Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
Anonymous Author(s)
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Abstract
Cryo-electron microscopy (cryo-EM) is an imaging modality that provides uniqueinsights into the dynamics of proteins and other building blocks of life.
The algorithmic challenge of jointly estimating the poses, 3D structure, and conformationalheterogeneity of a biomolecule from millions of noisy and randomly oriented 2Dprojections in a computationally efficient manner, however, remains unsolved.
Ourmethod, cryoFIRE, performs ab initio heterogeneous reconstruction with unknownposes in an amortized framework, thereby avoiding the computationally expensivestep of pose search while enabling the analysis of conformational heterogeneity.
Poses and conformation are jointly estimated by an encoder while a physics-baseddecoder aggregates the images into an implicit neural representation of the conformational space.
We show that our method can provide one order of magnitudespeedup on datasets containing millions of images, without any loss of accuracy.
We validate that the joint estimation of poses and conformations can be amortizedover the size of the dataset.
For the first time, we prove that an amortized methodcan extract interpretable dynamic information from experimental datasets.
Introduction
Proteins and other biological macromolecules in the cell function through a finely-tuned choreographyof transitions between metastable conformational states.
Analyzing the structural heterogeneity ofa biomolecule is therefore critical for applications such as drug design and, more generally, forunderstanding these essential building blocks of life.
In a single particle cryo-electron microscopy(cryo-EM) experiment, a set of biomolecules whose structures sample some thermodynamic distribution are flash-frozen in a thin layer of vitreous ice and imaged with a transmission electron microscope(Fig.
1 (a)).
A cryo-EM experiment outputs a large set of unlabeled images, each containing a uniqueprojected molecule that adopts an unknown conformation and is viewed from an unknown orientation( pose ) ϕ
i =
( R i , t i ) ∈
SO (3) × R 2 (Fig. 1 (b)).
While homogeneous reconstruction methods focuson estimating the average 3D structure (the consensus volume) of the studied molecule from thecollected images, heterogeneous methods take into account structural variability and introduce avariable
z i (the conformational state ) that characterizes the electron scattering potential (the volume )
V ( ., z i ) : R 3 → R associated with observation i [5].
Given the image formation model (detailed in
Section 3.1), heterogeneous reconstruction can be seen as an inverse problem where V , z i and ϕ imust be inferred from the images Y i .
A graphical formulation of the problem is given in Fig.
1 (c).
Spurred by recent advances in data collection capabilities, the number of images collected per cryoEM experiments has been steadily increasing, now reaching millions to tens of millions [2, 17].
Established methods break down 3D reconstruction into two alternating iterative refinement steps:1) the latent pose ϕ
i and conformational states z i are first estimated from the images and the currentestimate of V , and 2) the estimation of V is then updated using the current estimates of the latent
[Figure]
variables.
The primary computational bottleneck of this approach is pose estimation, which is doneby rendering images from view points distributed on the the 5-dimensional space SO (3) × R 2 .
Although previous methods came up with accelerated branch-and-bound [13] strategies, state-of-theart methods for pose search raise two problems: (1) solving this problem for a single image is timeconsuming, especially when the images are rendered using a neural model [35] and (2) the pose needsto be solved independently for each image in the dataset and does not use the fact that similar imagesare likely associated with the similar variables.
Furthermore, most heterogeneous reconstructionalgorithms limit their applicability by relying on an upstream homogeneous reconstruction, treatingposes as known to simplify the optimization problem for z
i or due to the sheer computational cost ofpose search.
In this work, we present an amortized inference approach to heterogeneous cryo-EM reconstruction,addressing both the increasing size of datasets and the opportunity to learn complex conformationalspaces of dynamic proteins.
Our method performs ab initio reconstruction, meaning it does not needan upstream homogeneous reconstruction.
Previous methods showed that the estimation of z i couldbe amortized in heterogeneous reconstruction [33, 35, 4, 36] and that the estimation of ϕ i couldbe amortized in homogeneous reconstruction [18, 14] but no jointly amortized inference techniquewas proven to work on experimental datasets.
By leveraging a tailored loss function and using aspecialized autoencoder-like architecture, cryoFIRE (Fast heterogeneous ab Initio Reconstruction forcryo-EM) jointly amortizes the estimation of poses and conformations over the size of the dataset.
