PredSulSite: prediction of protein tyrosine sulfation sites with multiple features and analysis

DOTAD: A Database of Therapeutic Antibody Developability

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Sulfoproteomics Workflow with Precursor Ion Accurate Mass Shift Analysis Reveals Novel Tyrosine Sulfoproteins in the Golgi

Tyrosine sulfation in the Golgi of secreted and membrane proteins is an important post-translational modification (PTM). However, its labile nature has limited analysis by mass spectrometry (MS), a major reason why no sulfoproteome studies have been previously reported. Here, we show that a phosphoproteomics experimental workflow, which includes serial enrichment followed by high resolution, high mass accuracy MS, and tandem MS (MS/MS) analysis, enables sulfopeptide coenrichment and identification via accurate precursor ion mass shift open MSFragger database search. This approach, supported by manual validation, allows the confident identification of sulfotyrosine-containing peptides in the presence of high levels of phosphorylated peptides, thus enabling these two sterically and ionically similar isobaric PTMs to be distinguished and annotated in a single proteomic analysis. We applied this approach to isolated interphase and mitotic rat liver Golgi membranes and identified 67 tyrosine sulfopeptides, corresponding to 26 different proteins. This work discovered 23 new sulfoproteins with functions related to, for example, Ca2+-binding, glycan biosynthesis, and exocytosis. In addition, we report the first preliminary evidence for crosstalk between sulfation and phosphorylation in the Golgi, with implications for functional control.

Sulfoconjugation of protein peptides and glycoproteins in physiology and diseases

Protein sulfoconjugation, or sulfation, represents a critical post-translational modification (PTM) process that involves the attachment of sulfate groups to various positions of substrates within the protein peptides or glycoproteins. This process plays a dynamic and complex role in many physiological and pathological processes. Here, we summarize the importance of sulfation in the fields of oncology, virology, drug-induced liver injury (DILI), inflammatory bowel disease (IBD), and atherosclerosis. In oncology, sulfation is involved in tumor initiation, progression, and migration. In virology, sulfation influences viral entry, replication, and host immune response. In DILI, sulfation is associated with the incidence of DILI, where altered sulfation affects drug metabolism and toxicity. In IBD, dysregulation of sulfation compromises mucosal barrier and immune response. In atherosclerosis, sulfation influences the development of atherosclerosis by modulating the accumulation of lipoprotein, and the inflammation, proliferation, and migration of smooth muscle cells. The current review underscores the importance of further research to unravel the underlying mechanisms and therapeutic potential of targeting sulfoconjugation in various diseases. A better understanding of sulfation could facilitate the emergence of innovative diagnostic or therapeutic strategies.

Co-translational Installation of Posttranslational Modifications by Non-canonical Amino Acid Mutagenesis

Protein posttranslational modifications (PTMs) play critical roles in regulating cellular activities. Here we provide a survey of genetic code expansion (GCE) methods that were applied in the co-translational installation and studies of PTMs through noncanonical amino acid (ncAA) mutagenesis. We begin by reviewing types of PTM that have been installed by GCE with a focus on modifications of tyrosine, serine, threonine, lysine, and arginine residues. We also discuss examples of applying these methods in biological studies. Finally, we end the piece with a short discussion on the challenges and the opportunities of the field.

A potential antibody repertoire diversification mechanism through tyrosine sulfation for biotherapeutics engineering and production

The diversity of three hypervariable loops in antibody heavy chain and light chain, termed the complementarity-determining regions (CDRs), defines antibody's binding affinity and specificity owing to the direct contact between the CDRs and antigens. These CDR regions typically contain tyrosine (Tyr) residues that are known to engage in both nonpolar and pi stacking interaction with antigens through their complementary aromatic ring side chains. Nearly two decades ago, sulfotyrosine residue (sTyr), a negatively charged Tyr formed by Golgi-localized membrane-bound tyrosylprotein sulfotransferases during protein trafficking, were also found in the CDR regions and shown to play an important role in modulating antibody-antigen interaction. This breakthrough finding demonstrated that antibody repertoire could be further diversified through post-translational modifications, in addition to the conventional genetic recombination. This review article summarizes the current advances in the understanding of the Tyr-sulfation modification mechanism and its application in potentiating protein-protein interaction for antibody engineering and production. Challenges and opportunities are also discussed.

