dataesr/scientific-paragraphs-categorization huggingface dataset:
SDS-PAGE and Western blot analysis In some collected dataset (see subsection 9. 1. 3), the initiation factors seemed to be missing from the reconstructed cryo-EM maps. We thus wanted to verify that both factors were clearly present in the purified sample. To do this we analyzed the purified sample on a SDS-PAGE 12 % gel and by Western blot revelation using antibodies targeted towards the histidine of the two IFs. In addition to the purified IC3 complex, a control sample of both factors were loaded on the gels. The results are shown in (Figure  5. 2, page 70). Both factors are clearly present in the purified complex.
Particle picking and extraction Locating the particles on the micrographs is the first step in the process of particle picking. One option is to load each micrograph and manually pick every particle. Manual picking quickly becomes tedious and extremely time consuming as there are usually thousands of micrographs to process in a dataset. An alternative option, and widely preferred, is to make use of semi-automated particle-picking programs. There are various automated particle-picking programs that use different strategies to locate particles in a micrograph such as template-based picking, reference-free picking and neural networks picking. Within the context of my thesis I mainly used 2D template-based picking and LoG (Gaussian blob) reference-free picking programs.
Reference-free particle picking Alternatively, one can use a template-free picking based on a Laplacian-of-Gaussian (LoG) filter  (Figure 6. 3, page 78). The user provides the expected dimensions (minimum and maximum diameter of the object) to locate the particles within the micrographs. This approach removes any danger of reference bias and orientation bias. It is important to note that LoG based picking within RELION does not keep away from micrographs edges. Thus one must manually curate the located particles by removing particles close to the edges to avoid non information pixels as shown in Figure  6. 4, page 79. As both approaches have their limitations, we often use both techniques and optimize their parameters on a small subset of micrographs (3-5 micrographs) with different defoci values prior to picking the whole dataset.
2D classification Historically, 2D classification was the only approach to sort out particles. Moreover, 2D analysis of cryo-EM data may sometimes be a useful tool to answer biological questions. Especially, when no 3D reconstruction can be achieved  (Sosnowski et al. 2018; Ulbrich et al. 2009 ). Most often it is used as a sample quality assessment tool and allows to remove bad particles from the dataset. RELION uses a completely unsupervised, maximum-likelihood (ML) 2D classification that greatly reduces the pitfalls of model bias  (Henderson 2013). The only prior structural needed from the user is the total number of classes to be used, that should ideally reflect the number of different conformations present in the dataset. 2D classification is an iterative process. Particles are first randomly distributed into the classes asked for by the user.
This introduces the E step parameters that we used in multiple 2D and 3D classifications jobs. The E step parameter allows to introduce a user defined resolution (or spatial frequency) above which the algorithm is not allowed to align particles even if a higher-resolution can be computed from the class averages. We found that limiting the E step between 8 and 12 Å in 3D classifications helped isolate classes where aIF5B was bound to the SSU. In practice, we often used three rounds of Fourier space 2D classification with various sampling parameters to clean out the dataset.
Initial Model The initial reference can be obtained from various approaches. If an existing structure of a similar object exist, one can generate the reference by low pass filtering this available structure to avoid model bias and use it as initial reference. If no structure is available, one could make use of strategies as random conical tilt  (Radermacher et al. 1987)  or the common line approach  (Heel and Harauz 1988) , though these methods were difficult to generate a good initial ab initio model. Recently, the implementation of a Stochastic Gradient Descent (SGD) in cryoSPARC  (Punjani et al. 2017 ) have significantly improved generating initial model of previously unknown structures and was latter implemented in the RELION package as well. For the initial datasets presented in this thesis we used as initial reference a volume generated from the previously published structure of archaeal 30S  (Coureux et al.
3D refinement Selected particles after 3D classification are then subjected to 3D reconstruction refinement commonly called 3D refinement. During 3D refinement, the data is split into two half datasets and are iteratively and independently refined against two separate 3D reference volumes. This approach is termed "gold-standard refinement" and avoids over-fitting and inflated resolution estimates, as it resembles the R-free approach in X-ray crystallography. In the initial iterations of 3D refinement, the initial volumes to which particles are aligned are at low resolution. The aligned particles are then back-projected and new reference volumes are obtained. These two half maps are then compared by Fourier Shell Correlation (FSC) where a cross-correlation is computed between the two half maps in function of spatial frequency.
