dataesr/scientific-paragraphs-categorization huggingface dataset:
Domain tethering influences the functions of homologous protein domains with different domain compositions To understand further the influence of tethering of domains on the function of proteins, a comparative analysis was performed for homologous domain pairs, where one member is a single-domain protein while the other member is a part of a multi-domain protein. Both the members are full-length gene products. Four pairs of proteins namely (a) phosphoribosylanthranilate isomerase from E. coli (PDB id: 1PII) and Jonesia denitrificans (PDB id: 4WUI), (b) cyclophilin from Bos taurus (PDB id: 1IHG) and Homo sapiens (PDB id: 3ICH), (c) sialidase from Micromonospora viridifaciens (PDB id: 1EUT) and Homo sapiens (PDB id: 1SO7) and, (d) hexokinase-1 from Homo sapiens (PDB id: 1HKC) and Saccharomyces cerevisiae (PDB id: 3B8A) were studied.
Discussion Conformational and structural alterations have been observed in proteins as they bind to other proteins  [27, 28]. This line of thinking is extended in the current work to understand the structural, dynamic and energetic effects of tethering of protein domains in multi-domain proteins on the constituent domains. The extent of similarity between the physical and geometrical properties of protein-protein interaction and domain-domain interaction in multi-domain proteins  [15]  motivated us for the study. A dataset of 20 protein domain pairs of known 3-D structure has been used in the analysis. Each pair comprises of an entry with one or more domains of a multi-domain protein and the other entry has at least one additional domain tethered. Fifty percent of the protein domain pairs show differences in the global conformation on tethering.
Dataset preparation Structures of same protein domains in tethered and isolated forms. The dataset for the analyses was prepared using domain definition available in SCOPe 2. 03 ver. [48]. All the protein domain entries in SCOPe 2. 03, whose structures have been elucidated using X-ray crystallography, were filtered for domain entries with single chain both in the asymmetric unit (ASU) and biological unit (BU) and no other biological entity is present. This constraint was applied to ensure that no variances are observed due to the oligomeric condition of the protein or its interaction with other biomolecules. The filtered dataset was then clustered at 100% sequence identity using CD-HIT  [49]  and pairs of proteins were selected from each cluster, where the pair of entries differed only by the presence of extra domain/s in one entry and the common identical domains differed maximum by ± 10 amino acid residues.
Further, those pairs of proteins that have different cognate ligands were removed from the dataset. This step assures that no variances are observed due to the presence of ligands. After applying all the aforementioned filters, 20 pairs of structures (Table C in S1 Text) were obtained. The small number of entries in the dataset is the reflection of the rigorous quality checks we have employed and the relatively small number of multi-domain proteins in the PDB and SCOPe database (21% of the dataset). It is also important to note that the longer member in each pair need not be a full-length gene product. For all the analyses, various features of the common domains of each pair have been compared. Information on function-associated residues (mentioned as functional residues) was taken from the literature survey and UNIPROT  [50].
Domain boundary definition for all the multi-domain protein structures was taken from SCOPe 2. 03  [48]. Domain-domain interface residues were identified using the sum of van der Waals radii + 0. 5Å cut-off as inter-atomic distance criteria. Any pair of residues with the atomic distance less than the sum of van der Waals radii + 0. 5Å was considered as domaindomain interface residues. Single and multi-domain proteins with shared homologous domains-homologous domains data set. SCOPe 2. 03 database was screened for protein entries solved using X-ray crystallography, having a single chain in both asymmetric and biological unit and not bound to any other macromolecule. Crystal structures of only full-length proteins were selected. The selected proteins were clustered using local alignment tool of CD-HIT  [49]  at 25% sequence identity.
