Diagnosing, predicting disease outcome, and identifying effective treatment targets for virus-related cancers are lacking. Protein biomarkers have the potential to bridge the gap between prevention and treatment for these types of cancers. While it has been shown that certain antibodies against EBV proteins could be used to detect nasopharyngeal carcinoma (NPC), antibodies targeting are solely a tiny part of the about 80 proteins expressed by the EBV genome. Furthermore, it remains unclear what role other viruses play in NPC since many diseases are the result of multiple viral infections. For the first time, this study measured both IgA and IgG antibody responses against 646 viral proteins from 23 viruses in patients with NPC and control subjects using nucleic acid programmable protein arrays. Candidate seromarkers were then validated by ELISA using 1665 serum samples from three clinical cohorts. We demonstrated that the levels of five candidate seromarkers (EBV-BLLF3-IgA, EBV-BLRF2-IgA, EBV-BLRF2-IgG, EBV-BDLF1-IgA, EBV-BDLF1-IgG) in NPC patients were significantly elevated than controls. Additional examination revealed that NPC could be successfully diagnosed by combining the clinical biomarker EBNA1-IgA with the five anti-EBV antibodies. The sensitivity of the six-antibody signature at 95% specificity to diagnose NPC was comparable to the current clinically- approved biomarker combination, VCA-IgA, and EBNA1IgA. However, the recombinant antigens of the five antibodies are easier to produce and standardize compared to the native viral VCA proteins. This suggests the potential replacement of the traditional VCA-IgA assay with the 5-antibodies combination to screen and diagnose NPC. Additionally, we investigated the prognostic significance of these seromarkers titers in NPC. We showed that NPC patients with elevated BLLF3-IgA and BDLF1-IgA titers in their serum exhibited significantly poorer disease-free survival, suggesting the potential of these two seromarkers as prognostic indicators of NPC. These findings will help develop serological tests to detect and treat NPC in the future.

Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.

Both dysregulation of mechanistic target of rapamycin (mTOR) signalling and DNA methylation patterns have been shown to be closely associated with tumor progression and serve as promising targets for hepatocellular carcinoma (HCC) therapy. Although their respective roles in HCC have been extensively revealed, the existence of molecular interactions between them remains largely unknown. The association of DNA methylation and mTOR signalling in HCC tissues and cell lines was assessed. A Kaplan‒Meier analysis was applied to estimate the overall survival (OS) and recurrence-free survival (RFS) of HCC patients. The modulation of DNMT1 by mTOR in HCC cell lines was determined. The effect of the drug combination in cell lines and mouse models was examined. The results showed that the DNA methylation level was positively associated with the activation of mTOR signalling in HCC tissues and cell lines. Moreover, HCC patients with higher DNA methylation levels and enhanced activation of mTOR signalling exhibited the worst prognosis. Then, we screened methylation-related enzymes and found that the activation of mTOR signalling increased DNMT1 expression and activity. In addition, mTOR enhanced the translational efficiency of DNMT1 in a 4E-BP1-dependent manner, which is based on the pyrimidine rich translational element (PRTE)-containing 5′UTR of DNMT1. Moreover, we demonstrated that the combined inhibition of mTOR and DNMT synergistically inhibited HCC growth in vitro and in vivo. In addition to some already identified pro-cancer downstream molecules, the activation of mTOR signalling was found to promote DNA methylation by increasing the translation of DNMT1. Furthermore, combined targeting of mTOR and DNMT1 has been demonstrated to have a more effective tumor suppressive function in HCC.

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.

Aim: Conservative treatment approaches for thyroid carcinoma (TC) patients with wild-type B-type Raf kinase (BRAF) pose risks of long-term recurrence. The association of DNA methylation with TC metastasis is unclear. Patients & methods: Here we analyzed data from 179 BRAF wild-type TC patients in the The Cancer Genome Atlas database, identifying significant metastasis-associated CpGs. A logistic regression model was developed and validated for discriminating lymphatic metastasis in BRAF wild-type TC. Results: The model showed high accuracy (AUC: 0.924 training set; 0.812 and 0.773 external cohorts). TAGLN, MRPL4, CLDN10 and GRIK2 emerged as diagnostic markers. GRIK2, downregulated due to promoter hypermethylation, acted as a TC suppressor. Conclusion: Our 5-CpG epigenetic signature effectively discriminates lymphatic metastasis in BRAF wild-type TC, highlighting GRIK2's tumor-suppressive role influenced by promoter hypermethylation.

