dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We are grateful to  Ann Reum  ( Institute of Virology, Freie Universität Berlin ) and  Marion Müller  and  Yvonne Weber  ( Institute of Immunology, Freie Universität Berlin ) for their technical assistance. The authors thank all lab members for the helpful discussions. This work was supported by the  Deutsche Forschungsgemeinschaft  grant  KA 3492/8-1  awarded to B. B. K. All authors critically read and revised the manuscript and approved the final version. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
g. , by minimizing the occurrence of "property cliffs"-pairs of compounds perceived as highly similar in spite of using widely different property values  [15]. A similarity search is often used as a first step in VS. For example, Aoyub et al. [16]  and Guo et al. [17]  performed 2D similarity searches in large compound databases as initial phases of drug design cycles that resulted in development of novel MTAs binding to the taxane and colchicine site, respectively. Several novel colchicine-site targeting agents were also discovered by  Mangiatordi et al. , who based their design on a 3D shape similarity screening  [18]. Another two colchicine-site targeting hits were found by Federico et al. , who used not only 3D shape, but also electrostatic potential similarity in their VS campaign  [19].
[28]  and Pandit et al. [29]. Both works used CoMFA and CoMSIA methods to rationalize structure-activity data for limited datasets of similar scaffold-based compounds, suggesting possible structure optimization patterns, which, in case of the latter work, yielded a new class of cytotoxic in vitro tubulysin derivatives targeting the vinca binding site. A summary of the experimental conditions for the above-mentioned QSAR works is provided in Table  A2. It is worth noting that the use of machine learning in this field has been limited due to the scarcity of publicly available data. The lack of large, diverse tubulin-related structureactivity datasets makes it difficult to train adequate machine learning models that can be used in a large-scale virtual screening context. For example, querying the ChEMBL database (v.
26) for "Tubulin" returns more than 8000 raw structure-activity records, but these are a heterogeneous collection of results from widely different assays at diverse experimental setups, using the MTs or tubulin of widely different species (from Arabidopsis to Homo Sapiens). Or, machine learning requires homogeneous, comparable experimental activity entries to serve for calibration of empirical functions trying to approximate them upon input of a molecular structure. Thus, only entries sourcing from a same experimental setup (listed under a same ChEMBL Assay ID) can be safely compared. Deceivingly, there is only one such assay (CHEMBL817769; Inhibition of tubulin polymerization interacting at the colchicine binding site of Sus Scrofa) featuring more than 100 entries (103, precisely)-a ruleof-thumb minimal threshold of training set size to start envisaging machine learning.
The tubulin Designed Ankryin Repeat Protein DARPin crystallization system (Figure  3B )  [45]  is the second most frequently used one. This system allows to achieve even higher resolution compared to the T 2 R-TTL one, with the best resolved structure ranking at 1. 5 Å resolution (PDB ID 6S8K,  [46] ). In this system, only one tubulin dimer is coordinated by the selected DARPin, resulting in a much more densely packed and smaller unit cell. The very first structural information on tubulin was obtained in 1998 by Nogales et al. using electron crystallography on taxol stabilized zinc-induced protofilaments. This allowed the determination of a first model of the structure of tubulins, the assignment of domains and identified the taxol binding site on β-tubulin  [40]. However, the arrangement of the protofilaments in this crystal system is antiparallel and does not reflect the protofilament-assembly found in MTs.
In the T2R-TTL system, the TTL protein is bound at the same end of the tetramer on α1 tubulin. The overall tubulin structure does not differ significantly between the two setups. Since the SLDs and TTL used in these crystallization systems may prevent binding of proteins to tubulin, alternatives have been developed. The tubulin Designed Ankryin Repeat Protein DARPin crystallization system (Figure  3B )  [45]  is the second most frequently used one. This system allows to achieve even higher resolution compared to the T2R-TTL one, with the best resolved structure ranking at 1. 5 Å resolution (PDB ID 6S8K,  [46] ). In this system, only one tubulin dimer is coordinated by the selected DARPin, resulting in a much more densely packed and smaller unit cell. Up to now, the described systems T2R, T2R-TTL and TD1 are the only ones that have been used to elucidate the structures of tubulin-small molecule complexes.
