Systematic characterization and prediction of post-translational modification cross-talk

SETDB1 activity is globally directed by H3K14 acetylation via its Triple Tudor Domain

SETDB1 (SET domain bifurcated histone lysine methyltransferase 1) is a major protein lysine methyltransferase trimethylating lysine 9 on histone H3 (H3K9) which is involved in heterochromatin formation and silencing of repeat elements (REs). It contains a unique Triple Tudor Domain (3TD), which specifically binds the dual modification of H3K14ac in the presence of H3K9me1/2/3. Here, we explored the role of the 3TD H3-tail interaction for the H3K9 methylation activity of SETDB1. We generated a binding reduced 3TD mutant and demonstrate in biochemical methylation assays on peptides and recombinant nucleosomes containing H3K14ac and H3K14ac analogs, respectively, that H3K14 acetylation is crucial for the 3TD mediated recruitment of SETDB1. We also observe this effect in cells where SETDB1 binding and activity is globally correlated with H3K14ac, and knockout of the H3K14 acetyltransferase HBO1 causes a drastic reduction in H3K9me3 levels at SETDB1 dependent sites. Regions with DNA hypomethylation after SETDB1 knockout also show an enrichment in SETDB1-dependent H3K9me3 and H3K14ac. Further analyses revealed that 3TD is particularly important at specific target regions like L1M REs, where H3K9me3 cannot be efficiently reconstituted by the 3TD mutant of SETDB1. In summary, our data demonstrate that the H3K9me3 and H3K14ac are not antagonistic marks but rather the presence of H3K14ac is required for SETDB1 recruitment via 3TD binding to H3K9me1/2/3-K14ac regions and establishment of H3K9me3.

The role of novel protein acylations in cancer

Novel protein acylations are a class of protein post-translational modifications, such as lactylation, succinylation, crotonylation, palmitoylation, and β-hydroxybutyrylation. These acylation modifications are common in prokaryotes and eukaryotes and play pivotal roles in various key cellular processes by regulating gene transcription, protein subcellular localization, stability and activity, protein-protein interactions, and protein-DNA interactions. The diversified acylations are closely associated with various human diseases, especially cancer. In this review, we provide an overview of the distinctive characteristics, effects, and regulatory factors of novel protein acylations. We also explore the various mechanisms through which novel protein acylations are involved in the occurrence and progression of cancer. Furthermore, we discuss the development of anti-cancer drugs targeting novel acylations, offering promising avenues for cancer treatment.

Rapid flow-based synthesis of post-translationally modified peptides and proteins: a case study on MYC's transactivation domain

Protein-protein interactions of c-Myc (MYC) are often regulated by post-translational modifications (PTMs), such as phosphorylation, and crosstalk thereof. Studying these interactions requires proteins with unique PTM patterns, which are challenging to obtain by recombinant methods. Standard peptide synthesis and native chemical ligation can produce such modified proteins, but are time-consuming and therefore typically limited to the study of individual PTMs. Herein, we report the development of flow-based methods for the rapid synthesis of phosphorylated MYC sequences (up to 84 AA), and demonstrate the versatility of this approach for the incorporation of other PTMs (N ε-methylation, sulfation, acetylation, glycosylation) and combinations thereof. Peptides containing up to seven PTMs and phosphorylation at up to five sites were successfully prepared and isolated in high yield and purity. We further produced ten PTM-decorated analogues of the MYC Transactivation Domain (TAD) to screen for binding to the tumor suppressor protein, Bin1, using heteronuclear NMR and native mass spectrometry. We determined the effects of phosphorylation and glycosylation on the strength of the MYC:Bin1 interaction, and reveal an influence of MYC sequence length on binding. Our platform for the rapid synthesis of MYC sequences up to 84 AA with distinct PTM patterns thus enables the systematic study of PTM function at a molecular level, and offers a convenient way for expedited screening of constructs.

PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk

Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast

Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.

PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins

Posttranslational modifications (PTMs), which are processes of adding covalent groups in protein amino acids after the translation, play an important role in regulating proteins' localization, degradation, and functions. Different PTMs both within a single protein and across multiple proteins can work together or regulate reciprocally, known as PTM cross talk. However, high-throughput experimental identifications of PTM cross talk are lack due to technical limitations. In this chapter, we review in silico prediction approaches and illustrate the usage of PTM-X, a suite of recently proposed machine learning methods to predict both intra- and interprotein PTM cross talk.

