Journal of Molecular Biology最新文献

Histones are one of the fundamental chromatin proteins of life. In eukaryotes and megaviruses, they form nucleosome structures that wrap DNA. However, in prokaryotes, histones are much more diverse in how they organize DNA. In bacteria, histones bend and wrap DNA while in archaea they wrap and bridge DNA. These differences in DNA organizing properties are primarily due to distinct modes of histone multimerization. Here we present ProHistoneDB, an online database describing and categorizing prokaryotic and viral histones. For each histone, monomer, dimer, tetramer, and hexamer predictions are viewable and downloadable. ProHistoneDB contains 7334 histones, categorized into 24 groups based on the multimer predictions. For each category, interactive phylogenetic trees and HMM profile logos are available to identify conserved residues and explore the relative evolutionary relationships of histones. ProHistoneDB can be accessed at https://prohistonedb.org/.

The ongoing evolution of coronaviruses (CoVs) poses a long-term threat to global public health, requiring dissection of virus-host interactions to develop broad-spectrum antivirals. Existing data resources are often limited to specific interaction types, hindering a systematic understanding of the complete viral life cycle. To address this, CovInter 2.0 (https://idrblab.org/COVINTER) has been developed as a comprehensively upgraded database of coronavirus interactomics, which is the first to systematically integrate the six major classes of molecular interactions that drive the viral life cycle, compiling over 61,000 entries. Furthermore, data for 229 potential anti-CoV drugs and their targets have been included, bridging molecular interactions with therapeutic development. The platform features an interactive network visualization tool for intuitive exploration of these complex relationships. As an open-access resource, CovInter 2.0 provides a powerful tool for virology and drug discovery, computational biology, designed to accelerate the identification of novel antiviral targets and the development of next-generation therapeutics.

Long non-coding RNAs (lncRNAs) regulate gene expression, cellular functions, and disease-related processes through extensive and diverse interactions with various molecules, thereby forming heterogeneous functional regulatory networks. LncRNAs often exert their regulatory effects in a cooperative manner, functional enrichment analysis is considered a powerful approach for systematically elucidating their functions. However, most existing methods focus on the functions of individual lncRNAs, overlooking complex interactions among them, which limits the scope of enrichment analysis. In this paper, we developed TLSEA 2.0 (http://www.lirmed.com/tlsea2/), an updated web-based tool with an expanded repository of functional lncRNA sets, for enhancing enrichment analysis capabilities. Compared with the earlier version of TLSEA, TLSEA 2.0 has tripled its reference lncRNA sets and expanded enrichment analysis categories from one to five: Disease, Drug, RNA-RNA Interaction, RNA-Protein Interaction, and Cancer Phenotype. TLSEA 2.0 introduces an Expansion option, which extends the user-submitted lncRNA list by incorporating additional lncRNAs that exhibit strong association with the input set based on similarity networks, thereby expanding the user-submitted lncRNA list and enabling the discovery of more potential associations. Additional functional similarity networks have been incorporated, including lncRNA-protein interactions and co-expression data. Furthermore, TLSEA 2.0 employs combining Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN) to replace traditional graph representation learning during the process of extracting lncRNA features. These enhancements make TLSEA 2.0 a more comprehensive and robust online platform for functional enrichment analysis, facilitating deeper insights into the complex biological regulatory functions of lncRNAs.