PLMD: An updated data resource of protein lysine modifications

Current computational tools for protein lysine acylation site prediction

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

Species-specific model based on sequence and structural information for ubiquitination sites prediction

Ubiquitination is a common post-translational modification of proteins in eukaryotic cells, and it is also a significant method of regulating protein biological function. Computational methods for predicting ubiquitination sites can serve as a cost-effective and time-saving alternative to experimental methods. Existing computational methods often build classifiers based on protein sequence information, physical and chemical properties of amino acids, evolutionary information, and structural parameters. However, structural information about most proteins cannot be found in existing databases directly. The features of proteins differ among species, and some species have small amounts of ubiquitinated proteins. Therefore, it is necessary to develop species-specific models that can be applied to datasets with small sample sizes. To solve these problems, we propose a species-specific model (SSUbi) based on a capsule network, which integrates proteins' sequence and structural information. In this model, the feature extraction module is composed of two sub-modules that extract multi-dimensional features from sequence and structural information respectively. In the submodule, the convolution operation is used to extract encoding dimension features, and the channel attention mechanism is used to extract feature map dimension features. After integrating the multi-dimensional features from both types of information, the species-specific capsule network further converts the features into capsule vectors and classifies species-specific ubiquitination sites. The experimental results show that SSUbi can effectively improve the prediction performance of species with small sample sizes and outperform other models.

Glypred: Lysine Glycation Site Prediction via CCU-LightGBM-BiLSTM Framework with Multi-Head Attention Mechanism

Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature types─AAC, KMER, DR, PWAA, and EBGW─were selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.

Acetylome Analyses Provide New Insights into the Effect of Chronic Intermittent Hypoxia on Hypothalamus-Dependent Endocrine Metabolism Impairment

Paediatric obstructive sleep apnoea (OSA) is a highly prevalent sleep disorder resulting in chronic intermittent hypoxia (CIH) that has been linked to metabolism and endocrine impairment. Protein acetylation, which is a frequently occurring posttranslational modification, plays pivotal roles in the regulation of hypothalamic processes. However, the effects of CIH-induced global protein acetylation on hypothalamic function and endocrine metabolism remain poorly understood. To bridge this knowledge gap, we conducted a study utilizing liquid chromatography-mass spectrometry to analyse the lysine acetylome and proteome of the hypothalamus in healthy infantile mice exposed to 3 weeks of intermittent hypoxia (as a CIH model) compared to normoxic mice (as controls). Our analysis identified and quantified 2699 Kac sites in 2453 proteins. These acetylated proteins exhibited disruptions primarily in endocrine metabolism, the citrate cycle (TCA cycle), synapse function, and circadian entrainment. Additionally, we observed significant down-regulation of proteins that are known to be involved in endocrine hormone secretion. This study aimed to elucidate the molecular mechanisms underlying CIH-induced alterations in protein acetylation within the hypothalamus. By providing valuable insights into the pathophysiological processes associated with CIH and their impacts on hypothalamic function, our findings contribute to a deeper understanding of the consequences stemming from CIH-induced changes in protein acetylation within the hypothalamus as well as its potential role in endocrine impairment.

DEGRONOPEDIA: a web server for proteome-wide inspection of degrons

E3 ubiquitin ligases recognize substrates through their short linear motifs termed degrons. While degron-signaling has been a subject of extensive study, resources for its systematic screening are limited. To bridge this gap, we developed DEGRONOPEDIA, a web server that searches for degrons and maps them to nearby residues that can undergo ubiquitination and disordered regions, which may act as protein unfolding seeds. Along with an evolutionary assessment of degron conservation, the server also reports on post-translational modifications and mutations that may modulate degron availability. Acknowledging the prevalence of degrons at protein termini, DEGRONOPEDIA incorporates machine learning to assess N-/C-terminal stability, supplemented by simulations of proteolysis to identify degrons in newly formed termini. An experimental validation of a predicted C-terminal destabilizing motif, coupled with the confirmation of a post-proteolytic degron in another case, exemplifies its practical application. DEGRONOPEDIA can be freely accessed at degronopedia.com.

Bacterial protein acetylation: mechanisms, functions, and methods for study

Lysine acetylation is an evolutionarily conserved protein modification that changes protein functions and plays an essential role in many cellular processes, such as central metabolism, transcriptional regulation, chemotaxis, and pathogen virulence. It can alter DNA binding, enzymatic activity, protein-protein interactions, protein stability, or protein localization. In prokaryotes, lysine acetylation occurs non-enzymatically and by the action of lysine acetyltransferases (KAT). In enzymatic acetylation, KAT transfers the acetyl group from acetyl-CoA (AcCoA) to the lysine side chain. In contrast, acetyl phosphate (AcP) is the acetyl donor of chemical acetylation. Regardless of the acetylation type, the removal of acetyl groups from acetyl lysines occurs only enzymatically by lysine deacetylases (KDAC). KATs are grouped into three main superfamilies based on their catalytic domain sequences and biochemical characteristics of catalysis. Specifically, members of the GNAT are found in eukaryotes and prokaryotes and have a core structural domain architecture. These enzymes can acetylate small molecules, metabolites, peptides, and proteins. This review presents current knowledge of acetylation mechanisms and functional implications in bacterial metabolism, pathogenicity, stress response, translation, and the emerging topic of protein acetylation in the gut microbiome. Additionally, the methods used to elucidate the biological significance of acetylation in bacteria, such as relative quantification and stoichiometry quantification, and the genetic code expansion tool (CGE), are reviewed.

