CYCLOPEp: a computational platform for the design of therapeutic cyclic peptides through computational simulations, Big Data analysis and Artificial Intelligence
Antimicrobial resistance is a critical global health challenge, associated with over one million deaths in 2019 and projected to cause up to 10 million annually by 2050. This crisis stems from antibiotic misuse, which drives bacteria to evolve resistance mechanisms. Because traditional antibiotics t...
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| Tipo de documento: | tese |
| Data de publicação: | 2026 |
| País: | España |
| Recursos: | Universidad de Santiago de Compostela (USC) |
| Repositório: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglês |
| OAI Identifier: | oai:dnet:minerva_____::cb14d8afca60062a7f40b926701dc5a2 |
| Acesso em linha: | https://hdl.handle.net/10347/47293 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Molecular Dynamics Cyclic Peptides Artificial Intelligence Lipid Membranes 230224 Péptidos 230226 Bioquímica física |
| Resumo: | Antimicrobial resistance is a critical global health challenge, associated with over one million deaths in 2019 and projected to cause up to 10 million annually by 2050. This crisis stems from antibiotic misuse, which drives bacteria to evolve resistance mechanisms. Because traditional antibiotics target specific cellular processes that bacteria can bypass, a paradigm shift in drug design is essential. Bacterial membranes offer a promising alternative target due to their unique lipid composition; unlike neutral eukaryotic membranes, bacterial membranes are rich in anionic lipids. This allows for the design of antimicrobial peptides that selectively attack these negatively charged surfaces. However, the therapeutic use of such peptides is often limited by poor stability and low bioavailability. |
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