pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework

Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model

Post-translational modifications (PTMs) are pivotal in modulating protein functions and influencing cellular processes like signaling, localization, and degradation. The complexity of these biological interactions necessitates efficient predictive methodologies. In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It utilizes a custom prompt to guide the model through the subtle linguistic patterns encoded in amino acid sequences, generating tokens indicative of PTM sites. To provide interpretability, we visualize attention profiles from the model's final decoder layer to elucidate sequence motifs essential for molecular recognition and analyze the effects of mutations at or near PTM sites to offer deeper insights into protein functionality. Comparative assessments reveal that PTMGPT2 outperforms existing methods across 19 PTM types, underscoring its potential in identifying disease associations and drug targets.

Sperm-Specific CatSper is Not Conserved in All Vertebrates and May Not be the Only Progesterone-Responsive Ion Channel Present in Sperm

Progesterone (P4) acts as a key conserved signalling molecule in vertebrate reproduction. P4 is especially important for mature sperm physiology and subsequent reproductive success. "CatSpermasome", a multi-unit molecular complex, has been suggested to be the main if not the only P4-responsive atypical Ca2+-ion channel present in mature sperm. Altogether, here we analyse the protein sequences of CatSper1-4 from more than 500 vertebrates ranging from early fishes to humans. CatSper1 becomes longer in mammals due to sequence gain mainly at the N-terminus. Overall the conservation of full-length CatSper1-4 as well as the individual TM regions remain low. The lipid-water-interface residues (i.e. a 5 amino acid stretch sequence present on both sides of each TM region) also remain highly diverged. No specific patterns of amino acid distributions were observed. The total frequency of positively charged, negatively charged or their ratios do not follow in any specific pattern. Similarly, the frequency of total hydrophobic, total hydrophilic residues or even their ratios remain random and do not follow any specific pattern. We noted that the CatSper1-4 genes are missing in amphibians and the CatSper1 gene is missing in birds. The high variability of CatSper1-4 and gene-loss in certain clades indicate that the "CatSpermasome" is not the only P4-responsive ion channel. Data indicate that the molecular evolution of CatSper is mostly guided by diverse hydrophobic ligands rather than only P4. The comparative data also suggest possibilities of other Ca2+-channel/s in vertebrate sperm that can also respond to P4.

Structural and mechanistic insights into Quinolone Synthase to address its functional promiscuity

Quinolone synthase from Aegle marmelos (AmQNS) is a type III polyketide synthase that yields therapeutically effective quinolone and acridone compounds. Addressing the structural and molecular underpinnings of AmQNS and its substrate interaction in terms of its high selectivity and specificity can aid in the development of numerous novel compounds. This paper presents a high-resolution AmQNS crystal structure and explains its mechanistic role in synthetic selectivity. Additionally, we provide a model framework to comprehend structural constraints on ketide insertion and postulate that AmQNS's steric and electrostatic selectivity plays a role in its ability to bind to various core substrates, resulting in its synthetic diversity. AmQNS prefers quinolone synthesis and can accommodate large substrates because of its wide active site entrance. However, our research suggests that acridone is exclusively synthesized in the presence of high malonyl-CoA concentrations. Potential implications of functionally relevant residue mutations were also investigated, which will assist in harnessing the benefits of mutations for targeted polyketide production. The pharmaceutical industry stands to gain from these findings as they expand the pool of potential drug candidates, and these methodologies can also be applied to additional promising enzymes.

Location of S-nitrosylated cysteines in protein three-dimensional structures

Although S-nitrosylation of cysteines is a common protein posttranslational modification, little is known about its three-dimensional structural features. This paper describes a systematic survey of the data available in the Protein Data Bank. Several interesting observations could be made. (1) As a result of radiation damage, S-nitrosylated cysteines (Snc) are frequently reduced, at least partially. (2) S-nitrosylation may be a protection against irreversible thiol oxidation; because the NO group of Snc is relatively accessible to the solvent, it may act as a cork to protect the sulfur atoms of cysteines from oxidation by molecular oxygen to sulfenic, sulfinic, and sulfonic acid; moreover, Snc are frequently found at the start or end of helices and strands and this might shield secondary structural elements from unfolding.

PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information

The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological function mechanisms. In this document, we have constructed a predictor, named PPSNO, forecasting protein SNO sites using stacked integrated learning. PPSNO integrates multiple machine learning techniques into an ensemble model, enhancing its predictive accuracy. First, we established benchmark datasets by collecting SNO sites from various sources, including literature, databases, and other predictors. Second, various techniques for feature extraction are applied to derive characteristics from protein sequences, which are subsequently amalgamated into the PPSNO predictor for training. Five-fold cross-validation experiments show that PPSNO outperformed existing predictors, such as PSNO, PreSNO, pCysMod, DeepNitro, RecSNO, and Mul-SNO. The PPSNO predictor achieved an impressive accuracy of 92.8%, an area under the curve (AUC) of 96.1%, a Matthews correlation coefficient (MCC) of 81.3%, an F1-score of 85.6%, an SN of 79.3%, an SP of 97.7%, and an average precision (AP) of 92.2%. We also employed ROC curves, PR curves, and radar plots to show the superior performance of PPSNO. Our study shows that fused protein sequence features and two-layer stacked ensemble models can improve the accuracy of predicting SNO sites, which can aid in comprehending cellular processes and disease mechanisms. The codes and data are available at https://github.com/serendipity-wly/PPSNO .

PbrWRKY62-PbrADC1 module involves in superficial scald development of Pyrus bretschneideri Rehd.fruit via regulating putrescine biosynthesis

Putrescine plays a role in superficial scald development during the cold storage of pear fruit. However, the molecular mechanism behind this phenomenon has not been un-fully clarified until recently. In this study, a conjoint analysis of metabolites and gene expression profiles in the putrescine-metabolic pathway of P. bretschneideri Rehd. fruit followed by experimental validation revealed that PbrADC1, forming a homodimer in the chloroplast, was involved in putrescine biosynthesis and thus fruit chilling resistance. Additionally, the substrate-binding residue Cys546 in PbrADC1, whose activity was modified by H2O2, played a crucial role in arginine decarboxylation into agmatine. Through a combined analysis of the distribution of cis-acting elements in the PbrADC1 promoter as well as the expression profiles of related transcription factors (TFs), several TFs were identified as upstream regulators of PbrADC1 gene. Further investigation revealed that the nuclear PbrWRKY62 could directly bind to the W-box elements in the PbrADC1 promoter, activate its expression, enhance putrescine accumulation, and thus increase fruit chilling tolerance. In conclusion, our results suggest that the PbrWRKY62-PbrADC1 module is involved in the development of superficial scald in P. bretschneideri Rehd. fruit via regulating putrescine biosynthesis. Consequently, these findings could serve as valuable genetic resources for breeding scald-resistant pear fruit.

Identification of Protein Targets of S-Nitroso-Coenzyme A-Mediated S-Nitrosation Using Chemoproteomics

S-Nitrosation is a cysteine post-translational modification fundamental to cellular signaling. This modification regulates protein function in numerous biological processes in the nervous, cardiovascular, and immune systems. Small molecule or protein nitrosothiols act as mediators of NO signaling by transferring the NO group (formally NO+) to a free thiol on a target protein through a transnitrosation reaction. The protein targets of specific transnitrosating agents and the extent and functional effects of S-nitrosation on these target proteins have been poorly characterized. S-nitroso-coenzyme A (CoA-SNO) was recently identified as a mediator of endogenous S-nitrosation. Here, we identified direct protein targets of CoA-SNO-mediated transnitrosation using a competitive chemical-proteomic approach that quantified the extent of modification on 789 cysteine residues in response to CoA-SNO. A subset of cysteines displayed high susceptibility to modification by CoA-SNO, including previously uncharacterized sites of S-nitrosation. We further validated and functionally characterized the functional effects of S-nitrosation on the protein targets phosphofructokinase (platelet type), ATP citrate synthase, and ornithine aminotransferase.

Filamentous actin destabilization by H2O2 favors DnmA aggregation, with crucial roles of cysteines 450 and 776 in mitochondrial and peroxisomal division in Aspergillus nidulans

IMPORTANCE

Mitochondria constitute major sources of H2O2 and other reactive oxygen species in eukaryotic cells. The division of these organelles is crucial for multiple processes in cell biology and relies on highly regulated mechano-GTPases that are oligomerization dependent and belong to the dynamin-related protein family, like A. nidulans DnmA. Our previous work demonstrated that H2O2 induces mitochondrial constriction, division, and remodeling of the outer membrane. Here, we show that H2O2 also induces a DnmA aggregation consistent with higher-order oligomerization and its recruitment to mitochondria. The study of this response uncovered that H2O2 induces the depolymerization and reorganization of actin as well as the critical role that cysteines 450 and 776 play in DnmA function. Our results provide new insights into the mechanisms of reactive oxygen species cell signaling and how they can regulate the dynamics of the actin cytoskeleton and the division of mitochondria and peroxisomes.

NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool

The nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways. Existing NR prediction tools only use a few types of sequence-based features and are tested on relatively similar independent datasets; thus, they may suffer from overfitting when extended to new genera of sequences. To address this problem, we developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized. The first level of NRPreTo allows for the successful prediction of a query protein as NR or non-NR and further subclassifies the protein into one of the seven NR subfamilies in the second level. We developed Random Forest classifiers to test on benchmark datasets, as well as the entire human protein datasets from RefSeq and Human Protein Reference Database (HPRD). We observed that using additional feature groups improved the performance. We also observed that NRPreTo achieved high performance on the external datasets and predicted 59 novel NRs in the human proteome. The source code of NRPreTo is publicly available at https://github.com/bozdaglab/NRPreTo.

Molecular determinants of acrylamide neurotoxicity through covalent docking

Acrylamide (ACR) is formed during food processing by Maillard reaction between sugars and proteins at high temperatures. It is also used in many industries, from water waste treatment to manufacture of paper, fabrics, dyes and cosmetics. Unfortunately, cumulative exposure to acrylamide, either from diet or at the workplace, may result in neurotoxicity. Such adverse effects arise from covalent adducts formed between acrylamide and cysteine residues of several neuronal proteins via a Michael addition reaction. The molecular determinants of acrylamide reactivity and its impact on protein function are not completely understood. Here we have compiled a list of acrylamide protein targets reported so far in the literature in connection with neurotoxicity and performed a systematic covalent docking study. Our results indicate that acrylamide binding to cysteine is favored in the presence of nearby positively charged amino acids, such as lysines and arginines. For proteins with more than one reactive Cys, docking scores were able to discriminate between the primary ACR modification site and secondary sites modified only at high ACR concentrations. Therefore, docking scores emerge as a potential filter to predict Cys reactivity against acrylamide. Inspection of the ACR-protein complex structures provides insights into the putative functional consequences of ACR modification, especially for non-enzyme proteins. Based on our study, covalent docking is a promising computational tool to predict other potential protein targets mediating acrylamide neurotoxicity.

pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module

Background

Lysine succinylation is a newly discovered protein post-translational modifications. Predicting succinylation sites helps investigate the metabolic disease treatments. However, the biological experimental approaches are costly and inefficient, it is necessary to develop efficient computational approaches.

Results

In this paper, we proposed a novel predictor based on ensemble dense blocks and an attention module, called as pSuc-EDBAM, which adopted one hot encoding to derive the feature maps of protein sequences, and generated the low-level feature maps through 1-D CNN. Afterward, the ensemble dense blocks were used to capture feature information at different levels in the process of feature learning. We also introduced an attention module to evaluate the importance degrees of different features. The experimental results show that Acc reaches 74.25%, and MCC reaches 0.2927 on the testing dataset, which suggest that the pSuc-EDBAM outperforms the existing predictors.

Conclusions

The experimental results of ten-fold cross-validation on the training dataset and independent test on the testing dataset showed that pSuc-EDBAM outperforms the existing succinylation site predictors and can predict potential succinylation sites effectively. The pSuc-EDBAM is feasible and obtains the credible predictive results, which may also provide valuable references for other related research. To make the convenience of the experimental scientists, a user-friendly web server has been established (http://bioinfo.wugenqiang.top/pSuc-EDBAM/), by which the desired results can be easily obtained.

CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications

The unique chemical reactivity of cysteine residues results in various posttranslational modifications (PTMs), which are implicated in regulating a range of fundamental biological processes. With the advent of chemical proteomics technology, thousands of cysteine PTM (CysPTM) sites have been identified from multiple species. A few CysPTM-based databases have been developed, but they mainly focus on data collection rather than various annotations and analytical integration. Here, we present a platform-dubbed CysModDB, integrated with the comprehensive CysPTM resources and analysis tools. CysModDB contains five parts: (1) 70 536 experimentally verified CysPTM sites with annotations of sample origin and enrichment techniques, (2) 21 654 modified proteins annotated with functional regions and structure information, (3) cross-references to external databases such as the protein-protein interactions database, (4) online computational tools for predicting CysPTM sites and (5) integrated analysis tools such as gene enrichment and investigation of sequence features. These parts are integrated using a customized graphic browser and a Basket. The browser uses graphs to represent the distribution of modified sites with different CysPTM types on protein sequences and mapping these sites to the protein structures and functional regions, which assists in exploring cross-talks between the modified sites and their potential effect on protein functions. The Basket connects proteins and CysPTM sites to the analysis tools. In summary, CysModDB is an integrated platform to facilitate the CysPTM research, freely accessible via https://cysmoddb.bioinfogo.org/.