Structure-based prediction of post-translational modification cross-talk within proteins using complementary residue- and residue pair-based features

Post-translational modification (PTM)-based regulation can be mediated not only by the modification of a single residue but also by the interplay of different modifications. Accurate prediction of PTM cross-talk is a highly challenging issue and is in its infant stage. Especially, less attention has been paid to the structural preferences (except intrinsic disorder and spatial proximity) of cross-talk pairs and the characteristics of individual residues involved in cross-talk, which may restrict the improvement of the prediction accuracy. Here we report a structure-based algorithm called PCTpred to improve the PTM cross-talk prediction. The comprehensive residue- and residue pair-based features were designed for paired PTM sites at the sequence and structural levels. Through feature selection, we reserved 23 newly introduced descriptors and 3 traditional descriptors to develop a sequence-based predictor PCTseq and a structure-based predictor PCTstr, both of which were integrated to construct our final prediction model. According to pair- and protein-based evaluations, PCTpred yielded area under the curve values of approximately 0.9 and 0.8, respectively. Even when removing the distance preference of samples or using the input of modeled structures, our prediction performance was maintained or moderately reduced. PCTpred displayed stable and reliable improvements over the state-of-the-art methods based on various evaluations. The source code and data set are freely available at https://github.com/Liulab-HZAU/PCTpred or http://liulab.hzau.edu.cn/PCTpred/.

Systematic characterization and prediction of post-translational modification cross-talk between proteins

MOTIVATION

Protein post-translational modifications (PTMs) regulate a wide range of cellular protein functions. Many PTM sites from the same (intra) or different (inter) proteins often cooperate with each other to perform a function, which is defined as PTM cross-talk. PTM cross-talk within proteins attracted great attentions in the past a few years. However, the inter-protein PTM cross-talk is largely under studied due to its large protein pair space and lack of a gold standard dataset, even though the PTM interplay between proteins is a key element in cell signaling and regulatory networks.

RESULTS

In this study, 199 inter-protein PTM cross-talk pairs in 82 pairs of human proteins were collected from literature, which to our knowledge is the first effort in compiling such dataset. By comparing with background PTM pairs from the same protein pairs, we found that inter-protein cross-talk PTM pairs have higher sequence co-evolution at both PTM residue and motif levels. Also, we found that cross-talk PTMs have higher co-modification across multiple species and 88 human tissues or conditions. Furthermore, we showed that these features are predictive for PTM cross-talk between proteins, and applied a random forest model to integrate these features with achieving an area under the receiver operating characteristic curve of 0.81 in 10-fold cross-validation, prevailing over using any single feature alone. Therefore, this method would be a valuable tool to identify inter-protein PTM cross-talk at proteome-wide scale.

AVAILABILITY AND IMPLEMENTATION

A web server for prioritization of both intra- and inter-protein PTM cross-talk candidates is at http://bioinfo.bjmu.edu.cn/ptm-x/. Python code for local computer is also freely available at https://github.com/huangyh09/PTM-X.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

mUSP: a high-accuracy map of the in situ crosstalk of ubiquitylation and SUMOylation proteome predicted via the feature enhancement approach

Reversible post-translational modification (PTM) orchestrates various biological processes by changing the properties of proteins. Since many proteins are multiply modified by PTMs, identification of PTM crosstalk site has emerged to be an intriguing topic and attracted much attention. In this study, we systematically deciphered the in situ crosstalk of ubiquitylation and SUMOylation that co-occurs on the same lysine residue. We first collected 3363 ubiquitylation-SUMOylation (UBS) crosstalk site on 1302 proteins and then investigated the prime sequence motifs, the local evolutionary degree and the distribution of structural annotations at the residue and sequence levels between the UBS crosstalk and the single modification sites. Given the properties of UBS crosstalk sites, we thus developed the mUSP classifier to predict UBS crosstalk site by integrating different types of features with two-step feature optimization by recursive feature elimination approach. By using various cross-validations, the mUSP model achieved an average area under the curve (AUC) value of 0.8416, indicating its promising accuracy and robustness. By comparison, the mUSP has significantly better performance with the improvement of 38.41 and 51.48% AUC values compared to the cross-results by the previous single predictor. The mUSP was implemented as a web server available at http://bioinfo.ncu.edu.cn/mUSP/index.html to facilitate the query of our high-accuracy UBS crosstalk results for experimental design and validation.

