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Article ; Online: 3DFlex: determining structure and motion of flexible proteins from cryo-EM.

Punjani, Ali / Fleet, David J

2023 Volume 20, Issue 6, Page(s) 860–870

Abstract: Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible ... ...

Abstract	Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein's motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVβ8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape.
MeSH term(s)	Humans ; Cryoelectron Microscopy/methods ; COVID-19/metabolism ; SARS-CoV-2 ; Proteins/chemistry ; Ribosomes/metabolism
Chemical Substances	Proteins
Language	English
Publishing date	2023-05-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	2169522-2
ISSN	1548-7105 ; 1548-7091
ISSN (online)	1548-7105
ISSN	1548-7091
DOI	10.1038/s41592-023-01853-8
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Article ; Online: Hand Grasp Classification in Egocentric Video After Cervical Spinal Cord Injury.

Dousty, Mehdy / Fleet, David J / Zariffa, Jose

IEEE journal of biomedical and health informatics

2024 Volume 28, Issue 2, Page(s) 645–654

Abstract: Objective: The hand function of individuals with spinal cord injury (SCI) plays a crucial role in their independence and quality of life. Wearable cameras provide an opportunity to analyze hand function in non-clinical environments. Summarizing the ... ...

Abstract	Objective: The hand function of individuals with spinal cord injury (SCI) plays a crucial role in their independence and quality of life. Wearable cameras provide an opportunity to analyze hand function in non-clinical environments. Summarizing the video data and documenting dominant hand grasps and their usage frequency would allow clinicians to quickly and precisely analyze hand function. Method: We introduce a new hierarchical model to summarize the grasping strategies of individuals with SCI at home. The first level classifies hand-object interaction using hand-object contact estimation. We developed a new deep model in the second level by incorporating hand postures and hand-object contact points using contextual information. Results: In the first hierarchical level, a mean of 86% ±1.0% was achieved among 17 participants. At the grasp classification level, the mean average accuracy was 66.2 ±12.9%. The grasp classifier's performance was highly dependent on the participants, with accuracy varying from 41% to 78%. The highest grasp classification accuracy was obtained for the model with smoothed grasp classification, using a ResNet50 backbone architecture for the contextual head and a temporal pose head. Discussion: We introduce a novel algorithm that, for the first time, enables clinicians to analyze the quantity and type of hand movements in individuals with spinal cord injury at home. The algorithm can find applications in other research fields, including robotics, and most neurological diseases that affect hand function, notably, stroke and Parkinson's.
MeSH term(s)	Humans ; Quality of Life ; Cervical Cord ; Hand ; Spinal Cord Injuries ; Hand Strength
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2023.3269692
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Article ; Online: 3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM.

Punjani, Ali / Fleet, David J

Journal of structural biology

2021 Volume 213, Issue 2, Page(s) 107702

Abstract: Single particle cryo-EM excels in determining static structures of protein molecules, but existing 3D reconstruction methods have been ineffective in modelling flexible proteins. We introduce 3D variability analysis (3DVA), an algorithm that fits a ... ...

Abstract	Single particle cryo-EM excels in determining static structures of protein molecules, but existing 3D reconstruction methods have been ineffective in modelling flexible proteins. We introduce 3D variability analysis (3DVA), an algorithm that fits a linear subspace model of conformational change to cryo-EM data at high resolution. 3DVA enables the resolution and visualization of detailed molecular motions of both large and small proteins, revealing new biological insight from single particle cryo-EM data. Experimental results demonstrate the ability of 3DVA to resolve multiple flexible motions of α-helices in the sub-50 kDa transmembrane domain of a GPCR complex, bending modes of a sodium ion channel, five types of symmetric and symmetry-breaking flexibility in a proteasome, large motions in a spliceosome complex, and discrete conformational states of a ribosome assembly. 3DVA is implemented in the cryoSPARC software package.
MeSH term(s)	Algorithms ; Archaeal Proteins/chemistry ; Cryoelectron Microscopy/methods ; Databases, Protein ; Endopeptidases/chemistry ; Imaging, Three-Dimensional/methods ; NAV1.7 Voltage-Gated Sodium Channel/chemistry ; NAV1.7 Voltage-Gated Sodium Channel/metabolism ; Plasmodium falciparum/chemistry ; Receptors, Cannabinoid/chemistry ; Ribosome Subunits, Large, Bacterial/chemistry ; Ribosomes/chemistry ; Signal-To-Noise Ratio ; Spliceosomes/chemistry
Chemical Substances	Archaeal Proteins ; NAV1.7 Voltage-Gated Sodium Channel ; Receptors, Cannabinoid ; Endopeptidases (EC 3.4.-) ; proteasome, Thermoplasma (EC 3.4.99.-)
Language	English
Publishing date	2021-02-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	1032718-6
ISSN	1095-8657 ; 1047-8477
ISSN (online)	1095-8657
ISSN	1047-8477
DOI	10.1016/j.jsb.2021.107702
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Article ; Online: Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections.

