CiCLE 単粒子解析リモート講習会
2021/06/24-25

Tutorial of cryoSPARC : Innexin-6
Takeshi Kawabata (Protein Research Foundation, Osaka University)
川端 猛 （蛋白質研究奨励会・大阪大学）

2021/06/24-25
1Data processing for EM Single Particle Analysis
INX-6

2D images

3D reconstruction

EMPIAR-10291
300 images
3710 x 3838 x 1; 32 bit real

15.9 GBEMPIAREMPIAR-PDBj

300 movies
3710 x 3838 x 30; 32 bit real

478 GB(not registered in EMPIAR.
personal communication)

Atomic modeling

3D image

Atomic model

180 x 180 x 180 voxels

20840 atoms

emd_9973.map

22.24 MB

EMDB

(3.6Å)

PDB_ID: 6kfh

1.7 MB

wwPDB

Summary of innexin-6 EM data
Batuujin Burendei, Ruriko Shinozaki, Masakatsu Watanabe, Tohru Terada, Kazutoshi Tani,*, Yoshinori Fujiyoshi and Atsunori Oshima.
Cryo-EM structures of undocked innexin-6 hemichannels in phospholipids. Science Advances 12 Feb 2020: Vol. 6, no. 7, eaax3157
DOI: 10.1126/sciadv.aax3157

EMPIAREMDB Resol PDB

molecules

Details of EMPIAR

Data size of EMPIAR entry

ution

10289

9971

3.8
Å

6kff

WT INX-6
in ananodisc

Motion corrected 2D projection
images of innexin-6 gap
junction hemichannels in
nanodiscs.

49.5 GB. 933 *.mrc files.
Each mrc files has 3710 x
3838 x 1 pixels.

10290

9972

3.8
Å

6kfg

WT INX-6
in adetergent

Motion corrected 2D projection
images of innexin-6 gap
junction hemichannels in
detergent.

26.4 GB. 497 *.mrc files.
Each mrc files has 3710 x
3838 x 1 pixels.

10291

9973

3.6
Å

6kfh

WT INX6ΔN in a
nanodisc

Motion corrected 2D projection
images of N-terminal deleted
innexin-6 gap junction
hemichannels in nanodisc.

15.9 GB. 300 *.mrc files.
Each mrc files has 3710 x
3838 x 1 pixels.

・EMPIARに登録されているのはMotionCorrection後の静止画像のみ
・undocked hemichannelはEMPIAR, EMDB,PDBで公開。Docked hemichannelは、EMDB (9570,
9571), PDB(5h1q, 5h1r)で公開されているが、EMPIARにはデータがない。
3

JEM-3000SFF(JEOL)
The data were collected using a JEM-3000SFF (JEOL) electron microscope at 300 kV
equipped with a K2 summit direct electron detector camera (Gatan).
Burendei, B.,Shinozaki, R.,Watanabe, M.,Terada, T.,Tani, K.,Fujiyoshi, Y.,Oshima, A. Sci Adv, 6:eaax3157-eaax3157, 2020

Gatan社製K2 Summit直接検出型カメラ+

4

Innexin-6 : docked and undocked states
Structural component of the gap junctions.

Docked innexin-6

117 A

185 A

Undocked innexin-6
hemichannel
EMPIAR-10291

117 A

EMD-9973/PDB_ID:6kfh
Burendei, B.,Shinozaki, R.,Watanabe,
M.,Terada, T.,Tani, K.,Fujiyoshi, Y.,Oshima, A.
Sci Adv, 6:eaax3157-eaax3157, 2020

EMD-9570/PDB_ID:5h1q

EMD-9571/PDB_ID:5h1r

Oshima, A.,Tani, K.,Fujiyoshi, Y.Nat Commun, 7:13681-13681,
5ギャップ結合 (gap junction)
ギャップ結合（ギャップけつごう、英: Gap junction）は、隣り合う上皮細胞をつなぎ、水溶性の小さいイオ
ンや分子を通過させる細胞間結合のこと。並んだ2つの細胞の細胞膜にはコネクソンと呼ばれるタンパク複合体の末端が複数並んでおり、橋渡し構造をなしている。このコネクソンがチャネルとなり、ここを通って無機イオンや小さい水溶性分子が隣接細胞の細胞質から細胞質へと直接移動することができる。
また、細胞同士を電気的に結合するため、心筋組織などの興奮伝播にも関わっている。
Gap junctions are a specialized intercellular connection between a multitude of animal cell-types. They directly
connect the cytoplasm of two cells, which allows various molecules, ions and electrical impulses to directly passthrough a regulated gate between cells. One gap junction channel is composed of two connexons (orhemichannels), which connect across the intercellular space. Gap junctions are analogous to
the plasmodesmata that join plant cells.

