Journal of Molecular Biology最新文献

RiboVision2 is a web server designed to visualize phylogenetic, structural, and evolutionary properties of ribosomal RNAs simultaneously at the levels of primary, secondary, and three-dimensional structure and in the context of full ribosomal complexes. RiboVision2 instantly computes and displays a broad variety of data; it has no login requirements, is open-source, free for all users, and available at https://ribovision2.chemistry.gatech.edu.

Accurate models of protein tertiary structures are now available from numerous advanced prediction methods, although the accuracy of each method often varies depending on the specific protein target. Additionally, many models may still contain significant local errors. Therefore, reliable, independent model quality estimates are essential both for identifying errors and selecting the very best models for further biological investigations. ModFOLD9 is a leading independent server for detecting the local errors in models produced by any method, and it can accurately discriminate between high-quality models from multiple alternative approaches. ModFOLD9 incorporates several new scores from deep learning-based approaches, leading to greatly improved prediction accuracy compared with earlier versions of the server. ModFOLD9 is continuously independently benchmarked, and it is shown to be highly competitive with other public servers. ModFOLD9 is freely available at https://www.reading.ac.uk/bioinf/ModFOLD/.

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Through an extensive literature survey, we have upgraded the Leaf Senescence Database (LSD v5.0; https://ngdc.cncb.ac.cn/lsd/), a curated repository of comprehensive senescence-associated genes (SAGs) and their corresponding mutants. Since its inception in 2010, LSD undergoes frequent updates to encompass the latest advances in leaf senescence research and its current version comprises a high-quality collection of 31,740 SAGs and 1,209 mutants from 148 species, which were manually searched based on robust experimental evidence and further categorized according to their functions in leaf senescence. Furthermore, LSD was greatly enriched with comprehensive annotations for the SAGs through meticulous curation using both manual and computational methods. In addition, it was equipped with user-friendly web interfaces that facilitate text queries, BLAST searches, and convenient download of SAG sequences for localized analysis. Users can effortlessly navigate the database to access a plethora of information, including literature references, mutants, phenotypes, multi-omics data, miRNA interactions, homologs in other plants, and cross-links to various databases. Taken together, the upgraded version of LSD stands as the most comprehensive and informative plant senescence-related database to date, incorporating the largest collection of SAGs and thus bearing great utility for a wide range of studies related to plant senescence.

We develop a novel database Alpha&ESMhFolds which allows the direct comparison of AlphaFold2 and ESMFold predicted models for 42,942 proteins of the Reference Human Proteome, and when available, their comparison with 2,900 directly associated PDB structures with at least a structure to sequence coverage of 70%. Statistics indicate that good quality models tend to overlap with a TM-score >0.6 as long as some PDB structural information is available. As expected, a direct model superimposition to the PDB structure highlights that AlphaFold2 models are slightly superior to ESMFold ones. However, some 55% of the database is endowed with models overlapping with TM-score <0.6. This highlights the different outputs of the two methods. The database is freely available for usage at https://alpha-esmhfolds.biocomp.unibo.it/.

Here we present TPPU_DSF (https://maciasnmr.net/tppu_dsf/). This is a free and open-source web application that opens, converts, fits, and calculates the thermodynamic parameters of protein unfolding from standard differential scanning fluorimetry (DSF) data in an automated manner. The software has several applications. In the context of screening compound libraries for protein binders, obtaining thermodynamic parameters provides a more robust approach to detecting hits than the changes in the melting temperature (T_m) alone, thereby helping to increase the number of positive hits in screening campaigns. Moreover, changes in ΔG_u^o indicate protein response to binding at lower compound concentrations than those in the T_m, thereby reducing the costs associated with the amounts of protein and compounds required for the assays. Also, by adding thermodynamic information to the T_m comparison, the software can contribute to the optimization of protein constructs and buffer conditions, a common practice before structural and functional projects.

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.

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.