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Zenodo
2025
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| Online Access: | https://doi.org/10.1093/braincomms/fcaf106 |
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| author | Marques, Sérgio Borko, Simeon Vavra, Ondrej Dvorsky (Stourac), Jan Kohout, Pavel Kabourek, Petr Hejtmánek, Lukáš Damborsky, Jiri Bednar, David |
| author_facet | Marques, Sérgio Borko, Simeon Vavra, Ondrej Dvorsky (Stourac), Jan Kohout, Pavel Kabourek, Petr Hejtmánek, Lukáš Damborsky, Jiri Bednar, David |
| contents | <p>Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at <a href="https://loschmidt.chemi.muni.cz/caverweb" target="_blank" rel="noopener">https://loschmidt.chemi.muni.cz/caverweb</a>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_1093_braincomms_fcaf106 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Caver Web 2.0: analysis of tunnels and ligand transport in dynamic ensembles of proteins Marques, Sérgio Borko, Simeon Vavra, Ondrej Dvorsky (Stourac), Jan Kohout, Pavel Kabourek, Petr Hejtmánek, Lukáš Damborsky, Jiri Bednar, David Drug Approval Evolution Biochemistry Molecular biology <p>Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at <a href="https://loschmidt.chemi.muni.cz/caverweb" target="_blank" rel="noopener">https://loschmidt.chemi.muni.cz/caverweb</a>.</p> |
| title | Caver Web 2.0: analysis of tunnels and ligand transport in dynamic ensembles of proteins |
| topic | Drug Approval Evolution Biochemistry Molecular biology |
| url | https://doi.org/10.1093/braincomms/fcaf106 |