MOTIVATION Targeted therapy for cancer-related genetic variants is critical for precision medicine. Although several databases including The Clinical Interpretation of Variants in Cancer (CIViC), The Oncology Knowledge Base (OncoKB), The Cancer Genome Interpreter (CGI), My Cancer Genome (MCG) provide clinical interpretations of variants in cancer, the clinical evidence was limited and miscellaneous. In this study, we developed the DrugCVar database, which integrated our manually curated cancer variant-drug targeting evidence from literature and the interpretations from the public resources. RESULTS In total, 7,830 clinical evidences for cancer variant-drug targeting were integrated and classified into ten evidence tiers. Searching and browsing functions were provided for quick queries of cancer variant-drug targeting evidence. Also, batch annotation module was developed for user-provided massive genetic variants in various formats. Details such as the mutation function, location of the variants in gene and protein structures, and mutation statistics of queried genes in various tumor types were also provided for further investigations. Thus, DrugCVar could serve as a comprehensive annotation tool to interpret potential drugs for cancer variants especially the massive ones from clinical cancer genomics studies. AVAILABILITY AND IMPLEMENTATION The database is available at http://drugcvar.omicsbio.info. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Animal gut microbiomes play important roles in the health, diseases, and production of animal hosts. The volume of animal gut metagenomic data, including both 16S amplicon and metagenomic sequencing data, has been increasing exponentially in recent years, making it increasingly difficult for researchers to query, retrieve, and reanalyze experimental data and explore new hypotheses. We designed a database called the domestic animal gut microbiome atlas (ADDAGMA) to house all publicly available, high-throughput sequencing data for the gut microbiome in domestic animals. ADDAGMA enhances the availability and accessibility of the rapidly growing body of metagenomic data. We annotated microbial and metadata from four domestic animals (cattle, horse, pig, and chicken) from 356 published papers to construct a comprehensive database that is equipped with browse and search functions, enabling users to make customized, complicated, biologically relevant queries. Users can quickly and accurately obtain experimental information on sample types, conditions, and sequencing platforms, and experimental results including microbial relative abundances, microbial taxon-associated host phenotype, and P-values for gut microbes of interest. The current version of ADDAGMA includes 290,422 quantification events (changes in abundance) for 3215 microbial taxa associated with 48 phenotypes. ADDAGMA presently covers gut microbiota sequencing data from pig, cattle, horse, and chicken, but will be expanded to include other domestic animals. ADDAGMA is freely available at (http://addagma.omicsbio.info/).

Abstract As a crucial molecular mechanism, post-translational modifications (PTMs) play critical roles in a wide range of biological processes in plants. Recent advances in mass spectrometry-based proteomic technologies have greatly accelerated the profiling and quantification of plant PTM events. Although several databases have been constructed to store plant PTM data, a resource including more plant species and more PTM types with quantitative dynamics still remains to be developed. In this paper, we present an integrative database of quantitative PTMs in plants named qPTMplants (http://qptmplants.omicsbio.info), which hosts 1 242 365 experimentally identified PTM events for 429 821 nonredundant sites on 123 551 proteins under 583 conditions for 23 PTM types in 43 plant species from 293 published studies, with 620 509 quantification events for 136 700 PTM sites on 55 361 proteins under 354 conditions. Moreover, the experimental details, such as conditions, samples, instruments and methods, were manually curated, while a variety of annotations, including the sequence and structural characteristics, were integrated into qPTMplants. Then, various search and browse functions were implemented to access the qPTMplants data in a user-friendly manner. Overall, we anticipate that the qPTMplants database will be a valuable resource for further research on PTMs in plants.

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.