Wikipediaからの引用

6

117 A

Undocked innexin-6 hemichannel

EMD-9971/PDB_ID:6kff
EMPIAR-10289
WT INX-6 in a nanodisc

EMD-9972/PDB_ID:6kfg
EMPIAR-10290
WT INX-6 in a detergent

EMD-9973/PDB_ID:6kfh
EMPIAR-10291
7
WT INX-6ΔN in a nanodisc

nanodisc

PDB_ID: 1av1
Apolipoprotein A-1(APOA1_HUMAN)

To investigate the structure in a lipid bilayer,
we reconstituted undocked WT INX-6
hemichannels in nanodiscs using themembrane scaffold protein 2N2 (MSP2N2)
and 1-palmitoyl-2-oleoyl-sn-glycero-3phosphocholine (POPC), as confirmed by
Coomassie brilliant blue–stained gel andnegatively stained electron micrographs (fig
S1A).
Burendei, B.,Shinozaki, R.,Watanabe, M.,Terada,
T.,Tani, K.,Fujiyoshi, Y.,Oshima, A. 8
Sci Adv, 6:eaax3157-eaax3157, 2020EMPIAR-10289
WT INX-6 in nanodisc
INX6hemiNano001

Pixel_width: 1.232Å
After CtfFind,
SigmaContrast:3
Lowpass filter 10 Å

These long circles
are not proteins.
micelle ? Nanodiscs ?

9

EMPIAR-10290
WT INX-6 in detergent
INX6hemiDet002

Pixel_width: 1.232Å
After CtfFind,
SigmaContrast:3
Lowpass filter 10 Å

10

EMPIAR-10291
WT INX-6DN in nanodisc
INX6hemiDet001

Pixel_width: 1.232Å
After CtfFind,
SigmaContrast:3
Lowpass filter 10 Å

11Download tutorial data
1) Googled by “EMPIAR PDBj”

2) Click [Workshop] at the header

3) Download “EMPIAR-10291_50mic.tar” (2.7 GB)

12cryoSPARC プログラム• トロント大学 計算機科学科で2017年に最初の版が公開・出版• 現在、Vector InstituteとStructura Biotechnology Inc.の二つの企業によって、開発・配布・販売• アカデミックな利用は無料だが、商用目的は有料• ソースは非公開• GPU、複数コア、複数ノードによる並列計算環境を想定• Webサーバから、計算ジョブを投入するインターフェース

13

Structura Biotechnology Inc.

https://structura.bio

14https://www.youtube.com/watch?v=eRPue0-Pkw4&t=3152s

15https://www.youtube.com/watch?v=eRPue0-Pkw4&t=3152s

16Articles of cryoSPARC
ArticlesComment

Punjani A, Rubinstein JL, Fleet DJ, Brubaker MA.
cryoSPARC: algorithms for rapid unsupervised cryo-EM structure
determination. Nat Methods. 2017 Mar;14(3):290-296. doi:
10.1038/nmeth.4169.

Stochastic Gradient
Descent

Punjani A, Brubaker MA, Fleet DJ. Building Proteins in a Day:
Efficient 3D Molecular Structure Estimation with Electron
Cryomicroscopy. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2017, vol. 39, no. 4, 706-718, 1 April 2017, doi:
10.1109/TPAMI.2016.2627573.
Punjani A, Zhang H, Fleet DJ. Non-uniform refinement: adaptive
regularization improves single-particle cryo-EM reconstruction. Nat
Methods. 2020 Dec;17(12):1214-1221. doi: 10.1038/s41592-02000990-8.

Non-uniform refinement

Punjani A, Fleet DJ. 3D variability analysis: Resolving continuous
flexibility and discrete heterogeneity from single particle cryo-EM. J
Struct Biol. 2021 Feb 11;213(2):107702. doi:
10.1016/j.jsb.2021.107702

3D variability analysis

Punjani A, Fleet DJ. 3D Flexible Refinement: Structure and Motion of
Flexible Proteins from Cryo-EM. bioRxiv 2021 doi:
https://doi.org/10.1101/2021/04.22.440893

3D flexible refinement
17

Who are they ?

