dbPTM 2025 update: comprehensive integration of PTMs and proteomic data for advanced insights into cancer research.

Post-translational modifications (PTMs) are essential for modulating protein function and influencing stability, activity and signaling processes. The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.

A Study of Disease Prognosis in Lung Adenocarcinoma Using Single-Cell Decomposition and Immune Signature Analysis.

The development of tumors is a highly complex process that entails numerous interactions and intricate relationships between the host immune system and cancer cells. It has been demonstrated in studies that the treatment response of patients can be correlated with the tumor microenvironment (TME). Consequently, the examination of diverse immune profiles within the TME can facilitate the elucidation of tumor development and the development of advantageous models for diagnoses and prognoses.

An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria.

The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: ATCC 25923, ATCC 25922, and ATCC 27853.

Corrigendum: Systolic blood pressure as the mediator of the effect of early menarche on the risk of coronary artery disease: A Mendelian randomization study.

[This corrects the article DOI: 10.3389/fcvm.2022.

Hypertension as a Novel Link for Shared Heritability in Age at Menarche and Cardiometabolic Traits.

Context: Extremely early age at menarche, also called precocious puberty, has been associated with various cardiometabolic traits, but their shared heritability remains unclear.

Objectives: This work aimed to identify new shared genetic variants and their pathways for age at menarche and cardiometabolic traits and to investigate the influence of central precocious puberty on childhood cardiometabolic traits.

Methods: Using the conjunction false discovery rate method, this study analyzed genome-wide association study data from the menarche-cardiometabolic traits among 59 655 females of Taiwanese ancestry and systemically investigated pleiotropy between age at menarche and cardiometabolic traits.

Targeted next-generation sequencing for genetic variants of left ventricular mass status among community-based adults in Taiwan.

Left ventricular mass is a highly heritable disease. Previous studies have suggested common genetic variants to be associated with left ventricular mass; however, the roles of rare variants are still unknown. We performed targeted next-generation sequencing using the TruSight Cardio panel, which provides comprehensive coverage of 175 genes with known associations to 17 inherited cardiac conditions.

Systolic blood pressure as the mediator of the effect of early menarche on the risk of coronary artery disease: A Mendelian randomization study.

Background: Menarche timing may not be directly associated with the risk of coronary artery disease (CAD). Therefore, we investigated the roles of metabolic factors in explaining the effect of age at menarche on CAD risk.

Methods: We identified women with age at menarche and CAD by using three analytical methods: Mendelian randomization (MR), logistic regression analysis, and Cox proportional hazard regression.

Anti-Angiogenetic and Anti-Lymphangiogenic Effects of a Novel 2-Aminobenzimidazole Derivative, MFB.

Background And Purpose: Benzimidazoles have attracted much attention over the last few decades due to their broad-spectrum pharmacological properties. Increasing evidence is showing the potential use of benzimidazoles as anti-angiogenic agents, although the mechanisms that impact angiogenesis remain to be fully defined. In this study, we aim to investigate the anti-angiogenic mechanisms of MFB, a novel 2-aminobenzimidazole derivative, to develop a novel angiogenesis inhibitor.

Identification of Differentially Expressed Genes in Different Glioblastoma Regions and Their Association with Cancer Stem Cell Development and Temozolomide Response.

The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA; 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing; 3589 cells) were included in this study.

dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications.

Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version.

Computational analysis for identification of the extracellular matrix molecules involved in endometrial cancer progression.

Recurrence and poorly differentiated (grade 3 and above) and atypical cell type endometrial cancer (EC) have poor prognosis outcome. The mechanisms and characteristics of recurrence and distal metastasis of EC remain unclear. The extracellular matrix (ECM) of the reproductive tract in women undergoes extensive structural remodelling changes every month.

Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method.

Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods.

Identification of potential biomarkers related to glioma survival by gene expression profile analysis.

Background: Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.

Methods: In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups.

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites.

Background: Glutarylation, the addition of a glutaryl group (five carbons) to a lysine residue of a protein molecule, is an important post-translational modification and plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified glutarylated peptides increases, it becomes imperative to investigate substrate motifs to enhance the study of protein glutarylation. We carried out a bioinformatics investigation of glutarylation sites based on amino acid composition using a public database containing information on 430 non-homologous glutarylation sites.

Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas.

The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in addition to histologic phenotypes. We aim to determine whether clinical MRI can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas. The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive.

MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs.

Background: Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson's disease, and Alzheimer's disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures.

Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions.

Background: Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in.

Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles.

Machinery of pre-mRNA splicing is carried out through the interaction of RNA sequence elements and a variety of RNA splicing-related proteins (SRPs) (e.g. spliceosome and splicing factors).

An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs.

Background: Functional RNA molecules participate in numerous biological processes, ranging from gene regulation to protein synthesis. Analysis of functional RNA motifs and elements in RNA sequences can obtain useful information for deciphering RNA regulatory mechanisms. Our previous work, RegRNA, is widely used in the identification of regulatory motifs, and this work extends it by incorporating more comprehensive and updated data sources and analytical approaches into a new platform.

GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group.

Background: Sequence features in promoter regions are involved in regulating gene transcription initiation. Although numerous computational methods have been developed for predicting transcriptional start sites (TSSs) or transcription factor (TF) binding sites (TFBSs), they lack annotations for do not consider some important regulatory features such as CpG islands, tandem repeats, the TATA box, CCAAT box, GC box, over-represented oligonucleotides, DNA stability, and GC content. Additionally, the combinatorial interaction of TFs regulates the gene group that is associated with same expression pattern.