CPLM 4.0: an updated database with rich annotations for protein lysine modifications

Lysine lactylation-based insight to understanding the characterization of cervical cancer

Lysine lactylation (Kla), a recently discovered post-translational modification (PTM), is not only present in histone proteins but also widely distributed among non-histone proteins in tumor cells and immunocytes. However, the precise characterization and functional implications of these non-histone Kla proteins remain to be explored. Herein, a comprehensive proteomic analysis of Kla was conducted in HeLa cells. As a result, a total of 3633 Kla sites on 1637 proteins were identified. Subsequently, the stable Kla substrates were obtained and sorted to investigate the characterization and function of Kla proteins. Moreover, we characterized the Kla-related features of cervical cancers through integrative analyses of multiple datasets with proteomes, transcriptomes and single-cell transcriptome profiling. Kla-related genes (KRGs) were used to stratify cervical cancers into two clusters (C1 and C2). C2 cluster display inhibition in glycosylation and increased oxidative phosphorylation activity with high survival rate. In addition, we constructed a prognostic model based on two lactate signature genes, namely ISY1 and PPP1R14B. Interestingly, our findings revealed a negative correlation between PPP1R14B expression and the infiltration of CD8+ T cells, as well as a lower survival rate. This observation was further validated at the single-cell resolution. Simultaneously, we found that K140R mutant of PPP1R14B resulted in the decrease of Kla level and enhanced the proliferation and migration capabilities of cervical cancer cell lines, suggesting PPP1R14B-K140la has an effect on tumor behaviors. Collectively, we provides a Kla-based insight to understanding the characterization of cervical cancer, offering a potential avenue for therapeutic approaches.

Cellular Site-Specific Incorporation of Noncanonical Amino Acids in Synthetic Biology

Over the past two decades, genetic code expansion (GCE)-enabled methods for incorporating noncanonical amino acids (ncAAs) into proteins have significantly advanced the field of synthetic biology while also reaping substantial benefits from it. On one hand, they provide synthetic biologists with a powerful toolkit to enhance and diversify biological designs beyond natural constraints. Conversely, synthetic biology has not only propelled the development of ncAA incorporation through sophisticated tools and innovative strategies but also broadened its potential applications across various fields. This Review delves into the methodological advancements and primary applications of site-specific cellular incorporation of ncAAs in synthetic biology. The topics encompass expanding the genetic code through noncanonical codon addition, creating semiautonomous and autonomous organisms, designing regulatory elements, and manipulating and extending peptide natural product biosynthetic pathways. The Review concludes by examining the ongoing challenges and future prospects of GCE-enabled ncAA incorporation in synthetic biology and highlighting opportunities for further advancements in this rapidly evolving field.

Ketogenic diet reshapes cancer metabolism through lysine β-hydroxybutyrylation

Lysine β-hydroxybutyrylation (Kbhb) is a post-translational modification induced by the ketogenic diet (KD), a diet showing therapeutic effects on multiple human diseases. Little is known how cellular processes are regulated by Kbhb. Here we show that protein Kbhb is strongly affected by the KD through a multi-omics analysis of mouse livers. Using a small training dataset with known functions, we developed a bioinformatics method for the prediction of functionally important lysine modification sites (pFunK), which revealed functionally relevant Kbhb sites on various proteins, including aldolase B (ALDOB) Lys108. KD consumption or β-hydroxybutyrate supplementation in hepatocellular carcinoma cells increases ALDOB Lys108bhb and inhibits the enzymatic activity of ALDOB. A Kbhb-mimicking mutation (p.Lys108Gln) attenuates ALDOB activity and its binding to substrate fructose-1,6-bisphosphate, inhibits mammalian target of rapamycin signalling and glycolysis, and markedly suppresses cancer cell proliferation. Our study reveals a critical role of Kbhb in regulating cancer cell metabolism and provides a generally applicable algorithm for predicting functionally important lysine modification sites.

