dataesr/scientific-paragraphs-categorization huggingface dataset:
The literature focuses on drug promiscuity, which is a drug's ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms.
Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.
The protein binding site similarity confers similar binding interactions with a common ligand. For instance, Haupt et al. [2]  demonstrated that the global structure and the binding site similarity within the structures of the Protein Data Bank (PDB)  [10]  had greater influence on drug promiscuity than physicochemical drug properties, such as hydrophobicity, molecular weight, or flexibility. The field of binding site comparison has emerged as a powerful approach for investigating drug promiscuity. Naderi et al. [11]  surveyed 12 widely used computational tools to match the pockets and underlined the important pharmacological applications of pocket matching. For related targets, small molecules often bind with a high affinity to multiple members of a protein family due to the high sequence similarity in its active sites  [2, 12].
Hu et al. [25]  analyzed both the compound and target promiscuity and the target's ability to interact with an increasing number of structurally diverse compounds and concluded that most of the targets bind to varying numbers of promiscuous compounds. To the best of our knowledge, no exhaustive work has been dedicated to quantifying and characterizing the structural features of target binding sites to understand their propensity to be promiscuous versus selective (that is, able to interact with only one type of ligand). It is possible to explore the target binding site space and multiple binding site-ligand interactions while taking advantage of the increasing number of three-dimensional (3D) structures and their redundancy. In this study, we used the interaction database, Mother Of All Databases (MOAD), which is one of the largest databases that provides more than 25,000 protein-ligand complexes with only high-quality resolution structures extracted from the PDB that are suitable for studying the promiscuity of binding sites  [26].
MOAD Protein and Ligand Space The MOAD provides 25,769 high-resolution protein-ligand complexed structures, which include 12,440 unique ligands and a total of 44,675 protein-ligand interactions. First, we extracted 10,500 different valid ligands (see Section 4) from 9,556 PDB complex structures that were involved in 18,317 pocket-ligand interactions (Figure  1 ). Selected molecules were filtered based on their drug-likeliness, and 5,824 PDB complex structures containing 4,058 valid and drug-like ligands were retained. From these PDB files, we aligned 10,271 protein mono-chains while using TM-Align, clustering them into 1,137 homologous chain clusters using H-CD-HIT. The final filtered dataset included 8,669 protein-ligand interactions. Next, similar ligands were clustered while using a Tanimoto coefficient threshold of 0.
The protocol developed to extract Druggable Binding Sites (DBS) from the Mother Of All Databases (MOAD) data and to determine their promiscuity. We extracted 3,488 valid and drug-like ligands that were regrouped into 1,969 clusters while using the Tanimoto coefficient (green), 459 homologous chain clusters among the 4,749 Protein Data Bank (PDB) files selected using TM-Align  [28]  to calculate the identity between all the protein chains, and H-CD-HIT  [29]  clustering algorithms to group the homologous protein chains (orange). The 7,267 pockets clustered in 481 druggable binding sites (DBS) (blue) correspond to the ligands previously selected and all of the homologous protein chains. The promiscuity was calculated from these data.
Thus, the higher the occurrence of a DBS in the MOAD, the higher the possibility of observing its interactions with different Ligand-Clusters, and the higher the reliability in its qualification of promiscuous or selective DBS. We selected a compromise between having enough occurrences of each DBS (important enough to limit the characterization of DBS as "selective" when not true) to analyze the promiscuity of each DBS, but also to retain a high number of different DBS to obtain reliable promiscuity information from the MOAD. In this study, we chose to analyze the promiscuity of the set of 481 DBS observed four or more times, which is referred to as the DBS4 dataset (the complete dataset is available at the following DOI: 10. 6084/m9. figshare. 8313185). These corresponded to 37% of the DBS that were described by 7,267 pocket-ligand interactions from 459 homologous chain clusters and a large part of the binding pockets (83%) in interactions with 86% of the ligands.