Our contributions include:
• an autoencoder-based architecture that process images 10 times faster than existing methods
and enables amortization of the runtime over the size of the dataset,
• a demonstration that our model accurately learns the structure of a low-dimensional mani-
fold in the conformational space on benchmark synthetic datasets with both discrete and
continuous heterogeneity, and
• the first instance of amortized inference for ab initio heterogeneous reconstruction of
experimental cryo-EM datasets.
Related Work
Heterogeneous reconstruction methods must jointly infer the conformational space V and the latentvariables (the poses ϕ
and the conformational states z i ).
All existing methods make this problemtractable using an iterative strategy that alternates between the estimation of V and the estimation ofthe latent variables.
They can all be differentiated depending on how latent variables are estimated [5].
Expectation-Maximization over Conformations With an Expectation-Maximization (EM) algorithm, posterior distributions over the conformational states are computed (or approximated) for eachimage at expectation step.
In cryo-EM, this method was first popularized by RELION with 3D Classification [27] which implemented a discrete class indicator z i ∈ { 1 , . . .
, K } , where K is the numberof classes defined by the user.
More recently, a continuous reconstruction method called multi-bodyrefinement was made available in RELION [16].
In this approach, a homogeneous reconstruction issegmented by the user into a set of rigid bodies, where each rigid body is free to move relative tothe others.
The relative pose (orientation and translation) of each body is subsequently estimatedthrough EM for each particle, and further reduced through Principal Component Analysis (PCA).
Various methods have been developed to estimate the conformational space as a linear deformationaround a known reference.
HEMNMA [8] uses the normal modes, or eigenvolumes, of the referenceto model its deformation and proceeds through a projection-matching-based elastic alignment of eachsingle-particle image with the reference structure to yield a reduced representation of the variabilityin the dataset. 3DVA [21] relaxes the dependence on known normal modes and instead implementsa variant of the EM algorithm for Probabilistic PCA which iteratively updates the eigenvolumesand the projection of the particles on them, until convergence, following [30].
Other approachesexplicitly estimate the deformation field mapping the reference structure to the best-fit structure foreach image.
In Herreros et al.
[7], the deformation field is defined as a linear expansion over Zernikepolynomials, with z i as their coefficients.
In 3DFlex [20], the deformation field is generated from z ithrough a neural flow generator, alleviating the linear constraint.
Formally, each z i can be seen asa point estimate that maximizes the posterior distribution over conformational states.
An in-depthreview of EM-based reconstruction method can be found in [29].
Amortization over Conformations Instead of optimizing each variable independently, amortizedinference learns a function q ψ , parameterized by ψ , that maps images Y i to probability (posterior)distributions over the space of latent variables [10].
CryoDRGN [33] introduced amortized variational inference to estimate the conformational state z i in the setting where poses are known.
IncryoDRGN [33], distributions over the conformational states are predicted by the encoder of a Variational Autoencoder (VAE) [10, 11] and the conformational space is parameterized with animplicit neural representation.
E2GMM [4] and cryoFold [36] both reconstruct a deformable atomicrepresentation of the molecule and train an encoder to associate each image with a low-dimensionalz i that characterizes the deformation.
These two methods require the initialization of the backbonestructure of the molecule.
Although these methods can be used to analyze the structural heterogeneityin a dataset, they assume the poses to be given by another reconstruction method.
In cryoDRGNBNB [34] and cryoDRGN2 [35] the poses are not given anymore but instead are estimated using anexhaustive search strategy.
The estimation of the poses therefore does not amortize over the size ofthe dataset and, in spite of a branch-and-bound approach [13], the 5D pose search remains the mostcomputationally expensive step in the pipeline.
Amortization over Poses In the context of homogeneous reconstruction, previous methods usedamortized inference to predict the poses.
In cryoVAEGAN [15], Miolane et al.
showed that thein-plane rotation could be disentangled from the contrast transfer function (CTF) parameters inthe latent space of an encoder.
CryoPoseNet [18] first demonstrated the possibility of using anautoencoder architecture with synthetic datasets.
CryoAI [14] later introduced the symmetrized lossto help the model avoid local minima and showed that homogeneous reconstruction could be done onexperimental datasets.
All of these methods, however, reconstruct a 3D consensus volume and do notaddress the question of conformational heterogeneity.
Joint Amortization Rosenbaum et al. .
[24] were the first to demonstrate heterogeneous reconstruction from unknown poses in a jointly amortized framework on synthetic data: both z i and ϕ areestimated with a variational encoder and predicted conformations are used to deform an atomic model,while predicted poses are used to compute projections analytically.