Shi, Bin / Zhang, Kevin / Fleet, David J / McLeod, Robert A / Dwayne Miller, R J / Howe, Jane Y

Journal of structural biology

2024 Volume 216, Issue 2, Page(s) 108073

Abstract: Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruction. Recently, deep neural networks, e.g., CryoDRGN, have ... ...

Abstract	Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruction. Recently, deep neural networks, e.g., CryoDRGN, have demonstrated conformational and compositional heterogeneity of complexes. However, the lack of ground-truth conformations poses a challenge to assess the performance of heterogeneity analysis methods. In this work, variational autoencoders (VAE) with three types of deep generative priors were learned for latent variable inference and heterogeneous 3D reconstruction via Bayesian inference. More specifically, VAEs with "Variational Mixture of Posteriors" priors (VampPrior-SPR), non-parametric exemplar-based priors (ExemplarPrior-SPR) and priors from latent score-based generative models (LSGM-SPR) were quantitatively compared with CryoDRGN. We built four simulated datasets composed of hypothetical continuous conformation or discrete states of the hERG K + channel. Empirical and quantitative comparisons of inferred latent representations were performed with affine-transformation-based metrics. These models with more informative priors gave better regularized, interpretable factorized latent representations with better conserved pairwise distances, less deformed latent distributions and lower within-cluster variances. They were also tested on experimental datasets to resolve compositional and conformational heterogeneity (50S ribosome assembly, cowpea chlorotic mottle virus, and pre-catalytic spliceosome) with comparable high resolution. Codes and data are available: https://github.com/benjamin3344/DGP-SPR.
Language	English
Publishing date	2024-03-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	1032718-6
ISSN	1095-8657 ; 1047-8477
ISSN (online)	1095-8657
ISSN	1047-8477
DOI	10.1016/j.jsb.2024.108073
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Article ; Online: Grasp Analysis in the Home Environment as a Measure of Hand Function After Cervical Spinal Cord Injury.

Dousty, Mehdy / Bandini, Andrea / Eftekhar, Parvin / Fleet, David J / Zariffa, José

Neurorehabilitation and neural repair

2023 Volume 37, Issue 7, Page(s) 466–474

Abstract: Background: Following a spinal cord injury, regaining hand function is a top priority. Current hand assessments are conducted in clinics, which may not fully represent real-world hand function. Grasp strategies used in the home environment are an ... ...

Abstract	Background: Following a spinal cord injury, regaining hand function is a top priority. Current hand assessments are conducted in clinics, which may not fully represent real-world hand function. Grasp strategies used in the home environment are an important consideration when examining the impact of rehabilitation interventions. Objective: The main objective of this study is to investigate the relationship between grasp use at home and clinical scores. Method: We used a previously collected dataset in which 21 individuals with spinal cord injuries (SCI) recorded egocentric video while performing activities of daily living in their homes. We manually annotated 4432 hand-object interactions into power, precision, intermediate, and non-prehensile grasps. We examined the distributions of grasp types used and their relationships with clinical assessments. Results: Moderate to strong correlations were obtained between reliance on power grasp and the Spinal Cord Independence Measure III (SCIM; Conclusion: The types of grasp types used in naturalistic activities at home are related to upper limb impairment after cervical SCI. This study provides the first direct demonstration of the importance of hand grasp analysis in the home environment.
MeSH term(s)	Humans ; Quadriplegia/rehabilitation ; Activities of Daily Living ; Cervical Cord ; Home Environment ; Spinal Cord Injuries ; Hand Strength ; Upper Extremity
Language	English
Publishing date	2023-06-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491637-x
ISSN	1552-6844 ; 1545-9683 ; 0888-4390
ISSN (online)	1552-6844
ISSN	1545-9683 ; 0888-4390
DOI	10.1177/15459683231177601
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Zs.B 3096: Show issues

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Article ; Online: Image Super-Resolution via Iterative Refinement.

Saharia, Chitwan / Ho, Jonathan / Chan, William / Salimans, Tim / Fleet, David J / Norouzi, Mohammad

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 4, Page(s) 4713–4726

Abstract: We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a ... ...

Abstract	We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8× face super-resolution task on CelebA-HQ for which SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GAN baselines do not exceed a fool rate of 34%. We evaluate SR3 on a 4× super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images. We further show the effectiveness of SR3 in cascaded image generation, where a generative model is chained with super-resolution models to synthesize high-resolution images with competitive FID scores on the class-conditional 256×256 ImageNet generation challenge.
Language	English
Publishing date	2023-03-07
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2022.3204461
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Monocular Depth Estimation using Diffusion Models

Saxena, Saurabh / Kar, Abhishek / Norouzi, Mohammad / Fleet, David J.

2023

Abstract: We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation. To that end, we introduce innovations to address problems arising due to noisy, incomplete depth maps in ... ...