Lysine Phoshoglycerylation Is Widespread in Bacteria and Overlaps with Acylation

Phosphoglycerylation is a non-enzymatic protein modification in which a phosphoglyceryl moiety is covalently bound to the ε-amino group of lysine. It is enriched in glycolytic enzymes from humans and mice and is thought to provide a feedback mechanism for regulating glycolytic flux. We report the first proteomic analysis of this post-translational modification in bacteria by profiling phosphoglyceryl-lysine during the growth of Streptococcus pyogenes in different culture media. The identity of phosphoglyceryl-lysine was confirmed by a previously unknown diagnostic cyclic immonium ion generated during MS/MS. We identified 370 lysine phosphoglycerylation sites in 123 proteins of S. pyogenes. Growth in a defined medium on 1% fructose caused a significant accumulation of phosphoglycerylation compared to growth in a rich medium containing 0.2% glucose. Re-analysis of phosphoproteomes from 14 bacterial species revealed that phosphoglycerylation is generally widespread in bacteria. Many phosphoglycerylation sites were conserved in several bacteria, including S. pyogenes. There was considerable overlap between phosphoglycerylation, acetylation, succinylation, and other acylations on the same lysine residues. Despite some exceptions, most lysine phosphoglycerylations in S. pyogenes occurred with low stoichiometry. Such modifications may be meaningless, but it is also conceivable that phosphoglycerylation, acetylation, and other acylations jointly contribute to the overall regulation of metabolism.

GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs

Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.

KbhbXG: A Machine learning architecture based on XGBoost for prediction of lysine β-Hydroxybutyrylation (Kbhb) modification sites

Lysine β-hydroxybutyrylation is an important post-translational modification (PTM) involved in various physiological and biological processes. In this research, we introduce a novel predictor KbhbXG, which utilizes XGBoost to identify β-hydroxybutyrylation modification sites based on protein sequence information. The traditional experimental methods employed for the identification of β-hydroxybutyrylated sites using proteomic techniques are both costly and time-consuming. Thus, the development of computational methods and predictors can play a crucial role in facilitating the rapid identification of β-hydroxybutyrylation sites. Our proposed KbhbXG model first utilizes machine learning algorithm XGBoost to predict β-hydroxybutyrylation modification sites. On the independent test set, KbhbXG achieves an accuracy of 0.7457, specificity of 0.7771, and an impressive area under the curve (AUC) score of 0.8172. The high AUC score achieved by our method demonstrates its potential for effectively identifying novel β-hydroxybutyrylation sites, thereby facilitating further research and exploration of the β-hydroxybutyrylation process. Also, functional analyses have revealed that different organisms preferentially engage in distinct biological processes and pathways, which can provide valuable insights for understanding the mechanism of β-hydroxybutyrylation and guide experimental verification. To promote transparency and reproducibility, we have made both the codes and dataset of KbhbXG publicly available. Researchers interested in utilizing our proposed model can access these resources at https://github.com/Lab-Xu/KbhbXG.

Lactylation: The emerging frontier in post-translational modification

Lactate, a metabolic byproduct, has gained recognition as a highly influential signaling molecule. Lactylation, an emerging form of post-translational modification derived from lactate, plays a crucial role in numerous cellular processes such as inflammation, embryonic development, tumor proliferation, and metabolism. However, the precise molecular mechanisms through which lactylation governs these biological functions in both physiological and pathological contexts remain elusive. Hence, it is imperative to provide a comprehensive overview of lactylation in order to elucidate its significance in biological processes and establish a foundation for forthcoming investigations. This review aims to succinctly outline the process of lactylation modification and the characterization of protein lactylation across diverse organisms. Additionally, A summary of the regulatory mechanisms of lactylation in cellular processes and specific diseases is presented. Finally, this review concludes by delineating existing research gaps in lactylation and proposing primary directions for future investigations.