System Selection for Virtual Screening (VS) and MD Simulations Not all out of the more than 300 crystal structures within the PDB database were equally often used in computational experiments, as we noticed in our analysis of the most recent MD simulation literature (overview in Table  A6 ). Surprisingly, we found that even 20 years after the first description of the tubulin structure at near-atomic resolution  [54] , simulations of taxane-site ligands or apo tubulin are often based on some of the very first tubulin datasets obtained with electron diffraction in 1998 (PDB ID 1TUB, 3. 7 Å,  [40] ) and 2001 (PDB ID 1JFF, 3. 5 Å,  [54] ). There is a bit more of variety in the colchicine site structures that were selected for simulations, although only a fraction of the great number of available high-resolution tubulin colchicine site structures have been considered: PDB ID 1SA0 2004 3.
Protein-ligand docking is a powerful VS tool that alone can produce high-quality results. For example, Zúñiga-Bustos et al. used only protein-ligand docking to screen a large compound library, with virtual hits being confirmed promotors of tubulin polymerization targeting the laulimalide binding site  [91]. In another study, Liu et al. screened a large database with consecutive docking experiments with increasing rigor of conformational sampling, eventually yielding six hits with in vitro antitumor activity due to targeting the colchicine binding site  [92]. In a similar manner, Liu et al. docked a large compound library and discovered two colchicine-site targeting in vitro inhibitors of tubulin polymerization among the highest ranked molecules  [93]. Often, protein-ligand docking is used as a way to provide rationale for a tubulintargeting agent's biological action.
Fusani et al. compared the binding mode of epothilone A in complex with tubulin of the first published 3D structure solved by Nettles et al. (PDB: 1TVK) and a later one solved by Prota et al. (PDB: 4I50) using BPMD. Fusani et al. wanted  to differentiate between the correct and incorrect ligand binding poses by applying BPMD  [109]. Moreover, Gaspari et al. used MetaD to induce the cis-to-trans isomerization of a colchicine binder in complex with tubulin. This allowed the authors to calculate the difference in binding free energy between the cis and trans isomers of the ligand via a thermodynamic cycle. Furthermore, Gaspari et al. also used MetaD to gain insight into the differences in the unbinding process of colchicine and another colchicine site binder studied in complex with tubulin  [110].
The authors wanted to investigate how the change in the substituent position could alter the free energy of binding and compare the binding mode of the molecules in the tubulin sub-pocket. The computational results aligned with the experimental ones, concluding that the meta-substituted molecule was a better colchicine site binder than the para-substituted compound  [116]. Stroylov et al. used FEP calculations based on MD simulations for predicting tubulinligand free binding energy differences of new tubulin polymerization inhibitors targeting the colchicine site  [117]. Mao et al. with the goal of discovering new tubulin inhibitors capable of binding to the taxane site, performed a VS of ~1. 6M molecules retrieved from the ChemDiv database. After applying different computational filters, 17 hit compounds were selected and submitted for experimental evaluation.
Author Contributions: H. P. -P. , A. -C. A. and M. S. -extensive literature review and original draft preparation, A. E. P. , S. P. and D. H. -editing, review and supervision. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the H2020-MSCA-ITN-2019 (860070 TUBINTRAIN). Institutional Review Board Statement: No bibliographic database was harmed during this study. Informed Consent Statement: Not applicable. Data Availability Statement: This is a review article, no new data was created.
For simulations of other ligands, since a lower number of structures is available, the choice of the starting model was obvious: vinca-site ligands PDB ID 3E22 2008 3. 8 Å [59], PDB ID 4O4J 2014 2. 2 Å [56], PDB ID 5JH7 2016 2. 2 Å [67], and laulimalide site: PDB ID 4O4H 2014 2. 1Å.
Biomolecules 2023, 13 , 285 7 of 32 Figure 2. 13322 Figure 2. The "curved" and "straight" tubulin conformations. (A) A straight protofilament, as present in the MT lattice, is shown in ribbon presentation in light gray (PDB ID 7SJ7). A protofilament constituted of tubulin in a curved conformation is shown in blue (from PDB ID 5LXT). (B) The intrinsic curvature and structural differences on a single dimer are shown: A heterodimer in the straight conformation is depicted in light gray and the curved conformation in light blue. The main differences in the structures are within the intermediate domain (residues 206-384), highlighted in darker blue, which upon curved-to-straight transition moves relative to the other domains. This is also indicated in the schematic drawing of both straight and curved dimers.
