H2020Индивидуална стипендия2020–2022

EM-PRIOR · Single Particle Cryo-EM Reconstruction with Convolutional Neural Networks

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

Период: 2020-08-01 → 2022-07-31
Финансиране от ЕС: 224 934 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Невронни мрежи се използват за по-точно възстановяване на 3D структури на молекули от шумни 2D изображения. Това помага за изследване на по-малки протеини, като рецепторите GPCR, които са важни за разработването на нови 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

Single Particle Cryo-EM Reconstruction with Convolutional Neural Networks

The initial objective was to explore machine learning methods that have been successful in other imaging modalities, like computed tomography, to produce more informative priors for cryo-EM structure determination. In cryo-EM data processing, the aim is to reconstruct an unknown 3D molecular structure from 2D projection images, which view the structure from unknown relative orientations. From a mathematical point of view, cryo-EM structure determination belongs to the field of ill-posed inverse problems. It is ill-posed because the high levels of noise and the many unknown parameters result in a situation where the data alone does not provide sufficient information to determine a unique solution. I proposed to use the regularization by denoising (RED) framework to inject prior knowledge into cryo-EM reconstruction to better handle the ill-posedness. The denoiser would be a deep neural network that is trained on cryo-EM data from publica databases. By tapping into the vast amounts of prior knowledge about protein structures available in public databases, the proposed methods have the potential to not only make existing cryo-EM applications better, but also to enhance the scope of cryo-EM structure determination to many more targets than currently possible. This includes important drug targets, like GPCRs, that currently are outside the size limit of what can be resolved by cryo-EM to high resolution.

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

Цел на проекта

Electron cryo-microscopy (cryo-EM) is the fastest growing technique to explore the structure of biological macromolecules. To limit radiation damage, images are recorded under low-dose conditions, which leads to high levels of experimental noise. To reduce the noise, one averages over many images, but this requires alignment and classification algorithms that are robust to the high levels of noise. When signal-to-noise ratios drop, cryo-EM 3D reconstruction algorithms become susceptible to overfitting, ultimately limiting their applicability. The algorithms can be improved by incorporating prior knowledge. The most widely used approaches in the field to date incorporate the prior knowledge that cryo-EM reconstructions are smooth in a Bayesian approach. However, in terms of information content, the smoothness prior reflects poorly compared to the vast amount of prior knowledge that structural biology has gathered in the past 50 years. I aim to develop a computational pipeline that can exploit much more of the existing knowledge about biological structures in the cryo-EM structure determination process. I will express this prior knowledge through convolutional neural networks that have been trained on many reconstructions, and use these networks in novel algorithms that optimise a regularised likelihood function. Similar approaches have excelled in image denoising and reconstruction in related areas. Preliminary results with simulated data suggest that significant improvements beyond the existing methods are possible, both in computational speed and in signal recovery capabilities. The proposed methods will enable faster computations with less user involvement, but most importantly, they will extend the applicability of cryo-EM structure determination to many more samples, alleviating the existing experimental requirements of particle size, ice thickness and sample purity.

Оригинален текст от CORDIS (на английски).

Участници

UNITED KINGDOM RESEARCH AND INNOVATION · SWINDONКоординаторОбединеното кралство