Targeting deubiquitinase OTUB1 protects vascular smooth muscle cells in atherosclerosis by modulating PDGFRβ

Atherosclerosis is a chronic artery disease that causes various types of cardiovascular dysfunction. Vascular smooth muscle cells (VSMCs), the main components of atherosclerotic plaque, switch from contractile to synthetic phenotypes during atherogenesis. Ubiquitylation is crucial in regulating VSMC phenotypes in atherosclerosis, and it can be reversely regulated by deubiquitinases. However, the specific effects of deubiquitinases on atherosclerosis have not been thoroughly elucidated. In this study, RNAi screening in human aortic smooth muscle cells was performed to explore the effects of OTU family deubiquitinases, which revealed that silencing OTUB1 inhibited PDGF-BB-stimulated VSMC phenotype switch. Further in vivo studies using Apoe-/- mice revealed that knockdown of OTUB1 in VSMCs alleviated atherosclerosis plaque burden in the advanced stage and led to a stable plaque phenotype. Moreover, VSMC proliferation and migration upon PDGF-BB stimulation could be inhibited by silencing OTUB1 in vitro. Unbiased RNA-sequencing data indicated that knocking down OTUB1 influenced VSMC differentiation, adhesion, and proliferation. Mass spectrometry of ubiquitinated protein confirmed that proteins related to cell growth and migration were differentially ubiquitylated. Mechanistically, we found that OTUB1 recognized the K707 residue ubiquitylation of PDGFRβ with its catalytic triad, thereby reducing the K48-linked ubiquitylation of PDGFRβ. Inhibiting OTUB1 in VSMCs could promote PDGFRβ degradation via the ubiquitin-proteasome pathway, so it was beneficial in preventing VSMCs' phenotype switch. These findings revealed that knocking down OTUB1 ameliorated VSMCs' phenotype switch and atherosclerosis progression, indicating that OTUB1 could be a valuable translational therapeutic target in the future.

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.

Mechanistic and Structural Insights on Difluoromethyl-1,3,4-oxadiazole Inhibitors of HDAC6

Histone deacetylase 6 (HDAC6) is increasingly recognized for its potential in targeted disease therapy. This study delves into the mechanistic and structural nuances of HDAC6 inhibition by difluoromethyl-1,3,4-oxadiazole (DFMO) derivatives, a class of non-hydroxamic inhibitors with remarkable selectivity and potency. Employing a combination of nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) kinetic experiments, comprehensive enzymatic characterizations, and X-ray crystallography, we dissect the intricate details of the DFMO-HDAC6 interaction dynamics. More specifically, we find that the chemical structure of a DMFO and the binding mode of its difluoroacetylhydrazide derivative are crucial in determining the predominant hydrolysis mechanism. Our findings provide additional insights into two different mechanisms of DFMO hydrolysis, thus contributing to a better understanding of the HDAC6 inhibition by oxadiazoles in disease modulation and therapeutic intervention.

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.

Simulation and Computational Study of RING Domain Mutants of BRCA1 and Ube2k in AD/PD Pathophysiology

Lysine-based post-translational modification (PTM) such as acylation, acetylation, deamination, methylation, SUMOylation, and ubiquitination has proven to be a major regulator of gene expression, chromatin structure, protein stability, protein-protein interaction, protein degradation, and cellular localization. However, besides all the PTMs, ubiquitination stands as the second most common PTM after phosphorylation that is involved in the etiology of neurodegenerative diseases (NDDs) namely, Alzheimer's disease (AD) and Parkinson's disease (PD). NDDs are characterized by the accumulation of misfolded protein aggregates in the brain that lead to disease-related gene mutation and irregular protein homeostasis. The ubiquitin-proteasome system (UPS) is in charge of degrading these misfolded proteins, which involve an interplay of E1, E2, E3, and deubiquitinase enzymes. Impaired UPS has been commonly observed in NDDs and E3 ligases are the key members of the UPS, thus, dysfunction of the same can accelerate the neurodegeneration process. Therefore, the aim of this study is firstly, to find E3 ligases that are common in both AD and PD through data mining. Secondly, to study the impact of mutation on its structure and function. The study deciphered 74 E3 ligases that were common in both AD and PD. Later, 10 hub genes were calculated of which protein-protein interaction, pathway enrichment, lysine site prediction, domain, and motif analysis were performed. The results predicted BRCA1, PML, and TRIM33 as the top three putative lysine-modified E3 ligases involved in AD and PD pathogenesis. However, based on structural characterization, BRCA1 was taken further to study RING domain mutation that inferred K32Y, K32L, K32C, K45V, K45Y, and K45G as potential mutants that alter the structural and functional ability of BRCA1 to interact with Ube2k, E2-conjugating enzyme. The most probable mutant observed after molecular dynamics simulation of 50 ns is K32L. Therefore, our study concludes BRCA1, a potential E3 ligase common in AD and PD, and RING domain mutation at sites K32 and K45 possibly disturbs its interaction with its E2, Ube2k.

iSUMO-RsFPN: A predictor for identifying lysine SUMOylation sites based on multi-features and feature pyramid networks

SUMOylation is a protein post-translational modification that plays an essential role in cellular functions. For predicting SUMO sites, numerous researchers have proposed advanced methods based on ordinary machine learning algorithms. These reported methods have shown excellent predictive performance, but there is room for improvement. In this study, we constructed a novel deep neural network Residual Pyramid Network (RsFPN), and developed an ensemble deep learning predictor called iSUMO-RsFPN. Initially, three feature extraction methods were employed to extract features from samples. Following this, weak classifiers were trained based on RsFPN for each feature type. Ultimately, the weak classifiers were integrated to construct the final classifier. Moreover, the predictor underwent systematically testing on an independent test dataset, where the results demonstrated a significant improvement over the existing state-of-the-art predictors. The code of iSUMO-RsFPN is free and available at https://github.com/454170054/iSUMO-RsFPN.

Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches

Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.

