dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, to explore whether HAPMs might be permissive or compensatory in nature, we assessed the temporal occurrence of mutation using a molecular clock analysis. Our analysis is explained below. Large-scale phylogenetic analysis. We downloaded from the Influenza Virus Database all available nucleotide sequences (>80,000 sequences) corresponding to complete AIV genome sets for the H7NX (n ≈ 3000) and H5NX (n ≈ 10,000) subtypes, from all hosts (excluding humans) and geographical regions. Genomes from non-avian hosts included mammals (mostly equine and swine influenza A viruses), environmentally derived sequences (usually from avian faecal sampling) and ND/NA (host species Not Determined/Not Available). Non-avian isolates comprise <15% of the large-scale datasets; most of them representing sporadic cross-species transmissions, with exception of the established equine H7NX lineage  62.
Sequence datasets were aligned using MAFFT  63  and preliminary large-scale phylogenies for each genome segment were estimated using RAxML under a general time reversible nucleotide substitution model with gamma-distributed among-site rate variation (GTR + G). Branch support was assessed using a bootstrap approach with 100 replicates  64. We used the large-scale phylogenies to identify all HP lineages. HP strains were identified by (i) detecting >1 basic residue insertions within the consensus cleavage site in the HA protein alignments  11  and (ii) by confirming HP status with case reports in the literature  5, 11. Sequences in other alignments were labelled HP/LP according to their status in the HA tree. Phylogenetically informed subsampling. To render analyses computationally feasible, we subsampled the large-scale datasets in a phylogenetically informed manner.
All non-avian viruses were excluded from all subsampled alignments, as viruses the rate and nature of molecular evolution can vary in different hosts  62. For the H7 and H5 HA datasets we retained (i) the selected HP clusters, (ii) their immediate LP ancestors in the tree, (iii) up to 250 randomly selected LP reference sequences to maintain overall tree topology, and (iv) a group of ancestral sequences for rooting purposes. In total, 313 sequences were sampled for H7 and H5 (Supplementary Data 1). For the NA datasets, we extracted all sequences corresponding to the NA types that are associated with the HP phenotype (for H7NX: N1 = 136 sequences, N3 = 367, N7 = 196 and N9 = 18, and for H5NX: N1 = 2053 subsampled to 200, N2 = 367, N7 = 757, N3 = 78, N8 = 93 and N9 = 223). We then merged the individual NA subtype alignments to create two global NA alignments (for H7NX: NX = 919 and for H5NX: NX = 1146 subsampled to 600).
Finally, to illustrate the genetic diversity of NA, we extracted from the largescale datasets all NA sequences of the isolates present in the HA subsampled alignments (211 for H7NX and 150 for H5NX) (Supplementary Fig. 4 ). AIVs of different subtypes undergo genome reassortment, especially within the internal segments  23. In order to account for this, when inferring the evolutionary history of internal genes, we used a single phylogeny/alignment that includes all AIV subtypes (not just viruses belonging to the H5 and H7 subtypes). In this way, we maximise our chances of including those phylogenetic nodes that are closely related LP ancestors to HP clusters, irrespective of AIV subtype. This procedure aims to (i) reduce estimation error, (ii) increase accuracy of phylogeny reconstruction, and (iii) ensure the most closely related strains to each HP lineage are included.
Consequently, for each internal segment (PB2, PB1, PA, NP M1, NS1), we constructed a consensus sequence for each HP cluster and used blastn to identify closely related sequences belonging all AIV subtypes other than H5NX and H7NX, and retained the top ten hits for each search. Sequences from these hits were added to the merged internal segment datasets (both the H5NX and H7NX viruses, including the selected HP strains). The resulting final number of sequences in each internal segment dataset was: PB2 = 514, PB1 = 541, PA = 485, NP = 503, M1 = 443 and NS1 = 471. Each dataset was re-aligned using MAFFT  63  and alignments were screened for recombination using the RDP, GENECOV and BOOTSCAN components of the RDP3 package  65. No evidence for intra-segment recombination was detected by three or more methods under a Bonferroni-corrected p value < 0.
