Journal of Molecular Biology最新文献

The Papilloma Virus Episteme (PaVE) https://pave.niaid.nih.gov/ was initiated by NIAID in 2008 to provide a highly curated bioinformatic and knowledge resource for the papillomavirus scientific community. It rapidly became the fundamental and core resource for papillomavirus researchers and clinicians worldwide. Over time, the software infrastructure became severely outdated. In PaVE 2.0, the underlying libraries and hosting platform have been completely upgraded and rebuilt using Amazon Web Services (AWS) tools and automated CI/CD (continuous integration and deployment) pipelines for deployment of the application and data (now in AWS S3 cloud storage). PaVE 2.0 is hosted on three AWS ECS (elastic container service) using the NIAID Operations & Engineering Branch's Monarch tech stack and terraform. A new Celery queue supports longer running tasks. The framework is Python Flask with a JavaScript/JINJA template front end, and the database switched from MySQL to Neo4j. A Swagger API (Application Programming Interface) performs database queries, and executes jobs for BLAST, MAFFT, and the L1 typing tooland will allow future programmatic data access. All major tools such as BLAST, the L1 typing tool, genome locus viewer, phylogenetic tree generator, multiple sequence alignment, and protein structure viewer were modernized and enhanced to support more users. Multiple sequence alignment uses MAFFT instead of COBALT. The protein structure viewer was changed from Jmol to Mol*, the new embeddable viewer used by RCSB (Research Collaboratory for Structural Bioinformatics). In summary, PaVE 2.0 allows us to continue to provide this essential resource with an open-source framework that could be used as a template for molecular biology databases of other viruses.

Molecular dynamics (MD) simulations can be used by protein scientists to investigate a wide array of biologically relevant properties such as the effects of mutations on a protein's structure and activity, or probing intermolecular interactions with small molecule substrates or other macromolecules. Within the world of computational structural biology, several programs have become popular for running these simulations, but each of these programs requires a significant time investment from the researcher to run even simple simulations. Even after learning how to run and analyse simulations, many elements remain a "black box." This greatly limits the accessibility of molecular dynamics simulations for non-experts. Here we present drMD, an automated pipeline for running MD simulations using the OpenMM molecular mechanics toolkit. We have created drMD with non-experts in computational biology in mind. The drMD codebase has several functions that automatically handle routine procedures associated with running MD simulations. This greatly reduces the expertise required to run MD simulations. We have also introduced a series of quality-of-life features to make the process of running MD simulations both easier and more pleasant. Finally, drMD explains the steps it is taking interactively and, where useful, provides relevant references so the user can learn more. All these features make drMD an effective tool for learning MD while running publication-quality simulations. drMD is open source and can be found on GitHub: https://github.com/wells-wood-research/drMD.

Evaluating the frequencies of drug-side effects is crucial in drug development and risk-benefit analysis. While existing deep learning methods show promise, they have yet to explore using heterogeneous networks to simultaneously model the various relationship between drugs and side effects, highlighting areas for potential enhancement. In this study, we propose DSE-HNGCN, a novel method that leverages heterogeneous networks to simultaneously model the various relationships between drugs and side effects. By employing multi-layer graph convolutional networks, we aim to mine the interactions between drugs and side effects to predict the frequencies of drug-side effects. To address the over-smoothing problem in graph convolutional networks and capture diverse semantic information from different layers, we introduce a layer importance combination strategy. Additionally, we have developed an integrated prediction module that effectively utilizes drug and side effect features from different networks. Our experimental results, using benchmark datasets in a range of scenarios, show that our model outperforms existing methods in predicting the frequencies of drug-side effects. Comparative experiments and visual analysis highlight the substantial benefits of incorporating heterogeneous networks and other pertinent modules, thus improving the accuracy of DSE-HNGCN predictions. We also provide interpretability for DSE-HNGCN, indicating that the extracted features are potentially biologically significant. Case studies validate our model's capability to identify potential side effects of drugs, offering valuable insights for subsequent biological validation experiments.