Each cluster was searched for pairs of homologous domains, where one entry is a single-domain protein and the other entry is a multi-domain protein. Further, these pairs of entries were refined for the presence of same cognate ligands and same conformation. It was ensured that the EC number was same for the homologous protein pairs. It enabled us to establish a functional relationship between the homologous protein pairs and also to extract the catalytic site information reliably. Further, it was made sure that the binding sites share high sequence identity (>70%) and show low structural deviation (< 2Å). 4 pairs of homologous proteins satisfied these restraints and were used for further analysis (Table D in S1 Text). Control dataset 1: All the protein domain entries in SCOPe 2. 03  [48] , whose structures have been elucidated using X-ray crystallography, were filtered for domain entries with single chain both in the asymmetric unit (ASU) and biological unit (BU) and no other biological entity is present.
This constraint was applied to ensure that no variances are observed due to the oligomeric condition of the protein or its interaction with other biomolecules. The filtered dataset was then clustered at 100% sequence identity using CD-HIT  [49]  and pairs of proteins with identical domain composition were selected. 607 pairs of protein were identified and it constituted the control dataset 1. This dataset was generated to study the impact of crystal packing on the conformation of protein domains. Control dataset 2: The control dataset 2 was generated by in-silico removal of the uncommon domain from MD of domains pairs. The length and sequence of the domains (referred as AD) are identical to ID. This dataset was generated to study the impact of crystal packing on dynamics of proteins. Control dataset 3: The control dataset 3 was generated by superimposing the ID onto MD followed by in-silico removal of the common domain from MD and ligation of the superimposed ID with the uncommon domain of MD.
Structural analysis Proteins in the datasets were structurally aligned using TM-align  [51]. For the same-domain dataset, the structural variations were studied at the global and local level. Global Root Mean Square Deviation (RMSD) and Global Distance Test-Total Score (GDT-TS)  [52]  score were used to define global deviations. GDT-TS, henceforth mentioned as GDT, is used to define structural similarity between domains of identical sequences. Unlike RMSD it is largely insensitive to outliers arising especially due to differences in loop conformations. It is defined as the number of alpha carbons falling within a cut-off from the corresponding Cα of the other structure. MAXCLUSTER, an improved version of the maxsub algorithm  [53] , with a cut-off of 4Å was used for calculation of GDT score.
Dynamics: Normal mode analysis and molecular dynamics To study dynamics of domains, we have used two approaches namely normal mode analysis (NMA) and molecular dynamics. Crystal structures were energy minimized using GROMACS package  [55]  with conjugate gradient as the energy minimization method. Prior to energy minimization, the structures were repaired for missing residues and missing atoms. The missing residues were modelled using Rosetta 3. 4  [56]  and missing atoms were built using WHAT IF 10. 1a algorithm  [57]. Normal modes were calculated by generating coarse-grained anisotropic network model (ANM) for proteins, with 15 Å as the cut-off for connecting the nodes. Distance-dependent spring constants (the closer the nodes, stiffer is the edge) were used for the edges. Calculation of normal modes as well as the associated calculations and analyses were done using the ProDy package  [58].
Frustration calculation Energetics calculation was performed only a dataset of identical protein domains. As the homologous protein domains differ in sequence identity, it is futile to compare their energetics. Frustratometer algorithm  [22]  was used to perform the energetics calculation. The algorithm systematically perturbs each residue and contact to generate the decoys and compute energy according to Associative Memory Hamiltonian with Water-mediated interaction energy function (AMW)  [62]. Then the energy of the native protein is compared with the energy distribution of the decoys to calculate the frustration index, which is the Z-score of the energy of the native with respect to the decoys. A residue or a contact is considered as minimally frustrated if the frustration index is greater than 0.
Fig, two-sample KS-test, p-value: 0. 04). It has to noted that ~7% (291 residues out of 4284 residues) of residues show significant differences in centrality score (|centrality score (MD)centrality score (ID)| > 1. 5) (Fig 3A). These 291 residues belong to 12 domain pairs in the dataset. Only ~3% of these 291 residues form a part of domain-domain interface regions. Many residues showing a significant difference in coc score (|centrality score (MD)-centrality score (ID)| > 1. 5) showed low structural deviation (S2B Fig) implying that rewiring of the residue-residue contact can happen without any significant structural deviation. An example of the coc distribution of a domain pair (fibronectin) is shown in Fig 3D. Fibronectin domain shows differences in centrality score both at the domain-domain interface residues (boxed as black in Fig 3D) as well as residues other than domain-domain interface residues (boxed as red in Fig 3D).