For simulations of other ligands, since a lower number of structures is available, the choice of the starting model was obvious: vinca-site ligands PDB ID 3E22 2008 3. 8 Å [59], PDB ID 4O4J 2014 2. 2 Å [56], PDB ID 5JH7 2016 2. 2 Å [67], and laulimalide site: PDB ID 4O4H 2014 2. 1Å , PDB ID 1Z2B 2005 4. 1 Å [58], PDB ID 3E22 2008 3. 8 Å [59], PDB ID 3HKC 2009 3. 8 Å [57], PDB ID 4O2B 2014 2. 3 Å [65], PDB ID 6Y6D 2020 2. 2 Å [66].
7 × 10 6 Phase (Schrödin-ger) Atom-type-based 3D shape Atom-type volume scoring, 0. 65 similarity threshold 31 virtual hits have been confirmed to decrease microtubule polymerization in vitro. Federico 2020 [19] ZINC *, Chembridge Diverset CL, Chembridge Diverset EXP, BindingDB FDA, MayBridge 164 × 717 ROCS (OpenEye) EON (OpenEye) Smooth 3D Gaussian functions for each atom Electrostatic potential maps of pre-aligned molecules Tanimoto similarities of aligned overlap volumes (no threshold, top 5000 selected for ROCS, top 2000 for EON) Two virtual hits established by shape and electrostatic similarity to a known active were shown to inhibit tubulin polymerization in vitro. * (Drug Database, Naturals).
006. Possibility of scaffold-hopping for vinca-site-targeting compounds design was shown Niu 2014 [34] Specs Screening Database, 202,919 molecules 26 compounds designed to target colchicine site with known cytotoxic action HypoGen module from Discovery Studio 2. 5 (Accelrys) HBD, HBA, HP, and R-A 1 66 colchicine site-targeting compounds with known cytotoxicity (26 actives, 40 inactives) Cost difference Two compounds with good fitness to the developed pharmaco-phore model were shown to be tubulin polymerization inhibitors in vitro. Stefanski 2018 [27] A custom-designed virtual combinatorial library of 1159 combretastatin A-4 analogs 21 active colchicine site-targeting molecules mined from literature Discovery Studio 3. 5 (Accelrys) HBA, HBD, HP, and HP-A 1 20 tubulin inhibitors and 800 decoys mined from ChEMBL Area under receiver-operator curve (AUROC) Two virtual hits were established as in vitro cytotoxic agents targeting colchicine binding site.
) 970 inactive molecules and 30 known inhibitors with experimental activity mined from literature Specs database, 202,919 molecules The screening established five virtual hits that are cytotoxic in vitro, one most potent hit confirmed to bind at the colchicine site Ensemble of many pharmacophore models 118 crystal structures of LigandScout v3. 1 81 co-crystalized ligands and A subset of specifically based on colchicine Zhou 2019 [83] tubulin co-crystalized with colchicine site binding (Inte:Ligand), Phase v3. 4 (Schrödinger), 3354 decoys randomly extracted from the DUD-E selected 8918 purchasable compounds site-bound ligands structures was used in virtual screening ligands Pharmer database from the ZINC database which led to discovery of a potent tubulin-targeting cytotoxic agent Zhang 2021 [32] 1JFF Discovery Studio 3.
5 (Accelrys) 467 inactive molecules from ZINC15 database and 33 known inhibitors with experimental activity BioDiversity, 30,000 molecules Large database filtered to focus on a subset that eventually led to discovery of two taxane-site targeting cytotoxic agents Protein-ligand interaction Virtual screening campaign Gallego-Yerga 2021 [84] 1SA0 fingerprints (PLIF) implemented in MOE (Chemical Computing Group No additional validation performed A subset of 100, 000 compounds from ZINC15 database yielded a novel cytotoxic agent disrupting tubulin polymerization by binding at Inc. ) colchicine site Elseginy 2022 [85] 3E22, 3HKD, 3HKE, 3HKC, 1Z2B, and 1SA1 Discovery Studio 2. 5 (Accelrys) 40 literature-mined tubulin inhibitors targeting the colchicine site, 2000 decoy molecules randomly selected from ChemDiv library ChemDiv library, 700,000 molecules A virtual screening campaign discovered an in vitro potent cytotoxic hit targeting the colchicine binding site.