Druggable Binding Site Promiscuity Quantification Complementary to selective DBS, we defined a DBS interacting with several Ligand-Clusters as a "promiscuous DBS". We quantified the DBS interacting with only one type of ligand versus several types of ligands in the DBS4 dataset to determine the promiscuity of the DBS. The DBS was associated with 5. 6 (±8. 9) Ligand-Clusters on average, from 1 to 90 Ligand-Clusters (Figure  2 ). Almost one-quarter of the DBS (21%, 100/481) were selective, whereas 79% were promiscuous DBS (Table  1 ). Amongst the promiscuous DBS, we distinguished 34% (166) as moderately promiscuous (MP) DBS, which were associated with two or three Ligand-Clusters, from 45% (215) highly promiscuous (HP) DBS, which were associated with four or more Ligand-Clusters. The most promiscuous DBS corresponded to the Human Cyclin-dependent kinase 2 (CDK2) transferase, which has been well studied due to its role in the regulation of the acid-base balance in the organism  [30].
Its high pocket (164) and Ligand-Cluster (90) numbers were consistent with the high occurrence of complexed structures, the protein high sequence homology of CDK families, and the numerous inhibitors that were developed against CDK2  [31]. Table  1. The number (and proportion) of DBS in the DBS4 dataset according to their promiscuity level and correspondence with the number (and proportion) of binding pockets: selective (S, in interaction with one Ligand-Cluster), moderately promiscuous (MP, two or three Ligand-Clusters), and highly promiscuous (HP, four or more Ligand-Clusters). Eight and seven pockets, on average, describe Selective and MP DBS, respectively, whereas 23 pockets describe HP DBS. The 45% HP DBS corresponded to 69% (5,029) of the 7,267 pockets (Table  1 ). However, it was difficult to interpret whether these high pocket occurrences of HP DBS are due to these DBS that belong to targets of therapeutic interest and they are highly crystallized (which contributes to revealing their high promiscuity), or a consequence of their known promiscuity, which explains that they have been intensively studied and crystallized with different ligands.
ANOVA tests between 2,931 ligands from the dedicated Ligand-Clusters versus 557 ligands from the mixed Ligand-Clusters showed that ligands were able to interact with DBS presenting different promiscuity (mixed Ligand-Clusters) and they tended to be more hydrophilic (low logP values) with a low number of rotatable bonds (RototableB) (Table  S3 ). Table  2. Description of the Ligand-Clusters according to the different promiscuity levels using the DBS4 dataset. Ligands associated with selective (S), MP, and HP DBS are considered as dedicated, and those associated with DBS of different promiscuity levels are considered mixed. Second column gives the occurrence (and frequency) of Ligand-Clusters according to the promiscuity of DBS to which they bind: 29 were S, 182 were MP, the majority (1,631) were HP, and 127 were mixed, where they bind to DBS of different promiscuities.
Complementary Study of the Ligand-Cluster Promiscuity Subsequently, we studied the contribution of the DBS promiscuity and the Ligand-Cluster promiscuity on the multiple interactions between the different protein classes. First, we conducted a complementary study of the promiscuity of the Ligand-Clusters in the DBS4 dataset. We observed a limited Ligand-Cluster promiscuity: 82. 2% were selective (in interaction with only one DBS) and 17. 8% were promiscuous. This weak rate of promiscuous Ligand-Clusters can be partly explained by 71% (1,407/1,969) of the Ligand-Clusters being composed of only one ligand (where 59% (832/1,407) only had one occurrence) in the DBS4 dataset. These promiscuous Ligand-Clusters were associated, on average, with 3. 11 (±2. 54) DBS extracted from 1. 76 (±1. 02) different MOAD protein classes.
This is coherent with their tendency to be flexible and small for interacting with more difficult pockets that are associated with selective DBS. These promiscuous Ligand-Clusters interacted with a high number of DBS: 4. 15 DBS corresponding to 2. 13 MOAD protein classes and 6. 71 DBS corresponding to 2. 95 MOAD protein classes, respectively. Table  3. Distribution of the Ligand-Clusters relative to their ability to interact with DBS of different levels of promiscuity (rows). The last row, "All DBS", indicates the values of all the Ligand-Clusters in the DBS4 dataset. The columns indicate the total number of Ligand-Clusters, and those that interacted with one DBS (selective) or more (promiscuous). For the promiscuous Ligand-Clusters, we detailed the average number of DBS bound and the average number of protein class bound (not indicated for the selective Ligand-Clusters, as they bind to one DBS).
