H2020Индивидуална стипендия2020–2022

DeepNOE · Leveraging deep learning for protein structure solving at ultra-high resolution on the basis of NMR measurements with exact nuclear Overhauser enhancement

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-03-01 → 2022-02-28
Финансиране от ЕС: 191 149 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Дълбокото обучение се прилага за автоматизиране на анализа на данни от ЯМР спектроскопия, за да се определят точните структури на протеините. Това съкращава времето за обработка на данните и помага за по-бързото разбиране на биологичните процеси и разработването на лекарства.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

DeepNOE: Leveraging deep learning for protein structure solving at ultra-high resolution on the basis of NMR measurements with exact nuclear Overhauser enhancement

Development of new techniques that facilitate protein structure studies is one of the major undertakings in molecular biology. All key techniques, namely X-ray crystallography (X-ray), Electron Microscopy (EM) and Nuclear Magnetic Resonance (NMR) spectroscopy, contributed extensively to our fundamental understanding of biological processes, which govern the organization of living organisms at the molecular level. Although the above methods have been used successfully in research for decades, there are still major open problems, which offer an opportunity for their further improvement. In this research project, we placed full emphasis on NMR spectroscopy. This technique has certain characteristics, which makes it indispensable in molecular biology research. It allows protein structures to be solved in nearly physiological conditions, and provides insight into dynamics and interactions at the atomic level. Additionally, NMR spectroscopy features high-precision interatomic distance measurements (up to 0.1 Å), making it possible to reveal multiple simultaneously populated conformational states of a protein. One of the main limitations of NMR spectroscopy is the tedious data analysis process. It takes weeks or months of a trained expert’s work to transform the set of measured spectra into a protein structure model. This bottleneck not only reduces the throughput of the experimental work, but also makes certain studies prohibitively expensive. Automation of this process is an open problem, which has been formulated in the field over 30 years ago. Its solution could emerge as a powerful tool for the elucidation of protein structure and dynamics, opening new avenues in structural biology and structure-based drug discovery. The primary objective of the DeepNOE project was to address this long-lasting challenge, by combining deep learning with methods implemented in the existing software package CYANA. Over the course of the project, we managed to propose the first comprehensive solution to this problem. Our new method analyses NMR spectra strictly without human intervention, making it possible to obtain results within hours after the measurement has been finished.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH · ZuerichКоординаторШвейцария