Deciphering functional roles of protein succinylation and glutarylation using genetic code expansion

Post-translational modifications (PTMs) dynamically regulate cellular processes. Lysine undergoes a range of acylations, including malonylation, succinylation (SucK) and glutarylation (GluK). These PTMs increase the size of the lysine side chain and reverse its charge from +1 to -1 under physiological conditions, probably impacting protein structure and function. To understand the functional roles of these PTMs, homogeneously modified proteins are required for biochemical studies. While the site-specific encoding of PTMs and their mimics via genetic code expansion has facilitated the characterization of the functional roles of many PTMs, negatively charged lysine acylations have defied this approach. Here we describe site-specific incorporation of SucK and GluK into proteins via temporarily masking their negative charge through thioester derivatives. We prepare succinylated and glutarylated bacterial and mammalian target proteins, including non-refoldable multidomain proteins. This allows us to study how succinylation and glutarylation impact enzymatic activity of metabolic enzymes and regulate protein-DNA and protein-protein interactions in biological processes from replication to ubiquitin signalling.

Predicting lysine methylation sites using a convolutional neural network

Protein lysine methylation is a particular type of post translational modification that plays an important role in both histone and non-histone function regulation in proteins. Deregulation caused by lysine methyltransferases has been identified as the cause of several diseases including cancer as well as both mental and developmental disorders. Identifying lysine methylation sites is a critical step in both early diagnosis and drug design. This study proposes a new Machine Learning method called CNN-Meth for predicting lysine methylation sites using a convolutional neural network (CNN). Our model is trained using evolutionary, structural, and physicochemical-based presentation along with binary encoding. Unlike previous studies, instead of extracting handcrafted features, we use CNN to automatically extract features from different presentations of amino acids to avoid information loss. Automated feature extraction from these representations of amino acids as well as CNN as a classifier have never been used for this problem. Our results demonstrate that CNN-Meth can significantly outperform previous methods for predicting methylation sites. It achieves 96.0%, 85.1%, 96.4%, and 0.65 in terms of Accuracy, Sensitivity, Specificity, and Matthew's Correlation Coefficient (MCC), respectively. CNN-Meth and its source code are publicly available at https://github.com/MLBC-lab/CNN-Meth.

A chemical proteomics approach for global mapping of functional lysines on cell surface of living cell

Cell surface proteins are responsible for many crucial physiological roles, and they are also the major category of drug targets as the majority of therapeutics target membrane proteins on the surface of cells to alter cellular signaling. Despite its great significance, ligand discovery against membrane proteins has posed a great challenge mainly due to the special property of their natural habitat. Here, we design a new chemical proteomic probe OPA-S-S-alkyne that can efficiently and selectively target the lysines exposed on the cell surface and develop a chemical proteomics strategy for global analysis of surface functionality (GASF) in living cells. In total, we quantified 2639 cell surface lysines in Hela cell and several hundred residues with high reactivity were discovered, which represents the largest dataset of surface functional lysine sites to date. We discovered and validated that hyper-reactive lysine residues K382 on tyrosine kinase-like orphan receptor 2 (ROR2) and K285 on Endoglin (ENG/CD105) are at the protein interaction interface in co-crystal structures of protein complexes, emphasizing the broad potential functional consequences of cell surface lysines and GASF strategy is highly desirable for discovering new active and ligandable sites that can be functionally interrogated for drug discovery.

Orthogonal Translation for Site-Specific Installation of Post-translational Modifications

Post-translational modifications (PTMs) endow proteins with new properties to respond to environmental changes or growth needs. With the development of advanced proteomics techniques, hundreds of distinct types of PTMs have been observed in a wide range of proteins from bacteria, archaea, and eukarya. To identify the roles of these PTMs, scientists have applied various approaches. However, high dynamics, low stoichiometry, and crosstalk between PTMs make it almost impossible to obtain homogeneously modified proteins for characterization of the site-specific effect of individual PTM on target proteins. To solve this problem, the genetic code expansion (GCE) strategy has been introduced into the field of PTM studies. Instead of modifying proteins after translation, GCE incorporates modified amino acids into proteins during translation, thus generating site-specifically modified proteins at target positions. In this review, we summarize the development of GCE systems for orthogonal translation for site-specific installation of PTMs.

SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model

SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2 and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64%, 73.36%, 0.48% and 74.00%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.