Thiol groups on cysteines can undergo multiple post-translational modifications (PTMs), acting as a molecular switch to maintain redox homeostasis and regulating a series of cell signaling transductions. Identification of sophistical protein cysteine modifications is crucial for dissecting its underlying regulatory mechanism. Instead of a time-consuming and labor-intensive experimental method, various computational methods have attracted intense research interest due to their convenience and low cost. Here, we developed the first comprehensive deep learning based tool pCysMod for multiple protein cysteine modification prediction, including S -nitrosylation, S -palmitoylation, S -sulfenylation, S -sulfhydration, and S -sulfinylation. Experimentally verified cysteine sites curated from literature and sites collected by other databases and predicting tools were integrated as benchmark dataset. Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. Cross-validations indicated our model showed excellent robustness and outperformed existing tools, which was able to achieve an average AUC of 0.793, 0.807, 0.796, 0.793, and 0.876 for S -nitrosylation, S -palmitoylation, S -sulfenylation, S -sulfhydration, and S -sulfinylation, demonstrating pCysMod was stable and suitable for protein cysteine modification prediction. Besides, we constructed a comprehensive protein cysteine modification prediction web server based on this model to benefit the researches finding the potential modification sites of their interested proteins, which could be accessed at http://pcysmod.omicsbio.info . This work will undoubtedly greatly promote the study of protein cysteine modification and contribute to clarifying the biological regulation mechanisms of cysteine modification within and among the cells.

To date, various studies have found that the occurrence of cancer may be related to viral infections. Therefore, it is important to explore the relationship between viruses and diseases. The International Agency for Research on Cancer has defined six types of viruses as Class 1 human carcinogens, including Epstein-Barr virus, hepatitis C virus, hepatitis B virus, human T-cell lymphotropic virus, human herpesvirus 8 and human papillomavirus, while Merkel cell polyomavirus is classified as 'probably carcinogenic to humans' (Group 2A). Therefore, in-depth research on these viruses will help clarify their relationship with diseases, and substantial efforts have been made to sequence their genomes. However, there is no complete database documenting these cancer-associated viruses, and researchers are not able to easily access and retrieve the published genomes. In this study, we developed iCAV, a database that integrates the genomes of cancer-related viruses and the corresponding phenotypes. We collected a total of 18 649 genome sequences from seven human disease-related viruses, and each virus was further classified by the associated disease, sample and country. iCAV is a comprehensive resource of cancer-associated viruses that provides browse and download functions for viral genomes.

The gut microbiota plays important roles in human health through regulating both physiological homeostasis and disease emergence. The accumulation of metagenomic sequencing studies enables us to better understand the temporal and spatial variations of the gut microbiota under different physiological and pathological conditions. However, it is inconvenient for scientists to query and retrieve published data; thus, a comprehensive resource for the quantitative gut metagenome is urgently needed. In this study, we developed gut MEtaGenome Atlas (gutMEGA), a well-annotated comprehensive database, to curate and host published quantitative gut microbiota datasets from Homo sapiens. By carefully curating the gut microbiota composition, phenotypes and experimental information, gutMEGA finally integrated 59 132 quantification events for 6457 taxa at seven different levels (kingdom, phylum, class, order, family, genus and species) under 776 conditions. Moreover, with various browsing and search functions, gutMEGA provides a fast and simple way for users to obtain the relative abundances of intestinal microbes among phenotypes. Overall, gutMEGA is a convenient and comprehensive resource for gut metagenome research, which can be freely accessed at http://gutmega.omicsbio.info.