8 Å AutoDock 4. 2 645 virtual hits yielded by similarity search in PubChem 1 20% Established hit got satisfactory and tested predicted physiochemical analog compound synthesized properties; later work saw an Ayoub 2013 [16] Extracted from 1JFF as a AutoDock Vina 1309 virtual hits established by Anti-proliferative activity (IC 50 ) Taxane sphere containing the residues Gold a pharmacophore screening of 11 22% against four cancer cell lines Zhang 2021 [32] within 11. 5 Å from the ligand CDOCKER the BioDiversity database ranging from 10. 31 µM to Colchicine Extracted from 1SA1 as all residues around the ligand at a 6. 5 Å distance SurFlex-Dock 1739 virtual hits found by pharmacophore screening of the ChemDiv library 1 1. 78% 21. 04 µM Tubulin polymerization inhibition IC 50 value of 17. 6 µM Nagarajan 2015 [82] Around 3000 virtual hits Colchicine Extracted from 4O2A as a sphere of 8 Å radius around the native ligand GOLD procured by ligand-based virtual screening of six chemical libraries Three databases: Chembridge 3 43% IC 50 of 83.
Consequently, diagnosis frequently occurs during later, debilitating stages of the illness, with elevated morbidity rates. In leprosy, co-infections often involve pathogens prevalent in developing countries and can modify disease progression, increase the risk of leprosy reactions, or affect the efficacy of treatment  [3]. Antimicrobial drug interactions between leprosy multidrug therapy and other treatments can also pose challenges  [4]. Publications on leprosy co-infections have focused on Tuberculosis, Leishmaniasis, Chromoblastomycosis and Helminthic infections. There is a growing body of evidence on these co-infections, however, there are still many knowledge gaps and a lack of consensus on the topic. We performed a preliminary search on MEDLINE, Cochrane Database of Systematic Reviews and JBI Evidence Synthesis that did not identify any scoping review on bacterial, fungal, and parasitic co-infections in people with leprosy.
Search strategy Between September 20th to 30th, 2022, a librarian conducted searches in the PubMed/MED-LINE, Virtual Health Library-VHL (excluding Medline), Embase, Scopus, and Google Scholar databases. The search strategies were established by incorporating keywords structured using the acronym PCC (Population: individuals with leprosy; Concept: clinical and epidemiological characteristics; Context: co-infections with Tuberculosis, Leishmaniasis, Schistosomiasis, Chagas Disease, fungal and helminthic infections). The complete search strategy can be accessed at https://osf. io/wvcuh/?view_only=b7d8554cc46c4ba2aaecff9cd9e02f50.
Regarding study characteristics, 73% of the papers discussed issues related to treatment, while 45% disclosed financial support and conflict-of-interest statements. Ethnicity of the study participants was reported in only 6. 7% of the studies. Table  1  summarizes the most relevant clinical and epidemiological findings. Outcomes data for each source of evidence are fully available in a spreadsheet table published elsewhere (https://osf. io/wvcuh/?view_only=b7d8554cc46c4ba2aaecff9cd9e02f50).
Ideally, studies should begin with population databases to identify the largest possible number of cases. Our survey identified only two such studies on leprosy-leishmaniasis co-infection  [98, 99] , highlighting the need for further research on co-infections. Researchers residing in endemic areas for any co-infection are encouraged to conduct similar studies for other infections instead of solely reporting cases they encounter in their clinical practice, except when population databases are not available. Additionally, we encourage studies that explore coinfection of leprosy and syphilis, given the near absence of investigations in this domain, despite the exceedingly high prevalence of syphilis. Furthermore, given the substantial migration flow, clinicians in developed countries must be cognizant of neglected diseases, particularly in populations with elevated social vulnerability and immunosuppression, which may result in elevated morbidity, transmission, and disabilities.
Moreover, among Lp-associated larvae, control larvae are more distant of ATF4 knock-down and GCN2 knock-down. This pattern was expected because ATF4 is downstream GCN2, and thus genes regulated by ATF4 should also be regulated by GCN2. However, ATF4 knocked-down samples and GCN2 knocked-down samples are clearly separated, which indicates that our dataset may allow us to identify signatures of GCN2-dependent ATF4-independent gene regulation (Fig. S7A ). Even though control larvae associated with B02. 04 and F07. 08 were closer, most samples were separated from each other by PC2 (Fig. S7B ). We applied the four aforementioned selective filters (Fig. 6A ) to our dataset and found 104 genes consistently differentially expressed (25 up-regulated and 79 downregulated) by Lp in a GCN2-dependent, ATF4-independent and r/tRNA operondependent manner (Fig.
