dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of the scoring functions. Binding affinity accuracy. The best model of each machinelearning algorithm was selected according to the Pearson's Correlation Coefficient ( R ) using the tenfold cross- validation strategy. Then, we applied the scoring functions to the respective test sets to validate their affinity predictability according to R and root mean squared error (RMSE). Both R and RMSE were calculated using the experimental and predicted free energy of binding (ΔG bind ): where y i and t i are respectively the predicted and the experimental binding affinities for the i-th complex, y and t are the arithmetic average values for y and t and N is the number of points in the data set. where N is the number of points in the dataset, y i is the predicted binding affinity and t i is the experimental binding affinity.
Virtual screening experiments. In order to evaluate the success of our scoring functions to discriminate active and decoys compounds, we performed docking experiments using the protein-ligand docking program DockThor  51, 52  and re-scoring with DockTScore on core set and the DUD-E datasets  63  for the proteases FA7 (coagulation factor VII, PDB code 1W7X), RENI (renin, PDB code 3G6Z), TRYB1 (tryptase β1, PDB code 2ZEC), and UROK (urokinase-type plasminogen activator, PDB code 1SQT), and the kinases AKT2 (serine/ threonine-protein kinase AKT2, PDB code 3D0E), KIT (stem cell growth factor receptor, PDB code 3G0E) and MK01 (MAP kinase ERK2, PDB code 2OJG). Proteases were selected to evaluate the screening success of the DockTScore general and target-specific scoring functions trained on the PDBbind refined set due to the large size of the training set used to calibrate the focused scoring functions for proteases.
The protease and kinase datasets from DUD-E were chosen according to the following criteria: (i) no metal ions interacting with the ligand, and (ii) co-crystallized ligand successfully redocked with the top-energy solution with RMSD ≤ 2. 0 Å. For PPIs, we constructed screening datasets for Bcl2-like/BAX and MDM2/p53 systems composed of actives taken from the iPPI-DB  64  database (https ://ippid b. paste ur. fr/) and inactive compounds taken from the BDM chemical library available at ChemREST (https ://chem-rest. paste ur. fr/#?&versi oned_sourc es=8&used_filte rs =). The iPPI-DB is a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of about 2000 modulators of protein-protein interactions. It contains exclusively small molecules and therefore no peptides.
BDM compounds have been previously shown to be negative on MDM2 and Bcl2 interactions via fluorescence polarization assays  65. For the PPIs screening datasets, we selected only the compounds without chiral centers and having only one protonation/tautomer state as predicted by Epik. Following the DUD-E sets construction, we selected randomly 50 inactives for each active compound to keep an adequate balance between actives and inactives to evaluate the scoring functions performance on virtual screening experiments. The PDB codes 3QKD and 4IPF were used for the receptor structures of the Bcl-2-like protein 1 and MDM2, respectively. The docking poses were generated with the program DockThor for protein-ligand docking freely available as a web server at https ://dockt hor. lncc. br). The DockThor program uses a grid box to define the search space, the DMRTS genetic algorithm as the search algorithm, and an MMFF94S-based scoring function for pose R = N i=1 y i -y t i -t N i=1 y i -y 2 N i=1 t i -t 2 RMSE = 1 N N i=1 y i -t i 2 prediction  51, 52.
It has been widely demonstrated that iPPIs have higher hydrophobicity, aromaticity and molecular weight compared to enzyme inhibitors, as usually interacting within flatter, larger and more hydrophobic binding sites than the enzyme catalytic sites  41, 71, 72. Given this, it is expected that the hydrophobic effect due to the binding represented here by the lipophilic contact and "oneSolv" solvation terms exhibit a strongly favorable contribution for this class of complexes. The unfavorable contribution of the E vdWS term might be due to some overlapping with the lipophilic contact and the "oneSolv" solvation terms. Further, a larger dataset set would allow to better evaluate the solvation contribution for inhibiting PPI. Regarding the ligand entropy, it is clearly unfavorable for the binding.