1 The row "All DBS" is not the sum of the other rows because some Ligand-Cluster bind several types of DBS Next, we analyzed the 68 Ligand-Clusters that interacted with selective DBS of different MOAD protein classes in further detail. These were split in Ligand-Clusters different in terms of promiscuity. We studied the characteristics and frequency of the 29 selective and the 39 promiscuous Ligand-Clusters. The 29 selective Ligand-Clusters interacted with 29 selective DBS that belonged to nine different MOAD protein classes. This indicated that only 1. 4% (29/1969) of the Ligand-Clusters and 6% (29/481) of the DBS supported the "one drug, one target" concept in the DBS4 dataset. The 39 promiscuous Ligand-Clusters were highly promiscuous-these interacted with 6. 71 DBS on average. The high promiscuity of these 39 Ligand-Clusters can be explained by their ligands having to be rather small and adaptable to be able to interact with selective DBS pockets.
From the Ligand-Cluster point of view, we observed 85 (72%) dedicated and 32 (28%) promiscuous Ligand-Clusters according to the small portion of promiscuous Ligand-Clusters in the DBS4 dataset (Table  3 ). However, some of the promiscuous Ligand-Clusters were highly promiscuous, as illustrated by Ligand-Cluster number 1 in interaction with 27 DBS (Figure  7 ). From the DBS point of view, only a small number (five) of the lyase DBS were not connected to another one DBS by some common Ligand-Clusters (two selective and three HP lyase DBS). Most of the lyase DBS shared at least one Ligand-Cluster with other DBS, and mainly with DBS from other protein classes. We observed one lyase DBS that was particularly promiscuous: DBS 1_1, corresponding to a human carbonic anhydrase protein, interacting with 73 Ligand-Clusters whose 55 were dedicated to this DBS.
Circles represent the DBS; these are colored according to the MOAD protein class to which they belong and are named according to their promiscuity (S for selective, MP for moderately promiscuous, and HP for highly promiscuous). The squares represent the Ligand-Clusters and are colored according to their level of promiscuity (beige for selective, binding to one DBS, or green for promiscuous) and their name is written inside the square. The grey line represents the boundary between the Pocket-Clusters and the Ligand-Clusters. This network visualization was created using the igraph package (see Section 4).
Discussion In this study, we used the high-quality protein-ligand complex database, the MOAD, to study the promiscuity of the druggable binding sites. MOAD PDB chains were clustered according to a sequence identity threshold of 90% in a homologous chain cluster using H-CD-HIT to identify DBS promiscuity by taking advantage of the increasing protein redundancy information. The high quality of the MOAD's complexes is an advantage for characterizing binding site properties, but it can result in a limited number of available chains. Consequently, we studied the promiscuity of DBS bound to at least four drug-like valid ligands in the MOAD to reduce any false conclusions regarding selectivity due to the limited complex structures that are available. The resulting 481 DBS and 3,488 drug-like ligands, corresponding to 7,267 pocket-ligand interactions, confirmed that the MOAD can provide a valuable and large amount of data to study binding site promiscuity, for instance, through a comparison with datasets that Mestres et al.
These pocket properties are expected to facilitate drug-like ligand interactions, as they are well adapted to bind to several and diverse ligands (their interacting Ligand-Clusters were associated, on average, with a Tanimoto coefficient of 0. 44 (±0. 10) per HP DBS). Accordingly, their ligands do not need a particular flexibility and they tended to be large with high carbon and heavy atom frequencies, and a few were hydrophilic. The opposite promiscuity trend was observed in the MOAD for DBS and Ligand-Clusters: only 18% of Ligand-Clusters were promiscuous, whereas 80% of the DBS were promiscuous. The high frequency of selective Ligand-Clusters in the DBS4 dataset can be partly explained by the high frequency of ligands (24%) only observed once and by the clustering of homologous chains in one DBS, which thus reduces the number of DBS.
