CheSweet: An application to predict glycan's chemicals shifts
The knowledge of the tridimensional structure of glycans is necessary to understand in detail, at atomic level, the molecular processes in which they are involved. Chemical Shifts (CS) are observables obtained from Nuclear Magnetic Resonance experiments that are highly sensitive probes to sense conf...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2018 |
| País: | Argentina |
| Recursos: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositório: | CONICET Digital (CONICET) |
| Idioma: | inglês |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/94554 |
| Acesso em linha: | http://hdl.handle.net/11336/94554 |
| Access Level: | Acceso aberto |
| Palavra-chave: | GLYCANS CHEMICAL SHIFTS PYTHON https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Resumo: | The knowledge of the tridimensional structure of glycans is necessary to understand in detail, at atomic level, the molecular processes in which they are involved. Chemical Shifts (CS) are observables obtained from Nuclear Magnetic Resonance experiments that are highly sensitive probes to sense conformational changes. CS can be calculated accurately using quantum chemical tools, although these computations are very demanding for routine computations of more than a few conformations. For that reason we have developed CheSweet, a Python module for accurate and fast computation of CS. |
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