Suppression of ITPKB degradation by Trim25 confers TMZ resistance in glioblastoma through ROS homeostasis

Temozolomide (TMZ) represents a standard-of-care chemotherapeutic agent in glioblastoma (GBM). However, the development of drug resistance constitutes a significant hurdle in the treatment of malignant glioma. Although specific innovative approaches, such as immunotherapy, have shown favorable clinical outcomes, the inherent invasiveness of most gliomas continues to make them challenging to treat. Consequently, there is an urgent need to identify effective therapeutic targets for gliomas to overcome chemoresistance and facilitate drug development. This investigation used mass spectrometry to examine the proteomic profiles of six pairs of GBM patients who underwent standard-of-care treatment and surgery for both primary and recurrent tumors. A total of 648 proteins exhibiting significant differential expression were identified. Gene Set Enrichment Analysis (GSEA) unveiled notable alterations in pathways related to METABOLISM_OF_LIPIDS and BIOLOGICAL_OXIDATIONS between the primary and recurrent groups. Validation through glioma tissue arrays and the Xiangya cohort confirmed substantial upregulation of inositol 1,4,5-triphosphate (IP3) kinase B (ITPKB) in the recurrence group, correlating with poor survival in glioma patients. In TMZ-resistant cells, the depletion of ITPKB led to an increase in reactive oxygen species (ROS) related to NADPH oxidase (NOX) activity and restored cell sensitivity to TMZ. Mechanistically, the decreased phosphorylation of the E3 ligase Trim25 at the S100 position in recurrent GBM samples accounted for the weakened ITPKB ubiquitination. This, in turn, elevated ITPKB stability and impaired ROS production. Furthermore, ITPKB depletion or the ITPKB inhibitor GNF362 effectively overcome TMZ chemoresistance in a glioma xenograft mouse model. These findings reveal a novel mechanism underlying TMZ resistance and propose ITPKB as a promising therapeutic target for TMZ-resistant GBM.

DeepNphos: A deep-learning architecture for prediction of N-phosphorylation sites

MOTIVATION

Phosphorylation, a prevalent post-translational modification, plays a crucial role in regulating cellular activities. This process encompasses O-phosphorylation (e.g., phosphoserine) and N-phosphorylation (e.g., phospho-lysine (pK), phospho-arginine (pR), and phospho-histidine (pH)). While significant research has focused on O-phosphorylation, resulting in the development of various algorithms for predicting O-phosphorylation sites with commendable performance, there has been a notable absence of models designed to predict N-phosphorylation sites. This study introduces an integrated model named DeepNphos, designed to predict N-phosphorylation sites. This model is developed based on the analysis of thousands of experimentally identified pK, pR and pH sites.

RESULTS

Observing that the Convolutional Neural Network (CNN) model, incorporating the One-Hot encoding feature, demonstrates favorable performance in comparison to other models when predicting pK, pR, and pH sites. Additionally, pK exhibits similarities to other lysine modification types, and integrating the CNN model with a deep-transfer learning (DTL) strategy based on tens of thousands of known lysine modification sites could enhance pK prediction performance. In contrast, pR exhibits little similarity to other arginine modification types, and the integration of DTL has minimal impact on pR prediction performance. Furthermore, the decision was made to refrain from incorporating the DTL strategy in predicting pH sites, given the scarcity of histidine modification sites beyond those associated with pH. The final classifiers for predicting pK, pR, and pH sites achieve AUC values of 0.856, 0.805 and 0.802 for ten-fold cross-validation, respectively. Overall, DeepNphos is the first classifier for predicting N-phosphorylation sites, accessible at https://github.com/ChangXulinmessi/DeepNPhos.

Acetylome analyses provide novel insights into the effects of chronic intermittent hypoxia on hippocampus-dependent cognitive impairment

Introduction

Chronic intermittent hypoxia (CIH) can negatively affect hippocampal function through various molecular mechanisms. Protein acetylation, a frequently occurring modification, plays crucial roles in synaptic plasticity and cognitive processes. However, the global protein acetylation induced by CIH in the hippocampus and its specific effects on hippocampal function and behavior remain poorly understood.

Methods

To address this gap, we conducted a study using liquid chromatography-tandem mass spectrometry to analyze the lysine acetylome and proteome of the hippocampus in healthy adult mice exposed to intermittent hypoxia for 4 weeks (as a CIH model) compared to normoxic mice (as a control).

Results

We identified and quantified a total of 2,184 lysine acetylation sites in 1,007 proteins. Analysis of these acetylated proteins revealed disturbances primarily in oxidative phosphorylation, the tricarboxylic acid (TCA) cycle, and glycolysis, all of which are localized exclusively to mitochondria. Additionally, we observed significant changes in the abundance of 21 proteins, some of which are known to be associated with cognitive impairments.

Discussion

This study helps to elucidate the molecular mechanisms underlying CIH-induced changes in protein acetylation in the hippocampus. By providing valuable insights into the pathophysiological processes associated with CIH and their impacts on hippocampal function, our findings contribute to a better understanding of the consequences of CIH-induced changes in protein acetylation in the hippocampus and the potential role of CIH in cognitive impairment.