This weak frequency of promiscuous Ligand that was observed both in our analysis and in a previous study  [25]  may be due to the fact that most of the ligands are not usually tested against other proteins beyond their target family, while proteins are usually tested against many different ligands, thus there is much more room for the observed promiscuity. Moreover, the known protein DBS universe is significantly smaller than the universe of possible ligand scaffolds. Rifaioglu et al. [42]  recently underlined that there are tens of millions of compounds that are available in compound and bioactivity databases, (about 9,000 FDA-approved small molecule drugs approved by experimental), while there are roughly 550,000 reviewed protein records available (20,244 of which are human proteins) in protein sequence and annotations resources (e.
The weak frequency of selective Ligand-Cluster could be due to the choice of a high quality but sparse database, such as MOAD. Nevertheless, this highlights that the high promiscuity of a large number of DBS compensates for a low number of promiscuous Ligand-Clusters. This analysis confirms that the DBS promiscuity strongly contributes to multiple interactions between Ligand-Clusters and DBS belonging to different protein classes. This is in accordance with Meyers et al. [14] , who concluded that pocket similarity analysis only partly explains the drug promiscuity across different target families. Computational approaches have been demonstrated to be very beneficial and promising for polypharmacological studies, but also drug off-target studies, whose mechanisms are poorly understood or largely unknown, in most cases  [43].
Drug-Like Ligand Clustering and Description The diversity of this drug-like ligand set was analyzed while using Tanimoto similarity, based on MACCS fingerprints  [48]. A Tanimoto coefficient that is equal to 1 corresponds to identical MACCS fingerprints, whereas a null coefficient does not share any fingerprint similarity. The Tanimoto coefficients were computed on all ligand pairs using OpenBabel software  [49]. Similar ligands were gathered by hierarchical clustering while using the Butina algorithm  [50]  and Ward method aggregation criterion. Similar ligands with a Tanimoto coefficient threshold greater than or equal to 0. 8, as in  [29] , were clustered and defined as a Ligand-Cluster. The resulting ligands and Ligand-Clusters were described while using 21 geometrical and physicochemical descriptors that were proposed by FAF-Drugs3 software  [47, 51]  to characterize their main tendencies relative to the promiscuity of their associated DBS.
• Two pockets i 1 and i 2 are fall in the same Pocket-Cluster when d(g(i 1 ), g(i 2 )) < C, meaning that the distance between i 1 barycenter and i 2 barycenter is lower than the cutoff value. For our dataset, we experimentally found that k = 2 was an optimal value ensuring the transitivity property of our clustering criterion (i. e. , when pockets i 1 and i 2 are in the same cluster and pockets i 2 and i 3 are in the same cluster, therefore i 1 and i 3 must be in the same cluster). Indeed, we do not obtain any non-overlapping pockets when we calculated the score of overlap (SO) between the p pockets of each DBS. It means that the disconnected pockets are clustered in two different Pocket-Clusters. Here, the score of overlap of  [34, 58]  corresponds to the proportion of atoms that are common between two pockets that are associated with one binding site (2): SO = n common n i 1 + n i 2 -n common (2) where n common is the number of atoms belonging to both the pocket i 1 and pocket i 2 , n i 1 and n i 2 are the numbers of atoms in pockets i 1 and i 2 , respectively.
Figure1. The protocol developed to extract Druggable Binding Sites (DBS) from the Mother Of All Databases (MOAD) data and to determine their promiscuity. We extracted 3,488 valid and drug-like ligands that were regrouped into 1,969 clusters while using the Tanimoto coefficient (green), 459 homologous chain clusters among the 4,749 Protein Data Bank (PDB) files selected using TM-Align [28]  to calculate the identity between all the protein chains, and H-CD-HIT [29]  clustering algorithms to group the homologous protein chains (orange). The 7,267 pockets clustered in 481 druggable binding sites (DBS) (blue) correspond to the ligands previously selected and all of the homologous protein chains. The promiscuity was calculated from these data.