A multienzyme S-nitrosylation cascade regulates cholesterol homeostasis

Accumulating evidence suggests that protein S-nitrosylation is enzymatically regulated and that specificity in S-nitrosylation derives from dedicated S-nitrosylases and denitrosylases that conjugate and remove S-nitrosothiols, respectively. Here, we report that mice deficient in the protein denitrosylase SCoR2 (S-nitroso-Coenzyme A Reductase 2; AKR1A1) exhibit marked reductions in serum cholesterol due to reduced secretion of the cholesterol-regulating protein PCSK9. SCoR2 associates with endoplasmic reticulum (ER) secretory machinery to control an S-nitrosylation cascade involving ER cargo-selection proteins SAR1 and SURF4, which moonlight as S-nitrosylases. SAR1 acts as a SURF4 nitrosylase and SURF4 as a PCSK9 nitrosylase to inhibit PCSK9 secretion, while SCoR2 counteracts nitrosylase activity by promoting PCSK9 denitrosylation. Inhibition of PCSK9 by an NO-based drug requires nitrosylase activity, and small-molecule inhibition of SCoR2 phenocopies the PCSK9-mediated reductions in cholesterol observed in SCoR2-deficient mice. Our results reveal enzymatic machinery controlling cholesterol levels through S-nitrosylation and suggest a distinct treatment paradigm for cardiovascular disease.

Targeting the YXXΦ Motifs of the SARS Coronaviruses 1 and 2 ORF3a Peptides by In Silico Analysis to Predict Novel Virus-Host Interactions

The emerging SARS-CoV and SARS-CoV-2 belong to the family of "common cold" RNA coronaviruses, and they are responsible for the 2003 epidemic and the current pandemic with over 6.3 M deaths worldwide. The ORF3a gene is conserved in both viruses and codes for the accessory protein ORF3a, with unclear functions, possibly related to viral virulence and pathogenesis. The tyrosine-based YXXΦ motif (Φ: bulky hydrophobic residue-L/I/M/V/F) was originally discovered to mediate clathrin-dependent endocytosis of membrane-spanning proteins. Many viruses employ the YXXΦ motif to achieve efficient receptor-guided internalisation in host cells, maintain the structural integrity of their capsids and enhance viral replication. Importantly, this motif has been recently identified on the ORF3a proteins of SARS-CoV and SARS-CoV-2. Given that the ORF3a aa sequence is not fully conserved between the two SARS viruses, we aimed to map in silico structural differences and putative sequence-driven alterations of regulatory elements within and adjacently to the YXXΦ motifs that could predict variations in ORF3a functions. Using robust bioinformatics tools, we investigated the presence of relevant post-translational modifications and the YXXΦ motif involvement in protein-protein interactions. Our study suggests that the predicted YXXΦ-related features may confer specific-yet to be discovered-functions to ORF3a proteins, significant to the new virus and related to enhanced propagation, host immune regulation and virulence.

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

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

S-Nitrosation of E3 Ubiquitin Ligase Complex Components Regulates Hormonal Signalings in Arabidopsis

E3 ubiquitin ligases mediate the last step of the ubiquitination pathway in the ubiquitin-proteasome system (UPS). By targeting transcriptional regulators for their turnover, E3s play a crucial role in every aspect of plant biology. In plants, SKP1/CULLIN1/F-BOX PROTEIN (SCF)-type E3 ubiquitin ligases are essential for the perception and signaling of several key hormones including auxins and jasmonates (JAs). F-box proteins, TRANSPORT INHIBITOR RESPONSE 1 (TIR1) and CORONATINE INSENSITIVE 1 (COI1), bind directly transcriptional repressors AUXIN/INDOLE-3-ACETIC ACID (AUX/IAA) and JASMONATE ZIM-DOMAIN (JAZ) in auxin- and JAs-depending manner, respectively, which permits the perception of the hormones and transcriptional activation of signaling pathways. Redox modification of proteins mainly by S-nitrosation of cysteines (Cys) residues via nitric oxide (NO) has emerged as a valued regulatory mechanism in physiological processes requiring its rapid and versatile integration. Previously, we demonstrated that TIR1 and Arabidopsis thaliana SKP1 (ASK1) are targets of S-nitrosation, and these NO-dependent posttranslational modifications enhance protein-protein interactions and positively regulate SCFTIR1 complex assembly and expression of auxin response genes. In this work, we confirmed S-nitrosation of Cys140 in TIR1, which was associated in planta to auxin-dependent developmental and stress-associated responses. In addition, we provide evidence on the modulation of the SCFCOI1 complex by different S-nitrosation events. We demonstrated that S-nitrosation of ASK1 Cys118 enhanced ASK1-COI1 protein-protein interaction. Overexpression of non-nitrosable ask1 mutant protein impaired the activation of JA-responsive genes mediated by SCFCOI1 illustrating the functional relevance of this redox-mediated regulation in planta. In silico analysis positions COI1 as a promising S-nitrosation target, and demonstrated that plants treated with methyl JA (MeJA) or S-nitrosocysteine (NO-Cys, S-nitrosation agent) develop shared responses at a genome-wide level. The regulation of SCF components involved in hormonal perception by S-nitrosation may represent a key strategy to determine the precise time and site-dependent activation of each hormonal signaling pathway and highlights NO as a pivotal molecular player in these scenarios.

