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.

Global analysis of phosphorylation networks in humans.

Phosphorylation-mediated signaling plays a crucial role in nearly every aspect of cellular physiology. A recent study based on protein microarray experiments identified a large number of kinase-substrate relationships (KSRs), and built a comprehensive and reliable phosphorylation network in humans. Analysis of this network, in conjunction with additional resources, revealed several key features. First, comparison of the human and yeast phosphorylation networks uncovered an evolutionarily conserved signaling backbone dominated by kinase-to-kinase relationships. Second, although most of the KSRs themselves are not conserved, the functions enriched in the substrates for a given kinase are often conserved. Third, the prevalence of kinase-transcription factor regulatory modules suggests that phosphorylation and transcriptional regulatory networks are inherently wired together to form integrated regulatory circuits. Overall, the phosphorylation networks described in this work promise to offer new insights into the properties of kinase signaling pathways, at both the global and the protein levels. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Isoform-specific interactions between meprin metalloproteases and the catalytic subunit of protein kinase A: significance in acute and chronic kidney injury.

Meprin metalloproteases are abundantly expressed in the brush-border membranes of kidney proximal tubules. Meprins are implicated in ischemia-reperfusion (IR)-induced renal injury and diabetic nephropathy. The protein kinase A (PKA) signaling pathway modulates extracellular matrix metabolism in diabetic kidneys. The present study evaluated isoform-specific interactions between the catalytic subunit of PKA (PKA C) and meprins. To this end, cytosolic-enriched kidney proteins from meprin αß double knockout mice, and purified forms of recombinant mouse PKA Cα, Cß1, and Cß2, were incubated with activated forms of either homomeric meprin A or meprin B. The cleaved protein products were subjected to SDS-PAGE and analyzed by Coomassie staining and Western blot analysis. While meprin A only cleaved PKA Cß1, meprin B cleaved all three PKA C isoforms. Analysis of the proteolytic fragments by mass spectrometry revealed that meprin A and B cleave the PKA C isoforms at defined sites, resulting in unique cleavage products. Michaelis-Menten enzyme kinetics demonstrated that meprin B-mediated cleavage of PKA Cα occurs at a rate consistent with that of other physiologically relevant meprin substrates. Meprin cleavage decreased the kinase activity of PKA Cα, Cß1, and Cß2. PKA C levels were higher in diabetic kidneys, with evidence of in vivo fragmentation in wild-type diabetic kidneys. Confocal microscopy showed localization of meprin A in the glomeruli of diabetic kidneys. At 3 h post-IR, PKA C levels in proximal tubules decreased compared with distal tubules, which lack meprins. These data suggest that meprins may impact kidney injury, in part, via modulation of PKA signaling pathways.

PhosphoNetworks: a database for human phosphorylation networks.

SUMMARY: Phosphorylation plays an important role in cellular signal transduction. Current phosphorylation-related databases often focus on the phosphorylation sites, which are mainly determined by mass spectrometry. Here, we present PhosphoNetworks, a phosphorylation database built on a high-resolution map of phosphorylation networks. This high-resolution map of phosphorylation networks provides not only the kinase-substrate relationships (KSRs), but also the specific phosphorylation sites on which the kinases act on the substrates. The database contains the most comprehensive dataset for KSRs, including the relationships from a recent high-throughput project for identification of KSRs using protein microarrays, as well as known KSRs curated from the literature. In addition, the database also includes several analytical tools for dissecting phosphorylation networks. PhosphoNetworks is expected to play a prominent role in proteomics and phosphorylation-related disease research. AVAILABILITY AND IMPLEMENTATION: http://www.phosphonetworks.org

Construction of human activity-based phosphorylation networks.

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)-based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase-substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high-quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high-resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B-cell receptor signaling. Overall, these studies provide global insights into kinase-mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.

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.

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.

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.

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.

Small molecules and chemical tools at the interface.

As our understanding of the molecular mechanisms driving complex biological processes grows, the role of chemical biology will continue to expand. The 2008 American Society for Biochemistry and Molecular Biology meeting showcased recent progress in the field of chemical biology while simultaneously pointing toward the future of research at the interface between chemistry and biology.

The structure and function of fluorescent proteins.

The current complement of fluorescent proteins (FPs) contains color variants whose emission spectra span most of the visible spectrum, providing researchers with a versatile toolset of fluorescent probes for live cell imaging applications. FP family members generate their chromophores autocatalytically through a series of posttranslational modifications. The fluorescence characteristics of GFP-family members are influenced in important ways by the local microenvironment surrounding the chromophore. In this tutorial review, we first examine the molecular factors that influence the photophysical properties of FP family members and then briefly discuss some of the ways in which these fascinating proteins have been applied to the field of live cell imaging.

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.

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.

Fucci: street lights on the road to mitosis.

Tracking cell-cycle progression in live cells within their endogenous environment has been an outstanding challenge. In a recent issue of Cell, Sakaue-Sawano et al. describe Fucci, a technique designed to track cell-cycle progression with high spatiotemporal resolution in a multicellular context.