The co-crystallized ligand is bound with only one "frozen" rotatable bond in the dimer out of four rotatable bonds (Fig. 6 ). Therefore, we also evaluated the performance of the DockTScore PPI-specific scoring functions on the TRYB1 target (Fig. 7 ). Interestingly, the PPI-specific MLR scoring function outperformed the other scoring functions evaluated (i. e. , general and protease-specific, linear and nonlinear), achieving an AUC ROC curve of 0. 762 (SMOreg protease was 0. 651), EF 1% = 15. 626 (SMOreg protease was 7. 473), BEDROC20 = 0. 291 (SMOreg protease was 0. 203) and BEDROC100 = 0. 272 (SMOreg protease was 0. 167). The screening of actives and inactives on the two PPIs datasets resulted in AUC values better than 0. 70 for the two targets for almost all scoring functions (Table  6  and Fig.
8 ), while the early recognition problem was successfully addressed only for the Bcl2-like system, reaching high BEDROC values of 0. 474 (α = 20) for SMOreg and 0. 539 (α = 100) for MLR. For the Bcl2-like protein/BAX system, the SMOreg scoring functions generally Table  5. Screening success of the general and target-specific scoring functions trained with MLR, SMOreg and RF for the FA7, RENI, TRYB1 and UROK datasets from DUD-E. ac, dec and tot are the number of active, decoy compounds and the total number of molecules in the final dataset (i. e. , compounds that were docked and rescored with DockThor and DockTScore, respectively). Only the top-scored protonation state of a compound according to each scoring function (SF) was kept. outperformed the other machine learning methods, whereas the PPI-specific scoring functions improved the EF 1% and BEDROC for all algorithms.
Interestingly, the linear PPI-specific scoring function, with a satisfactory AUC ROC value of 0. 709, obtained the best EF 1% value and the highest BEDROC100 value of 0. 539. In the case of MDM2 target, the nonlinear general scoring functions outperformed the specific models in terms of AUC ROC, whereas the RF-based achieved the best overall screening performance. However, for this target all methods exhibited insufficient early recognition capacity according to the EF 1% and the BEDROC values. In addition to the proteases and PPIs targets, we also evaluated the performance of our general scoring functions trained with MLR, SMOreg and RF on three protein kinases datasets taken from DUD-E. Kinases are considered as challenging targets mainly due to binding site flexibility, which frequently leads to induced-fit effects due to ligand binding.
However, the use of strategies that account for large movements of the binding site, such as ensemble docking with more than one representative structure of the protein, might be necessary to achieve better screening results on highly flexible systems such as kinases. Table  6. Screening success of the general and PPI-specific scoring functions trained with MLR, SMOreg and RF evaluated on the Bcl2-like protein/BAX and MDM2/p53 datasets. ac, inac and tot are the number of active, inactive compounds and the total number of molecules in the final dataset (i. e. , compounds that were docked and rescored with DockThor and DockTScore, respectively). Only the top-scored protonation state of each compound according to each scoring function (SF) was kept.
Screening success of the general scoring functions trained with MLR, SMOreg and RF evaluated on the AKT2, KIT, and MK01 datasets from DUD-E. ac, dec and tot are the number of active, decoy compounds and the total number of molecules in the final dataset (i. e. , compounds that were docked and rescored with DockThor and DockTScore, respectively). Only the top-scored protonation state of each compound according to each scoring function (SF) was kept. v2013 core set  39. On the other hand, it has been recently suggested that linear scoring functions, which can be less-accurate for binding affinity prediction but are composed of meaningful protein-ligand interaction terms, can be more robust than nonlinear scoring functions based only on element-element distance counts  4. Definitely, element-element pair approaches are less sensitive to the proper dataset preparation, discarding the necessity of the time-consuming task of a careful assignment of the protonation states and atom types.
Taking into consideration the very few scoring functions dedicated to score properly inhibitors of PPI both HADDOCK2P2I and Dock-TScore scoring functions can be very helpful e. g. , for consensus scoring strategies. Furthermore, the growth of the number of experimentally derived iPPI structures available with associated affinity data enables the further development of more robust scoring functions specific for PPIs. The variable performances achieved by the DockTScore models on the screening validation for the three different classes of proteins (e. g. , proteases, PPIs and kinases) are in agreement with other works published in the literature showing that the accuracy of scoring functions is strongly target-dependent. Further, although our scoring functions consider most of the interactions key for ligand binding, yet we do not take into account some contributions like the vibrational entropy  16  or particular cases as water molecules present in the binding pocket.