As important post-translational modifications, protein cysteine modifications (PCMs) occurring at cysteine thiol group play critical roles in the regulation of various biological processes in eukaryotes. Due to the rapid advancement of high-throughput proteomics technologies, a large number of PCM events have been identified but remain to be curated. Thus, an integrated resource of eukaryotic PCMs will be useful for the research community. In this work, we developed an integrative database for protein cysteine modifications in eukaryotes (iCysMod), which curated and hosted 108 030 PCM events for 85 747 experimentally identified sites on 31 483 proteins from 48 eukaryotes for 8 types of PCMs, including oxidation, S-nitrosylation (-SNO), S-glutathionylation (-SSG), disulfide formation (-SSR), S-sulfhydration (-SSH), S-sulfenylation (-SOH), S-sulfinylation (-SO2H) and S-palmitoylation (-S-palm). Then, browse and search options were provided for accessing the dataset, while various detailed information about the PCM events was well organized for visualization. With human dataset in iCysMod, the sequence features around the cysteine modification sites for each PCM type were analyzed, and the results indicated that various types of PCMs presented distinct sequence recognition preferences. Moreover, different PCMs can crosstalk with each other to synergistically orchestrate specific biological processes, and 37 841 PCM events involved in 119 types of PCM co-occurrences at the same cysteine residues were finally obtained. Taken together, we anticipate that the database of iCysMod would provide a useful resource for eukaryotic PCMs to facilitate related researches, while the online service is freely available at http://icysmod.omicsbio.info.

Recent studies demonstrate that immune checkpoint inhibitor (ICI) therapy has achieved success in many types of advanced cancers including advanced hepatocellular carcinoma (HCC). However, ICI therapy is beneficial in only some HCC patients, suggesting that immune-responses are highly variable in HCCs. Therefore, understanding the immune status in HCC microenvironment will facilitate ICI immunotherapy and guide patient selection for the therapy. In this study, we first analyzed the expression profile of immune-modulating genes and their relationship with survival of HCC patients using the data downloaded from The Cancer Genome Atlas - Liver Hepatocellular Carcinoma (TCGA-LIHC) database, and found that the higher expressions of CD276 (B7-H3) and CD47 were significantly associated with poor survival. Then we identified 4 immune subtypes of HCCs with different survivals by using the combination expression of B7-H3 (or CD47) and CD8. Patients with B7-H3low/CD8high or CD47low/CD8high have the best survival while ones with B7-H3high/CD8low or CD47high/CD8low have the worst survival. The 4 immune subtypes were validated in another 72 HCC patients obtained from South China. In conclusion, our findings suggest that HCC patient prognosis is associated with immunophenotypes by T cell infiltration (CD8 expression) and the expression of the adaptive immune resistance gene (B7-H3 or CD47), and this immune classification system will facilitate HCC patient selection for ICI immunotherapy.

Ferroptosis is a type of cell death related to cancer; however, the characteristics of ferroptosis in cancers are still uncertain. Based on the data in The Cancer Genome Atlas, we found that most ferroptosis regulator genes (FRGs) were differentially expressed in tumors, somatic copy number alterations (SCNA) and DNA methylation contributed to their aberrant expression. We established the ferroptosis potential index (FPI) to reveal the functional roles of ferroptosis and noticed that the FPI was higher in tumors than in normal tissues in most cancers and was associated with subtypes and clinical features. The FPI was negatively correlated with several metabolic pathways but positively associated with several important metastasis-related pathways and immune-related pathways. High FPI predicted poor prognosis in several tumors, whereas FPI and FRGs impacted drug sensitivity. Our study presents a systematic analysis of ferroptosis and its regulatory genes and highlights the potential of ferroptosis-based cancer therapy.

Ferroptosis is a type of cell death that related to cancer, however, the characteristics of ferroptosis in cancers are still uncertain. Based on the data in The Cancer Genome Atlas, we found that most ferroptosis regulator genes (FRGs) were differentially expressed in tumors, copy number alterations (CNA) and DNA methylation contributed to their aberrant expression. We established the ferroptosis potential index (FPI) to reveal the functional roles of ferroptosis and noticed that the FPI was higher in tumors than in normal tissues in most cancers, and was associated with subtypes and clinical features. The FPI was negatively correlated with several metabolism pathways but positively associated with several important metastasis-related pathways and immune-related pathways. Higher FPI predicted worse prognosis in several tumors, while FPI and FRGs impacted drug sensitivity. Our study presents a systematical analysis of ferroptosis and its regulatory genes, and highlights the potential of ferroptosis-based cancer therapy.