Peptide-templated gold nanoparticle nanosensor for simultaneous detection of multiple posttranslational modification enzymes

We developed a peptide-templated gold nanoparticle (AuNP) nanosensor for simultaneous detection of multiple posttranslational modification (PTM) enzymes with a detection limit of 28 pM for histone deacetylase (HDAC) and 0.8 pM for protein tyrosine phosphatase 1B (PTP1B), and it can be further applied for the screening of PTM enzyme inhibitors and the measurement of PTM enzymes in cancer cells.

Shot-gun proteomics: why thousands of unidentified signals matter

Mass spectrometry-based proteomics has become a constitutional part of the multi-omics toolbox in yeast research, advancing fundamental knowledge of molecular processes and guiding decisions in strain and product developmental pipelines. Nevertheless, post-translational protein modifications (PTMs) continue to challenge the field of proteomics. PTMs are not directly encoded in the genome; therefore, they require a sensitive analysis of the proteome itself. In yeast, the relevance of post-translational regulators has already been established, such as for phosphorylation, which can directly affect the reaction rates of metabolic enzymes. Whereas, the selective analysis of single modifications has become a broadly employed technique, the sensitive analysis of a comprehensive set of modifications still remains a challenge. At the same time, a large number of fragmentation spectra in a typical shot-gun proteomics experiment remain unidentified. It has been estimated that a good proportion of those unidentified spectra originates from unexpected modifications or natural peptide variants. In this review, recent advancements in microbial proteomics for unrestricted protein modification discovery are reviewed, and recent research integrating this additional layer of information to elucidate protein interaction and regulation in yeast is briefly discussed.

Crosstalk of intracellular post-translational modifications in cancer

Post-translational modifications (PTMs) have been reported to play pivotal roles in numerous cellular biochemical and physiological processes. Multiple PTMs can influence the actions of each other positively or negatively, termed as PTM crosstalk or PTM code. During recent years, development of identification strategies for PTMs co-occurrence has revealed abundant information of interplay between PTMs. Increasing evidence demonstrates that deregulation of PTMs crosstalk is involved in the genesis and development of various diseases. Insight into the complexity of PTMs crosstalk will help us better understand etiology and provide novel targets for drug therapy. In the present review, we will discuss the important functional roles of PTMs crosstalk in proteins associated with cancer diseases.

[Structural-functional diversity of p53 proteoforms]

Protein p53 is one of the most studied proteins. This attention is primarily due to its key role in the cellular mechanisms associated with carcinogenesis. Protein p53 is a transcription factor involved in a wide variety of processes: cell cycle regulation and apoptosis, signaling inside the cell, DNA repair, coordination of metabolic processes, regulation of cell interactions, etc. This multifunctionality is apparently determined by the fact that p53 is a vivid example of how the same protein can be represented by numerous proteoforms bearing completely different functional loads. By alternative splicing, using different promoters and translation initiation sites, the TP53 gene gives rise to at least 12 isoforms, which can additionally undergo numerous (>200) post-translational modifications. Proteoforms generated due to numerous point mutations in the TP53 gene are adding more complexity to this picture. The proteoforms produced are involved in various processes, such as the regulation of p53 transcriptional activity in response to various factors. This review is devoted to the description of the currently known p53 proteoforms, as well as their possible functionality.

Protein Post-Translational Modification Crosstalk in Acute Myeloid Leukemia Calls for Action

BACKGROUND

Post-translational modification (PTM) crosstalk is a young research field. However, there is now evidence of the extraordinary characterization of the different proteoforms and their interactions in a biological environment that PTM crosstalk studies can describe. Besides gene expression and phosphorylation profiling of acute myeloid leukemia (AML) samples, the functional combination of several PTMs that might contribute to a better understanding of the complexity of the AML proteome remains to be discovered.