CURTAIN-A unique web-based tool for exploration and sharing of MS-based proteomics data

To facilitate analysis and sharing of mass spectrometry (MS)-based proteomics data, we created online tools called CURTAIN (https://curtain.proteo.info) and CURTAIN-PTM (https://curtainptm.proteo.info) with an accompanying series of video tutorials (https://www.youtube.com/@CURTAIN-me6hl). These are designed to enable non-MS experts to interactively peruse volcano plots and deconvolute primary experimental data so that replicates can be visualized in bar charts or violin plots and exported in publication-ready format. They also allow assessment of overall experimental quality by correlation matrix and profile plot analysis. After making a selection of protein "hits", the user can analyze known domain structure, AlphaFold predicted structure, reported interactors, relative expression as well as disease links. CURTAIN-PTM permits analysis of all identified PTM sites on protein(s) of interest with selected databases. CURTAIN-PTM also links with the Kinase Library to predict upstream kinases that may phosphorylate sites of interest. We provide examples of the utility of CURTAIN and CURTAIN-PTM in analyzing how targeted degradation of the PPM1H Rab phosphatase that counteracts the Parkinson's LRRK2 kinase impacts cellular protein levels and phosphorylation sites. We also reanalyzed a ubiquitylation dataset, characterizing the PINK1-Parkin pathway activation in primary neurons, revealing data of interest not highlighted previously. CURTAIN and CURTAIN-PTM are free to use and open source, enabling researchers to share and maximize the impact of their proteomics data. We advocate that MS data published in volcano plot format be reported containing a shareable CURTAIN weblink, thereby allowing readers to better analyze and exploit the data.

iGly-IDN: Identifying Lysine Glycation Sites in Proteins Based on Improved DenseNet

Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective computational methods need to be developed. In this study, we proposed the new model called iGly-IDN based on the improved densely connected convolutional networks (DenseNet). First, one hot encoding was adopted to obtain the original feature maps. Afterward, the improved DenseNet was adopted to capture feature information with the importance degrees during the feature learning. According to the experimental results, Acc reaches 66%, and Mathews correlation coefficient reaches 0.33 on the independent testing data set, which indicates that the iGly-IDN can provide more effective glycation site identification than the current predictors.

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.

Characterizing lysine acetylation of glucokinase

Glucokinase (GK) catalyzes the phosphorylation of glucose to form glucose-6-phosphate as the substrate of glycolysis for energy production. Acetylation of lysine residues in Escherichia coli GK has been identified at multiple sites by a series of proteomic studies, but the impact of acetylation on GK functions remains largely unknown. In this study, we applied the genetic code expansion strategy to produce site-specifically acetylated GK variants which naturally exist in cells. Enzyme assays and kinetic analyses showed that lysine acetylation decreases the GK activity, mostly resulting from acetylation of K214 and K216 at the entrance of the active site, which impairs the binding of substrates. We also compared results obtained from the glutamine substitution method and the genetic acetyllysine incorporation approach, showing that glutamine substitution is not always effective for mimicking acetylated lysine. Further genetic studies as well as in vitro acetylation and deacetylation assays were performed to determine acetylation and deacetylation mechanisms, which showed that E. coli GK could be acetylated by acetyl-phosphate without enzymes and deacetylated by CobB deacetylase.

Phosphorylated Adapter RNA Export Protein Is Methylated at Lys 381 by an Methyltransferase-like 21C (METTL21C)

Methyltransferase-like 21C (METTL21C) is a member of the non-histone methyltransferase superfamily, which mainly mediates the methylation of lysine (Lys) residues. The main types of modification are Lys dimethylation and trimethylation. However, at present, most of the studies on METTL21C are focused on humans and mice, and there are few reports on poultry. Therefore, chicken embryo fibroblasts (DF-1) were selected as the object of study. To explore the function of chicken METTL21C (chMETTL21C) in the proliferation of DF-1 cells, flow cytometry and qPCR were used to detect the function of chicken METTL21C in the proliferation of DF-1 cells. The results showed that overexpression of METTL21C blocked the cell cycle in the G1max S phase, thus inhibiting cell proliferation. In addition, based on proteomic analysis, stable overexpression of METTL21C may inhibit the proliferation of DF-1 cells by mediating lysine trimethylation of proliferation-related proteins phosphorylated adapter RNA export protein (PHAX), nucleoside diphosphate kinases (NDPKs), eukaryotic transcription extension factor (eukaryotic translation elongation factor 1A,e EF1A), and inversin (Invs). Through immunoprecipitation (co-IP) and liquid chromatography-mass spectrometry (LC-MS/MS) analysis, METTL21C-mediated PHAX Lys-381 methylation was confirmed to be involved in the regulation of DF-1 cell proliferation. The results of this study provide a reference for analyzing the methylation function of METTL21C and the mechanism of regulating the growth and development of chicken cells.

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier

BACKGROUND

Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention.

RESULTS

In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively.

CONCLUSION

GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite .

DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons

Protein-targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.

MSTL-Kace: Prediction of Prokaryotic Lysine Acetylation Sites Based on Multistage Transfer Learning Strategy

As one of the most important post-translational modifications (PTM), lysine acetylation (Kace) plays an important role in various biological activities. Traditional experimental methods for identifying Kace sites are inefficient and expensive. Instead, several machine learning methods have been developed for Kace site prediction, and hand-crafted features have been used to encode the protein sequences. However, there are still two challenges: the complex biological information may be under-represented by these manmade features and the small sample issue of some species needs to be addressed. We propose a novel model, MSTL-Kace, which was developed based on transfer learning strategy with pretrained bidirectional encoder representations from transformers (BERT) model. In this model, the high-level embeddings were extracted from species-specific BERT models, and a two-stage fine-tuning strategy was used to deal with small sample issue. Specifically, a domain-specific BERT model was pretrained using all of the sequences in our data sets, which was then fine-tuned, or two-stage fine-tuned based on the training data set of each species to obtain the species-specific BERT models. Afterward, the embeddings of residues were extracted from the fine-tuned model and fed to the different downstream learning algorithms. After comparison, the best model for the six prokaryotic species was built by using a random forest. The results for the independent test sets show that our model outperforms the state-of-the-art methods on all six species. The source codes and data for MSTL-Kace are available at https://github.com/leo97king/MSTL-Kace.