Abstract	We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation. To that end, we introduce innovations to address problems arising due to noisy, incomplete depth maps in training data, including step-unrolled denoising diffusion, an $L_1$ loss, and depth infilling during training. To cope with the limited availability of data for supervised training, we leverage pre-training on self-supervised image-to-image translation tasks. Despite the simplicity of the approach, with a generic loss and architecture, our DepthGen model achieves SOTA performance on the indoor NYU dataset, and near SOTA results on the outdoor KITTI dataset. Further, with a multimodal posterior, DepthGen naturally represents depth ambiguity (e.g., from transparent surfaces), and its zero-shot performance combined with depth imputation, enable a simple but effective text-to-3D pipeline. Project page: https://depth-gen.github.io
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-02-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Clark, Kevin / Vicol, Paul / Swersky, Kevin / Fleet, David J

2023

Abstract: We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-tuning diffusion models to maximize differentiable reward functions, such as scores from human preference models. We first show that it is possible to backpropagate the ... ...

Abstract	We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-tuning diffusion models to maximize differentiable reward functions, such as scores from human preference models. We first show that it is possible to backpropagate the reward function gradient through the full sampling procedure, and that doing so achieves strong performance on a variety of rewards, outperforming reinforcement learning-based approaches. We then propose more efficient variants of DRaFT: DRaFT-K, which truncates backpropagation to only the last K steps of sampling, and DRaFT-LV, which obtains lower-variance gradient estimates for the case when K=1. We show that our methods work well for a variety of reward functions and can be used to substantially improve the aesthetic quality of images generated by Stable Diffusion 1.4. Finally, we draw connections between our approach and prior work, providing a unifying perspective on the design space of gradient-based fine-tuning algorithms.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Publishing date	2023-09-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction.

Punjani, Ali / Zhang, Haowei / Fleet, David J

Nature methods

2020 Volume 17, Issue 12, Page(s) 1214–1221

Abstract: Cryogenic electron microscopy (cryo-EM) is widely used to study biological macromolecules that comprise regions with disorder, flexibility or partial occupancy. For example, membrane proteins are often kept in solution with detergent micelles and lipid ... ...

Abstract	Cryogenic electron microscopy (cryo-EM) is widely used to study biological macromolecules that comprise regions with disorder, flexibility or partial occupancy. For example, membrane proteins are often kept in solution with detergent micelles and lipid nanodiscs that are locally disordered. Such spatial variability negatively impacts computational three-dimensional (3D) reconstruction with existing iterative refinement algorithms that assume rigidity. We introduce non-uniform refinement, an algorithm based on cross-validation optimization, which automatically regularizes 3D density maps during refinement to account for spatial variability. Unlike common shift-invariant regularizers, non-uniform refinement systematically removes noise from disordered regions, while retaining signal useful for aligning particle images, yielding dramatically improved resolution and 3D map quality in many cases. We obtain high-resolution reconstructions for multiple membrane proteins as small as 100 kDa, demonstrating increased effectiveness of cryo-EM for this class of targets critical in structural biology and drug discovery. Non-uniform refinement is implemented in the cryoSPARC software package.
MeSH term(s)	Algorithms ; Cryoelectron Microscopy/methods ; Imaging, Three-Dimensional/methods ; Intrinsically Disordered Proteins/analysis ; Membrane Proteins/analysis ; Software
Chemical Substances	Intrinsically Disordered Proteins ; Membrane Proteins
Language	English
Publishing date	2020-11-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2169522-2
ISSN	1548-7105 ; 1548-7091
ISSN (online)	1548-7105
ISSN	1548-7091
DOI	10.1038/s41592-020-00990-8
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Zs.A 6022: Show issues

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Article ; Online: 3D Variability Analysis: Directly resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM images

Punjani, Ali / Fleet, David J.

bioRxiv

Abstract: Proteins are dynamic molecules that constitute the molecular machinery of living cells. Single particle cryo-EM excels as an imaging technique to solve static structures of proteins, but existing computational 3D reconstruction methods for cryo-EM data ... ...

Abstract	Proteins are dynamic molecules that constitute the molecular machinery of living cells. Single particle cryo-EM excels as an imaging technique to solve static structures of proteins, but existing computational 3D reconstruction methods for cryo-EM data have not been effective in practice for solving structures or modelling motion of flexible proteins. We introduce 3D variability analysis (3DVA), an algorithm that models the conformational landscape of a molecule as a linear subspace, and is able to fit the model to experimental cryo-EM data at high resolution. 3DVA makes it possible, for the first time, to resolve and visualize detailed molecular motions of both large and small proteins. Through extensive experimental results on cryo-EM data, we demonstrate the ability of 3DVA to resolve multiple flexible motions of α-helices in the sub-50 kDa transmembrane domain of a GPCR complex, bending modes of a sodium ion channel, five types of symmetric and symmetry-breaking flexibility in a proteasome, and large motions in a spliceosome complex. We also show that 3DVA can uncover discrete conformational states of heterogenous mixtures of ribosome particles from a single sample. The results demonstrate that 3DVA enables new biological insight to be extracted from single particle cryo-EM data. 3DVA is implemented in the cryoSPARC software package, and has already been used successfully in several notable structural studies.
Keywords	covid19
Publisher	BioRxiv
Document type	Article ; Online
DOI	10.1101/2020.04.08.032466
Database	COVID19

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