Punjani A, Brubaker MA, Fleet DJ. Building Proteins in a Day: Efficient 3D Molecular Structure
Estimation with Electron Cryomicroscopy. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2017, vol. 39, no. 4, 706-718, 1 April 2017, doi: 10.1109/TPAMI.2016.2627573.
18

Patent ?
Declaration of Competing Interest
A.P. is CEO of Stuctura Biotechnology Inc. which builds the cryoSPARC software package,
distributed freely for academic non-profit use with software licenses available forcommercial use. D.J.F. is an advisor to Stuctura Biotechnology Inc. The novel aspects ofthe method presented are described in a provisional patent application.
Punjani A, Fleet DJ. 3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single
particle cryo-EM. J Struct Biol. 2021 Feb 11;213(2):107702. doi: 10.1016/j.jsb.2021.107702

Competing Interests
The novel aspects of the method presented are described in a provisional patent application.
Punjani A, Fleet DJ. 3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM. bioRxiv 2021
doi: https://doi.org/10.1101/2021/04.22.440893

19

Algorithm for 3D reconstruction
Observed projection images

initial 3D map

PDB ID:5uz7
Resolution: 6 A

M-stepE-step(1) 2D projections are
generated from 3D map.

(2) Chose the closest projection
for each observed projection
to estimate its orientation(3) 3D reconstruction is performed
using estimated orientations

3D reconstruction of cryoSPARC
・3D Refinement : based on Relion-like Bayes estimation framework
Input : Prepare an initial 3D map
E-step： Estimate projection orientation for each 2D particle image
M-step： Reconstruct 3D map from 2D particles images
using the estimated orientations, in Fourier space.

・E-step : Efficient search of projection orientations
using Branch and Bound・M-step : the standard EM algorithm may be employed.

・Ab initio reconstruction:
M-step: gradient-based algorithm is employed, not EM algorithm.EMア
Furthermore, SGD(Stochastic Gradient Descent) has been introduced.
*SGD: gradient is obtained from a part of particle images, to avoid a trapping into
local
21
optimum.

Stochastic Gradient Descent for M-step22

Branch-and-bound for E-step23

Hardware required for Relion/cryoSPARC
I am buying new GPUs, what do you recommend to run RELION on?
Our collaborator in Stockholm, Erik Lindahl, has made a useful blog with GPU hardware recommendations. Briefly, you'll need an NVIDIA
GPU with a CUDA compute ability of at least 3.5, but you don't need the expensive double-precision NVIDIA cards, i.e. the high-end gamer
cards will also do, but do see Erik's blog for details! Note that 3D auto-refine will benefit from 2 GPUs, while 2D and 3D classification can be
run just as well with 1 GPU. Apart from your GPUs you'll need a decent amount of RAM on the CPU (at least 64Gb), and you may also
benefit from a fast (e.g. a 400Gb SSD!) scratch disk, especially of your working directories will be mounted over the network connecting
multiple machines.

OS : Linux。GPU用のライブラリとしてCUDAが必要。
GPU の種類: NVIDIA社製。ゲーム用の安価なカードでかまわない。GeForce 1080など。高価な倍精度のGPU(例えば、Tesla)は必要ない。
GPUの枚数：2D分類は1枚でも動く。3D auto-refineは２枚以上必要。
主メモリ：できるだけ多く。最低でも64Gbyte。
ディスク：scratch領域にSSDがあるとよい。
CPU: CPUだけしか動かないタスクもあるので、コア数も多いほうがよい。
・事前にインストールが必要なライブラリ・CUDA (GPU計算のためのライブラリ)・openmpi (ジョブ並列用のライブラリ)
・以下のライブラリは無ければ、relionのコンパイラがインストール時にローカルにインストールする。
・FFTW (高速フーリエ変換のライブラリ)
・FLTK (GUIのライブラリ)

NVIDIA GeForce 1080 Ti

NVIDIA GeForce RTX™ 2080 Ti
24This tutorial assumes 2 persons share 1 Linux machine with 4GPUs.

ライセンス IDの取得(1) non-profit academic useに限り無料(2) 申請すると24時間以内に登録メール
アドレスにライセンスIDが送付される(3) 登録メールアドレスとして、@gmail.comや@yahoo.co.jpを入れると即座にPlease enter a valid
institution/company email addressというエラーで棄却。@docomo.ne.jpでは受け付けられるがライセンスIDが送付されてこない….
(4)一つのライセンスIDでは、一つのmasterノードしかインストール・実行できない。

(5)研究室などで、複数の人が共有で使用する場合は、Master-workersかCluster
25の様式でセットアップする必要

Install: Single Workstation Setup
Simplest setup.