The vibrational entropy is strongly related to the protein flexibility and to solvent entropy, and their precise estimation is not evident to be included in classical scoring functions. Other approaches as molecular dynamics or normal mode analysis can help to resolve such problems, however they are unpractical for a huge number of ligands and thus they are out of the scope of this work. Kinases are known to be very flexible proteins, and in our study KIT is the kinase protein for which our models exhibited the lowest performances on both AUC ROC and early recognition capacity evaluated through EF1% and BEDROC. The protein conformation of KIT provided by the DUD-E database and used here as the reference structure is complexed with the kinase inhibitor sunitinib. That KIT state corresponds to a more closed conformation of the ATP-binding site.
The superposition of the autoinhibited KIT complexed with sunitinib (PDB code 3G0E) and the KIT-ponatinib complex (PDB code 4U0I), ponatinib being larger than sunitinib, shows an induced inactive DFG-out conformation of the enzyme, illustrating thus two possible distinct conformations adopted by the enzyme due to different ligands (Figure  S1 ). Such results reinforce the importance of a careful selection of the receptor conformation to be used for virtual screening campaigns and the consideration of the protein flexibility to some extent  81. Next, many inhibitors of proteases such as TRYB1 and UROK are known to displace water molecules interacting with catalytic residues of the binding site, however, in some cases such molecules can serve as a bridge between the receptor and the ligand. The analysis of the nine experimental complexes used in the virtual screening experiments showed that some of them contain ligands able to displace water molecules (e.
The better performance of our MLR scoring function specific for PPIs on the protease TRYB1 dataset indicates that it could be applied on targets with similar profiles with those observed for PPI interfaces, such as those with highly solvent-exposed binding sites. We have recently reported similar observations when analyzing solventexposed co-crystallized ligands to support the design of novel protein-protein interaction inhibitors  83. Our scoring function specific for PPIs also reinforces the fact that nonlinear scoring functions are more dependent on larger training sets, while robust linear models can be developed even when scarce data for training is available. Future growth of data for new PPI interfaces including dimer interfaces will allow to develop more robust nonlinear scoring functions specific for protein targets with binding site profiles similar to those found in PPIs.
As expected, the nonlinear scoring functions generally performed better than the respective MLR models. Finally, we demonstrated that the scoring functions developed in this work also exhibited good performances on virtual screening experiments to distinguish actives from inactive/decoy compounds for various protein targets. DockTScore functions are independent of docking software and can be used for affinity prediction or consensus scoring to improve the performance of docking-scoring approaches on virtual screening experiments. Currently, the MLR DockTScore predictions are provided for the DockThor docking at the DockThor-VS web server (available at www. dockt hor. lncc. br). All the developed scoring functions in this work are under implementation in a dedicated web server.
Acknowledgements The authors thank  CNPq  (Grant  308202/2016-3 ),  Faperj  (Grant  E-26/010. 001229/2015 ), PCI-LNCC  (Grants  300463/2019-7  and  312604/2016-5 ), the  French ANR agency  (Grant ToxME),  INSERM institute and University of Paris  for financial support. We gratefully acknowledge the support of the  Brazilian Sistema Nacional de Processamento de Alto Desempenho (SINAPAD)  and the availability of the computational resources provided by the  Supercomputer SDumont (LNCC/MCTIC ).
(short name) Total a Affinities (kcal mol -1 ) Training b Test c Training and test sets. All datasets were randomly separated into a training set with 75% of the structures and an independent test set with the remaining 25% structures (Table www. nature. com/scientificreports/ Bcl2-like/BAX 10 -12. 636 d , -5. 244 e 7 3 Bromodomain2/Histone 2 -9. 968, -8. 561 2 0 Bromodomain4/Histone 11 -9. 931, -6. 145 9 2 K-Ras/SOS1 1 -4. 712 1 0 MDM2-like/P53 20 -12. 768, -6. 737 14 6 Menin 1 -10. 404 0 1 Xiap/Smac 7 -11. 278, -5. 378 6 1 E1/E2 1 -10. 051 1 0 IL2/IL2R 1 -6. 910 1 0 LEDGF/Integrase 4 -10. 490, -6. 676 2 2 ZipA/ftsZ 2 -6. 685, -5. 544 2 0 Total 60 45 15.