001. The subsampled alignments were analysed using jModelTest2 to identify well-fitting substitution models  66. For each alignment, trees were estimated using RAxML under the GTR + G model and bootstrapped with 100 replicates  64. Prior to tree estimation, HA alignments were edited to remove the cleavage site, whilst regions with poor alignment scores were removed from the NA alignments. Alignment sizes for the subsampled datasets are provided in Supplementary Data 1. The varying sizes and/or missing HP clusters in some alignments are due to the lack of complete genomes for all strains in the public database. Detecting candidate HAPMs co-occurring across HP clusters. To identify candidate HAPMs within AIV genomes, all mutations (deletions, insertions and non-synonymous variable sites) present in ≥1% of the sequences in each subsampled alignment were coded as a discrete taxon trait and subjected to ancestral state reconstruction on the subsampled RAXML trees, using the maximum likelihood framework implemented in the 'ape' R package  67.
We therefore undertook structural analyses to predict, based on existing structural data and the literature, if the HAPMs identified in previous steps could have possible implications for protein structure and stability, and/or have any potential functional impacts (see references in Table  1 ). We modelled protein 3D structures from existing structural data in the PDB database (https://pdb101. rcsb. org/), using representative HP sequences from our datasets as queries. For H7, we used the crystal structure of isolate A/H7N2/New York/107/2003 AIV (PDB ID 3M5G), whilst for H5 we used the crystal structure of isolate A/Vietnam/30262III/04 AIV (PDB ID For the PB2/PB1/PA complex), the high-resolution structure of the A/ H5N1/duck/Fujian/01/2002 virus (PDB 6QPF) was used as a template  76. For all structures, we used the I-TASSER server  77  to generate models with the mutations of interest, which were visually compared to PDB files for validation.
Finally, to predict which HAPMs might possible affect the stability of protein structures, we used the PIPS (Phylogenetic Inference of Protein Stability) program  35. This program implements an informed Bayesian approach and estimates, for each amino acid state, a value of free energy change (ddG) in terms of the thermodynamic stability of an unfolding/refolding protein. A statistical model is used to calculate the probability of free energy change along phylogeny branches in a manner analogous to the computation of nucleotide substitution models, and thereby computationally predict the occurrence of mutations altering protein stability  35. Negative ddG values are predicted to be stabilising for protein structure. Values close to zero are predicted to have little effect or to be only weakly stabilising, whilst positive values are predicted to destabilise protein structure  35.
Acknowledgements M. E. -Z was supported by an  EMBO   Long Term Fellowship  ( ALTF376-2017 ) during this work, and is currently supported by  Leverhulme Trust ECR Fellowship  ( ECF-2019-542 ). M. G. , J. T. and O. G. P. are supported by the  EU's Seventh Framework Programme  (FP7/ 2007-2013)/ European Research Council  ( 614725-PATHPHYLODYN ). B. G. is supported by the 2017  Universities of Academic Excellence Scholarship Program of the Secretariat for Higher Education, Science, Technology and Innovation of the Republic of Ecuador  ( ARSEQ-BEC-003163-2017 ) and by  Universidad San Francisco de Quito. J. R. K. and L. C. are supported by the  Wellcome   Investigator award  ( 200835/Z/16/Z ). The  Wellcome Centre for Human Genetics  is supported by grant  203141/Z/16/Z. T. A. B. is supported by the  Medical Research Council  ( MR/L009528/1 ).
The mobility is often discussed in terms of the rotational correlation time τ c , which is derived from the diffusion tensor: for a nitroxide characterized by an axial diffusion τ c = 1/6(D || D ⊥ ) 1/2. We used the MultiComponentEPR827. vi program by Christian Altenbach to perform the simulations. The program is written in LabVIEW (National Instruments) and can be freely downloaded from the following website: http://www. biochemistry. ucla. edu/Faculty/Hubbell/.
Circular Dichroism CD measurements were performed with a Jasco J-1500 spectropolarimeter. Experiments were performed at 298 K using a Jasco PTC-423 Peltier cell holder connected to a Jasco PTC-423S Peltier controller. Far-UV CD spectra were collected using a cylindrical cell (121-0. 20-40 from Hellma) with 0. 2 mm optical path length using 50 µL of protein solution. Data were acquired at a scan speed of 20 nm/min and at least three scans were averaged. Proteins were used at a concentration of 50 µM, in a 0. 5 mM Tris-HCl pH 7. 0, 1 mM KCl buffer. For experiments with ferrous iron, the cuvette was filled and sealed under anaerobic atmosphere in a glove box; the compartment of the CD spectropolarimeter is anaerobic since it is constantly under nitrogen flow.