A representative example (fibronectin) is shown in Fig 5C and cross-correlation matrices for 20 protein domain pairs are shown in S4 Fig. To ensure that differences are not observed due to crystal packing or other artefact, R v coefficient between cross-correlation of ID and control dataset 2 and cross-correlation between MD and control dataset 2 were calculated (S5 Fig). The comparison ruled out any other factor apart from tethering for the behavior observed. An important point to note here is that this characteristic has been observed irrespective of the number of interactions between the domains. For example, the domains in rat DNA polymerase β do not interact with each other but still, low R v coefficient is observed (1BPD in Fig 5B).
98) (Fig 6A) but ~12% (region II, III, IV, VI, VII and VIII of Fig 6A) of the residues showed differences in the single residue level frustration (SRLF) with 5 residues (region III and region VII of Fig 6A) showing drastic substitution from high frustration to minimal frustration and vice-versa. These 12% residues are distributed over the entire domain dataset i. e. each domain pair have at least one residue showing different frustration indices. Residues apart from domain-domain interface residues and functional residues were also observed to differ in the frustration index (S7A Fig). Moreover, differences in the frustration index of MD and ID were observed to be independent of the structural deviation observed. Equivalent numbers of substitutions were observed at structural deviation greater than 1Å and lower than or equal to 1Å (Fig 6D).
IISc-DBT partnership programme, India DST, India (Mathematical Biology Initiative & J. C. Bose National Fellowship, FIST program) UGC, India -Centre for Advanced Studies, Ministry of Human Resource Development, India Ministry of Research (France) ,  University of Paris Diderot ,  Sorbonne Paris Cite, National Institute for Blood Transfusion (INTS, France), Institute for Health and GR-Ex , reference  ANR-11-LABX-0051. The labex GR-Ex is funded by the program Investissements d'avenir of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. Indo-French Centre for the Promotion of Advanced Research/CEFIPRA  for a collaborative grant (number  5302-2 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
During each nesting attempt, we record (a) the identity of the (social) sire and the dam; (b) lay, hatch and fledge dates; and (c) the corresponding numbers of eggs/chicks at each stage. Surviving fledglings are measured, banded and blood-sampled. The intense monitoring (since 1995), combined with a microsatellite genotyping effort started in 2004  (Brekke, Dawson, Horsburgh, & Ewen, 2009) , allowed us to reconstruct a long-term pedigree of the Tiritiri Matangi population, while accounting for extrapair paternity  (Brekke et al. , 2012). We used temperature data from 1995 to 2010, downloaded from the New Zealand National Climate Database (https://cliflo. niwa. co. nz/), to assess the relationship between laying date and temperature. The years cover a period over which a weather station from New Zealand's National Climate was present on the island.
| Phenotypic information This study is based on the breeding seasons spanning from 1997/1998 to 2013/2014. Lay date was derived directly from the data, except for the first season 1997/1998 where the data were missing. Instead, it was computed as being 17 days before the recorded hatch date (regression of laying date on hatch date, N obs = 1,207, a = -17. 07 days, b = 1. 0, R 2 = 0. 998). Dates require a point of reference, so for the analyses of this article, the laying date was defined as the number of days since the 1st of September of the year corresponding to the breeding season. Attempts at breeding by females were numbered with an increasing clutch number, independently of the success of the clutches. In the following, "start of breeding season" corresponds to the laying date of the first of these successive clutches or of the sole clutch if only one has been recorded.
The fitness of the breeding female was computed as the number of offspring recruited as breeders in the following generations (using both the social and genetic pedigrees, see below). The age of females was taken as the number of years between the focal season and the season of the birth of the female. Because hihi have a limited growth after fledging  (Low, 2006) , we used the tarsus length measured when individuals are banded (as 21-day-old nestlings) as a measure of the individual adult size. The total data set consisted of 1,369 breeding events (855 whole-season events) for 330 females, spanning 16 years.