Identification of Acetylation Sites of Fatty Acid Synthase (FASN) by Mass Spectrometry and FASN Activity Assay

Lysine acetylation is a conserved post-translational modification and a key regulatory mechanism for various cellular processes, including metabolic control, epigenetic regulation, and cellular signaling transduction. Recent advances in mass spectrometry (MS) enable the extensive identification of acetylated lysine residues of histone and non-histone proteins. However, protein enrichment before MS analysis may be necessary to improve the detection of low-abundant proteins or proteins that exhibit low acetylation levels. Fatty acid synthase (FASN), an essential enzyme catalyzing the de novo synthesis of fatty acids, has been found to be acetylated in various species, from fruit flies to humans. Here, we describe a step-by-step process of antibody-based protein enrichment and sample preparation for acetylation identification of endogenous FASN protein by MS-based proteomics analysis. Meanwhile, we provide a protocol for nicotinamide adenine dinucleotide phosphate (NADPH) absorbance assay for FASN activity measurement, which is one of the primary functional readouts of de novo lipogenesis. Key features • A comprehensive protocol for protein immunoprecipitation and sample preparation for acetylation site identification by mass spectrometry. • Step-by-step procedures for measurement of FASN activity of fruit fly larvae using an absorbance assay.

Deciphering the functional landscape of phosphosites with deep neural network

Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series of in vitro and cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server.

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.

RNF167-mediated ubiquitination of Tollip inhibits TNF-α-triggered NF-κB and MAPK activation

Toll-interacting protein (Tollip) is a multifunctional regulator in cellular activities. However, whether its functions are subjected to post-translational modifications remains elusive. Here, we identified ubiquitination as a post-translational modification on Tollip. We found that Tollip interacted with ring finger protein 167 (RNF167) through its C-terminal coupling of ubiquitin to ER degradation (CUE) domain, and RNF167 functioned as the potential E3 ligase to attach K33-linked poly-ubiquitin chains to the Lys235 (K235) site of Tollip. Furthermore, we discovered Tollip could inhibit TNF-α-induced nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) activation, and substitution of Lys235 on Tollip to arginine failed to suppress TNF-α-NF-κB/MAPK (JNK) cascades, revealing the role of Tollip and its ubiquitination in NF-κB/MAPK pathways. Thus, our study reveals the novel biological function of Tollip and RNF167-dependent ubiquitination of Tollip in TNF-α signaling.

Studying lysine acetylation of citric acid cycle enzymes by genetic code expansion

Lysine acetylation is one of the most abundant post-translational modifications in nature, affecting many key biological pathways in both prokaryotes and eukaryotes. It has not been long since technological advances led to understanding of the roles of acetylation in biological processes. Most of those studies were based on proteomic analyses, which have identified thousands of acetylation sites in a wide range of proteins. However, the specific role of individual acetylation event remains largely unclear, mostly due to the existence of multiple acetylation and dynamic changes of acetylation levels. To solve these problems, the genetic code expansion technique has been applied in protein acetylation studies, facilitating the incorporation of acetyllysine into a specific lysine position to generate a site-specifically acetylated protein. By this method, the effects of acetylation at a specific lysine residue can be characterized with minimal interferences. Here, we summarized the development of the genetic code expansion technique for lysine acetylation and recent studies on lysine acetylation of citrate acid cycle enzymes in bacteria by this approach, providing a practical application of the genetic code expansion technique in protein acetylation studies.

Long noncoding RNA DIAPH2-AS1 promotes neural invasion of gastric cancer via stabilizing NSUN2 to enhance the m5C modification of NTN1

Neural invasion (NI) is a vital pathological characteristic of gastric cancer (GC), which correlates with tumor recurrence and a worse prognosis. Long noncoding RNAs (lncRNAs) play critical roles in various biological processes. However, the involvement of lncRNAs in NI of GC (GC-NI) remains unclear. DIAPH2-AS1 was upregulated in NI-positive GC tissues, which was confirmed by qRT-PCR. The higher expression of DIAPH2-AS1 predicted NI and worse survival for GC patients. Both in vitro and in vivo experiments, including wound-healing assay, Transwell assay, DRG-GC cells co-culture model, the mouse sciatic nerve model, and the lung metastasis model, indicated that DIAPH2-AS1 promoted the migration, invasion, and NI potential of GC cells. Mechanistically, pulldown assay and RNA immunoprecipitation assay revealed that DIAPH2-AS1 interacted with NSUN2. Subsequent experiments indicated that DIAPH2-AS1 stabilized NSUN2 from ubiquitin-proteasomal degradation via masking the K577 and K579 of NSUN2. The protection of DIAPH2-AS1 on NSUN2 improved the stability of NTN1 mRNA via m5C modification, which finally induced GC-NI. Our work uncovered DIAPH2-AS1 as a novel oncogenic lncRNA in GC-NI and validated the DIAPH2-AS1-NSUN2-NTN1 axis as a potential therapeutic target for NI-positive GC.

iHypoxia: An Integrative Database of Protein Expression Dynamics in Response to Hypoxia in Animals

Mammals have evolved mechanisms to sense hypoxia and induce hypoxic responses. Recently, high-throughput techniques have greatly promoted global studies of protein expression changes during hypoxia and the identification of candidate genes associated with hypoxia-adaptive evolution, which have contributed to the understanding of the complex regulatory networks of hypoxia. In this study, we developed an integrated resource for the expression dynamics of proteins in response to hypoxia (iHypoxia), and this database contains 2589 expression events of 1944 proteins identified by low-throughput experiments (LTEs) and 422,553 quantitative expression events of 33,559 proteins identified by high-throughput experiments from five mammals that exhibit a response to hypoxia. Various experimental details, such as the hypoxic experimental conditions, expression patterns, and sample types, were carefully collected and integrated. Furthermore, 8788 candidate genes from diverse species inhabiting low-oxygen environments were also integrated. In addition, we conducted an orthologous search and computationally identified 394,141 proteins that may respond to hypoxia among 48 animals. An enrichment analysis of human proteins identified from LTEs shows that these proteins are enriched in certain drug targets and cancer genes. Annotation of known posttranslational modification (PTM) sites in the proteins identified by LTEs reveals that these proteins undergo extensive PTMs, particularly phosphorylation, ubiquitination, and acetylation. iHypoxia provides a convenient and user-friendly method for users to obtain hypoxia-related information of interest. We anticipate that iHypoxia, which is freely accessible at https://ihypoxia.omicsbio.info, will advance the understanding of hypoxia and serve as a valuable data resource.