Osteoarthritis (OA) is the most common degenerative joint disease and the second leading cause of disability worldwide. Single-omics analyses are far from elucidating the complex mechanisms of lipid metabolic dysfunction in OA. This study identified a shared lipid metabolic signature of OA by integrating metabolomics, single-cell and bulk RNA-seq, as well as metagenomics. Compared to the normal counterparts, cartilagesin OA patients exhibited significant depletion of homeostatic chondrocytes (HomCs) (P = 0.03) and showed lipid metabolic disorders in linoleic acid metabolism and glycerophospholipid metabolism which was consistent with our findings obtained from plasma metabolomics. Through high-dimensional weighted gene co-expression network analysis (hdWGCNA), weidentified PLA2G2A as a hub gene associated with lipid metabolic disorders in HomCs. And an OA-associated subtype of HomCs, namely HomC1 (marked by PLA2G2A, MT-CO1, MT-CO2, and MT-CO3) was identified, which also exhibited abnormal activation of lipid metabolic pathways. This suggests the involvement of HomC1 in OA progression through the shared lipid metabolism aberrancies, which were further validated via bulk RNA-Seq analysis. Metagenomic profiling identified specific gut microbial species significantly associated with the key lipid metabolism disorders, including Bacteroides uniformis (P < 0.001, R = -0.52), Klebsiella pneumonia (P = 0.003, R = 0.42), Intestinibacter_bartlettii (P = 0.009, R = 0.38), and Streptococcus anginosus (P = 0.009, R = 0.38). By integrating the multi-omics features, a random forest diagnostic model with outstanding performance was developed (AUC = 0.97). In summary, this study deciphered the crucial role of a integrated lipid metabolic signature in OA pathogenesis, and established a regulatory axis of gut microbiota-metabolites-cell-gene, providing new insights into the gut-joint axis and precision therapy for OA.

Alzheimer's disease (AD) is a complex neurodegenerative disorder, with existing therapeutic drugs typically targeting specific disease stages. Traditional Chinese medicine (TCM), known for its multi-target and multi-mechanism therapeutic approach, has demonstrated efficacy in treating various stages of AD. In the present work, through a systematic review of classical Chinese medical texts, the formulae for preventing and treating AD were identified. Meanwhile, the active ingredients within these formulae were extracted and cataloged. A comprehensive bioinformatics analysis of omics data was performed to identify differentially expressed genes across different functional brain zones in AD patients at various stages. Finally, by integrating the multidimensional data, we proposed the first database, TCM-ADIP, dedicated to TCM based AD prevention and treatment, which is freely available at https://cbcb.cdutcm.edu.cn/TCM-ADIP/. TCM-ADIP not only supports interactive searching of multidimensional data, but also provides tools for gene localization and functional enrichment analysis of formulae, herbs, and ingredients for AD intervention in specific brain zones. TCM-ADIP fills a crucial gap in existing databases, offering a comprehensive resource for TCM in the treatment of AD.

Precise regulation of protein kinase activity is crucial in cell functions, and its loss is implicated in various diseases. The kinase activity is regulated by interconverting active and inactive states in the conformational landscape. However, how protein kinases switch conformations in response to different signals such as the binding at distinct sites remains incompletely understood. Here, we predict the binding mode for the peptide substrate to Src tyrosine kinase using enhanced conformational sampling simulations (totaling 24 μs) and then investigate changes in the conformational landscape upon substrate binding by conducting unbiased molecular dynamics simulations (totaling 50 μs) initiated from the apo and substrate-bound forms. Unexpectedly, the peptide substrate binding significantly facilitates the transitions from active to inactive conformations in which the αC helix is directed outward, the regulatory spine is broken, and the ATP-binding domain is perturbed. We also explore an underlying residue-contact network responsible for the allosteric conformational changes. Our results are in accord with the recent experiments reporting the negative cooperativity between the peptide substrate and ATP binding to tyrosine kinases and will contribute to advancing our understanding of the regulation mechanisms for kinase activity.