At high resolution, correcting for these local defocus variation can improve the resolution of reconstructions. Furthermore, CTF refinement benefit from the 3D reference structure as it exploits its phase and amplitude for a more reliable estimation of the CTF  (Zivanov et al. 2018 ). In our case, we observed significant improvement of resolution after applying these approaches to the dataset collected on a high end microscope as reported in subsection 11. 2. 3.
Initial datasets -ds38 & ds39 The first two datasets I worked on were collected a few months prior to my arrival at the lab, in August and December 2017 called thereafter dataset-38 (ds38) and dataset-39 (ds39). The numbering of the datasets relates to the total number of collected cryo-EM dataset since the arrival at the laboratory of Pierre-Damien Coureux, one of my doctoral co-advisors. The same numbering strategy is applied throughout this manuscript. Both samples were prepared and collected using very similar procedures. August data acquisition session was shortened due to a technical issue on the microscope, and the collected grid had very thick vitreous ice (Figure  7. 1, page 93). A compensation session was scheduled in December 2017. Micrographs showed good contrast (Figure  7. 5, page 97) but unfortunately was heavily contaminated by cubic ice, probably due to a rise in the temperature during grid preparation and storage.
Cryo-EM grid preparation and data collection We used copper 300 mesh Quantifoil R2/2 grid covered with a thin layer of continuous carbon; 3 nm thick for the August dataset and 2 nm thick for the December dataset. The grids were glow-discharged 30 s at -20 mA in an EMS 100 Glow discharge unit. The purified IC3 complex was boosted with a 5-fold concentration of aIF1A, aIF5B, Met-tRNA i and GDPNP, activated by heating 30S at 51 °C and kept at 37 °C before spotting.
91 The sample was then diluted at a concentration of 100 nM in a pre-heated (37 °C) buffer, before applying 4 µL to the grid at 20 °C and 90% humidity for 10 s. The sample was vitrified by plunging into liquid ethane at -182 °C, after 1. 4 s blotting using a Leica EM-GP plunger. High-resolution data were collected on a Titan Krios microscope operating at 300 kV equipped with a Falcon III (ThermoFisher) direct electron detector, at the Diamond Light Source facility, Oxfordshire, UK with the help of Alistair Siebert. August dataset (ds38) was collected at a nominal magnification of 128,440× with a pixel size of 1. 09 Å/pixel. Each movie consisted of 20 frames with a total exposure of 45 e -/Å 2. In total 1,963 movies were collected. The December dataset (ds39) was collected at a calibrated magnification of 129,270× with a calibrated pixel size of 1.
Data processing of dataset 38 Data processing of the August dataset (ds38) was done using RELION-2. 1  (Scheres 2012 ). The frames were aligned with dose-weighting using MotionCorr2 (S. Q. Zheng et al. 2017) , and CTF estimation was done using gCTF (Zhang 2016) (see chapter 6). After careful inspection of the micrographs and their associated power spectrum we kept 972 micrographs (49%). Automated particle picking located 190 k particles (195 particles per micrograph (ppm)) using 2D-templates from a thousand manually-picked particles. Particles were extracted with a 3-fold downscale, and 2D-classifications were used to discard bad particles, poorly aligned ones and false positive picks. Using the resulting class averages, 86 k particles (45% -88 ppm) remained (Figure  7. 1, page 93). The selected particles were refined into a single model using a 30S volume generated from the PDB model 6SWC as initial reference  (Coureux et al.
Data processing dataset 39 Data processing of the December dataset (ds39) was done using RELION-2. 1  (Scheres 2012 ). The frames were aligned with dose-weighting using MotionCorr2 (S. Q. Zheng et al. 2017) , and CTF estimation was done using gCTF  (Zhang 2016). After careful inspection of the micrographs and their associated power spectrum only 418 micrographs (8%) were kept for further processing. As previously mentioned many micrographs were heavily contaminated by cubic-ice and had to be discarded. Automated particle picking selected 91 k particles (217 ppm) using 2D-templates from manually-picked particles. Particles were extracted with a 3-fold downscale, and 2D-classifications were used to discard bad particles, poorly aligned ones and false positive picks. Around 72 k particles (79% -172 particles per micrograph) were selected from the 2D-class averages.