All-Atom Molecular Dynamics Simulations Molecular dynamics simulations were performed with the GROMACS 5 package  [42] [43] [44] [45] [46]  using the Amber99SB-ILDN force field for proteins that has been shown to yield an accurate description of many structural and dynamical properties of proteins  [47] [48] [49] [50]. Side chain protonation states of titratable amino acids were assigned using a value of pH = 7. 4 with the help of the pdb2pqr software  [51]. Capping acetyl and methyl-amino groups were added to the N and C termini of both AC5 domains and Gαi to avoid strong artifacts between non-natural termini  [23, 36]. The four states we study (Gαi_sym + AC5, Gαi_sym + AC5 + ATP, Gαi_tilted + AC5, Gαi_tilted + AC5 + ATP) were each placed in a truncated octahedral box and solvated with TIP3P water molecules  [52]  to a depth of at least 11 Å.
The angles were measured using helical axes derived from the residues that remain in stable α-helical conformations throughout the simulations (C1: 408-420 and 468-475, C2: 910-918 and 978-988) as defined in  [25]. We also computed the distances between the center of the helices in each domain (d C1 and d C2 , respectively). To characterize protein-protein interfaces, we computed the interface contacts with the python/C code available at https://github. com/MMSB-MOBI/ccmap, using a fixed cutoff of 5 Å between heavy atoms. To characterize the ATP binding site, we computed two distances between ATP and two key residues for AC5 activity (distance between O 2 γ and LYS 1065 and between O 2 α and ARG 1029) and the distances between ASP 396 and ASP 440 and the two Mg 2+ ions  [68]. For comparison purposes, we here use the numbering scheme from reference  [68] ; the corresponding numbering in P84309 is LYS 1245, ARG 1209, ASP 475, and ASP 519.
Acknowledgments: We thank  Alexis Michon  and  Samuel Bosquin  ( UMS 3760 ,  Institut de Biologie et Chimie des Protéines, Lyon, France ) for technical assistance and hardware support. We wish to acknowledge  GENCI  for a generous allocation of computer time on the CINES supercomputer OCCIGEN (Grant  2016-c201607758 , Grant  2017-A0020707585  and Grant  2018-A0040710357 ).
In this study we revisit the evolutionary history of PVs with newly available ancestral fish PV genomes. We selected our calibration points and dated the PV tree using four different data sets based on the consistencies in virus-host codivergence patterns. Two of these data sets contain the virtually complete PV collection hitherto described, while the other two are different versions of reduced data sets to correct for the over-representation of humans and of other economically important hosts. First, we investigate whether a timedependent rate model better fits PV evolution data than previously used models. Second, we compare evolutionary rates, diversification rates, and node age estimates among the different PV data sets. In addition, we compare the PV evolutionary-and diversification rate estimates with those of the corresponding hosts.
Data collection and alignments. CC-BY-NC 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is For this study, 359 full length PV genomes were downloaded from the PaVE (https://pave. niaid. nih. gov/, Van Doorslaer,  Li, et al. , 2017)  and GenBank (https://www. ncbi. nlm. nih. gov/genbank/) databases (Table  S1 ). The E1, E2, L2 and L1 genes were extracted and aligned individually at the amino acid level using MAFFT v. 7. 271  (Katoh & Standley, 2013) , corrected manually, and backtranslated to nucleotides using PAL2NAL v. 14  (Suyama et al. , 2006). The alignment was filtered using Gblocks v. 0. 91b  (Castresana, 2000) , with the following parameters: -t=c, -b1=50% of the number of sequences + 1, -b2=50% of the number of sequences + 1, -b3=8, -b4=3, -b5=a, -b0=3.
, 2010; Burk et al. , 2013; Bzhalava et al. , 2015; De Villiers et al. , 2004; Van Doorslaer et al. , 2018 ; see also https://talk. ictvonline. org/taxonomy/). Our FD, RD1 and RD2 contained one single fish PV genome (SaPV1), but during the study four other fish PV genomes were made available in GenBank (accessions: MH510267, MH616908, MH617143, MH617579;  Tisza et al. , 2020). As the sequences of these new PVs add relevant information to the basal clade of the tree, these genomes were added to the FD, and this new data set containing 347 PV genomes was named FDF (FD + additional fish PVs). For all four data sets (FDF, FD, RD1, and RD2), the concatenated E1-E2-L2-L1 alignments were used to construct ML trees with RAxML v. 8. 2. 9 under the GTR+Γ4 model for the nucleotide alignments, using twelve partitions (three for each gene corresponding to each codon position), under the LG+I+Γ model for the amino acid alignments using four partitions (one for each gene), and in both cases using 1000 bootstrap replicates (Fig.