The method was only proven towork on simulated datasets.
To the best of our knowledge, no amortized inference technique wasproven to work with experimental datasets in a heterogeneous reconstruction setting with unknownposes.
Our approach (cryoFIRE) uses a conditioned neural network to implicitly represent the conformationalspace V .
An encoder amortizes the inference of both z
i and ϕ i over the size of the dataset and adifferentiable rendering pipeline generates noise-free images.
In contrast to previous techniques that jointly amortize the estimation of ϕ i and z i , cryoFIRE can perform heterogeneous reconstruction onexperimental datasets.
Methods
3.1 Image Formation Model
In single particle cryo-EM, probing electrons interact with the electrostatic potential created by themolecules embedded in a thin layer of vitreous ice (Figure 1 (a)).
During reconstruction, we assumethat this potential can be broken down into spatially bounded independent potentials ( volumes ) createdby individual molecules.
Each volume V i can be seen as a mapping from R 3 to R and is indexedby i ∈ { 1 , . .
. , N } .
We assume that the volumes { V i } i =1 ,...,N are drawn independently from aprobability distribution P V supported on a low-dimensional manifold (the conformational space ) inthe space F ( R 3 , R ) of all possible 3D potentials.
More specifically, we assume there exist d ∈ N and V : R 3 × R d → R such that P V is supported on the conformational space {V ( ., z ) , z ∈ R d } .
In the sample, each molecule is in an unknown orientation R i ∈
SO (3) ⊂ R 3 × 3 in the frame of theobserver.
The probing electron beam interacts with the electrostatic potential and its projections,
[Equation]
are considered mappings from R 2 to R .
The interaction between the beam and the lens is modeled bythe Point Spread Function (PSF) P i .
Imperfect centering of the molecule in the image is characterizedby small translations t i ∈
R 2 .
Finally, taking into account signal arising from the vitreous ice intowhich the molecules are embedded as well as the non-idealities of the lens and the detector, eachimage Y i is generally modeled as
[Equation]
where ∗ is the convolution operator, T t the t -translation kernel and η i white Gaussian noise on 143 R[32, 26].
The Fourier-Slice Theorem [3] (FST) avoids the computation of integrals and convolutions in Eq.
(2)and is also satisfied with the Hartley transform:
[Equation]
where H 2 D and H 3 D are the 2D and 3D Hartley transform operators [6].
The “slice” operator S i isdefined such that for any ˆ V : R 3 → R , [Equation] i
[Equation]
Finally, if ˆ Y i =
H 2 D
[ Y i ] and ˆ V ( ., z i ) =
H 3 D
[ V ( ., z i )] , the image formation model in Hartley spacecan be expressed as
[Equation]
where ⊙ indicates element-wise multiplication,
C i
=
H 2 D [ P i ] is the Contrast Transfer Function(CTF), ˆ T t the t -translation operator or phase-shift in Fourier space and ˆ η i represents complex white Gaussian noise on R 2 .
3.2 Overview of cryoFIRE
Fig.
2 summarizes the architecture of cryoFIRE.
Images Y i are fed into an encoder, parameterized byψ , that predicts a pose ϕ
=
( R i , t i ) and a conformational state z i .
R i is used to rotate a grid of D 23D-coordinates in Hartley space.
These coordinates are concatenated with the conformational statebefore being fed into a neural representation of the function ˆ V θ (in Hartley space), parameterized by θ .ˆ
V θ is a mapping from R 3+ d to R and can be seen as a parametric representation of the conformationalspace in Hartley domain (a manifold of dimension d ).
Queried for all k in the grid, the neuralrepresentation outputs a set of real values corresponding to a discrete sampling of the slice definedin Eq.
Based on the estimated translation t i and given CTF parameters C i , the rest of theimage formation model described in Eq.
(5) is simulated to obtain ˆ X
i , a noise-free estimation of ˆ Y i .
[Figure]
The whole forward model is refered to as Γ ψ,θ , such that ˆ X i =
Γ ψ,θ ( Y i ) .
Pairs of measured andreconstructed images are compared using the symmetrized loss (see Section 3.5) and gradients arebackpropagated throughout the differentiable model in order to optimize both the encoder and theneural representation.
3.3 Discriminative Model
In cryoFIRE, an encoder acts as a discriminative model by mapping images to estimates of ϕ i and z i .