A protein succinylation sites prediction method based on the hybrid architecture of LSTM network and CNN

The succinylation modification of protein participates in the regulation of a variety of cellular processes. Identification of modified substrates with precise sites is the basis for understanding the molecular mechanism and regulation of succinylation. In this work, we picked and chose five superior feature codes: CKSAAP, ACF, BLOSUM62, AAindex, and one-hot, according to their performance in the problem of succinylation sites prediction. Then, LSTM network and CNN were used to construct four models: LSTM-CNN, CNN-LSTM, LSTM, and CNN. The five selected features were, respectively, input into each of these four models for training to compare the four models. Based on the performance of each model, the optimal model among them was chosen to construct a hybrid model DeepSucc that was composed of five sub-modules for integrating heterogeneous information. Under the 10-fold cross-validation, the hybrid model DeepSucc achieves 86.26% accuracy, 84.94% specificity, 87.57% sensitivity, 0.9406 AUC, and 0.7254 MCC. When compared with other prediction tools using an independent test set, DeepSucc outperformed them in sensitivity and MCC. The datasets and source codes can be accessed at https://github.com/1835174863zd/DeepSucc.

In silico prediction of post-translational modifications in therapeutic antibodies

Monoclonal antibodies are susceptible to chemical and enzymatic modifications during manufacturing, storage, and shipping. Deamidation, isomerization, and oxidation can compromise the potency, efficacy, and safety of therapeutic antibodies. Recently, in silico tools have been used to identify liable residues and engineer antibodies with better chemical stability. Computational approaches for predicting deamidation, isomerization, oxidation, glycation, carbonylation, sulfation, and hydroxylation are reviewed here. Although liable motifs have been used to improve the chemical stability of antibodies, the accuracy of in silico predictions can be improved using machine learning and molecular dynamic simulations. In addition, there are opportunities to improve predictions for specific stress conditions, develop in silico prediction of novel modifications in antibodies, and predict the impact of modifications on physical stability and antigen-binding.

Tyrosine-O-sulfation is a widespread affinity enhancer among thrombin interactors

Tyrosine-O-sulfation is a common post-translational modification (PTM) of proteins following the cellular secretory pathway. First described in human fibrinogen, tyrosine-O-sulfation has long been associated with the modulation of protein-protein interactions in several physiological processes. A number of relevant interactions for hemostasis are largely dictated by this PTM, many of which involving the serine proteinase thrombin (FIIa), a central player in the blood-clotting cascade. Tyrosine sulfation is not limited to endogenous FIIa ligands and has also been found in hirudin, a well-known and potent thrombin inhibitor from the medicinal leech, Hirudo medicinalis. The discovery of hirudin led to successful clinical application of analogs of leech-inspired molecules, but also unveiled several other natural thrombin-directed anticoagulant molecules, many of which undergo tyrosine-O-sulfation. The presence of this PTM has been shown to enhance the anticoagulant properties of these peptides from a range of blood-feeding organisms, including ticks, mosquitos and flies. Interestingly, some of these molecules display mechanisms of action that mimic those of thrombin's bona fide substrates.

ABC-Gly: Identifying Protein Lysine Glycation Sites with Artificial Bee Colony Algorithm

Background:

Glycation is a nonenzymatic post-translational modification process by attaching a sugar molecule to a protein or lipid molecule. It may impair the function and change the characteristic of the proteins which may lead to some metabolic diseases. In order to understand the underlying molecular mechanisms of glycation, computational prediction methods have been developed because of their convenience and high speed. However, a more effective computational tool is still a challenging task in computational biology.

Methods:

In this study, we showed an accurate identification tool named ABC-Gly for predicting lysine glycation sites. At first, we utilized three informative features, including position-specific amino acid propensity, secondary structure and the composition of k-spaced amino acid pairs to encode the peptides. Moreover, to sufficiently exploit discriminative features thus can improve the prediction and generalization ability of the model, we developed a two-step feature selection, which combined the Fisher score and an improved binary artificial bee colony algorithm based on the support vector machine. Finally, based on the optimal feature subset, we constructed an effective model by using the Support Vector Machine on the training dataset.