Master and worker can run on the samemachine.

26

Install: Master Workers Setup

master-workshop(192.168.39.72)

embox1(192.168.39.130)

embox3(192.168.39.90)

27

Install: Cluster Setup

28

Hardware Requirements
Master nodeComponent Minimum Requirement

Recommended

CPU 4+ cores

8+ cores at 2.8GHz+

RAM 16GB+

32GB DDR4

Fast Local Storage Not Required

Not Required

GPU Not Required

Not Required

Network

1Gbps link to storage
servers

10Gbps link to storage
servers

Worker nodeComponent Minimum Requirement

Recommended

CPU 2+ cores per GPU

4 cores per GPU

CPU Memory Bandwidth 50+ GB/s
RAM 32GB+ per GPU
Fast Local Storage 1TB SSD

GPU
Network

100+ GB/s
64GB DDR4 per GPU
2TB PCIe SSD

1+ NVIDIA GPU with CC
3.5+, 11GB+ VRAM

1+ NVIDIA Tesla V100,
RTX2080Ti, RTX3090, etc

1Gbps link to storage
servers

10Gbps link to storage
servers29

Software Requirements
Component
Operating System
Shell
User AccountRequirement
Modern Linux OS (Ubuntu)(1) OSはUbuntuを強く推奨。CentOS7ではエラーの報告があるらしい。

Bashcryosparcuser

Software

CUDA ≥10 (worker nodes only)
GCC

Filesystem

Shared file system across all
nodes(2) Masterノードのインストール・使用ごとに１つのライセンスが必要。

Master-worker setupの場合の設定について(1)Master, workersで共通のアカウント cryosparcuserを設定(2) cryosparcuser はmasterからworkersへパスワード無しでsshログイン可能とする(3) ポート番号39000-39010が開放されていること(ubuntuはデフォルトが開放状態)(4) master, workers間で読み書きのできる共有のファイルシステム
30

CryoSPARCのユーザーアカウント(1) 実行にはcryoSPARCのアカウントが必要。Unixアカウントとは別。
(2) cryoSPARCのアカウントは Emailアドレスとパスワード(3) どのcryoSPARCのアカウントで操作しても、Unixからみると、
cryosparcuserというUnixアカウントが実行しているように見える。
(4) データのアップロードにcryoSPARCにUnixのアカウントも必要。計算結果のダ
ウンロードはWebからできるが、入力画像のアップロードはWebからできないので、WebマシンにUnixアカウントでログインして入力電顕画像のアップロード作業する必要がある。

31

今回の講習会用マシンの設定(1) 所内用： Master + 2 Workers

所内から http://192.168.39.72:39000
アカウント：

embox1(192.168.39.130)
master-workshop(192.168.39.72)

embox3(192.168.39.90)

guest01@master-workshop
guest02@master-workshop
guest03@master-workshop
guest04@master-workshop
guest10@master-workshop
計算済みの結果が見れます※４人のアシスタントの方だけに使っていただきます。

(2) 所外のリモート参加者用： Single Workstation

所内から http://ciclews.pdbj.org
アカウント：

embox2(192.168.39.89)

guest01@embox2
guest02@embox2
guest03@embox2
guest04@embox2

パスワード(共通)：
embox2remote

guest10@embox2 計算済みの結果が見れます32※８０人以上の人がアクセスする可能性があるので、くれぐれもジョブを実行しないでください。

リモート受講者用の計算途中結果のデータ提供
講習会のWEBページ https://pdbj.org/news/workshop20210624

すべて展開

ジョブごとのデータ（電顕画像、３Dマップデータなど）を収納

33

Job history

Day 2 ?

Day 1 ?