The encoder is structured sequentially with the following components:
A Convolutional Neural Network (CNN) containing 7 convolutional layers extracts high- level visual features from images and divides the width and height of images by 32 .
The
architecture of the CNN is inspired from the first layers of VGG16 [28], known to perform
well on visual tasks.
A shared Multi-Layer Perceptron (MLP) with 2 hidden layers that outputs a feature y i of dimension 256 .
A conformation MLP that maps y i to z i .
Rotation and translation MLPs that map the concatenation of y i and z i respectively to R
i
and t i .
The pose prediction is conditioned on z i since rotations and translations are only defined for a given conformation of the molecule and z i contains, with a small number of dimensions, all the required information to determine the conformational state.
The rotation is represented in the 6-dimensional space S 2 × S 2 [37].
We experimented with both variational (predicting an approximate posterior distribution over latentvariables) and non-variational (predicting a point estimate) approaches and did not observe any significant performance difference (although the variational approach is more computationally expensive).
Full details about the architecture of the encoder are given in Supplement A.
3.4 Generative Model
The generative model is a simulation of the image formation model in Hartley space, in order tomake use of the FST (Section 3.1).
The forward pass is differentiable with respect to z i , R i and t i .
For each i , a grid of D 2 coordinates on the x-y plane in Hartley space are rotated by R i (mappedfrom S 2 × S 2 to R 3 × 3 using Gram–Schmidt orthogonalization) via a matrix multiplication.
Rotatedpoints k are positionally encoded with a bank of D/ 2 random frequencies [31] and independentlyconcatenated with the conformational state z
i before being fed into an MLP mapping R 3+ d to R .
This neural network is an implicit representation of the function ˆ V , where ˆ V ( ., z )
=
H 3 D
[ V ( ., z )] .
Additional implementation details about the neural network are given in Supplement B.
The CTF is defined by the defoci parameters and the astigmatism angle (provided by the simulator orby an external software like CTFFIND [23] for experimental datasets).
It is then applied to the sliceof size D 2 outputted by the neural representation.
Finally, the slice is element-wise multiplied by ˆ T t i ,the Hartley transform of the t i -translation kernel.
3.5 Training Procedure
In the setting where poses are unknown, one cannot distinguish, given a set of 2D projections, twomolecules with different handedness.
This is called the “handedness ambiguity” [25].
Ref.
[14]showed that this leads amortized inference techniques to get stuck in local minima where the predictedmolecule contains spurious planar symmetries.
A solution suggested to alleviate this problem is touse the symmetrized loss
[Equation]
where B is a batch of indices and R π applies an in-plane rotation of π on images.
In order to enablethe model to converge to a consensus volume, we disable the optimization of the conformation MLPat the start of the training.
During this “pose-only phase”, z i are randomly sampled from a normalized Gaussian distribution.
Results
We evaluate cryoFIRE for ab initio heterogeneous reconstruction and compare it with the state-ofthe-art method cryoDRGN2 [35].
We first validate that using an encoder to predict poses, insteadof performing an exhaustive pose search, enables us to reduce the runtime of heterogeneous reconstruction on a synthetic dataset.
We show that the encoder is able to accurately predict ϕ
i and z ifor images it has never processed during training, thereby validating the ability of an encoder-likearchitecture to amortize the runtime over the size of the dataset.
Secondly, we show that cryoFIREenables the detection of discrete heterogeneity in a dataset, by clustering images in the z i -space.
Finally, we show that our method can perform heterogeneous reconstruction of real data, a first for amethod that jointly amortizes the estimation of poses and conformations.
4.1 Runtime Improvement and Amortization
Experimental Setup We prepare three synthetic datasets of varying sizes – small (50k images),medium (500k images) and large (5M images) (Supplement C).
Images are generated using asimulation of the image formation model on ground truth volumes of the 80S ribosome.
Each datasetcontains a mix of projections with 90% sampled from the major volume and the rest from the minorvolume.
CTFs are drawn randomly with a log-normal distribution over the defocus range.
We add Gaussian noise with standard deviation σ = 1 to the normalized projections.
With cryoFIRE, wefix d = 8 and activate the conformation MLP after the model has seen 1 .
5 M images.
We compareour method with cryoDRGN2 [35] with default parameters.
Images are masked with a circle ofradius 32 pixels for pose search.
Pose search is done every 5 epochs for the small datasets and everyepoch for the medium and large datasets.