DTL promotes head and neck squamous cell carcinoma progression by mediating the degradation of ARGLU1 to regulate the Notch signaling pathway

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with a high incidence in squamous epithelium. The E3 ubiquitin ligase DTL is a component of the CRL4A complex and is widely involved in tumor progression. We aimed to analyze the role of DTL in HNSCC and to explore its mechanism of action. Through clinical analysis, we found that DTL is upregulated in HNSCC tissues and is associated with the tumor microenvironment and poor survival in patients. Through gain-of-function and loss-of-function assays, we showed that DTL promotes cell proliferation and migration in vitro and tumor growth in vivo. Mass spectrometry analysis and immunoprecipitation assays showed that DTL interacts with ARGLU1 to promote K11-linked ubiquitination-mediated degradation of ARGLU1, thereby promoting the activation of the CSL-dependent Notch signaling pathway. Furthermore, siARGLU1 blocks the inhibitory effects of DTL knockdown on HNSCC cells. In this study, we showed that DTL promotes HNSCC progression through K11-linked ubiquitination of ARGLU1 to activate the CSL-dependent Notch pathway. These findings identify a promising therapeutic target for HNSCC.

Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences

The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety of biological functions, such as cellular regulation and pathogenesis. This modification plays a crucial role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life processes. However, accurately identifying malonylation sites is crucial to understand the molecular mechanism of malonylation, and the experimental identification can be a challenging and costly task. Recently, approaches based on machine learning (ML) have been suggested to address this issue. It has been demonstrated that these procedures improve accuracy while lowering costs and time constraints. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features and inefficient underlying classifiers. As a result, there is an urgent need for effective predictors and calculation methods. In this study, we provide a comprehensive analysis and review of existing prediction models, tools and benchmark datasets for predicting malonylation sites in protein sequences followed by a comparison study. The review consists of the specifications of benchmark datasets, explanation of features and encoding methods, descriptions of the predictions approaches and their embedding ML or deep learning models and the description and comparison of the existing tools in this domain. To evaluate and compare the prediction capability of the tools, a new bunch of data has been extracted based on the most updated database and the tools have been assessed based on the extracted data. Finally, a hybrid architecture consisting of several classifiers including classical ML models and a deep learning model has been proposed to ensemble the prediction results. This approach demonstrates the better performance in comparison with all prediction tools included in this study (the source codes of the models presented in this manuscript are available in https://github.com/Malonylation). Database URL: https://github.com/A-Golshan/Malonylation.

FLAMS: Find Lysine Acylations and other Modification Sites

SUMMARY

Today, hundreds of post-translational modification (PTM) sites are routinely identified at once, but the comparison of new experimental datasets to already existing ones is hampered by the current inability to search most PTM databases at the protein residue level. We present FLAMS (Find Lysine Acylations and other Modification Sites), a Python3-based command line and web-tool that enables researchers to compare their PTM sites to the contents of the CPLM, the largest dedicated protein lysine modification database, and dbPTM, the most comprehensive general PTM database, at the residue level. FLAMS can be integrated into PTM analysis pipelines, allowing researchers to quickly assess the novelty and conservation of PTM sites across species in newly generated datasets, aiding in the functional assessment of sites and the prioritization of sites for further experimental characterization.

AVAILABILITY AND IMPLEMENTATION

FLAMS is implemented in Python3, and freely available under an MIT license. It can be found as a command line tool at https://github.com/hannelorelongin/FLAMS, pip and conda; and as a web service at https://www.biw.kuleuven.be/m2s/cmpg/research/CSB/tools/flams/.

PCB126 impairs human sperm functions by affecting post-translational modifications and mitochondrial functions

