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DeepNOE

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DeepNOE Leveraging deep learning for protein structure solving at ultra high re...

DeepNOE Leveraging deep learning for protein structure solving at ultra high resolution on the basis of NMR measurements with exact nuclear Overhauser enhancement Nuclear Magnetic Resonance (NMR) spectroscopy is one of the leading techniques for protein structure analysis. In contrast to other methods, NMR spectroscopy allows the measurement of the dynamics and structure of a protein under... ver más

28/02/2022

ETH Zürich

191K€

Presupuesto del proyecto: 191K€

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-02-28

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

ETH Zürich

191.15K€ | Lider

MEGACOLOR PRODUCTOS CERAMICO...

277.54K€ | Participante

UBE CORPORATION EUROPE

315.64K€ | Participante

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Duración del proyecto: 24 meses Fecha Inicio: 2020-02-25
Fecha Fin: 2022-02-28

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 191K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Nuclear Magnetic Resonance (NMR) spectroscopy is one of the leading techniques for protein structure analysis. In contrast to other methods, NMR spectroscopy allows the measurement of the dynamics and structure of a protein under nearly physiological conditions, without the need for crystallization or freezing of a sample. Recent studies on exact Nuclear Overhauser enhancements (eNOEs), carried out in the laboratory of the host professor, have enabled distance measurements in proteins by NMR with accuracy of 0.1 Å. This allows to determine structures in solution and in living cells with unprecedented resolution. This biophysical achievement creates an outstanding opportunity for a computer scientist (the fellow candidate) to develop the first-of-its-kind model/algorithm that automatically transforms raw NMR measurements into high-resolution protein structures that reveal multiple simultaneously populated conformational states in atomic detail. This problem will be tackled with the use of deep learning (DL), a novel field in machine learning that has emerged after 2010 and has revolutionized data science and artificial intelligence. The project is divided into 3 parts. First, it is planned to investigate recent advances in DL to derive a model that extracts visual information from 2D and 3D NMR spectra. Afterwards, the proposed model will be integrated into CYANA to formulate a hierarchical DL/optimization routine, which automates all steps of protein structure solving. Finally, it is planned to explore the possibility of calculating protein structures directly from NOESY spectra, which constitutes a new protocol for protein structure solving by NMR spectroscopy. Summing up, the proposed DL approach has the potential to reduce the time required to solve proteins with NMR from months/years to days, while delivering very high resolution, multi-state structures. We expect this project to open new avenues in structural biology and drug discovery.

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