2 s blotting using a Leica EM-GP plunger. The data set (ds55) was collected on our in-house microscope at the CIMEX (for Centre Interdisciplinaire de Microscopie Electronique de l'Ecole Polytechnique (X)) fa- cility. The grids were loaded onto an Elsa Cryo-Transfer Holder (GATAN) and inserted into a 300 kV Titan Themis microscope (ThermoFisher) equipped with a Falcon III direct electron detector (ThermoFisher). The movies consisted of 39 frames over 1 s with a total exposure of 39 e -/Å 2 and a nominal pixel size of 1. 12 Å/pixel. In total 2,175 micrographs were collected. Unfortunately, the alignment of the microscope was not good and the dataset suffered from high astigmatism.
A complementary script, is the 3D-classification particle movement. This tool help us visualize the classes that share the same particles. Again, in the form of stacked histograms, we could visualize at every iteration for individual classes how many particles arrived from and departed to another class. This tool helped us identify or confirm the similarities between classes of a classification. These scripts were used in all the processed dataset and helped in decision making, like class selection after a 3D classification. showed the 30S and aIF1A. In all but one a Met-tRNA i is observed in the P site. One class showed the entire aIF5B, and three classes showed only domain IV of aIF5B bound to the 3'-end of the Met-tRNA i Met. In the three remaining classes aIF5B is absent from the potential map.
A new protocol to prepare IC3 The combined results of the previous datasets suggested that aIF5B binding to the 30S was not stable enough in our experimental conditions. Therefore, in order to stabilize the contact of aIF5B with the 30S we decided to use a cross-linking reagent. We chose bis(sulfosuccinimidyl)suberate (BS 3 ) as a cross-linking molecule. BS 3 is a water-soluble molecule due to its terminal N-hydroxysulfosuccinimide ester (NHS) substituents. This eliminates the need to use organic solvents that would interfere with the structure of the IC3 complex. Primary amines are the principal targets for NHS esters at pH between 7 and 9. Five amino acids are possible reagents to BS 3 but lysine and N-terminal residues seem to be the main reagents  (Mattson et al. 1993 ; ThermoFisher™ 2020).
Data processing Data processing was done using RELION-3. 1. 0. Motion correction was performed using RELION's own implementation  (Zivanov et al. 2018 (Zivanov et al. , 2019)). The frames were aligned using 5×5 patches with 1 e -/Å 2 /frame dose-weighting. CTF was estimated using gCTF  (Zhang 2016 ) on the dose-weighted micrographs. Aligned micrographs with gross surface contamination and obvious gCTF output-metrics outliers were discarded (Figure  8. 2, page 119). The final pool comprised of 995 micrographs, 80% of the initial dataset. Auto-picking procedure found 296,071 particles (298 particles per micrograph) using 12 references from 2,100 LoG-picked particles. The parameters used for the template- based picker were optimized on 20 micrographs of different defoci, before picking the whole dataset.
5, page 122). We were able to "re-align" part of the head-to-toe particles resulting in a final pool of 55,914 particles (18% -56 ppm) of well aligned particles. For the re-alignment, particles underwent 4 successive jobs. First a 3D classification with coarse sampling and a highly filtered reference (70 Å) to erase the signal of aIF5B for the first few iterations. Next, a re-extraction job using the re-centering option of RELION to reduce particle offset followed by a 3D reconstruction refinement with high In latter datasets when face with similarly misaligned particles, we applied a simpler procedure suggested by a CCPEM user where all particles are first aligned against one map with a loose mask. This worked perfectly well.
Sample washing problems We also faced problems with the washing procedure of the purified complex containing NH 4 Cl salts to be replaced by NaCl salts. One dataset (November 2020, ds60) showed an unstructured 30S particle, where h44 could not be seen in the cryo-EM map and the head of the SSU showed very large mobility with respect to the body (Figure  9. 2, page 132). This result was quickly attributed to a too low concentration of Mg 2+ ions in the washing buffer that was used by mistake. On two other datasets (December 2020, ds61 & ds62) we decided to switch to a semi-permeable membrane dialysis to was the complex. However, during dialysis some of the factors precipitated, and data processing resulted in cryo-EM maps were the Met-tRNA i Met was base-paired with the mRNA in the P site, but both initiation factors were absent from the complex (Figure  9.
Cryo-EM grid preparation and data collection The dataset was collected at the CIMEX facility. Data collection lasted 7 days from the 23 rd till the morning of the 29 th of March 2021, re-filling the sample holder every 8-10 hours with liquid nitrogen. The grids were loaded into a 300 kV Titan Themis microscope (ThermoFisher) equipped with a Falcon3 direct electron detector (GATAN) on a side entry Elsa sample holder (GATAN). Movies collection lasted for 1 s, collecting 40 frames in linear mode with a total exposure of 40 e -/Å 2 and defoci ranging from -0. 9 µm to -4 µm. In total 10,498 movies were collected at a nominal pixel size of 1. 12 Å/pixel (nominal magnification of 125,000×), from 40 different gridsquares, using the automated collection software EPU (ThermoFisher). Copper 300 mesh Quantifoil R2/1 grid covered with a 2 nm thick continuous layer of carbon was rinsed in an ethyl acetate solution and glow-discharged for 30 s at -25 mA and 0.