OBJECTIVE

By reviewing current workflows for the simultaneous enrichment of several PTMs and bioinformatics tools to analyze mass spectrometry (MS)-based data, our major objective is to introduce the PTM crosstalk field to the AML research community.

RESULTS

After an introduction to PTMs and PTM crosstalk, this review introduces several protocols for the simultaneous enrichment of PTMs. Two of them allow a simultaneous enrichment of at least three PTMs when using 0.5-2 mg of cell lysate. We have reviewed many of the bioinformatics tools used for PTM crosstalk discovery as its complex data analysis, mainly generated from MS, becomes challenging for most AML researchers. We have presented several non-AML PTM crosstalk studies throughout the review in order to show how important the characterization of PTM crosstalk becomes for the selection of disease biomarkers and therapeutic targets.

CONCLUSION

Herein, we have reviewed the advances and pitfalls of the emerging PTM crosstalk field and its potential contribution to unravel the heterogeneity of AML. The complexity of sample preparation and bioinformatics workflows demands a good interaction between experts of several areas.

TCellXTalk facilitates the detection of co-modified peptides for the study of protein post-translational modification cross-talk in T cells

MOTIVATION

Protein function is regulated by post-translational modifications (PTMs) that may act individually or interact with others in a phenomenon termed PTM cross-talk. Multiple databases have been dedicated to PTMs, including recent initiatives oriented towards the in silico prediction of PTM interactions. The study of PTM cross-talk ultimately requires experimental evidence about whether certain PTMs coexist in a single protein molecule. However, available resources do not assist researchers in the experimental detection of co-modified peptides.

RESULTS

Herein, we present TCellXTalk, a comprehensive database of phosphorylation, ubiquitination and acetylation sites in human T cells that supports the experimental detection of co-modified peptides using targeted or directed mass spectrometry. We demonstrate the efficacy of TCellXTalk and the strategy presented here in a proof of concept experiment that enabled the identification and quantification of 15 co-modified (phosphorylated and ubiquitinated) peptides from CD3 proteins of the T-cell receptor complex. To our knowledge, these are the first co-modified peptide sequences described in this widely studied cell type. Furthermore, quantitative data showed distinct dynamics for co-modified peptides upon T cell activation, demonstrating differential regulation of co-occurring PTMs in this biological context. Overall, TCellXTalk facilitates the experimental detection of co-modified peptides in human T cells and puts forward a novel and generic strategy for the study of PTM cross-talk.

AVAILABILITY AND IMPLEMENTATION

TCellXTalk is available at https://www.tcellxtalk.org. Source Code is available at https://bitbucket.org/lp-csic-uab/tcellxtalk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Recent advances in phosphoproteomics and application to neurological diseases

Phosphorylation has an incredible impact on the biological behavior of proteins, altering everything from intrinsic activity to cellular localization and complex formation. It is no surprise then that this post-translational modification has been the subject of intense study and that, with the advent of faster, more accurate instrumentation, the number of large-scale mass spectrometry-based phosphoproteomic studies has swelled over the past decade. Recent developments in sample preparation, phosphorylation enrichment, quantification, and data analysis strategies permit both targeted and ultra-deep phosphoproteome profiling, but challenges remain in pinpointing biologically relevant phosphorylation events. We describe here technological advances that have facilitated phosphoproteomic analysis of cells, tissues, and biofluids and note applications to neuropathologies in which the phosphorylation machinery may be dysregulated, much as it is in cancer.

Protein Language: Post-Translational Modifications Talking to Each Other

Post-translational modifications (PTMs) are at the heart of many cellular signaling events. Apart from a single regulatory PTM, there are also PTMs that function in orchestrated manners. Such PTM crosstalk usually serves as a fine-tuning mechanism to adjust cellular responses to the slightest changes in the environment. While PTM crosstalk has been studied in depth in various species; in plants, this field is just emerging. In this review, we discuss recent studies on crosstalk between three of the most common protein PTMs in plant cells, being phosphorylation, ubiquitination, and sumoylation, and we highlight the diverse underlying mechanisms as well as signaling outputs of such crosstalk.