Third, we studied whether female survival to the subsequent year depended on when she started breeding, the number of clutches per year and her quality in binomial mixed models with year and female identity as random effects. Fourth, we studied the existence of phenotypic plasticity with temperature, using the average temperature cue described above. To do so, we fitted an individual model of the start of breeding season using the best model from the first analysis above and tested whether the inclusion of the temperature cue as a new variable was significant in the model. To help convergence of the algorithms, continuous variables (except inbreeding) were standardized, that is, mean-centred and scaled to a variance of 1. The models were fitted in R (R Core Team, 2017) with the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015)  and were compared using Akaike's information criterion (AIC,  Akaike, 1981; Burnham & Anderson, 2002).
| Heritability of laying date To estimate the heritability of female laying date, we used the R package MCMCglmm  (Hadfield, 2010). We conducted the analysis either using all data available (i. e. , full pedigree) or restricting to years where molecular data were available to reconstruct the pedigree (i. e. , subset pedigree). Although the distribution of laying date is skewed, it was analysed as a Gaussian trait as using the clutch number within a year as a fixed effect was an efficient way to account for the skewness. In addition to the additive genetic effect, the female identity (permanent environment effect), the social male identity and the year were fitted as random effects. The phenotypic variance was computed as the sum of all random effect variances, the residual variance and the variance arising from fixed effects, following  de Villemereuil, Morrissey, Nakagawa, and Schielzeth (2018).
The prior for these random effect variances used the parameter extension implemented in MCMCglmm with parameters V = 1, nu = 1, alpha•mu=0 and alpha•V = 1,000. The prior parameters for the residual variance were set to V = 1 and nu = 0. 02. Clutch number was included as a fixed effect, and its significance was tested using the pMCMC value yielded by MCMCglmm. The models were run for 500,000 iterations with a thinning interval of 10, after a burn-in of 3,000. These parameters were chosen to ensure a MCMC effective sample size above 8,000 for all parameters. Convergence for all parameters was checked graphically and by using the  Heidelberger and Welch (1981)  test as implemented in the coda R package  (Plummer, Best, Cowles, & Vines, 2006). The heritability of laying date was computed as the ratio between the additive genetic variance and the sum of all variance components in the model excluding the between-year variance (i.
| Power analysis To evaluate the capacity of our data to estimate low levels of heritability, we performed a power analysis. We used our exact data structure (pedigree, number of individuals and structure of multiple measurements), but simulated a new phenotypic trait. We simulated breeding values according to our pedigree using the MCMCglmm rbv() function, as well as all the other random effects fitted in the above model (permanent environment, mate and year effects, all with the same variance). Variance components were set so that the total variance was comparable to our laying date data set and the resulting expected heritability would be 0. 1, as estimates below this threshold would be typically considered as small. As a comparison, using the meta-analysis data set from  Mittell, Nakagawa, and Hadfield (2015) , we found that heritabilities reported for passerine laying date typically range from 0.
The relatively narrow distribution for λ j = μ + u j was chosen to ensure that the fitted optimum belongs to the realm of possible laying date with boundaries -2 and 2 roughly corresponding to the 2% and 98% quantiles of the scaled laying date variable. For each fitness-related trait, the model above was implemented in JAGS  (Plummer, 2003). The total model was run in eight chains for 50,000 iterations with a thinning interval of 10 after a burn-in of 3,000. These parameters were chosen to ensure a total effective sample size of the MCMC above 10,000. The convergence was checked by comparing the eight chains using the potential scale reduction factor of  Gelman and Rubin (1992)  as implemented in the coda R package  (Plummer et al. , 2006). All parameters had a factor equal to 1, meaning that the same convergent state was reached by the different chains.
| Survival analysis and laying date We studied the survival between different stages of development (egg, hatchling, fledgling, recruit; with recruit being defined as breeding for at least 1 year) according to laying date (as a quadratic effect), clutch number and female quality (age, size and/or inbreeding). To keep the number of models tested low, only one model for age was tested: young (1-year-old)/middle (2-to 6-year-old)/old (>6-year-old). This was done using a binomial mixed model with year and female identity as random effects. As above, the models were fitted in R with the lme4 package and compared using AIC using the procedure described previously. To improve model fit, laying date and size were standardized (mean-centred and scaled to a variance of 1). When a quadratic effect of laying date was significant, we also used the optimum model described above to estimate the optimum of survival according to laying date.