RESULTS Cryo-EM -3D reconstructions of different datasets were performed to assess the Pab IC3 complex integrity. Depending on the dataset, the 3D reconstructions showed either the studied full complex IC3 or the IC3 complex lacking the initiation factors aIF1A and aIF5B. The resolution limits obtained in the different datasets varied around 3 Å (see FSCs on Supplementary Figure  2 ) and were sufficient to conclude on the presence or absence of translation initiation factors, even at low thresholds. Processed data from batch#3 (2 grids in total) gave 3D reconstructions where the potential map was clearly visible for the two initiation factors (Figure  1A ). Processed data from batch#1 and #2 (5 grids in total), using the same sample and preparing the grids in the same experimental conditions, gave systematic stripped complexes without any density in the 3D reconstructions for the initiation factors (Figure  1B ).
Data processing Data processing was done using RELION-3. 1. 1. Motion correction was performed using RELION's own implementation (S. Q. Zheng et al. 2017; Zivanov et al. 2018 Zivanov et al. , 2019)). The frames were aligned using 5×5 patches with 1 e -/Å 2 /frame dose-weighting. CTF Auto-picking procedure was done using the Laplacian-of-Gaussian (LoG) filter procedure. The parameters used for the LoG-pick were first optimized on 3 micrographs of different defoci, before picking the whole dataset. Particles were extracted using boxes that are roughly 1. 5 times (432 pixel) larger than the complex. To reduce computation time of the initial stages of processing, particles were extracted with a 3-fold downscale; 144 pixel box size, and 2. 58 Å pixel size. Particle boxes that extend over the edges of the micrographs were discarded.
The structure of full-length aIF5B was solved by molecular replacement with PHASER (65) using the structure of aIF5B from Aeropyrum pernix (PDB ID, 5FG3,  (40) ) as a search model with three independent modules containing domains I and II, domain III and domain IV, respectively. Coordinates and associated B factors were refined through several cycles of manual adjustments with COOT (  66 ) and positional refinement with BUSTER  (67)  and PHENIX  (68). During the course of structure refinement, we noted the presence of a GDP molecule within the active site although the crystals were prepared in the presence of 0. 5 mM GDPNP-Mg 2+. Final statistics are shown in Supplementary Table  S1. The final model contains all residues (from 2 to 598) with one GDP molecule bound to the active site. No magnesium atom was visible in the electron density.
The IC3 complex was further incubated 2 min at 51 • C and 5 min at room temperature. Finally, IC3 was purified by affinity chromatography (TALON resin, Clontech) using the two N-terminal histidine tags of aIF5B and aIF1A. IC3 was then dialyzed against buffer A to remove imidazole, concentrated using centricon 100 K and stored at -80 • C after flash-freezing into liquid nitrogen. The presence of all components was confirmed by SDS-PAGE and Western blot analysis (Supplementary Figure  S1 ). Before spotting onto R2/1 grids with an extra 2 nm carbon layer (Quantifoil, Inc), the complex was diluted to a final concentration of ∼100 nM and a 5-fold excess of Met-tRNA i Met , aIF1A and aIF5B was added. For the final data collection, in order to stabilize aIF5B onto the SSU, we used BS 3 ((bis(sulfosuccinimidyl)suberate), Thermo Fisher Scientific) as a crosslinker.
Data processing Motion correction was performed using RELION's implementation  (69). The frames were aligned using 5 × 5 patches with 1 e -/ Å2 /frame dose-weighting. CTF was estimated using gCTF  (70)  on the dose-weighted micrographs. Aligned micrographs with gross surface contamination and obvious gCTF output-metrics outliers were discarded. Power spectra with nominal resolutions better than 4 Å, objective-lens astigmatism better than 350 Å and within a [0. 7-3. 8 ] m defocus range were kept for further processing. All steps of data processing were performed in  RELION 3. 1 (69). The final pool comprised 17. 1k micrographs, 89% of the ini-tial dataset. Particles were extracted using 432 × 432 pixel boxes. To reduce computation time of the initial stages of processing, particles were downscaled 3-fold to a 144 × 144 pixel box size  (2.