Dissecting Structure-Encoded Determinants of Allosteric Cross-Talk between Post-Translational Modification Sites in the Hsp90 Chaperones

Post-translational modifications (PTMs) represent an important regulatory instrument that modulates structure, dynamics and function of proteins. The large number of PTM sites in the Hsp90 proteins that are scattered throughout different domains indicated that synchronization of multiple PTMs through a combinatorial code can be invoked as an important mechanism to orchestrate diverse chaperone functions and recognize multiple client proteins. In this study, we have combined structural and coevolutionary analysis with molecular simulations and perturbation response scanning analysis of the Hsp90 structures to characterize functional role of PTM sites in allosteric regulation. The results reveal a small group of conserved PTMs that act as global mediators of collective dynamics and allosteric communications in the Hsp90 structures, while the majority of flexible PTM sites serve as sensors and carriers of the allosteric structural changes. This study provides a comprehensive structural, dynamic and network analysis of PTM sites across Hsp90 proteins, identifying specific role of regulatory PTM hotspots in the allosteric mechanism of the Hsp90 cycle. We argue that plasticity of a combinatorial PTM code in the Hsp90 may be enacted through allosteric coupling between effector and sensor PTM residues, which would allow for timely response to structural requirements of multiple modified enzymes.

Co-occurring protein phosphorylation are functionally associated

Post-translational modifications (PTMs) add a further layer of complexity to the proteome and regulate a wide range of cellular protein functions. With the increasing number of known PTM sites, it becomes imperative to understand their functional interplays. In this study, we proposed a novel analytical strategy to explore functional relationships between PTM sites by testing their tendency to be modified together (co-occurrence) under the same condition, and applied it to proteome-wide human phosphorylation data collected under 88 different laboratory or physiological conditions. Co-occurring phosphorylation occurs significantly more frequently than randomly expected and include many known examples of cross-talk or functional connections. Such pairs, either within the same phosphoprotein or between interacting partners, are more likely to be in sequence or structural proximity, be phosphorylated by the same kinases, participate in similar biological processes, and show residue co-evolution across vertebrates. In addition, we also found that their co-occurrence states tend to be conserved in orthologous phosphosites in the mouse proteome. Together, our results support that the co-occurring phosphorylation are functionally associated. Comparison with existing methods further suggests that co-occurrence analysis can be a useful complement to uncover novel functional associations between PTM sites.

In addition to gene expression and translation control, post-translational modifications (PTMs) represent another level to regulate proteins functions. Different PTM sites within a protein usually co-operate to fulfill their functional roles. Recent advances in high-throughput mass spectrometry (MS) technologies have facilitated the proteome-wide identification of PTM sites, giving rise to both challenge and opportunity to understand their functional relationships. Previously, several data mining approaches have been developed to explore the global PTM interplays. In this study, we proposed to infer functional associations between PTM sites from the correlation of their modification status across many biological conditions, which was not exploited before. In practice, we tested if a pair of sites are modified together under the same condition significantly more often than expected (co-occurrence). As a proof of principle, we applied this analytical strategy to human phosphorylation because we could collect data sets of proteome-wide coverage under 88 different conditions. We demonstrated that sites with co-occurring phosphorylation status are functionally associated from several lines of evidence. The co-occurrence analysis can also uncover functionally connected phosphosites with clear biological evidence which are missed by other approaches. With increasing proteome-wide data for other types of PTMs under different conditions, the co-occurrence analysis can be integrated with other methods to identify novel PTM associations.

Prediction of post-translational modification sites using multiple kernel support vector machine

Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites by using protein local sequence information in the context of conserved motif. Here we proposed a novel computational method by using the combination of multiple kernel support vector machines (SVM) for predicting PTM sites including phosphorylation, O-linked glycosylation, acetylation, sulfation and nitration. To largely make use of local sequence information and site-modification relationships, we developed a local sequence kernel and Gaussian interaction profile kernel, respectively. Multiple kernels were further combined to train SVM for efficiently leveraging kernel information to boost predictive performance. We compared the proposed method with existing PTM prediction methods. The experimental results revealed that the proposed method performed comparable or better performance than the existing prediction methods, suggesting the feasibility of the developed kernels and the usefulness of the proposed method in PTM sites prediction.