Statistics and graphics Statistical analyses and graphics were done using R (R Core Team, 2014), with the aid of the packages "ape", "car", "dplyr", "ggfortify", "ggplot2", "ggtree", "lawstat", "overlapping", "pgirmess", "reshape", "stats", and "strap". Computation of optimal breakpoints in the LTT plots was performed using the R package "strucchange". The optimal number of breakpoints was first calculated using the default 'strucchange' parameters. From this analyses the optimal number of breakpoints for the host LTT plot was found to be 3. Since the older nodes contain very few observations (due to limited sampling efforts in basal PVs and thus a limited number of basal branching events), we refined the analyses by specifying a minimal sample size of 3 data points for each segment (parameter h), together with a maximal number of breaks to be calculated set at either 3, 4 or 5.
PV crown-groups form well-supported clades, albeit with unclear relative positions among them In this study we use four different data sets, with a less exhaustive (RD1 and RD2) and a more exhaustive (FD and FDF) representation of all PV genomes in the databases (see Materials and Methods). As some PV lineages ─mainly PVs infecting humans and some economically important species such as cattle, dogs and horses─ are over-represented in the full data sets (FD and FDF), we chose to also work with two different reduced representative data sets (RD1 and RD2). For the construction of RD1 and RD2, the full data sets were used to select one representative for each PV species and type. We would like to stress that for PVs, a type describes a unique genomic entity, genetically different from and within defined boundaries of nucleotide identity to other sister taxa (see also Materials and Methods).
We interpret that in addition to virus host co-evolution, the flattening slopes in periods III and especially IV, indicate a much slower diversification of PVs within their hosts (Fig. 3d  and Table  4 ). The lower slope values for PV diversification in the reduced data sets compared to the full data sets arises most likely from the reduction of the number of terminal taxa associated to individual hosts. The LTT plots and the LTT slope analysis suggest that the per-lineage speciation rates have not remained constant through time. Thus we can reject the null hypothesis of constant diversification rates on the FDF, FD, RD1, and RD2 data sets, suggesting that the observed trends in PV number of lineages through time is compatible with evolutionary events such as adaptive radiations and/or key adaptations.
Node age estimates on different data sets For the dated trees constructed on our full and reduced data sets, we obtained the tmrca estimates for different clades along the PV tree (Fig. 4  and Table  S6 ). For those nodes for which the posterior distribution of the age was sampled, we show to what extent the distributions overlap in Fig. S14. Overall, the tmrca estimates obtained by using different data sets are similar. Larger differences are found for the more ancient nodes: the root of the tree, AvesTestudines/Mammals, Aves/Testudines, Aves, and Mammals (Fig. 4  and Fig. S14 ). For these nodes, we obtained older tmrca estimates when using the reduced data sets (RD1 and RD2), as compared to the full data sets (FD and FDF). Due to the scarce number of PVs described for the hosts corresponding to the underlying PV clades, the representative extant taxa are the same in these data sets (see Table  S1 ).
Therefore, the ages of these ancient nodes appear to be mostly influenced by the number of mammalian PVs present in the data sets, where removal of overrepresented PVs leads to older time estimates. For the youngest nodes, Homo/Colobus, Homo/Pan and Pan troglodytes/Pan paniscus, inconsistencies in the tmrca estimates were also obtained (Fig. 4  and Fig. S14 ). For Homo/Colobus and Pan troglodytes/Pan paniscus, we obtained younger tmrca estimates when using RD1 and RD2, as compared to the FD and FDF, while for Homo/Pan this is the inverse. Ultimately.
Within these three clades, PVs further diversified some evolved the potential to degrade tumor suppressor proteins  (Fu et al. , 2010; Mesplede et al. , 2012; Mirabello et al. , 2017; Van Doorslaer et al. , 2015; Willemsen et al. , 2019). In this study, we used four different data sets to explore potential biases of the choice of terminal taxa to infer the evolutionary time scale of PVs. The main differences between the full and reduced data sets are apparent in (i) the inferred ages of the basal nodes of the PV tree and (ii) the strength of the time-dependent rate effect. First, the more basal nodes, between roughly 100 and 550 Ma, are generally inferred to be older when using the reduced representative sets of taxa compared to using all taxa (Fig. 3b , Fig. 4  and Fig. S14 ). Secondly, the time-dependent rate effect is stronger when using the reduced data sets compared to using the full data sets, as evidenced by a steeper slope in Fig.
