Journal of Molecular Biology最新文献

The Mouse Variation Registry (MVAR) resource is a scalable registry of mouse single nucleotide variants and small indels and variant annotation. The resource accepts data in standard Variant Call Format (VCF) and assesses the uniqueness of the submitted variants via a canonicalization process. Novel variants are assigned a unique, persistent MVAR identifier; variants that are equivalent to an existing variant in the resource are associated with the existing identifier. Annotations for variant type, molecular consequence, impact, and genomic region in the context of specific transcripts and protein sequences are generated using Ensembl’s Variant Effect Predictor (VEP) and Jannovar. Access to the data and annotations in MVAR are supported via an Application Programming Interface (API) and web application. Researchers can search the resource by gene symbol, genomic region, variant (expressed in Human Genome Variation Society syntax), refSNP identifiers, or MVAR identifiers. Tabular search results can be filtered by variant annotations (variant type, molecular consequence, impact, variant region) and viewed according to variant distribution across mouse strains. The registry currently comprises more than 99 million canonical single nucleotide variants for 581 strains of mice. MVAR is accessible from https://mvar.jax.org.

Extracellular vesicles and particles (EVPs) play a crucial role in mediating cell-to-cell communication by transporting various molecular cargos, with small non-coding RNAs (ncRNAs) holding particular significance. A thorough investigation into the abundance and sorting mechanisms of ncRNA within EVPs is imperative for advancing their clinical applications. We have developed EVPsort, which not only provides an extensive overview of ncRNA profiling in 3,162 samples across various biofluids, cell lines, and disease contexts but also seamlessly integrates 19 external databases and tools. This integration encompasses information on associations between ncRNAs and RNA-binding proteins (RBPs), motifs, targets, pathways, diseases, and drugs. With its rich resources and powerful analysis tools, EVPsort extends its profiling capabilities to investigate ncRNA sorting, identify relevant RBPs and motifs, and assess functional implications. EVPsort stands as a pioneering database dedicated to comprehensively addressing both the abundance and sorting of ncRNA within EVPs. It is freely accessible at https://bioinfo.vanderbilt.edu/evpsort/.

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein–ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein–ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

Knowledge of protein–ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner.

@TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.

Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes ($\sim$30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 h on the above 400 genomes, or $\sim 36\times$ speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold’s prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at:

https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).

A single protein structure is rarely sufficient to capture the conformational variability of a protein. Both bound and unbound (holo and apo) forms of a protein are essential for understanding its geometry and making meaningful comparisons. Nevertheless, docking or drug design studies often still consider only single protein structures in their holo form, which are for the most part rigid. With the recent explosion in the field of structural biology, large, curated datasets are urgently needed. Here, we use a previously developed application (AHoJ) to perform a comprehensive search for apo-holo pairs for 468,293 biologically relevant protein–ligand interactions across 27,983 proteins. In each search, the binding pocket is captured and mapped across existing structures within the same UniProt, and the mapped pockets are annotated as apo or holo, based on the presence or absence of ligands. We assemble the results into a database, AHoJ-DB (www.apoholo.cz/db), that captures the variability of proteins with identical sequences, thereby exposing the agents responsible for the observed differences in geometry. We report several metrics for each annotated pocket, and we also include binding pockets that form at the interface of multiple chains. Analysis of the database shows that about 24% of the binding sites occur at the interface of two or more chains and that less than 50% of the total binding sites processed have an apo form in the PDB. These results can be used to train and evaluate predictors, discover potentially druggable proteins, and reveal protein- and ligand-specific relationships that were previously obscured by intermittent or partial data.

Availability: www.apoholo.cz/db

We introduce XGR-model (or XGRm), a web server made accessible at http://www.xgrm.pro, with the aim of meeting the increasing demand for effectively interpreting summary-level genomic data in model organisms. Currently, it hosts two enrichment analysers and two subnetwork analysers to support enrichment and subnetwork analyses for user-input mouse genomic data, whether gene-centric or genomic region-centric. The enrichment analysers identify ontology term enrichments for input genes (GElyser) or for genes linked from input genomic regions (RElyser). The subnetwork analysers rely on our previously established network algorithm to identify gene subnetworks from input gene-centric summary data (GSlyser) or from input region-centric summary data (RSlyser), leveraging network information about either functional interactions or pathway-derived interactions. Collectively, XGRm offers an all-in-one solution for gaining systems biology insights into summary-level genomic data in mice, underpinned by our commitment to regular updates as well as natural extensions to other model organisms.

The red flour beetle Tribolium castaneum has emerged as a powerful model in insect functional genomics. However, a major limitation in the field is the lack of a detailed spatio-temporal view of the genetic signatures underpinning the function of distinct tissues and life stages. Here, we present an ontogenetic and tissue-specific web-based resource for Tribolium transcriptomics: BeetleAtlas (https://www.beetleatlas.org). This web application provides access to a database populated with quantitative expression data for nine adult and seven larval tissues, as well as for four embryonic stages of Tribolium. BeetleAtlas allows one to search for individual Tribolium genes to obtain values of both total gene expression and enrichment in different tissues, together with data for individual isoforms. To facilitate cross-species studies, one can also use Drosophila melanogaster gene identifiers to search for related Tribolium genes. For retrieved genes there are options to identify and display the tissue expression of related Tribolium genes or homologous Drosophila genes. Five additional search modes are available to find genes conforming to any of the following criteria: exhibiting high expression in a particular tissue; showing significant differences in expression between larva and adult; having a peak of expression at a specific stage of embryonic development; belonging to a particular functional category; and displaying a pattern of tissue expression similar to that of a query gene. We illustrate how the different feaures of BeetleAtlas can be used to illuminate our understanding of the genetic mechanisms underpinning the biology of what is the largest animal group on earth.

Nearest neighbor thermodynamic parameters are widely used for RNA and DNA secondary structure prediction and to model thermodynamic ensembles of secondary structures. The Nearest Neighbor Database (NNDB) is a freely available web resource (https://rna.urmc.rochester.edu/NNDB) that provides the functional forms, parameter values, and example calculations. The NNDB provides the 1999 and 2004 set of RNA folding nearest neighbor parameters. We expanded the database to include a set of DNA parameters and a set of RNA parameters that includes m⁶A in addition to the canonical RNA nucleobases. The site was redesigned using the Quarto open-source publishing system. A downloadable PDF version of the complete resource and downloadable sets of nearest neighbor parameters are available.

With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods.

In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs.