SUMMARY: The Integrated Database of Small Molecules (IDSM) integrates data from small-molecule datasets, making them accessible through the SPARQL query language. Its unique feature is the ability to search for compounds through SPARQL based on their molecular structure. We extended IDSM to enable mass spectra databases to be integrated and searched for based on mass spectrum similarity. As sources of mass spectra, we employed the MassBank of North America database and the In Silico Spectral Database of natural products. AVAILABILITY AND IMPLEMENTATION: The extension is an integral part of IDSM, which is available at https://idsm.elixir-czech.cz. The manual and usage examples are available at https://idsm.elixir-czech.cz/docs/ms. The source codes of all IDSM parts are available under open-source licences at https://github.com/idsm-src.

• Publikační typ
• časopisecké články MeSH

Research on animal venoms and their components spans multiple disciplines, including biology, biochemistry, bioinformatics, pharmacology, medicine, and more. Manipulating and analyzing the diverse array of data required for venom research can be challenging, and relevant tools and resources are often dispersed across different online platforms, making them less accessible to nonexperts. In this article, we address the multifaceted needs of the scientific community involved in venom and toxin-related research by identifying and discussing web resources, databases, and tools commonly used in this field. We have compiled these resources into a comprehensive table available on the VenomZone website (https://venomzone.expasy.org/10897). Furthermore, we highlight the challenges currently faced by researchers in accessing and using these resources and emphasize the importance of community-driven interdisciplinary approaches. We conclude by underscoring the significance of enhancing standards, promoting interoperability, and encouraging data and method sharing within the venom research community.

• Klíčová slova
• antivenom, drug discovery, genomics, machine learning, peptidomics, proteomics, toxin databases, transcriptomics, venom resources,
• MeSH
• big data * MeSH
• databáze faktografické MeSH
• internet * MeSH
• výpočetní biologie * metody   MeSH
• živočišné jedy * MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• živočišné jedy * MeSH

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project.

• Klíčová slova
• RDB-to-RDF mapping, Relational database, Resource Description Framework, SPARQL,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Protein tunnels are essential in transporting small molecules into the active sites of enzymes. Tunnels' geometrical and physico-chemical properties influence the transport process. The tunnels are attractive hot spots for protein engineering and drug development. However, studying the ligand binding and unbinding using experimental techniques is challenging, while in silico methods come with their limitations, especially in the case of resource-demanding virtual screening pipelines. Caver Web 1.2 is a new version of the web server combining the capabilities for the detection of protein tunnels with the calculation of the ligand trajectories. The new version of the Caver Web server was expanded with the ability to fetch novel ligands from the Integrated Database of Small Molecules and with the fully automated virtual screening pipeline allowing for the fast evaluation of the predefined set of over 4,300 currently approved drugs. The virtual screening pipeline is accompanied by a comprehensive user interface, making it a viable service for the broader spectrum of companies and the academic user community. The web server is freely available for academic use at https://loschmidt.chemi.muni.cz/caverweb.

• Klíčová slova
• CIF, Crystallographic Information File, CSA, Catalytic Site Atlas, Caver, CaverDock, Channel, FDA, U.S. Food and Drug Administration, FDA-approved drug, IDSM, Integrated Database of Small Molecules, PDB, Protein Data Bank, Tunnel, Virtual screening, Web,
• Publikační typ
• časopisecké články MeSH