34

Comparison with Relion 3.1
cryoSPARC v3.2.0

Comp. Time

Relion 3.1

Comp.Time

J2:Patch CTF estimation

02 min 06 sec

CTF estimation< 1minJ4:2D classification(100particles)[1GPU]

01 min 03 sec

2D classification(341 particles) [1GPU]

1 min

J9:Template picker

01 min 06 sec

Auto-picking, referencebased [1GPU]

2 min

J12:2D classification(27680 particles; 50 class)[2 GPUs]

06 min 31 sec

2D classification (38244
particles; 20 class)[2GPUs]23 minJ14: Ab-initio
05 min 38 secreconstruction(1283)[1GPU]

3D Initial model (643)[[1GPU]

10 minJ15: Homogeneous
Refinement (1803;1class)[1GPU]

3D classification(643;4classes)[2GPUs]12 min3D refinement (1803;1class)[2GPUs]25 min05 min 20 sec

35Comp.Time by Single and Four users
Single user

Four users

cryoSPARC v3.2.0

guest10

guest01

machines

embox3

embox3

J2:Patch CTF estimation

02:06

02:08

02:07

02:21

02:20

J4:2D classification(100particles)[1GPU]

01:03

01:06

01:15

01:03

01:18

J9:Template picker

01:06

01:03

02:30

02:25

01:54

J12:2D classification(27680 particles; 50 class)[2 GPUs]

06:31

11:26

11:33

14:56

14:34

J14: Ab-initio
reconstruction(1283)[1GPU]

05:38

09:30

09:34

04:48

04:54

J15: Homogeneous Refinement 05:20(1803;1class) [1GPU]

06:33

08:08

05:23

05:10

guest02

guest03

guest04

embox1*2D class and Template picker job with 4 users takes ~2 times time than those with 1 user.
36
This may due to DISC I/O; 4 users access to one shared_disc in the master node.

Step 0: Log-in

http://192.168.39.72:39000
guest01@master-workshop

37

Step 1: Create a Project(1) Click [Projects] icon

(2) Click [+ Add] button(3) Input “Title” and “Directory in which to create new project directory”(4)
EMPIAR-10291_50mic/shared_data_SSD4TB/cryosparc_projects/guest10

Project directory must exist.
Project directory must be in the shared directory.
38

Step 2: Create a Workspace(1) Click your Project number(2) Click[+ Add]

(3) Input Title of a new Workspace(4) A new workspace is shown.
Test Workspace

39

Download Tutorial Data by Command※この作業は今回は不要です。masterサーバの/shared_data_SD4TB/tutorial_data/EMPAR-10291_50micにコピー済みです。
(1) Log-in to the “master” server.

(2) Change directory to your project directory$ cd /shared_data_SSD4TB/cryosparc_projects/guest01(3) Change directory to your project directory$ wget ftp://empiar.pdbj.org/pub/empiar/workshop/10291/00001/EMPIAR10291_50mic.tar(4) Extract the tar file$ tar xvf EMPIAR-10291_50mic.tar
40

Step 4: J1:Import Micrographs(1)Click a Workspace(2) Choose [Job Builder](4) Input “Micrographs data path”
and other parameters.
then click [Queue]/shared_data_SSD4TB/tutorial_data/EMPIAR-10291_50mic/data/*.mrc
1.232

NOTE: file extension, such as *.mrc, *.tif,
has to be added.

300
1.6
35.0

(3) Select [Input Micrographs](5) Click [Create]
41

Step 4: J1:Import Micrographs

42

Step 6: J2:CTF estimation (2.5 min with 1GPU)(1) Select [Patch CTF estimation(multi)] job(2) Select previous [Import Micrographs] job

43

Step 6: J2:CTF estimation (2.5 min with 1GPU)(1)Drag and drop !
(2) Set Number of GPU 1
and click [Queue](3) Click [Create]

Number of GPU:1
44

Step 6: J2:CTF estimation (2.5 min with 1GPU)

Step 7: J3 Manual Picker(2) Select CTF estimation Job(1) Choose [Manual Picker] Job(3)Drag and drop !

(5) Click [Create](4) Click [Queue]

Step 7: J3: Manual Picker(1) Choose [Interactive](2) Set Low pass filter (A)=10, and Box size(px)=180(3) Pick particles by clicking theleft button of your mouse. At
least 100 particles should be
picked from the first 4
micrographs. About 25 particles
per one micrograph.
NOTE: Click the right mousebutton un-pick a particle.