Over the past few decades, there has been a consistent decline in semen quality across the globe, with environmental pollution being identified as the primary cause. Among the various contaminants present in the environment, persistent organic pollutants (POPs) have garnered significant attention due to their high toxicity, slow degradation, bio-accumulation, and long-range migration. PCBs, which include 210 congeners, are a crucial type of POPs that are known to have harmful effects on the environment and human health. Among the various PCB congeners, 3,3',4,4',5-pentachlorobiphenyl (PCB126) is a typical environmental endocrine-disrupting chemical that is widely distributed and has been associated with several health hazards. However, the impact and mechanism of PCB126 on human sperm function has not been fully elucidated. We aimed to investigate the effects of different concentrations of PCB126 (0.01, 0.1, 1, 10 μg/mL) on sperm motility, viability, hyperactivation, and acrosome reaction after incubation for different periods (1 and 2 h), delving deeper into the molecular mechanism of human sperm dysfunction caused by PCB126. First, we investigated the link between PCB126 treatment and the occurrence of protein modifications that are critical to sperm function regulation, such as tyrosine phosphorylation and lysine glutarylation. Second, we examined the potential impact of PCB126 on different parameters related to mitochondrial function, including reactive oxygen species, malondialdehyde levels, mitochondrial membrane potential, mitochondria respiration and adenosine triphosphate generation. Our findings indicate that exposure to environmental pollutants such as PCB126 in vitro may have a negative impact on human sperm functions by interfering with post-translational modifications and mitochondrial functions.

The functions and mechanisms of post-translational modification in protein regulators of RNA methylation: Current status and future perspectives

RNA methylation, an epigenetic modification that does not alter gene sequence, may be important to diverse biological processes. Protein regulators of RNA methylation include "writers," "erasers," and "readers," which respectively deposit, remove, and recognize methylated RNA. RNA methylation, particularly N6-methyladenosine (m6A), 5-methylcytosine (m5C), N3-methylcytosine (m3C), N1-methyladenosine (m1A) and N7-methylguanosine (m7G), has been suggested as disease therapeutic targets. Despite advances in the structure and pharmacology of RNA methylation regulators that have improved drug discovery, regulating these proteins by various post-translational modifications (PTMs) has received little attention. PTM modifies protein structure and function, affecting all aspects of normal biology and pathogenesis, including immunology, cell differentiation, DNA damage repair, and tumors. It is becoming evident that RNA methylation regulators are also regulated by diverse PTMs. PTM of RNA methylation regulators induces their covalent linkage to new functional groups, hence modifying their activity and function. Mass spectrometry has identified many PTMs on protein regulators of RNA methylation. In this review, we describe the functions and PTM of protein regulators of RNA methylation and summarize the recent advances in the regulatory mode of human disease and its underlying mechanisms.

Targeting succinylation-mediated metabolic reprogramming as a potential approach for cancer therapy

Metabolic reprogramming is a common hallmark of cancers and involves alterations in many metabolic pathways during tumor initiation and progression. However, the cancer-specific modulation of metabolic reprogramming requires further elucidation. Succinylation, a newly identified protein posttranslational modification (PTM), participates in many cellular processes by transferring a succinyl group to a residue of the target protein, which is related to various pathological disorders including cancers. In recent years, there has been a gradual increase in the number of studies on the regulation of tumors by protein succinylation. Notably, accumulating evidence suggests that succinylation can mediate cancer cell metabolism by altering the structure or activity of metabolism-related proteins and plays vital roles in metabolic reprogramming. Furthermore, some antitumor drugs have been linked to succinylation-mediated tumor-associated metabolism. To better elucidate lysine succinylation mediated tumor metabolic reprogramming, this review mainly summarizes recent studies on the regulation and effects of protein succinylation in tumors, focusing on the metabolic regulation of tumorigenesis and development, which will provide new directions for cancer diagnosis as well as possible therapeutic targets.

RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences

Post-translational modification (PTM) occurs after a protein is translated from ribonucleic acid. It is an important living creature life phenomenon because it is implicated in almost all cellular processes. Identification of PTM sites from a given protein sequence is a hot topic in bioinformatics. Lots of computational methods have been proposed, and they provide good performance. However, most previous methods can only tackle one PTM type. Few methods consider multiple PTM types. In this study, a multi-label classification model, named RMTLysPTM, was developed to recognize four types of lysine (K) PTM sites, including acetylation, crotonylation, methylation and succinylation. The surrounding sites of a lysine site were selected to constitute a peptide segment, representing the lysine at the center. Deep analysis was conducted to count the distribution of 2-residues with fixed location across the four types of lysine PTM sites. By aggregating the distribution information of 2-residues in one peptide segment, the peptide segment was encoded by informative features. Furthermore, a prediction engine that can precisely capture the traits of the above representations was designed to recognize the types of lysine PTM sites. The cross-validation results on two datasets (Qiu and CPLM training datasets) suggested that the model had extremely high performance and RMTLysPTM had strong generalization ability by testing it on protein Q16778 and CPLM testing datasets. The model was found to be generally superior to all previous models and those using popular methods and features. A web server was set up for RMTLysPTM, and it can be accessed at http://119.3.127.138/.