The manifold roles of protein S-nitrosylation in the life of insulin

Insulin, which is released by pancreatic islet β-cells in response to elevated levels of glucose in the blood, is a critical regulator of metabolism. Insulin triggers the uptake of glucose and fatty acids into the liver, adipose tissue and muscle, and promotes the storage of these nutrients in the form of glycogen and lipids. Dysregulation of insulin synthesis, secretion, transport, degradation or signal transduction all cause failure to take up and store nutrients, resulting in type 1 diabetes mellitus, type 2 diabetes mellitus and metabolic dysfunction. In this Review, we make the case that insulin signalling is intimately coupled to protein S-nitrosylation, in which nitric oxide groups are conjugated to cysteine thiols to form S-nitrosothiols, within effectors of insulin action. We discuss the role of S-nitrosylation in the life cycle of insulin, from its synthesis and secretion in pancreatic β-cells, to its signalling and degradation in target tissues. Finally, we consider how aberrant S-nitrosylation contributes to metabolic diseases, including the roles of human genetic mutations and cellular events that alter S-nitrosylation of insulin-regulating proteins. Given the growing influence of S-nitrosylation in cellular metabolism, the field of metabolic signalling could benefit from renewed focus on S-nitrosylation in type 2 diabetes mellitus and insulin-related disorders.

Mul-SNO: A novel prediction tool for S-nitrosylation sites based on deep learning methods

Protein s-nitrosylation (SNO is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immune response and treating various major human diseases. In recent years, SNO sites have become a hot research topic. Traditional biochemical methods for SNO site identification are time-consuming and costly. In this study, we developed an economical and efficient SNO site prediction tool named Mul-SNO. Mul-SNO ensembled current popular and powerful deep learning model bidirectional long short-term memory (BiLSTM and bidirectional encoder representations from Transformers (BERT . Compared with existing state-of-the-art methods, Mul-SNO obtained better ACC of 0.911 and 0.796 based on 10-fold cross-validation and independent data sets, respectively. The prediction server can be obtained for free at http://lab.malab.cn/~mjq/Mul-SNO/.

The Modus Operandi of Hydrogen Sulfide(H2S)-Dependent Protein Persulfidation in Higher Plants

Protein persulfidation is a post-translational modification (PTM) mediated by hydrogen sulfide (H₂S), which affects the thiol group of cysteine residues from target proteins and can have a positive, negative or zero impact on protein function. Due to advances in proteomic techniques, the number of potential protein targets identified in higher plants, which are affected by this PTM, has increased considerably. However, its precise impact on biological function needs to be evaluated at the experimental level in purified proteins in order to identify the specific cysteine(s) residue(s) affected. It also needs to be evaluated at the cellular redox level given the potential interactions among different oxidative post-translational modifications (oxiPTMs), such as S-nitrosation, glutathionylation, sulfenylation, S-cyanylation and S-acylation, which also affect thiol groups. This review aims to provide an updated and comprehensive overview of the important physiological role exerted by persulfidation in higher plants, which acts as a cellular mechanism of protein protection against irreversible oxidation.

GSNOR regulates cardiomyocyte differentiation and maturation through protein S-nitrosylation

S-nitrosoglutathione reductase (GSNOR) is a denitrosylase enzyme responsible for reverting protein S-nitrosylation (SNO). In this issue, Salerno et al. [1] provide evidence that GSNOR deficiency - and thus elevated protein S-nitrosylation - accelerates cardiomyocyte differentiation and maturation of induced pluripotent stem cells (iPSCs). GSNOR inhibition (GSNOR-/- iPSCs) expedites the epithelial-mesenchymal transition (EMT) and promotes cardiomyocyte progenitor cell proliferation, differentiation, and migration. These findings are consistent with emerging roles for protein S-nitrosylation in developmental biology (including cardiomyocyte development), aging/longevity, and cancer.