of the hydroxylase family of enzymes, which catalyze the conversion of an alkyne group at select lysine or proline residues on their target substrates to a hydroxyl. Traditionally, hydroxylation has been identified using expensive and time-consuming experimental methods, such as tandem mass spectrometry. Therefore, to facilitate identification of putative hydroxylation sites and to complement existing experimental approaches, computational methods designed to predict the hydroxylation sites in protein sequences have recently been developed. Building on these efforts, we have developed a new method, termed RF-hydroxysite, that uses random forest to identify putative hydroxylysine and hydroxyproline residues in proteins using only the primary amino acid sequence as input. RF-Hydroxysite integrates features previously shown to contribute to hydroxylation site prediction with several new features that we found to augment the performance remarkably. These include features that capture physicochemical, structural, sequence-order and evolutionary information from the protein sequences. The features used in the final model were selected based on their contribution to the prediction. Physicochemical information was found to contribute the most to the model. The present study also sheds light on the contribution of evolutionary, sequence order, and protein disordered region information to hydroxylation site prediction. The web server for RF-hydroxysite is available online at .

RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.