Developmental timing experiments GF flies were placed in a sterile breeding cage overnight to lay eggs on a dish of HD similar to the HD used for the experiment. At d0, we collected the eggs and placed them in the tubes containing the HD. Unless stated otherwise, each experimental condition consisted in 5 tubes each containing 40 eggs. Eggs were then inoculated with 100 µL of sterile PBS 1X (GF condition) or with 100 µL of a culture of bacteria resuspended in PBS 1X (yielding ~2x10 8 CFUs of Lp and ~10 7 CFUs of Ap per tube). For the Heat-Killed (HK) condition, the resuspension of Lp in PBS was incubated 3h at 65°C. After inoculation, the larvae were kept at 25°C with 12:12-h dark/light cycles. The number of newly emerged pupae was scored every day until all pupae have emerged. The data are represented either as curves of pupariation over time or as the median time of pupariation (D50) calculated using a custom script on RStudio.
We used the R package Coxme  (Therneau and Grambsch, 2000)  to make a Cox proportional hazard model adapted to curves of pupariation over time, replacing the event "death" with the event "pupariation"  (Rodrigues et al. , 2021). Each experiment was performed in five independent replicates (one replicate consists in a tube containing 40 eggs as explained above). To compare the effect of Lp on development across different diets (e. g. balanced HD vs imbalanced HD), we tested whether the interaction between absence/presence of Lp and the diet was significant using the model pupariation ~absence/presence of Lp * diet + (1|Replicate) (1|Replicate accounts for replicates as a random factor). When necessary, we used the FDR method to correct for multiple comparisons. To compare one treatment to other treatments (e.
Data were analyzed using Galaxy  (Afgan et al. , 2016). Briefly, for each mutant, we filtered all pairs of reads which had one of the two reads mapped on the transposon sequence. We gathered the paired reads and mapped them on the genome of Lp NC8  (Axelsson et al. , 2012)  to identify the region in contact of the transposon. The genome of Lp NC8 contains five operons encoding r/tRNAs that share high sequence similarities. Therefore, sequencing did not allow us to identify in which operon the insertion took place. We thus used operon-specific PCR to identify in which operon the transposon was inserted for each mutant. For each mutant, we used two primers specific of the transposon (OLB215 and OLB 221) and one primer specific of each r/t RNA operon (op1, op2, op3, op4 and op5). The sequences of all primers used in this study can be found in Table  S2.
Acknowledgements We would like to thank  Pr. Hyung Don Ryoo ,  Dr. Pierre Leopold ,  Dr. Nathalie Arquier , the  Vienna Drosophila Resource Center  and the  Bloomington Stock Center  for fly lines;  Dr. David Duneau  for his help with statistical analyses;  Dr. Gilles Storelli  for critical reading of the manuscript;  Lucie Fallone  and  Meline Garcia , trainees of the  Master Biosciences of ENS de Lyon  for their help with experiments; the  ArthroTools platform  and the PLATIM platform of the  SFR Biosciences  ( UAR3444/US8 ) for fly facility and microscopy equipments. Research in F. Leulier's lab is supported by the  "Fondation pour la Recherche Médicale " (« Equipe  FRM DEQ20180339196 ) and the  Scientific Breakthrough Project  from  Université de Lyon  " Microbehave". T. Grenier  is funded by a  PhD fellowship  from  ENS de Lyon.
Statistical Analysis Statistical analysis was carried out using XLStat software version 2019 (Addinsoft, Paris, France). The comparison of averages of each treatment was based on the analysis of variance (ANOVA) at the significance level of 5%. Values followed by the same letter are not statistically significant according to Tukey's multiple range test at a significance level of p < 0. 05. Principal components analysis was also performed on the measured parameters (TPC, TFC, hesperidin content, DPPH-RSA, ABTS-RSA, ICA), showing significant variations with the used extraction methods (CSE, UAE, ASE, DIC-CSE, DIC-UAE, and DIC-ASE).
Data Availability Statement: Data is contained within the article. Acknowledgments: M. C. and N. B. are thankful to Grand Reims, Département de la Marne and Région Grand Est for financial support. N. B. and M. B. A. are grateful to A. Z. (Herbes Tunisie), Ministry of Higher Education and Scientific Research and National Research Promotion Agency for their funding to M. B. A. as part of the program of research scholarship (MOBIDOC scheme, PromESsE project, reference N • 435). N. B. is grateful to N. A. for the English checking of the manuscript.