Strategies to reduce the electron dose are of main significance to preserve the sample integrity. In ET, several strategies based on sparse acquisition (i. e. , downsampling) of the data have been proposed to reduce radiation damages. Restoration of missing information is performed using data compression approaches based on algorithms such as compressive-sensing (CS) or inpainting. In previous work, several algorithms such as discrete algebraic reconstruction technique  [17] , total variation minimization  [18] , CS  [19] [20] [21]  have been developed. In these different works, the downsampling is performed either by (i) reducing the number of acquired tilt angles or by (ii) reducing the number of pixels collected per image (i. e. , sparse images). Very recently, the limits of the reduction of collected tilt-angles associated with sparsity-exploiting reconstructions have been explored and show that they are highly dependent on the sample properties  [22].
Experimental Setup of the Electron Microscope The 200 kV field emission gun electron microscope (2200FS, JEOL, Tokyo, Japan) in scanning mode was set up to enable imaging conditions for thick specimens using the following parameters (condenser lens aperture: 40 µm, camera length: 80 cm, semi-convergence angle was 9. 3 mrad and outer bright-field semi-collection angle was 5 mrad). Images collected between 20,000 × and 30,000 × magnifications (corresponding pixel sizes were 2. 08 and 1. 38 nm respectively) were digitized using a Digiscan II ADC module (16 bits). The beam current was 2. 2 pA.
These settings correspond to a significant electron dose reduction compared to the settings used on the crossed line grating test sample. The electron dose was deliberately diminished to match very low dose collection conditions. Using these collection conditions, the final electron dose received by the sample after completion of the tilt-series has been estimated around 70 e -/Å 2 for fully collected images. To study the impact of the electron dose reduction on the quality of the reconstruction, a total of eight sparse images were collected for each tilt angle. The downsampling amount in the sparse images ranged between 3. 125% and 25%. After collection of the whole tilt-series, block-based sparse images were reconstructed in Matlab using 250 iterations of the inpainting algorithm developed by Garcia  [35, 36].
Reconstruction Quality Assessment The quality of reconstructions was initially assessed by visual inspection. However, to better characterize the reconstructions, image quality descriptors (IQD) were also measured on individual Z-slices of the 3D volumes. Entropy (H), root mean square contrast (C rms ) and Michelson's contrast (C m ) were computed using the following equations  [39] : H = - n i=1 P i log 2 P i H is the entropy of the element containing n pixels and Pi is the appearance probability of the pixel value i in the element. C rms = 1 MN M i=1 N j=1 (I xy -I) C rms is the root mean square contrast, M and N are the dimensions of the data in x and y respectively, I xy is the value of the pixel at position xy and I is the mean pixel value.
Collection of Sparse STEM Images Collection of non-overlapping pixels blocks randomly distributed over the area of interest (ROI) was performed using an in-house developed Digital Micrograph (DM) script. The Digiscan II software of DM has built-in functions that allow the collection of pixel blocks which dimensions can be 16 × 4 pixels. This collection scheme was used on a carbon crossed line grating grid to collect 12. 5% of the pixels (Figure  1A ). The white background represents uncollected pixels. Inpainting was then computed to recover missing pixels (Figure  1B ). The pattern of the crossed line grating replica becomes apparent after the inpainting reconstruction and resembles the ground-truth image of the same ROI (Figure  1C ). In ET, the Z-dimension of a reconstruction is large enough so that the entirety of the object of interest can fit in the 3D volume.
Data Presentation Images presented in this work have been generated using ImageJ (v1. 51j8)  [40]. Artworks have been designed in Blender. The computation of IQD and corresponding figures were performed in Matlab. IQD ratios data were fitted to the following model function using non-linear least-squares regression: 𝑓 𝑥 𝐴 1/ 𝐵 𝐶𝑥.