CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM<i>via</i>Deep Adversarial Learning

Gupta, Harshit; McCann, Michael T.; Donati, Laurène; Unser, Michaël

doi:10.1101/2020.03.20.001016

Cited by 13 publications

(14 citation statements)

References 59 publications

(85 reference statements)

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“…Deep learning has revolutionized the field of artificial intelligence and its impact has been felt in many others including cryo-EM. Deep learning in cryo-EM was first applied to the problem of particle picking [9][10][11] and since then, it has evolved to deal with other questions such as map reconstruction 12,13 , map segmentation 14,15 , or local resolution determination 16,17 . As in most of those methods, our approach relies on a convolutional neural network (CNN) that is trained on massive quantities of data.…”

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confidence: 99%

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

et al. 2021

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Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on global B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to reduce noise levels and obtain more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase.

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confidence: 99%

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

et al. 2021

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“…Our method is unsupervised as it only relies on the given observations and does not use large paired datasets for training. Similar to [1], our method aims to find x and p such that the distribution of the partial noisy measurements generated from (1) matches the real measurements {ξ j real } N j=1 . To this end, we use a generative adversarial network (GAN) [17].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we train a generator discriminator pair, where the discriminator tries to distinguish between the measurements output by the generator and the real ones. Our approach is inspired by CryoGAN [1] in which the goal is to reconstruct a 3D structure given 2D noisy projection images from unknown projection views. Unlike CryoGAN, we assume the distribution of the latent variables, i.e.…”

Section: Introductionmentioning

confidence: 99%

MSR-GAN: Multi-Segment Reconstruction via Adversarial Learning

Zehni

Zhao

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Multi-segment reconstruction (MSR) is the problem of estimating a signal given noisy partial observations. Here each observation corresponds to a randomly located segment of the signal. While previous works address this problem using template or momentmatching, in this paper we address MSR from an unsupervised adversarial learning standpoint, named MSR-GAN. We formulate MSR as a distribution matching problem where the goal is to recover the signal and the probability distribution of the segments such that the distribution of the generated measurements following a known forward model is close to the real observations. This is achieved once a min-max optimization involving a generator-discriminator pair is solved. MSR-GAN is mainly inspired by CryoGAN [1]. However, in MSR-GAN we no longer assume the probability distribution of the latent variables, i.e. segment locations, is given and seek to recover it alongside the unknown signal. For this purpose, we show that the loss at the generator side originally is non-differentiable with respect to the segment distribution. Thus, we propose to approximate it using Gumbel-Softmax reparametrization trick. Our proposed solution is generalizable to a wide range of inverse problems. Our simulation results and comparison with various baselines verify the potential of our approach in different settings.

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“…Deep learning has revolutionized the field of Artificial Intelligence and its impact has been felt in many others including cryo-EM. Deep learning in cryo-EM was firstly applied for the problem of particle picking (Wagner et al, 2019;Wang et al, 2016;Zhu et al, 2017) and since then, it has evolved to deal with other questions such as map reconstruction (Gupta et al, 2020;Zhong et al, 2019), map segmentation (Maddhuri Venkata Subramaniya et al, 2019;Si et al, 2020) or local resolution determination (Avramov et al, 2019;Ramírez-Aportela et al, 2019). As in most of those methods, our approach relies on a convolutional neural network (CNN) that is trained on massive quantities of data.…”

Section: Introductionmentioning

confidence: 99%

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

Sánchez-García

Gómez-Blanco

Cuervo

et al. 2020

Preprint

201

218

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Cryo-electron microscopy (cryo-EM) maps are among the most valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed before modeling in order to improve their interpretability. To that end, approaches based on B-factor correction are the most popular choices, yet they suffer from some limitations such as the fact that the correction is applied globally, ignoring the presence of heterogeneity in the map local quality that cryo-EM reconstructions tend to exhibit. With the aim of overcoming these limitations, here we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental cryo-EM maps and maps sharpened by LocScape using their respective atomic models, DeepEMhancer has automatically learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer has been evaluated on a testing set of 20 different experimental maps, showing its ability to obtain much cleaner and detailed versions of the experimental maps, thus, improving their interpretability. Additionally, we have illustrated the benefits of DeepEMhancer with a use case in which the structure of the SARS-CoV 2 RNA polymerase is improved.

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CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EMviaDeep Adversarial Learning

Cited by 13 publications

References 59 publications

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

MSR-GAN: Multi-Segment Reconstruction via Adversarial Learning

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

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