Box size of particles
Pixel size: 3.465Å

Pixel size: 1.232Å
To reduce
Computation costs

Diameter160Å

Just before
Refine3D

Diameter160Å

Box size221.8Å

Box size221.8Å

180 pixel

64 pixel48

2D Class average images from the original article

Fig.S3c

Burendei et al. Science Advances. 2020. DOI: 10.1126/sciadv.aax3157

Pixel size: 1.232Å

Diameter160Å

Box size221.8Å

180 pixel
方形の１辺7.44cm = 222.75Å
マスク直径5.28cm = 158.08Å

1.67 cm : 50Å

49

Step 7: J3:Manual Picker

Step 7: J3:Manual Picker

Step 7: J3:Manual Picker

Step 7: Particle Picking

Step 7: J3:Manual Picker

Step 7: J3:Manual Picker

Step 7: J3:Manual Picker(1) After finishing picking, click [Done Picking! Extract Particles]

Step 7: J3:Manual Picker

Step 8: Template Picking – J4:2D classification(4) Setup parameters(1) Choose [2D Classification] Job(2) Click [Manual Picker] Job

10(3) Drag “particles” of Manual Picker
and Drop(5) Click [Create]1Step 8: Template Picking – J4:2D classification[1min with 1GPU]

Step 8: Template Picking – J8:Select 2D classes(2) Choose [Class 2D] Job(1) Choose [Select 2D classes] Job(3) Drag “particles” of Class2D and drop to “particles”
Drag “class averages” of Class2D and drop to “templates”(4) Click [Queue](5) Click [Create]Step 8: Template Picking – J8:Select 2D classes(1) Click [Interactive](2) Select 2-4 good averages for the templates(3) Click [Done]Step 8: Template Picking – J9:Template Picker [1min](1) Choose [Template picker] Job(2) Drag “templates_selected” ofSelect 2D and Drop to templates(4) Input Particle diameterand click [Queue] and [Create(3) Drag “exposures” of Patch
CTF(M) and Drop to micrographs

160

Step 8: Template Picking – J9:Template Picker [1min]

Step 9: J10:Inspect Picks(2) Drag “particles” of Template
picker and Drop to “particles”(3) Drag “exposures” of Patch
CTF(M) and Drop to “micrographs”(4) Click [Queue] and [Create]Step 9: J10:Inspect Picks

Low pass filter (A) = 10
Box size (px):180

Step 9: J10:Inspect Picks
NCC: Normalized Cross Correlation
Power Score: indicating the presence of significant signal

Low pass filter (A) = 10

(1) Set NCC threshold:0.33(2) Power threshold:700(3) Click [Done Picking ! Output Locations]Step 10: J11:Extract from Micrographs(1) Choose [Extract from Micrographs](4) Input Extraction box size(pix)and click [Queue] and [Create](3) Drag “particles” of Inspect
Particles and Drop to “particles”(2) Drag “exposures” of Patch
CTF(M) and Drop to “micrographs”

180Step 10: J11:Extract from Micrographs

27680 particles

Step 11: J12:2D Classification [ 6.5 min with 2GPUs](5) click [Run on specific GPUs](1) Choose [2D Classification] job(4) Input Circular mask diameter(A)and Number of GPUs
and click [Queue](2) Click [ExtractFromMicrographs]

50(3) Drag “particles” of [Extract From
Micrograph] and Drop to “particles”(6) Check two GPUs assigned for you
and click [Create]160

2Step 11: J12:2D Classification [ 6.5 min with 2GPUs]Step 12: J13:Select 2D classes(1) Choose [Select 2D classes](2) Choose [2D class] job(3) Drag “particles” of [2D Class]and Drop to “particles”(4) Drag “class averages” of [2DClass] and Drop to “templates”(5) Click[Queue]and [Create]Step 12: J13:Select 2D classes

Step 12: J13:Select 2D classes(1) Select “good” classes, considering
Nparticles and resolution.

(2) Click [Done]

In this case, I selected 11 classes.
It means 16152 particles among 27680 particles.

Step 13: J14:Ab-initio Reconstruction(5) click [Run on specific GPUs](1) Choose [Ab-initio Reconstruction](2) Choose [Select 2D] job(3) Drag “particles_selected” of[Select 2D] and Drop to “particles”(6) Check one GPU assigned for you
and click [Create](4) Click [Queue]

Step 13: J14:Ab-initio Reconstruction [5.5 min with 1GPU](1) select [Outputs] tabStep 13: J14:Ab-initio Reconstruction [5.5 min with 1GPU](1) Download “J*.volume_class_0.map”

Step 13: J14:Ab-initio Reconstruction [5.5 min with 1GPU][Tools]->[Volume Data]->[Volume Viewer]
Change : step = 1[File]->[Open…]