For the construction of the reduced data sets in this study, it should be noted that taxa pruning was not random, as one representative was chosen for each PV species and PV type (see Materials and Methods). Therefore, the same monophyletic lineages of PVs infecting the same hosts were subsampled for phylogenetic inference with the reduced data sets (RD1 and RD2). RD1 contains representative species with initial shorter branches as compared to those chosen for RD2. Meaning that for RD2 more diverse PV genomes are being compared, and thus RD2 represents a more greedy strategy as compared to RD1. Although it has been shown that greedy algorithms are rarely the best option for statistical power  (McAuliffe et al. , 2005) , it has also been shown that for genomic comparisons the greedy strategy of maximizing the evolutionary divergence among species chosen from a known phylogeny can provide optimal solutions  (Pardi & Goldman, 2005).
Concordantly, our results indicate an effect of taxa choice in the reduced data sets, where time inference using single node calibrations with more diverse terminal taxa (RD2) leads to lower short-term evolutionary rates, than when using less diverse terminal taxa (RD1), while no differences were observed for the long-term evolutionary rates between both data sets (Fig. 2  and Table  S3 ). When comparing the results for all data sets in this study, it also seems that RD2 renders closer estimates to those obtained for the full data sets than RD1 does (Fig. 3a-b  and Tables  S4-6 ). In the particular case of this study, increasing the number of taxa appears to reduce phylogenetic error, leading to more accurate evolutionary rate estimates, and younger basal node age estimates in the full data sets (FD and FDF).
The combination of multiple data sets in our study also allows us to evaluate the impact on the estimates for deeper nodes when increasing the number of taxa in the outgroup. In this case, FD and FDF only differ in the additional presence of four fish PV genomes that were made available during the study. Indeed, the increased presence of basal fish PVs in the FDF, results in younger values for the estimate of the age of the root than any other data set (Fig. 3b ). We interpret that the increased number of terminal taxa in the outgroup decreases the uncertainty about the number and nature of substitutions in this clade  (Heath et al. , 2008) , leading to a better estimate of the age of the root. Previous studies have shown that different substitution rates can be obtained for the same species depending on whether they are inferred on a long-term or short-term evolutionary scale.
Acknowledgements The authors declare no conflict of interest. We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing computing and storage resources. The authors acknowledge the  IRD itrop HPC (South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper. This work was supported by the  European Research Council  Consolidator Grant CODOVIREVOL (Contract Number  647916 ) to IGB and by the  European Union   Horizon 2020 Marie Sklodowska-Curie research and innovation programme  grant ONCOGENEVOL (Contract Number  750180 ) to AW.
X-ray data collection, processing and model refinement X-ray diffraction data were collected at 100K at Diamond Light Source on beamline I24 using a Pilatus3 6M detector. Data were integrated, processed and scaled using XDS  [22]. The structure was solved using the molecular replacement method as implemented in the software PHASER of the CCP4 suite  [23]. The structure 1AW8 from the PDB was used as the search model after removing the ligands and the water molecules. Crystallographic refinement was carried out using REFMAC5  [24]  from the CCP4 suite  [23]. COOT  [25]  was used for real-space modelling.
Theoretical Diffusion Coefficient Calculation The program HYDROPRO  [26]  was used to calculate the diffusion coefficients for the apo and D-serine bound ADC using the apo-ADC structure 1AW8  [19]  and D-serine complex structure determined in this work as inputs. The partial specific volume of ADC was calculated as 0. 70 cm 3 /g. The solvent viscosity for D 2 O was set to 0. 01830, 0. 01175, 0. 00830 poise for the temperatures T = 280, 295 and 310 K, respectively  [27].
Protein concentration determination Data were recorded using a DeNovix DS-11 spectrophotometer. Protein concentration was determined from absorbance at 280 nm and a molar extinction coefficient for ADC of. The accuracy of the spectrophotometric measurements was confirmed by dialysis against 20 mM ammonium acetate, pH 7. 0, 100 mM NaCl, lyophilyzing and weighing a known amount of ADC. The measurements were carried out at an average temperature of T = 298. 5 K. Lyophilization was carried out using a Martin Christ instrument at a vacuum pressure of 0. 06 mbar.