Background: Ferroptosis is a newly discovered type of cell death that is closely related to cancer, however, the characteristics of ferroptosis and its regulators in cancers are still largely unknown. Methods: Based on the cancer omics data in The Cancer Genome Atlas, we comprehensively analyzed the alterations of ferroptosis regulator genes (FRGs) and computationally established the ferroptosis potential index (FPI) to reveal the functional roles of ferroptosis in cancer. Findings: We found that the mutation rates of FRGs were generally low, but most FRGs were differentially expressed in tumors among various cancer types. Frequent copy number alterations (CNA) and differential DNA methylation of FRGs contributed greatly to their aberrant expression. In most cancers, the FPI was higher in tumors than in the adjacent normal tissues and was associated with histological/molecular subtypes and clinical features. We observed that the FPI was negatively correlated with a number of metabolism pathways such as terpenoid backbone biosynthesis, but positively associated with metastasis-related pathways such as epithelial-mesenchymal transition (EMT), WNT beta-catenin signaling and angiogenesis. The FPI was also correlated immune microenvironment since it was positively correlated with pathways such as IL6 JAK-STAT signaling, inflammatory response, complement. Furthermore, it was observed that high FPI predicted significantly worse overall survival in glioblastoma multiforme, renal clear/papillary cell carcinoma, hepatocellular carcinoma, and lung adenocarcinoma, while FPI and FRGs were intensively correlated with cancer drug sensitivity. Interpretation: Our study presents a systematical analysis of ferroptosis and its regulatory genes across cancers, and highlights the potential of ferroptosis-based cancer therapy. Funding Statement: This work was supported by grants from National Key RD and Science and Technology Program of Guangdong (2019B020227002 to RH.X.). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: Not required.

As a widespread and reversible post-translational modification of proteins, S-glutathionylation specifically generates the mixed disulfides between cysteine residues and glutathione, which regulates various biological processes including oxidative stress, nitrosative stress and signal transduction. The identification of proteins and specific sites that undergo S-glutathionylation is crucial for understanding the underlying mechanisms and regulatory effects of S-glutathionylation. Experimental identification of S-glutathionylation sites is laborious and time-consuming, whereas computational predictions are more attractive due to their high speed and convenience. Here, we developed a novel computational framework DeepGSH (http://deepgsh.cancerbio.info/) for species-specific S-glutathionylation sites prediction, based on deep learning and particle swarm optimization algorithms. 5-fold cross validation indicated that DeepGSH was able to achieve an AUC of 0.8393 and 0.8458 for Homo sapiens and Mus musculus. According to critical evaluation and comparison, DeepGSH showed excellent robustness and better performance than existing tools in both species, demonstrating DeepGSH was suitable for S-glutathionylation prediction. The prediction results of DeepGSH might provide guidance for experimental validation of S-glutathionylation sites and helpful information to understand the intrinsic mechanisms.

As a widespread post-translational modification of proteins, calpain-mediated cleavage regulates a broad range of cellular processes, including proliferation, differentiation, cytoskeletal reorganization, and apoptosis. The identification of proteins that undergo calpain cleavage in a site-specific manner is the necessary foundation for understanding the exact molecular mechanisms and regulatory roles of calpain-mediated cleavage. In contrast with time-consuming and labor-intensive experimental methods, computational approaches for detecting calpain cleavage sites have attracted wide attention due to their efficiency and convenience. In this study, we established a novel computational tool named DeepCalpain (http://deepcalpain.cancerbio.info/) for predicting the potential calpain cleavage sites by adopting deep neural network and the particle swarm optimization algorithm. Through critical evaluation and comparison, DeepCalpain exhibited superior performance against other existing tools. Meanwhile, we found that protein interactions could enrich the calpain-substrate regulatory relationship. Since calpain-mediated cleavage was critical for cancer development and progression, we comprehensively analyzed the calpain cleavage associated mutations across 11 cancers with the help of DeepCalpain, which demonstrated that the calpain-mediated cleavage events were affected by mutations and heavily implicated in the regulation of cancer cells. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of calpain cleavage in biological pathways and different cancer types, which might open new avenues for the diagnosis and treatment of cancers.

Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein-protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.