The mode of laying date was estimated as November 1st, hence outside of the 95% credible interval for the fitness optimum. Our model was a good fit for the fitness distribution, despite a slight enrichment in zero values in the data compared to replicated data in a posterior predictive check (Supporting Information Figure  S3 ). When using only birds of good "quality" a priori best able to target the optimum using two different criteria: (a) females from ages 2 to 6, (see Figure  1 ) or (b) females that survived to the next year, the effect is still significant (mode outside the 95% credible interval of the optimum, see Supporting Information Figure  S4 ). The discrepancy between the optimum and mode was also still significant when analysing the start of breeding season against the annual fitness (cumulated number of fledglings across breeding events within a year, see Supporting Information Figure  S5 ) Both for the general and fitter populations, the mode of laying date was thus significantly later than the optimum of fitness.
Materials and methods Strain isolation and identification Culture condition. Culture was realized in an aerobic atmosphere on a homemade culture medium consisting of a Columbia agar culture (Sigma-Aldrich, Saint-Quentin Fallavier, France) modified by adding (per liter): MgCl 2 6H 2 O, 5 g; MgSO 4 7H 2 O, 5 g; KCl, 2 g; CaCl 2 2H 2 O, 1 g; NaBr, 0. 5 g; NaHCO 3 , 0. 5 g, glucose, 2 g and 100 g/L of NaCl. The pH was adjusted to 7. 5 with 10 M NaOH before autoclaving  [3]. MALDI-TOF MS identification. The identification of our strain was carried out by a MALDI-TOF MS analysis with a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany) as previously described  [11]. Obtained spectra were then compared by using MALDI Biotyper 3. 0 software (Bruker) as well as the Unité des Maladies Infectieuses et Tropicales Emergentes's (URMITE) database, which is constantly updated.
If no identification was possible at the genus or species level (score <1. 7), sequencing of the 16S rRNA gene was performed to achieve a correct identification  [12, 13]. Sequencing of 16S rRNA gene. DNA extraction was performed using the EZ1 DNA Tissue Kit and BioRobot EZ1 Advanced XL (Qiagen, Courtaboeuf, France). The 16S rRNA gene was amplified using PCR technology and universal primers fD1 and rP2  [12]  (Eurogentec, Angers, France). The amplifications and sequencing of the amplified products were performed as previously described  [14]. Then 16S rRNA gene sequences were assembled and corrected using Codoncode Aligner software (http://www. codoncode. com/) and compared with those available in GenBank (http://www. ncbi. nlm. nih. gov/genbank/). Identification at the species level was defined by a 16S rRNA gene sequence similarity of 99% with the sequence of the type strain in GenBank.
Phylogenetic classification Phylogenetic analysis based on 16S rRNA of our isolate was performed to identify its phylogenetic affiliations with other close isolates, including other members of the genus Bacillus. MEGA 6 (Molecular Evolutionary Genetics Analysis) software allowed us to construct a phylogenetic tree  [16]. Sequence alignment of the different species was performed using CLUS-TAL W  [17] , and evolutionary distance matrices for the neighbour-joining method were calculated using the algorithm of the Kimura two-parameter model  [18].
FAMEs were prepared as described by Sasser  [20]. GC/MS analyses were carried out as previously described  [21]. Briefly, FAMEs were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S; Perkin Elmer, Courtaboeuf, France). Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (National Institute of Standards and Technology, Gaithersburg, MD, USA) and the FAME mass spectral database (Wiley, Chichester, UK).