Results:

The performance of the proposed predictor ABC-Gly was measured with the sensitivity of 76.43%, the specificity of 91.10%, the balanced accuracy of 83.76%, the Area Under the receiveroperating characteristic Curve (AUC) of 0.9313, a Matthew’s Correlation Coefficient (MCC) of 0.6861 by 10-fold cross-validation on training dataset, and a balanced accuracy of 59.05% on independent dataset. Compared to the state-of-the-art predictors on the training dataset, the proposed predictor achieved significant improvement in the AUC of 0.156 and MCC of 0.336.

Conclusion:

The detailed analysis results indicated that our predictor may serve as a powerful complementary tool to other existing methods for predicting protein lysine glycation. The source code and datasets of the ABC-Gly were provided in the Supplementary File 1.

Prediction of bio-sequence modifications and the associations with diseases

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components

BACKGROUND

The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological pro-cesses. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.

METHODOLOGY

In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are in-corporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and in-dependent testing.

RESULTS

Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing.

CONCLUSION

The proposed model has better performance as compared to the existing predictors, how-ever, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.

Research progress in protein posttranslational modification site prediction

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.

New tools for evaluating protein tyrosine sulfation: tyrosylprotein sulfotransferases (TPSTs) are novel targets for RAF protein kinase inhibitors

Protein tyrosine sulfation is a post-translational modification best known for regulating extracellular protein–protein interactions. Tyrosine sulfation is catalysed by two Golgi-resident enzymes termed tyrosylprotein sulfotransferases (TPSTs) 1 and 2, which transfer sulfate from the cofactor PAPS (3′-phosphoadenosine 5′-phosphosulfate) to a context-dependent tyrosine in a protein substrate. A lack of quantitative tyrosine sulfation assays has hampered the development of chemical biology approaches for the identification of small-molecule inhibitors of tyrosine sulfation. In the present paper, we describe the development of a non-radioactive mobility-based enzymatic assay for TPST1 and TPST2, through which the tyrosine sulfation of synthetic fluorescent peptides can be rapidly quantified. We exploit ligand binding and inhibitor screens to uncover a susceptibility of TPST1 and TPST2 to different classes of small molecules, including the anti-angiogenic compound suramin and the kinase inhibitor rottlerin. By screening the Published Kinase Inhibitor Set, we identified oxindole-based inhibitors of the Ser/Thr kinase RAF (rapidly accelerated fibrosarcoma) as low-micromolar inhibitors of TPST1 and TPST2. Interestingly, unrelated RAF inhibitors, exemplified by the dual BRAF/VEGFR2 inhibitor RAF265, were also TPST inhibitors in vitro. We propose that target-validated protein kinase inhibitors could be repurposed, or redesigned, as more-specific TPST inhibitors to help evaluate the sulfotyrosyl proteome. Finally, we speculate that mechanistic inhibition of cellular tyrosine sulfation might be relevant to some of the phenotypes observed in cells exposed to anionic TPST ligands and RAF protein kinase inhibitors.

Mosquito-Derived Anophelin Sulfoproteins Are Potent Antithrombotics

The anophelins are small protein thrombin inhibitors that are produced in the salivary glands of the Anopheles mosquito to fulfill a vital role in blood feeding. A bioinformatic analysis of anophelin sequences revealed the presence of conserved tyrosine residues in an acidic environment that were predicted to be post-translationally sulfated in vivo. To test this prediction, insect cell expression of two anophelin proteins, from Anopheles albimanus and Anopheles gambiae, was performed, followed by analysis by mass spectrometry, which showed heterogeneous sulfation at the predicted sites. Homogeneously sulfated variants of the two proteins were subsequently generated by chemical synthesis via a one-pot ligation-desulfurization strategy. Tyrosine sulfation of the anophelins was shown to significantly enhance the thrombin inhibitory activity, with a doubly sulfated variant of the anophelin from A. albimanus exhibiting a 100-fold increase in potency compared with the unmodified homologue. Sulfated anophelins were also shown to exhibit potent in vivo anticoagulant and antithrombotic activity.

Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine

Glycation is a non-enzymatic process occurring inside or outside the host body by attaching a sugar molecule to a protein or lipid molecule. It is an important form of post-translational modification (PTM), which impairs the function and changes the characteristics of the proteins so that the identification of the glycation sites may provide some useful guidelines to understand various biological functions of proteins. In this study, we proposed an accurate prediction tool, named Glypre, for lysine glycation. Firstly, we used multiple informative features to encode the peptides. These features included the position scoring function, secondary structure, AAindex, and the composition of k-spaced amino acid pairs. Secondly, the distribution of distinctive features of the residues surrounding the glycation and non-glycation sites was statistically analysed. Thirdly, based on the distribution of these features, we developed a new predictor by using different optimal window sizes for different properties and a two-step feature selection method, which utilized the maximum relevance minimum redundancy method followed by a greedy feature selection procedure. The performance of Glypre was measured with a sensitivity of 57.47%, a specificity of 90.78%, an accuracy of 79.68%, area under the receiver-operating characteristic (ROC) curve (AUC) of 0.86, and a Matthews’s correlation coefficient (MCC) of 0.52 by 10-fold cross-validation. The detailed analysis results showed that our predictor may play a complementary role to other existing methods for identifying protein lysine glycation. The source code and datasets of the Glypre are available in the Supplementary File.

Structural basis for the broad substrate specificity of the human tyrosylprotein sulfotransferase-1

Tyrosylprotein sulfotransferases (TPSTs) are enzymes that catalyze post-translational tyrosine sulfation of proteins. In humans, there are only two TPST isoforms, designated TPST1 and TPST2. In a previous study, we reported the crystal structure of TPST2, which revealed the catalytic mechanism of the tyrosine sulfation reaction. However, detailed molecular mechanisms underlying how TPSTs catalyse a variety of substrate proteins with different efficiencies and how TPSTs catalyze the sulfation of multiple tyrosine residues in a substrate protein remain unresolved. Here, we report two crystal structures of the human TPST1 complexed with two substrate peptides that are catalysed by human TPST1 with significantly different efficiencies. The distinct binding modes found in the two complexes provide insight into the sulfation mechanism for these substrates. The present study provides valuable information describing the molecular mechanism of post-translational protein modifications catalysed by TPSTs.

High-Throughput Screening of Sulfated Proteins by Using a Genome-Wide Proteome Microarray and Protein Tyrosine Sulfation System

Protein tyrosine sulfation (PTS) is a widespread posttranslational modification that induces intercellular and extracellular responses by regulating protein-protein interactions and enzymatic activity. Although PTS affects numerous physiological and pathological processes, only a small fraction of the total predicted sulfated proteins has been identified to date. Here, we localized the potential sulfation sites of Escherichia coli proteins on a proteome microarray by using a 3'-phosphoadenosine 5'-phosphosulfate (PAPS) synthase-coupled tyrosylprotein sulfotransferase (TPST) catalysis system that involves in situ PAPS generation and TPST catalysis. Among the 4256 E. coli K12 proteins, 875 sulfated proteins were identified using antisulfotyrosine primary and Cy3-labeled antimouse secondary antibodies. Our findings add considerably to the list of potential proteins subjected to tyrosine sulfation. Similar procedures can be applied to identify sulfated proteins in yeast and human proteome microarrays, and we expect such approaches to contribute substantially to the understanding of important human diseases.

Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition

As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/.

A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues

Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.

Tyrosine sulfation as a protein post-translational modification

Integration of inorganic sulfate into biological molecules plays an important role in biological systems and is directly involved in the instigation of diseases. Protein tyrosine sulfation (PTS) is a common post-translational modification that was first reported in the literature fifty years ago. However, the significance of PTS under physiological conditions and its link to diseases have just begun to be appreciated in recent years. PTS is catalyzed by tyrosylprotein sulfotransferase (TPST) through transfer of an activated sulfate from 3'-phosphoadenosine-5'-phosphosulfate to tyrosine in a variety of proteins and peptides. Currently, only a small fraction of sulfated proteins is known and the understanding of the biological sulfation mechanisms is still in progress. In this review, we give an introductory and selective brief review of PTS and then summarize the basic biochemical information including the activity and the preparation of TPST, methods for the determination of PTS, and kinetics and reaction mechanism of TPST. This information is fundamental for the further exploration of the function of PTS that induces protein-protein interactions and the subsequent biochemical and physiological reactions.