IC3 model building and refinement The structure of the 30S was first manually adjusted in map A (2. 25 Å resolution) using Coot  (66)  and P. abyssi 30S subunit (PDB ID: 6SWC) as an initial model  (20). In particular, a total of 67 rRNA modifications were modeled and the 16S rRNA structure was updated accordingly (Supple-mentary Table  S3 ). The mRNA and the tRNA anticodon stem-loop (ASL) were also modeled in Map A (Supplementary Figure  S4 ). Then, the complete 30S:mRNA:Met-tRNA i Met -ASL model was refined through cycles of manual adjustments in Coot  (66)  and energy minimization in Phenix  (73). This improved model of 30S:mRNA:Met-tRNA i Met -ASL was used as a starting model for building IC3, 30S:mRNA:aIF1A:aIF5B-DIV:Met-tRNA i Met and 30S:mRNA:aIF1A:Met-tRNA i.
Met structures. The models of 30S:mRNA:aIF1A:aIF5B-DIV:Met-tRNA i Met and 30S:mRNA:aIF1A:Met-tRNA i Met were built and refined in the corresponding unsharpened maps using Phenix (maps deposited as EMD-14579 and EMD-14581). aIF5B-DIV and aIF1A were poorly defined and the models of these two proteins were placed in the density without further positional refinement. The models of 30S:mRNA:aIF1A:aIF5B-DIV:Met-tRNA i Met and 30S:mRNA:aIF1A:Met-tRNA i Met were only used to analyze the position of the initiator tRNA. For IC3, models of aIF5B (this study, PDB IDs 7YYP and 7YZN), aIF1A  ((20) , PDB ID 4MNO) and Met-tRNA i  Met ((58), PDB ID 5L4O) were first adjusted in Map B. The sharpened multibody map B2 was then used to manually adjust and refine the model of aIF1A:aIF5B:Met-tRNA i Met (Supplementary Table  S2 ).
Structure of full-length aIF5B bound to GDP The overall crystallographic structure of full-length P. abyssi aIF5B shows the four domains arranged as described previously for other eukaryotic  (37, 38, 41)  or archaeal  (39, 40)  e/aIF5B (Figure  2A ). Consistent with the presence of GDP in the active site, the two switch regions involved in the binding of the nucleotide adopt the OFF conformation as previously observed in the two other aIF5B:GDP structures (Aeropyrum pernix, PDB ID 5FG3  (40)  and Methanobacterium thermoautotrophicum, PDB ID 1G7S  (39) ) or in the apo eIF5B from S. cerevisiae (PDB ID 3WBI  (41) ). Domains I of all these structures can be superimposed with very small rmsd values (Supplementary Figure  S6 ). According to this superimposition, position of do- main II only weakly varies whereas position of domain III varies significantly in the different structures.
This suggested that aIF5B binding to the 30S was not stable enough in our experimental conditions. Therefore, in order to improve the quality of the cryo-EM maps, we crosslinked IC3 using bis(sulfosuccinimidyl)suberate (BS 3 ) at 51 • C as previously done for other ICs (e. g. (78, 79) , see Materials and Methods). Cross-linking with BS 3 did not change the overall structure, but it greatly improved the quality of the potential map in the aIF5B binding regions. Cryo-EM images were collected on a Titan Krios microscope (Supplementary Table  S2 ) yielding a total of 19. 1 K micrographs. Images were processed using  RELION 3. 1 (69, 80, 81)  (Supplementary Figure  S2 ; Materials and Methods). Briefly, an initial dataset with 2 M particles was used for an initial 3D-reconstruction in which all components of IC3 were visible.
Perspective and conclusion As previously mentioned, water droplets caught by the grids were in their majority too thick for TEM imaging. To improve droplet spreading on the grids one could make use of self-wicking grids that absorb excess water leaving a thin film behind suitable for imaging. In addition, to improving droplet dispersion, we also need to work on a faster screening method to test droplet thickness. An interesting idea is the use of the Linkam correlative microscopy stage. One would spray a fluorescent, molecule on a plunging cryo-EM grid and then estimate ice thickness by measuring the fluorescence of the region of interest and compare it to a calibration curve. I was able to test the Linkam stage once, and the development of this screening method is ongoing. Although the progress made so far does not allow for a dataset collection yet, I have good faith in the micro-spray grid preparation technique.