We train the models on a single NVIDIA A100 SXM440GB GPU.
Images of size D = 128 are fed by batches of maximum sizes ( 128 for cryoFIRE, 32for cryoDRGN2) and loaded on the fly using multi-threading on 16 CPUs.
The model is optimizedwith the ADAM optimizer [9] and a learning rate of 10 − 4 .
Convergence is detected by monitoringthe pose prediction accuracy.
Once convergence is reached on the train set , latent variables can beestimated on a test set of 10k images.
With cryoDRGN2, poses from the test set are estimated with arandomly-initialized pose search on the model obtained at train time.
Metrics The error on rotations is given by the median and the mean of the angular error on theview direction, in degrees. The translation error is defined as the median and mean square error of thepredicted t i , in pixels.
Before computing pose errors, a rigid 6D-body alignment is applied on theset of predicted poses to align the model into the same global reference frame as the ground truthvolume.
We perform a Principal Component Analysis (PCA) on the set of predicted
z i ’s and, lookingat the first principal component, we group images in two classes, according to the cluster they fallinto.
The confusion error is defined as the ratio of misclassified images (images that fall in the wrong
Each dataset contains a mix of images, 90% from the major volume and 10% from the minor volume. After convergence on a train set, each method estimates the latent variables ( R i , t i , z i ) on a test set. We report the rotation accuracy (Rot. in degrees, ↓ ), the translation accuracy (Trans.
in pixels, ↓ ), the confusion error ( ↓ ) and the resolution (Res.
in pixels, ↓ ).
Convergence is detected on the pose accuracy (ep.
= epochs).
[Table]
cluster).
After convergence, we generate two volumes by taking the centroids of the clusters in z ispace and conditioning the neural network ˆ V θ on them.
We report the resolution (in pixels) obtainedon the major ( 90% ) and on the minor ( 10% ) volumes.
The reported resolution is the inverse of themaximum frequency for which the Fourier Shell Correlation is above 0 .
Results We report the runtime and quantitative metrics in Table 1.
At train time, the averagetime for processing one image is 18 ms (5:07h / ( 2 × 500 000 )) with cryoDRGN2 vs. 1 .
7 ms(1:36h / ( 7 × 500 000 ))
for cryoFIRE.
(The runtime of cryoDRGN2 is lower with the small datasetbecause pose search is only done every 5 epochs).
At test time, only the encoder of cryoFIRE isused, which further decreases the runtime per image ( 1 . 1 ms).
The accuracy obtained at test time onϕ i and z i validates that our method effectively amortizes the estimation of latent variables, and theerrors on the test sets decrease with the size of the dataset provided for training.
The accuracy ofpredicted poses is slightly worse with cryoFIRE than with an exhaustive pose search strategy (see
Supplement E for a comparison with a full non-amortized method [22]).
The quantitative resolutionof the reconstructed volumes remains however very similar with both methods (see Supplement D fora qualitative comparison).
4.2 Heterogeneous Reconstruction of Synthetic Datasets
Experimental Setup We prepare three heterogeneous datasets of 100 k images of the Plasmodiumfalciparum 80S ribosome (Supplement C).
The three datasets contain an equal mix of projectionsfrom 2, 3 or 10 different ground truth volumes, generated sequentially along a reaction path thatcorresponds to a rotation of the small ribosomal subunit relative to the large ribosomal subunitidentified in [33].
The image formation model is simulated with the same parameters as in Section 4.1. We run cryoFIRE with d = 8 and perform a PCA on the set of predicted z i ’s after 50 epochs.
Results In Fig.
3, we plot the first principal component (PC1) of the set of z
i ’s vs. the groundtruth index of the volume each image was generated from.
With the dataset containing K , imagescan be clustered in K groups, based on the value of PC1 ( K ∈ { 2 , 3 } ).
Looking for the optimalthresholds on PC1, we compute a confusion error of 9 × 10 − 2 (resp.
5 × 10 − 3 ) on the 2 classes(resp.
3 classes ) dataset.
On the 10 classes dataset, the 10 conformations cannot be separated but thevalue of PC1 embeds information about the movement of the ribosome, as indicated by the Spearmancorrelation [12].
Sampling different values for PC1 (and setting the other components to zero), wecan use the neural representation ˆ V θ to generate a set of volumes from a set of conformational states [Figure] z i .
Qualitative results are given in Fig.