CEBPB promotes gastrointestinal motility dysfunction after severe acute pancreatitis via the MALAT1/CIRBP/ERK axis

Severe acute pancreatitis (SAP) is a kind of reversible inflammatory process of the exocrine pancreas with gastrointestinal motility dysfunction involved. Studies have highlighted the role of long noncoding RNA metastasis associated lung adenocarcinoma transcript 1 (MALAT1) in AP. However, the mechanism underlying its role in the gastrointestinal motility dysfunction remains undefined. Hence, we explored the regulatory role of MALAT1 in gastrointestinal motility dysfunction following SAP. Then, the expression of CCAAT/enhancer-binding protein beta (CEBPB), MALAT1 and cold-inducible RNA binding protein (CIRBP) was detected in plasma of SAP patients and pancreatic and intestinal tissues of SAP mouse models with their correlation analyzed also. Additionally, the effect of MALAT1 on the pancreatic and intestinal injury, expression of inflammatory factors and the ERK pathway-related genes as well as gastrointestinal motility dysfunction was assessed using ectopic expression and depletion experiments. CEBPB, MALAT1 and CIRBP were highly expressed in plasma of SAP patients and pancreatic and intestinal tissues of SAP mice. Further analysis showed that knockdown of MALAT1 could alleviate pancreatic and intestinal injury, reduce inflammation, and prevent gastrointestinal motility dysfunction in SAP mice. The transcription factor CEBPB could bind to the promoter region of MALAT1, thus activating the transcription of MALAT1. MALAT1 interacted with CIRBP and inhibited the degradation of CIRBP, leading to activated extracellular signal-regulated kinase (ERK) pathway and the resultant gastrointestinal motility dysfunction. In conclusion, CEBPB exhibits a promoting activity towards gastrointestinal motility dysfunction in SAP by pumping up MALAT1 expression and activating the CIRBP-dependent ERK pathway.

Lysine Malonylation and Its Links to Metabolism and Diseases

Malonylation is a recently identified post-translational modification with malonyl-coenzyme A as the donor. It conserved both in prokaryotes and eukaryotes. Recent advances in the identification and quantification of lysine malonylation by bioinformatic analysis have improved our understanding of its role in the regulation of protein activity, interaction, and localization and have elucidated its involvement in many biological processes. Malonylation has been linked to diverse physiological processes, including metabolic disorders, inflammation, and immune regulation. This review discusses malonylation in theory, describes the underlying mechanism, and summarizes the recent progress in malonylation research. The latest findings point to novel functions of malonylation and highlight the mechanisms by which malonylation regulates a variety of cellular processes. Our review also marks the association between lysine malonylation, the enzymes involved, and various diseases, and discusses promising diagnostic and therapeutic biomolecular targets for future clinical applications.

Changes of energy metabolism in failing heart and its regulation by SIRT3

Heart failure (HF) is the leading cause of hospitalization in elderly patients and a disease with extremely high morbidity and mortality rate worldwide. Although there are some existing treatment methods for heart failure, due to its complex pathogenesis and often accompanied by various comorbidities, there is still a lack of specific drugs to treat HF. The mortality rate of patients with HF is still high, highlighting an urgent need to elucidate the pathophysiological mechanisms of HF and seek new therapeutic approaches. The heart is an organ with a very high metabolic intensity, mainly using fatty acids, glucose, ketone bodies, and branched-chain amino acids as energy substrates to supply energy for the heart. Loss of metabolic flexibility and metabolic remodeling occurs with HF. Sirtuin3 (SIRT3) is a member of the NAD⁺-dependent Sirtuin family located in mitochondria, and can participate in mitochondrial physiological functions through the deacetylation of metabolic and respiratory enzymes in mitochondria. As the center of energy metabolism, mitochondria are involved in many physiological processes. Maintaining stable metabolic and physiological functions of the heart depends on normal mitochondrial function. The damage or loss of SIRT3 can lead to various cardiovascular diseases. Therefore, we summarize the recent progress of SIRT3 in cardiac mitochondrial protection and metabolic remodeling.

Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

Succinylated lysine residue prediction revisited

Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy, and attention has been given in the literature to develop such models, albeit with only moderate success in the context of different evaluation metrics. One crucial aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids. In contrast, some others used them without considering the inter-dependency among the properties. The combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by combining all the properties. We propose three deep learning architectures: CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC outperforms the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we successfully achieve better sensitivity and specificity by varying the threshold value.

DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet

As a key issue in orchestrating various biological processes and functions, protein post-translational modification (PTM) occurs widely in the mechanism of protein's function of animals and plants. Glutarylation is a type of protein-translational modification that occurs at active ε-amino groups of specific lysine residues in proteins, which is associated with various human diseases, including diabetes, cancer, and glutaric aciduria type I. Therefore, the issue of prediction for glutarylation sites is particularly important. This study developed a brand-new deep learning-based prediction model for glutarylation sites named DeepDN_iGlu via adopting attention residual learning method and DenseNet. The focal loss function is utilized in this study in place of the traditional cross-entropy loss function to address the issue of a substantial imbalance in the number of positive and negative samples. It can be noted that DeepDN_iGlu based on the deep learning model offers a greater potential for the glutarylation site prediction after employing the straightforward one hot encoding method, with Sensitivity (Sn), Specificity (Sp), Accuracy (ACC), Mathews Correlation Coefficient (MCC), and Area Under Curve (AUC) of 89.29%, 61.97%, 65.15%, 0.33 and 0.80 accordingly on the independent test set. To the best of the authors' knowledge, this is the first time that DenseNet has been used for the prediction of glutarylation sites. DeepDN_iGlu has been deployed as a web server (https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/) that is available to make glutarylation site prediction data more accessible.