Protein post-translational modifications: In silico prediction tools and molecular modeling

Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins.

Bioinformatics Analysis of Functional Associations of PTMs

Post-translational modifications (PTMs) are an important source of protein regulation; they fine-tune the function, localization, and interaction with other molecules of the majority of proteins and are partially responsible for their multifunctionality. Usually, proteins have several potential modification sites, and their patterns of occupancy are associated with certain functional states. These patterns imply cross talk among PTMs within and between proteins, the majority of which are still to be discovered. Several methods detect associations between PTMs; these have recently combined into a global resource, the PTMcode database, which contains already known and predicted functional associations between pairs of PTMs from more than 45,000 proteins in 19 eukaryotic species.

Bioinformatics Analysis of PTM-Modified Protein Interaction Networks and Complexes

Normal cellular functioning is maintained by macromolecular machines that control both core and specialized molecular tasks. These machines are in large part multi-subunit protein complexes that undergo regulation at multiple levels, from expression of requisite components to a vast array of post-translational modifications (PTMs). PTMs such as phosphorylation, ubiquitination, and acetylation currently number more than 200,000 in the human proteome and function within all molecular pathways. Here we provide a framework for systematically studying these PTMs in the context of global protein-protein interaction networks. This analytical framework allows insight into which functions specific PTMs tend to cluster in, and furthermore which complexes either single or multiple PTM signaling pathways converge on.

Prediction of post translation modifications at the contact site between Anaplasma phagocytophilum and human host during autophagosome induction using a bioinformatic approach

Autophagy is crucial for maintaining physiological homeostasis, but its role in infectious diseases is not yet adequately understood. The binding of Anaplasma translocated substrate-1 (ATS1) to the human Beclin1 (BECN1) protein is responsible for the modulation of autophagy pathway. ATS1-BECN1 is a novel type of interaction that facilitates Anaplasma phagocytophilum proliferation, leading to intracellular infection via autophagosome induction and segregation from the lysosome. Currently, there is no report of post translational modifications (PTMs) of BECN1 or cross-talk required for ATS-BECN1 complex formation. Prediction/modeling of the cross-talk between phosphorylation and other PTMs (O-β-glycosylation, sumoylation, methylation and palmitoylation) has been attempted in this study, which might be responsible for regulating function after the interaction of ATS1 with BECN1. PTMs were predicted computationally and mapped onto the interface of the docked ATS1-BECN1 complex. Results show that BECN1 phosphorylation at five residues (Thr91, Ser93, Ser96, Thr141 and Ser234), the interplay with O-β-glycosylation at three sites (Thr91, Ser93 and Ser96) with ATS1 may be crucial for attachment and, hence, infection. No other PTM site at the BECN1 interface was predicted to associate with ATS1. These findings may have significant clinical implications for understanding the etiology of Anaplasma infection and for therapeutic studies.

Systematic Analysis and Prediction of In Situ Cross Talk of O-GlcNAcylation and Phosphorylation

Reversible posttranslational modification (PTM) plays a very important role in biological process by changing properties of proteins. As many proteins are multiply modified by PTMs, cross talk of PTMs is becoming an intriguing topic and draws much attention. Currently, lots of evidences suggest that the PTMs work together to accomplish a specific biological function. However, both the general principles and underlying mechanism of PTM crosstalk are elusive. In this study, by using large-scale datasets we performed evolutionary conservation analysis, gene ontology enrichment, motif extraction of proteins with cross talk of O-GlcNAcylation and phosphorylation cooccurring on the same residue. We found that proteins with in situ O-GlcNAc/Phos cross talk were significantly enriched in some specific gene ontology terms and no obvious evolutionary pressure was observed. Moreover, 3 functional motifs associated with O-GlcNAc/Phos sites were extracted. We further used sequence features and GO features to predict O-GlcNAc/Phos cross talk sites based on phosphorylated sites and O-GlcNAcylated sites separately by the use of SVM model. The AUC of classifier based on phosphorylated sites is 0.896 and the other classifier based on GlcNAcylated sites is 0.843. Both classifiers achieved a relatively better performance compared with other existing methods.