1283

Change contour Level

1283

Step 14: J15:Homogeneous Refinement(1) Choose [Homogeneous Refinement](2) Choose [Ab-initio] job(3) Drag “particles_class0”of [Ab-initio] and Drop to“particles”(5) Input Symmetry:C8 and click [Queue](4) Drag “volume_class0” of [Abinitio] and Drop to “volume”

C8(4) Click [Queue]

Step 14: J15:Homogeneous Refinement [5.5 min with 1GPU](1) click [Run on specific GPUs](2) Check one GPU assigned for you
and click [Create]Step 14: J15:Homogeneous Refinement [5.5 min with 1GPU]

Resolution from Fourier Shell Correlation (FSC)
1)Divide 2D particle images into two sets, and reconstruct two ３D maps f1(x) and f2 (x).
Their Fourier transferred maps are called F1(q) and F2 (q).
2)Calculate correlation FSC(r) on a spherical shell with spatial frequency r=|q|.
3) Plot FSC(r) versus spatial frequency r. Resolution is defined spatial frequency
with FSC(r) =0.143 (=1/7).
qy
r

qy
r

qx

F1(q)

FSC (r ) =|q | = r|q | = rqx

F2 (q)



Re F1 (q )  F2 (q )* dq| F1 (q ) |2 dq  |q | = r| F2 (q ) |2 dq

FSC curves in cryoSPARC

Apunjani cryoSPARC Teamhttps://discuss.cryosparc.com/t/tight-corrected-and-loose-gsfsc-curves/201/4
Mar '17
Here is a short explanation of the different FSC curves in cryoSPARC:#FSC calculations in CryoSPARC##No Mask:
This is the raw FSC calculated between two independent half-maps reconstructed from the data. There is no
masking applied, so both the structure and solvent are included in this FSC.
##Spherical:
This is the FSC calculated after applying a soft spherical mask to both half maps. The outer radius of the soft
sphere is equal to half the volume box-size (i.e. the sphere extends to the faces of the box in all directions). The
inner radius is 85 percent of the outer radius. Between inner and outer radii, a soft cosine edge transitions from amask value of one to a value of zero.
##Loose:This is the FSC calculated after applying a soft solvent mask to both half maps. The loose mask is calculated asfollows. First, the density map is thresholded at 50% of the maximum density value. The resulting volume isdilated to create a soft mask. Voxels in the mask that are within 25 angstroms of the thresholded region receivea mask value of 1.0. Voxels between 25 and 40 angstroms fall off with a soft cosine edge, and voxels outside 40
angstroms receive a value of 0.0.
##Tight:
This is the same as the loose mask, except the dilation distances are 6 angstroms for the value 1.0 distance and
12 angstroms for the value 0.0 distance.
##Corrected:
This is the FSC curve calculated using the tight mask with correction by noise substitution [1]. The two half maps
have their phases randomized beyond a certain resolution, then the tight mask is applied to both, and an FSC iscalculated. This FSC is used along with the original FSC before phase randomization to compute the corrected
FSC as in [1]. This accounts for correlation effects induced by masking. The resolution at which phaserandomization begins is the resolution at which the no-mask FSC drops below the FSC = 0.143 criterion.
1.Chen, S. et al. High-resolution noise substitution to measure overfitting and validate resolution in 3D structure
determination by single particle electron cryomicroscopy. Ultramicroscopy 135, 24–35 (2013).

Mask maps

volume_map

volume_mask_refine

Loose ?

volume_mask_fsc

Tight ?

volume_mask_fsc_auto

Tight ?
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Step 14: J15:Homogeneous Refinement [5.5 min with 1GPU](1) select [Outputs] tab(2) Download “J*.volume_map”

Step 14: Homogeneous Refinement [5.5 min with 1GPU][Tools]->[Volume Data]->[Volume Viewer]
Change : step = 1[File]->[Open…]

Change contour Level

1803

1803

Step 14: Homogeneous Refinement [5.5 min with 1GPU][Tools]->[Volume Data]->[Volume Viewer]
Change : step = 1[File]->[Open…]

Change contour Level

sharp

1803

sharp

1803

Step 14: Homogeneous Refinement [5.5 min with 1GPU][Tools]->[Volume Data]->[Volume Viewer]
Change : step = 1[File]->[Open…]

Change contour Level

mask

1803

mask

1803

Acknowledgements
We thank following people:
・ Develpers for cryoSPARC

・ Prof. Atsunori Oshima for providing his EM
data and helpful advices.
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