Methylation across the central dogma in health and diseases: new therapeutic strategies

The proper transfer of genetic information from DNA to RNA to protein is essential for cell-fate control, development, and health. Methylation of DNA, RNAs, histones, and non-histone proteins is a reversible post-synthesis modification that finetunes gene expression and function in diverse physiological processes. Aberrant methylation caused by genetic mutations or environmental stimuli promotes various diseases and accelerates aging, necessitating the development of therapies to correct the disease-driver methylation imbalance. In this Review, we summarize the operating system of methylation across the central dogma, which includes writers, erasers, readers, and reader-independent outputs. We then discuss how dysregulation of the system contributes to neurological disorders, cancer, and aging. Current small-molecule compounds that target the modifiers show modest success in certain cancers. The methylome-wide action and lack of specificity lead to undesirable biological effects and cytotoxicity, limiting their therapeutic application, especially for diseases with a monogenic cause or different directions of methylation changes. Emerging tools capable of site-specific methylation manipulation hold great promise to solve this dilemma. With the refinement of delivery vehicles, these new tools are well positioned to advance the basic research and clinical translation of the methylation field.

Deubiquitinase USP9X stabilizes RNA m6A demethylase ALKBH5 and promotes acute myeloid leukemia cell survival

Post-translational modifications including protein ubiquitination regulate a plethora of cellular processes in distinct manners. RNA N6-methyladenosine is the most abundant post-transcriptional modification on mammalian mRNAs and plays important roles in various physiological and pathological conditions including hematologic malignancies. We previously determined that the RNA N6-methyladenosine eraser ALKBH5 is necessary for the maintenance of acute myeloid leukemia (AML) stem cell function, but the post-translational modifications involved in ALKBH5 regulation remain elusive. Here, we show that deubiquitinase ubiquitin-specific peptidase 9X (USP9X) stabilizes ALKBH5 and promotes AML cell survival. Through the use of mass spectrometry as an unbiased approach, we identify USP9X and confirm that it directly binds to ALKBH5. USP9X stabilizes ALKBH5 by removing the K48-linked polyubiquitin chain at K57. Using human myeloid leukemia cells and a murine AML model, we find that genetic knockdown or pharmaceutical inhibition of USP9X inhibits leukemia cell proliferation, induces apoptosis, and delays AML development. Ectopic expression of ALKBH5 partially mediates the function of USP9X in AML. Overall, this study uncovers deubiquitinase USP9X as a key for stabilizing ALKBH5 expression and reveals the important role of USP9X in AML, which provides a promising therapeutic strategy for AML treatment in the clinic.

TRIM7/RNF90 promotes autophagy via regulation of ATG7 ubiquitination during L. monocytogenes infection