In order to assess mean changes and related uncertainties in incomplete MMEs, the QUALYPSO approach applies a data augmentation technique which consists of simulating missing climate change responses according to the additive ANOVA model. For a detailed description of the QUALYPSO method, we refer to  Evin et al. (2019)  and to Sect. S2 in the Supplement. The different steps are illustrated in Fig. 1. They can be summarized as follows. -Forced climate response. The forced climate response φ i,j,k (t) for RCM i, GCM j , and emission scenario k at time (year) t is estimated for each of the 87 available climate simulations Y i,j,k (t) by fitting a trend model using a cubic smoothing spline (implemented by the function smooth. spline in R software;  R, 2017). High smoothing parameters (spar argument of smooth.
8 for further discussion of this limitation). -Lastly, the relative contribution of the different uncertainty sources to the total variance and the contribution of each individual GCM (RCM) to the GCM (RCM) uncertainty are provided. For each scenario, the balanced mean change response obtained from QUALYPSO, BM k , results from a proper treatment of missing RCP-GCM-RCM combinations and can be opposed to the direct estimate of the mean change response M k taken as the direct average of the raw climate change responses (anomalies between future and reference periods). The QUALYPSO approach is accessible through the R package QUALYPSO available at the CRAN repository at https://CRAN. R-project. org/package=QUALYPSO (last access: 17 December 2021). The package includes functions that implement the different steps described above, process the outputs, and provide a suite of plotting tools.
1b , Table  4 , Table  S3 ). The cohort consists of 4,041 subjects aged 4 to 90 years of Caucasian (36%), Hispanic/LatinX (33%), Asian/Pacific Islander (9%), Black/African American (2%), and other or unknown (20%) descent. In this cohort, a total of 13 anti-IFN-2 positive individuals (0. 32%) were identified. Of these, 5 were male, 6 were female, and 2 were of unknown gender, and positive samples were identified across all represented ethnic groups. None of the participants who were confirmed positive for past or present SARS-CoV-2 infection (117/3,851 by serology and 64/3,758 by PCR) were positive for anti-IFN-2 antibodies, and all were ambulatory or asymptomatic at the time of testing. These data are consistent with the previously reported absence of autoantibodies in ambulatory COVID-19 patients  (14).
1e ). For immune cell profiling, we collected ~200 PBMC samples from up to 4 longitudinal timepoints: 0, 4, 7, and 14 days since hospitalization. Multiplexed single-cell epitope and transcriptome sequencing (muxCITE-seq) was performed across 9 pools of genetically distinct samples to simultaneously measure mRNA abundances transcriptome-wide and surface protein abundances of 189 markers from the same cell (Fig. 2a , Table  S4 ). A total of 971,550 cell-containing droplets were sequenced and 600,929 cells remained in the final dataset after quality control and removal of doublets, platelets and red blood cells (see Methods). Genetic demultiplexing using Freemuxlet resulted in an average of 3,020 cells per sample (Fig. S1a ).
026) (Table  S6 ). Given the heterogeneous expression of the IFN signaling cluster (cluster 1) within COVID-19 patients, we further compared the expression of type I-specific and type II-specific ISGs between C19+ cases and healthy controls and within C19+ cases of varying severity. To differentiate type I-and II-specific ISGs, we compared healthy donor PBMCs stimulated with recombinant IFN-beta or IFN-gamma from an independent single-cell RNA-sequencing (scRNA-seq) dataset to identify genes specifically upregulated by either interferon (Fig. 3b , see Methods). Strikingly, in myeloid cells (cM, ncM, pDC, cDC), the average expression of type I-specific and to a lesser extent type II-specific ISGs in critical cases on day 0 of hospitalization was significantly lower compared to moderate and severe cases (type I: log 2 FC = -0.