PGluS: prediction of protein S-glutathionylation sites with multiple features and analysis

S-Glutathionylation is a reversible protein post-translational modification, which generates mixed disulfides between glutathione (GSH) and cysteine residues, playing an important role in regulating protein stability, activity, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-glutathionylated sites is crucial. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of S-glutathionylated sites are very desirable due to their convenience and high speed. Therefore, in this study, a new bioinformatics tool named PGluS was developed to predict S-glutathionylated sites based on multiple features and support vector machines. The performance of PGluS was measured with an accuracy of 71.41% and a MCC of 0.431 using the 5-fold cross-validation on the training dataset. Additionally, PGluS was evaluated using an independent testing dataset resulting in an accuracy of 71.25%, which demonstrated that PGluS was very promising for predicting S-glutathionylated sites. Furthermore, feature analysis was performed and it was shown that all features adopted in this method contributed to the S-glutathionylation process. A site-specific analysis showed that S-glutathionylation was intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, PGluS is freely accessible at .

Human germline and pan-cancer variomes and their distinct functional profiles

Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations.

Structural and energetic determinants of tyrosylprotein sulfotransferase sulfation specificity

MOTIVATION

Tyrosine sulfation is a type of post-translational modification (PTM) catalyzed by tyrosylprotein sulfotransferases (TPST). The modification plays a crucial role in mediating protein-protein interactions in many biologically important processes. There is no well-defined sequence motif for TPST sulfation, and the underlying determinants of TPST sulfation specificity remains elusive. Here, we perform molecular modeling to uncover the structural and energetic determinants of TPST sulfation specificity.

RESULTS

We estimate the binding affinities between TPST and peptides around tyrosines of both sulfated and non-sulfated proteins to differentiate them. We find that better differentiation is achieved after including energy costs associated with local unfolding of the tyrosine-containing peptide in a host protein, which depends on both the peptide's secondary structures and solvent accessibility. Local unfolding renders buried peptide-with ordered structures-thermodynamically available for TPST binding. Our results suggest that both thermodynamic availability of the peptide and its binding affinity to the enzyme are important for TPST sulfation specificity, and their interplay results into great variations in sequences and structures of sulfated peptides. We expect our method to be useful in predicting potential sulfation sites and transferable to other TPST variants. Our study may also shed light on other PTM systems without well-defined sequence and structural specificities.

AVAILABILITY AND IMPLEMENTATION

All the data and scripts used in the work are available at http://dlab.clemson.edu/research/Sulfation.

Crystal structure of human tyrosylprotein sulfotransferase-2 reveals the mechanism of protein tyrosine sulfation reaction

Post-translational protein modification by tyrosine-sulfation plays an important role in extracellular protein-protein interactions. The protein tyrosine sulfation reaction is catalyzed by the Golgi-enzyme called the tyrosylprotein sulfotransferase (TPST). To date, no crystal structure is available for TPST. Detailed mechanism of protein tyrosine sulfation reaction has thus remained unclear. Here we present the first crystal structure of the human TPST isoform 2 (TPST2) complexed with a substrate peptide (C4P5Y3) derived from complement C4 and 3’-phosphoadenosine-5’-phosphate (PAP) at 1.9Å resolution. Structural and complementary mutational analyses revealed the molecular basis for catalysis being an S_N2-like in-line displacement mechanism. TPST2 appeared to recognize the C4 peptide in a deep cleft by using a short parallel β-sheet type interaction, and the bound C4P5Y3 forms an L-shaped structure. Surprisingly, the mode of substrate peptide recognition observed in the TPST2 structure resembles that observed for the receptor type tyrosine kinases.