L. monocytogenes is a widely used infection model for the research on pathogenesis and host defense against gram-positive intracellular bacteria. Emerging evidence indicates that posttranslational modifications play a critical role in the regulation of macroautophagy/autophagy. However, little is known about the posttranslational modifications of ATG7, the essential protein in the autophagy process. In this study, we demonstrated that the RING-type E3 ligase TRIM7/RNF90 positively regulated autophagosome accumulation by promoting the ubiquitination of ATG7 at K413, thereby affecting L. monocytogenes infection. TRIM7 expression was induced by a variety range of conditions, including starvation, rapamycin stimulation, and L. monocytogenes infection. TRIM7 deficiency in mice or cells resulted in elevated innate immune responses and increased L. monocytogenes infection. ATG7 was associated with TRIM7 and the positive regulatory role of TRIM7 in L. monocytogenes infection-, starvation- or rapamycin-induced autophagosome accumulation was suggested by TRIM7 deficiency, TRIM7 overexpression, and TRIM7 knockdown. Further mechanistic investigation indicated that TRIM7 promoted the K63-linked ubiquitination of ATG7 at K413 and ubiquitination at this site was required for the function of ATG7 in autophagy and L. monocytogenes infection. Thus, our findings suggested a new regulator in intracellular bacterial infection and autophagy, with a novel posttranslational modification targeting ATG7. This research may expand our understanding of host anti-bacterial defense and the role of autophagy in intracellular bacterial infection.Abbreviations: ATG3: autophagy related 3; ATG5: autophagy related 5; ATG7: autophagy related 7; ATG10: autophagy related 10; ATG12: autophagy related 12; ATG16L1: autophagy related 16 like 1; Baf A1: bafilomycin A1; CQ: chloroquine; BMDC: bone marrow-derived dendritic cell; BMDM: bone marrow-derived macrophage; CFUs: colony-forming units; CXCL10/IP-10: C-X-C motif chemokine ligand 10; EBSS: Earle's balanced salt solution; ELISA: enzyme-linked immunosorbent assay; IFIT1/ISG56: interferon induced protein with tetratricopeptide repeats 1; IFNB/IFN-β: interferon beta; IL6: interleukin 6; IRF3, interferon regulatory factor 3; Lm: L. monocytogenes; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MEF: mouse embryonic fibroblast; MOI: multiplicity of infection; PLA: proximity ligation assay; PMA: phorbol myristate acetate; PMA-THP1, PMA-differentiated THP1; PMs: peritoneal macrophages; PTMs: posttranslational modifications; STING1, stimulator of interferon response cGAMP interactor 1; TBK1, TANK binding kinase 1; TNF/TNF-α: tumor necrosis factor; TRIM7/RNF90: tripartite motif containing; Hainan Provincial Natural Science Foundation of China.

Small-sample learning reveals propionylation in determining global protein homeostasis

Proteostasis is fundamental for maintaining organismal health. However, the mechanisms underlying its dynamic regulation and how its disruptions lead to diseases are largely unclear. Here, we conduct in-depth propionylomic profiling in Drosophila, and develop a small-sample learning framework to prioritize the propionylation at lysine 17 of H2B (H2BK17pr) to be functionally important. Mutating H2BK17 which eliminates propionylation leads to elevated total protein level in vivo. Further analyses reveal that H2BK17pr modulates the expression of 14.7-16.3% of genes in the proteostasis network, and determines global protein level by regulating the expression of genes involved in the ubiquitin-proteasome system. In addition, H2BK17pr exhibits daily oscillation, mediating the influences of feeding/fasting cycles to drive rhythmic expression of proteasomal genes. Our study not only reveals a role of lysine propionylation in regulating proteostasis, but also implements a generally applicable method which can be extended to other issues with little prior knowledge.

Functional analysis of protein post-translational modifications using genetic codon expansion

Post-translational modifications (PTMs) of proteins not only exponentially increase the diversity of proteoforms, but also contribute to dynamically modulating the localization, stability, activity, and interaction of proteins. Understanding the biological consequences and functions of specific PTMs has been challenging for many reasons, including the dynamic nature of many PTMs and the technical limitations to access homogenously modified proteins. The genetic code expansion technology has emerged to provide unique approaches for studying PTMs. Through site-specific incorporation of unnatural amino acids (UAAs) bearing PTMs or their mimics into proteins, genetic code expansion allows the generation of homogenous proteins with site-specific modifications and atomic resolution both in vitro and in vivo. With this technology, various PTMs and mimics have been precisely introduced into proteins. In this review, we summarize the UAAs and approaches that have been recently developed to site-specifically install PTMs and their mimics into proteins for functional studies of PTMs.

Dynamic regulation and key roles of ribonucleic acid methylation

Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N ⁶-methyladenosine (m⁶A), 5-methylcytosine (m⁵C), N ¹-methyladenosine (m¹A), 5-hydroxymethylcytosine (hm⁵C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.

AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations

Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.