Genotyping, sample demultiplexing and doublet detection To assign cells to donors of origin in our multiplexed design, we used the genetic demultiplexing tools Freemuxlet and vcfmatch-sample-ids, each a part of the Popscle suite of statistical genetics tools (https://github. com/statgen/popscle). Freemuxlet leverages the single-nucleotide polymorphisms (SNPs) present in transcripts and performs unsupervised clustering on the droplet barcodes to assign each to a nameless donor, or assign them as doublets between geneticallydistinct nameless donors. The algorithm takes in a list of candidate loci throughout the genome at which to scan for SNPs, and returns droplet barcodes with donor assignments and a set of observed variants per donor. These sets of variants are then matched using genotypic similarity to those from an orthogonal bulk RNA-seq assay, done on an individual basis, to determine which donor is which patient.
Bulk RNA-sequencing Bulk RNA-seq data was generated to extract genotype information, so that single-cells could be demultiplexed and matched to single donors. For each donor, RNA was extracted from PBMCs using the Quick RNA MagBead kit (Zymo Research) on a KingFisher Flex system (Thermofisher Scientific) according to the company's protocol. RNA integrity was measured with the Fragment Analyzer (Agilent) and library generation was continued when integrity was at least 6. Total RNA-sequencing libraries were depleted from ribosomal and hemoglobin RNAs, and generated using FastSelect (Qiagen) and Universal Plus mRNA-seq with Nu Quant (Tecan) reagents. Pooled libraries were PE100 sequenced on an HiSeq4000 or PE150 sequenced on an Illumina NovaSeq 6000 S4 flow cell at the CZ Biohub.
Single-cell epitope and RNA-sequencing preprocessing and alignment CellRanger v3. 1. 0 (run 1 to 7, cDNA library generation of run 6 failed) or v4. 0. 0 (run 8 to 10) software with the default settings was used to demultiplex the sequencing data and generate FASTQ files (Cellranger mkfastq), align the sequencing reads to the hg38 reference genome, and generate a unique molecular identifier (UMI)-filtered gene and protein expression count matrix for each lane (Cellranger count for scRNA-seq and CITE-seq data). Count matrices were then concatenated across all 50 lanes to generate two matrices: one mRNA matrix with 971,550 cells and 36,601 genes, and one surface protein matrix with 971,550 cells and 189 proteins.
Single-cell epitope and RNA-sequencing processing and quality control Resulting gene and protein expression count matrices were further processed in the Python package Scanpy v1. 5. 1  (36). Processing of the concatenated mRNA count matrix was done using a novel two-step process. We have found empirically that traditional workflows for the processing of droplet-based RNA-seq data for PBMCs can sometimes create unwanted effects in the downstream endpoints. In particular, filtering of cells with a high percentage of mitochondrial cells may create visual artifacts in UMAP projections, and filtering of the matrix to only a few hundred highly-variable genes, while reducing the memory footprint of the data, can sometimes lead to spurious clustering of cells based on only a few genes. Our iterative process yields a UMAP projection that captures all available heterogeneity with minimal filtering in the first iteration, then removes non-target cells and corrects for non-biological signal in the second iteration.
By doing this, we use a relatively large number of components to inform projections and clustering, but observe that the outputs in our dataset match the known biology better (e. g. proximity of similar cell types and states in UMAP space) and yield higherconfidence annotations. In the first step, the mRNA matrix was filtered to remove doublet droplets, as annotated by freemuxlet, and very lowlyexpressed genes with less than 100 UMIs across the 9 runs. The matrix was then normalized to yield a constant UMI sum per cell and log transformed. Matrix values were scaled to yield a mean of zero and standard deviation of 1, per gene. Principal component analysis (PCA) was performed, followed by nearest neighbors, UMAP projection and Leiden clustering, using an input of the 150 PCs with the highest variance explained and otherwise default Scanpy parameters.
Finally, further filtration of a relatively small number of cells with high expression of platelet, red blood cell, and mitochondrial genes was performed, as well as removal of donors that declined study participation. After processing, 600,929 cells and 18,262 genes remained in the mRNA matrix. The surface protein matrix was filtered to the cells found in the mRNA matrix. One protein was removed from the matrix, as it appeared to have very low counts relative to the other surface proteins. The remaining proteins were then normalized using the centered-log ratio (CLR), computed for each gene independently. The CLR has typically been used for CITE-seq data with the recognition that antibody counts are typically not zero-inflated and FACS-like Gaussian distributions are achievable when treating the data as compositional.