Difluoromethyl-1,3,4-oxadiazoles are slow-binding substrate analog inhibitors of histone deacetylase 6 with unprecedented isotype selectivity

Histone deacetylase 6 (HDAC6) is an attractive drug development target because of its role in the immune response, neuropathy, and cancer. Knockout mice develop normally and have no apparent phenotype, suggesting that selective inhibitors should have an excellent therapeutic window. Unfortunately, current HDAC6 inhibitors have only moderate selectivity and may inhibit other HDAC subtypes at high concentrations, potentially leading to side effects. Recently, substituted oxadiazoles have attracted attention as a promising novel HDAC inhibitor chemotype, but their mechanism of action is unknown. Here, we show that compounds containing a difluoromethyl-1,3,4-oxadiazole (DFMO) moiety are potent and single-digit nanomolar inhibitors with an unprecedented greater than 104-fold selectivity for HDAC6 over all other HDAC subtypes. By combining kinetics, X-ray crystallography, and mass spectrometry, we found that DFMO derivatives are slow-binding substrate analogs of HDAC6 that undergo an enzyme-catalyzed ring opening reaction, forming a tight and long-lived enzyme-inhibitor complex. The elucidation of the mechanism of action of DFMO derivatives paves the way for the rational design of highly selective inhibitors of HDAC6 and possibly of other HDAC subtypes as well with potentially important therapeutic implications.

PTMint database of experimentally verified PTM regulation on protein-protein interaction

MOTIVATION

Post-translational modification (PTM) is an important biochemical process. which includes six most well-studied types: phosphorylation, acetylation, methylation, sumoylation, ubiquitylation and glycosylation. PTM is involved in various cell signaling pathways and biological processes. Abnormal PTM status is closely associated with severe diseases (such as cancer and neurologic diseases) by regulating protein functions, such as protein-protein interactions (PPIs). A set of databases was constructed separately for PTM sites and PPI; however, the resource of regulation for PTM on PPI is still unsolved.

RESULTS

Here, we firstly constructed a public accessible database of PTMint (PTMs that are associated with PPIs) (https://ptmint.sjtu.edu.cn/) that contains manually curated complete experimental evidence of the PTM regulation on PPIs in multiple organisms, including Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Currently, the first version of PTMint encompassed 2477 non-redundant PTM sites in 1169 proteins affecting 2371 protein-protein pairs involving 357 diseases. Various annotations were systematically integrated, such as protein sequence, structure properties and protein complex analysis. PTMint database can help to insight into disease mechanism, disease diagnosis and drug discovery associated with PTM and PPI.

AVAILABILITY AND IMPLEMENTATION

PTMint is freely available at: https://ptmint.sjtu.edu.cn/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction

Ubiquitination is an important post-translational modification of proteins that regulates many cellular activities. Traditional experimental methods for identification are costly and time-consuming, so many researchers have proposed computational methods for ubiquitination site prediction in recent years. However, traditional machine learning methods focus on feature engineering and are not suitable for large-scale proteomic data. In addition, deep learning methods are mostly based on convolutional neural networks and fuse multiple coding approaches to achieve classification prediction. This cannot effectively identify potential fine-grained features of the input data and has limitations in the representation of dependencies between low-level features and high-level features. A multi-dimensional feature recognition model based on a capsule network (MDCapsUbi) was proposed to predict protein ubiquitination sites. The proposed module consisting of convolution operations and channel attention was used to recognize coarse-grained features in the sequence dimension and the feature map dimension. The capsule network module consisting of capsule vectors was used to identify fine-grained features and classify ubiquitinated sites. With ten-fold cross-validation, the MDCapsUbi achieved 91.82% accuracy, 91.39% sensitivity, 92.24% specificity, 0.837 MCC, 0.918 F-Score and 0.97 AUC. Experimental results indicated that the proposed method outperformed other ubiquitination site prediction technologies.

PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy

Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating cell function. In recent years, many models have been developed to predict ubiquitination sites in humans, mice and yeast. However, few studies have predicted ubiquitination sites in Arabidopsis thaliana. In view of this, a deep network model named PrUb-EL is proposed to predict ubiquitination sites in Arabidopsis thaliana. Firstly, six features based on the protein sequence are extracted with amino acid index database (AAindex), dipeptide deviates from the expected mean (DDE), dipeptide composition (DPC), blocks substitution matrix (BLOSUM62), enhanced amino acid composition (EAAC) and binary encoding. Secondly, the synthetic minority over-sampling technique (SMOTE) is utilized to process the imbalanced data set. Then a new classifier named DG is presented, which includes Dense block, Residual block and Gated recurrent unit (GRU) block. Finally, each of six feature extraction methods is integrated into the DG model, and the ensemble learning strategy is used to gain the final prediction result. Experimental results show that PrUb-EL has good predictive ability with the accuracy (ACC) and area under the ROC curve (auROC) values of 91.00% and 97.70% using 5-fold cross-validation, respectively. Note that the values of ACC and auROC are 88.58% and 96.09% in the independent test, respectively. Compared with previous studies, our model has significantly improved performance thus it is an excellent method for identifying ubiquitination sites in Arabidopsis thaliana. The datasets and code used for the article are available at https://github.com/Tom-Wangy/PreUb-EL.git.

FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites

Glutarylation is a post-translational modification which plays an irreplaceable role in various functions of the cell. Therefore, it is very important to accurately identify the glutarylation substrates and its corresponding glutarylation sites. In recent years, many computational methods of glutarylation sites have emerged one after another, but there are still many limitations, among which noisy data and the class imbalance problem caused by the uncertainty of non-glutarylation sites are great challenges. In this study, we propose a new semi-supervised learning algorithm, named FCCCSR, to identify reliable non-glutarylation lysine sites from unlabeled samples as negative samples. FCCCSR first finds core objects from positive samples according to reverse nearest neighbor information, and then clusters core objects based on natural neighbor structure. Finally, reliable negative samples are selected according to clustering result. With FCCCSR algorithm, we propose a new method named FCCCSR_Glu for glutarylation sites identification. In this study, multi-view features are extracted and fused to describe peptides, including amino acid composition, BLOSUM62, amino acid factors and composition of k-spaced amino acid pairs. Then, reliable negative samples selected by FCCCSR and positive samples are combined to establish models and XGBoost optimized by differential evolution algorithm is used as the classifier. On the independent testing dataset, FCCCSR_Glu achieves 85.18%, 98.36%, 94.31% and 0.8651 in sensitivity, specificity, accuracy and Matthew's Correlation Coefficient, respectively, which is superior to state-of-the-art methods in predicting glutarylation sites. Therefore, FCCCSR_Glu can be a useful tool for glutarylation sites prediction and FCCCSR algorithm can effectively select reliable negative samples from unlabeled samples. The data and code are available on https://github.com/xbbxhbc/FCCCSR_Glu.git.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

Chronic exercise remodels the lysine acetylome in the mouse hippocampus

Physical exercise benefits hippocampal function through various molecular mechanisms. Protein acetylation, a conserved and widespread post-translational modification, is involved in the synaptic plasticity and memory. However, whether exercise can change global acetylation and the role of acetylated proteins in the hippocampus have remained largely unknown. Herein, using healthy adult mice running for 6 weeks as exercise model and sedentary mice as control, we analyzed the hippocampal lysine acetylome and proteome by Liquid chromatography-tandem mass spectrometry. As a result, we profiled the lysine acetylation landscape for the hippocampus and identified 3,876 acetyl sites and 1,764 acetylated proteins. A total of 272 acetyl sites on 252 proteins were differentially regulated by chronic exercise, among which 18.58% acetylated proteins were annotated in mitochondria. These proteins were dominantly deacetylated and mainly associated with carbon-related metabolism, the Hippo signaling pathway, ribosomes, and protein processing. Meanwhile, 21 proteins were significantly expressed and enriched in the pathway of complement and coagulation cascades. Our findings provide a new avenue for understanding the molecular mechanisms underlying the benefits of exercise for hippocampal function and can contribute to the promotion of public health.

NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes

Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.

Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites

Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.

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.

A Novel Method for Identification of Glutarylation Sites Combining Borderline-SMOTE With Tomek Links Technique in Imbalanced Data

Glutarylation is a type of post-translational modification that occurs on lysine residues. It plays an irreplaceable role in various cellular functions. Therefore, identification of glutarylation sites is significant for understanding the molecular mechanism of glutarylation. In this study, we proposed a method named DEXGB_Glu to identify lysine glutarylation sites using XGBoost as classifier which was optimized by differential evolution algorithm. Aiming at the imbalance between positive samples and negative samples, Borderline-SMOTE method was employed to synthesize positive samples, increasing their amount equal to negative samples. Then, Tomek links technique was applied to filter out noise data. Analysis of this method and its results showed that differential evolution algorithm obviously improved the performance and the combination of Borderline-SMOTE and Tomek links effectively solved the imbalance between positive samples and negative samples. Finally, the performance of this method was much better than other methods in prediction of glutarylation sites. The data and code are available on https://github.com/ningq669/DEXGB_Glu.

The gateway to guanine nucleotides: Allosteric regulation of IMP dehydrogenases

Inosine 5'-monophosphate dehydrogenase (IMPDH) is an evolutionarily conserved enzyme that mediates the first committed step in de novo guanine nucleotide biosynthetic pathway. It is an essential enzyme in purine nucleotide biosynthesis that modulates the metabolic flux at the branch point between adenine and guanine nucleotides. IMPDH plays key roles in cell homeostasis, proliferation, and the immune response, and is the cellular target of several drugs that are widely used for antiviral and immunosuppressive chemotherapy. IMPDH enzyme is tightly regulated at multiple levels, from transcriptional control to allosteric modulation, enzyme filamentation, and posttranslational modifications. Herein, we review recent developments in our understanding of the mechanisms of IMPDH regulation, including all layers of allosteric control that fine-tune the enzyme activity.

ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

Lysine SUMOylation plays an essential role in various biological functions. Several approaches integrating various algorithms have been developed for predicting SUMOylation sites based on a limited dataset. Recently, the number of identified SUMOylation sites has significantly increased due to investigation at the proteomics scale. We collected modification data and found the reported approaches had poor performance using our collected data. Therefore, it is essential to explore the characteristics of this modification and construct prediction models with improved performance based on an enlarged dataset. In this study, we constructed and compared 16 classifiers by integrating four different algorithms and four encoding features selected from 11 sequence-based or physicochemical features. We found that the convolution neural network (CNN) model integrated with residue structure, dubbed ResSUMO, performed favorably when compared with the traditional machine learning and CNN models in both cross-validation and independent tests. The area under the receiver operating characteristic (ROC) curve for ResSUMO was around 0.80, superior to that of the reported predictors. We also found that increasing the depth of neural networks in the CNN models did not improve prediction performance due to the degradation problem, but the residual structure could be included to optimize the neural networks and improve performance. This indicates that residual neural networks have the potential to be broadly applied in the prediction of other types of modification sites with great effectiveness and robustness. Furthermore, the online ResSUMO service is freely accessible.