3 (d), showing an accurate reconstruction of the movement ofa subunit of the ribosome.
4.3 Heterogeneous Reconstruction of an Experimental Dataset
Experimental Setup We use the publicly available dataset EMPIAR-10180 [19] of a pre-catalyticspliceosome (Supplement C).
We run cryoFIRE with d = 8 , activate the conformation MLP after 30epochs and train for a total of 100 epochs.
Results from cryoDRGN2 [35] and cryoDRGN-BNB [34]on the same dataset are obtained from [35].
Results The first two components of a PCA on the set of predicted
z i ’s is shown in Fig.4.
Bytraversing the conformational space along the direction of PC1 and generating volumes on set of 5points, cryoFIRE generates a trajectory, showing a large flexing motion of the spliceosome.
CryoFIREqualitatively recovers the non-uniform distribution of viewing directions, avoids the local minimacryoDRGN-BNB falls into, and reconstructs volumes which qualitatively match the state of the art(Supplement D).
Discussion
As the resolving power of a sampling method scales with the size of the dataset, an increasing numberof images need to be collected in cryo-EM in order to extract meaningful structural information.
CryoFIRE answers the need for reconstruction methods that scale appropriately with the datasetsize: heterogeneous ab initio reconstruction on a dataset of 5M images can be performed within 2hours.
CryoFIRE is also the first reconstruction method to perform joint amortization of poses andconformations on an experimental cryo-EM dataset, opening the door to fast analysis of structuralheterogeneity on real datasets. CryoFIRE successfully reconstructs heterogeneous distributions.
However, the interpretability ofthe conformational space remains an open question.
Since distances in this space are not meaningful ( z i conditions the volume via a nonlinear decoder), the probability distribution cannot bestraightforwardly interpreted in that space.
As shown in our experiments, in the case of discreteheterogeneity, general quantities like the number of states or the relative populations of the states canbe reliably inferred from the conformational space by clustering the predicted conformational states.
In the case of continuous heterogeneity, dimensionality reduction methods like PCA can highlightdirections of maximal variance and, by visual inspection of the reconstructed volumes, cryoFIRE canhelp understanding the main degrees of freedom of a continuously deformable molecule.
However,“small” and “large” deformations can stem from similar changes in the conformational space andno physically interpretable notion of distance is currently associated with the conformational space:providing this space with a physically interpretable metric is an interesting avenue for future work,that could lead to exploiting to a larger extent the information embedded in the probability distributionof the conformational states. [Figure] [Figure] [Figure]
It is worth highlighting that the parameterization proposed here is not uniquely defined: a rotationof the molecule can equivalently be represented by R i or by a change of conformation state z i .
Empirically, cryoFIRE decorrelates poses and conformation by relying on a “pose-only phase” atthe beginning of training and conditioning the pose estimation on the estimated z i in the encoder,but nothing explicitly prevents z i from containing information about R i .
Future work could exploreimprovements in the optimization procedure to further enforce minimal mutual information betweenposes and conformation.
Finally, we note that the accuracy of pose estimation (especially on translations) is lower withcryoFIRE than with exhaustive pose search methods.
Inaccurate translation prediction can lead to“blurry” reconstructions and therefore limits the resolution of reconstructed volumes.
This is a directreflection of the fact that amortization maximizes a non-tight lower-bound of the likelihood, allowingfor faster inference at the cost of accuracy [5].
Future work could investigate a hybrid approach thatwould estimate poses with an encoder at the beginning of training and switch, at the end, to a localpose search initialized from encoder-estimated poses.
Broader Impacts Statement While biomolecular reconstruction from cryo-EM images does notpresent any obvious ethical challenges, its consequences and its current development as a fieldcalls for careful consideration.
The cryo-EM field is generating data at a rapidly increasing pace,potentially leading to wasteful storage and inefficient computing.
The design of more efficientmethods such as CryoFIRE provides a path to mitigate sustainability risks.
In order to better comparemodern reconstruction methods, careful attention must be paid toward designing common datasetsand evaluation metrics.
By providing an open-source implementation of CryoFIRE upon publication,together with benchmark metrics, we hope to make cryo-EM research accessible to a broader classof researchers.
We also acknowledge that, by pushing further the boundaries of biological research,our method may facilitate the development of harmful biologics, however this is outweighed by itssignificant societal benefits, such as enabling antibody-vaccine development [1].
We encourage thecryo-EM community to play wisely with cryoFIRE.
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