dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, the identification of such motifs is more difficult. Fortunately, the quantity of protein structures and models available in the Protein Data Bank (PDB) archive  (12)  has increased rapidly  (13) , providing abundant data for structural analyses. The study of ligands and cavities or pockets to which they bind is of particular interest, especially for drug discovery. Different algorithms exist to compute the structure of ligand-binding pockets and to predict binding sites using geometric criteria, such as SURFNET  (14) , APROPOS  (15) , Q-SiteFinder  (16) , LIGSITEcsc  (17) , ConCavity  (17, 18) , fpocket  (19) ,DEPTH  (20)  or PDBinder  (21)  among others. Artificial intelligence also has been useful in this field with the development of algorithms for convolutional neural networks, such as PickPocket: Pocket binding prediction for specific ligand families using neural networks.
FRSite  (22) , DeepSite  (23)  and DeepDrug3D (24) or of tools based on the random forest algorithm, such as P2Rank (25). With the progressive increase in structure availability, the need to store and compare ligand-binding pocket data has led to the creation of dedicated databases, such as the Computed Atlas of Surface Topography of proteins (CASTp) (26,27) and the PoSSuM database (28). The collection of all pockets present in a single organism is called a pocketome and has its dedicated database: Pocketome  (29). The extensive knowledge of all the pocket structures and their comparison are valuable for drug designers. For special needs, the tool PocketPipe can be used to analyze the pocketome of a single organism  (30). Recently, comparative analyses of binding sites have gained momentum due to their capacity to reveal ligandbinding similarities among proteins, regardless of their evolution  (31, 32).
As different proteins can evolve to bind to the same ligand type  (33)  the accurate classification of binding sites has become an important tool for designing drugs and predicting their possible side effects through unwanted binding  (34, 35). PickPocket: Pocket binding prediction for specific ligand families using neural networks. One limitation of the available tools is that they are tailored for drug design or for the analysis of large pockets, thus excluding the possibility of executing other tasks, such as determining the specific ligand-pocket binding complementarity. Yet, to discover new binding sites, the reverse approach needs to be possible: to focus on a small set of specific ligands and to take into account their molecular and structural specificity. In this work, we developed a tool called PickPocket to generate a dataset of pockets that interact with a specific user-defined ligand family.
The workflow consists of different R  (36)  and python  (36, 37)  programs organized using bash scripts. From the pocket dataset, we computed a descriptive matrix of all pockets that contains structural information on the cavity and the residue secondary structure. Then, we trained a neural network multilayer perceptron  (38)  to predict whether a cavity is a 'true' pocket (i. e. can interact with ligands of the family under study) or a 'false' pocket. To test PickPocket, we decided to use fatty acid-like ligands due to their structural similarities and their under-representation in the ligandpocket binding literature  (39). The source code and all datasets contained PickPocket: Pocket binding prediction for specific ligand families using neural networks. in this work are freely available at https://github.
Discussion and perspectives Thanks to the increased availability of structural data and the improvement of protein 3D modeling, new pocket-based methodologies can now be developed to help protein function discovery. Our results show that combining pocket descriptors and the residue secondary structure is sufficient to train a model and to predict the pockets for specific ligand families with high accuracy, including when using unseen data. We believe that careful analysis of protein ligand families and their corresponding binding pockets coupled with the high prediction capacity of neural networks is the way forward to close the gap between protein sequences and their functions. PickPocket aims to simplify the procedure for building a ligand family dataset and train a model to recognize the corresponding cavity.
The ligand selection and the corresponding structure still need to be done manually, but these steps are made easier by the PDB ligand research tool. Other databases, such as PoSSuM (28), may be used to create the dataset. PickPocket: Pocket binding prediction for specific ligand families using neural networks. PickPocket automatic labelling of 'true' and 'false' pockets is very fast, but still needs manual checking because sometimes it makes mistakes when pockets are very close to each other. The training data quality is extremely important for good predictions. Therefore, we strongly recommend users to manually check the generated dataset. Looking in detail at the results for the 6S2M structure, fpocket considered that it had two different pockets because the fatty acid binding cavity is very deep and narrow.
This can be corrected by changing the alpha-sphere radius or the maximum distance between pockets. One of the challenges we faced was to tune the fpocket parameters in order to have a big enough pocket size without merging different cavities. PickPocket easy tuning of these parameters allows users to adapt the input to the ligand specificity. In order to cluster ligand protein complexes, Deepdrug3D and FRsite use an atom-based voxelization. This step also allows generating a compatible input for convolutional neural networks. On the other hand, our methodology uses matrix properties that are faster to generate, but contain less information. We also chose to use the fpocket software, although DeepSite is more accurate against the sc-PDB database of binding sites PickPocket: Pocket binding prediction for specific ligand families using neural networks.
All pockets were computed using fpocket (  19 ) and all secondary structure information with STRIDE  (41). The 'true' pockets containing ligands were identified using euclidean distance. For each pocket in each structure, if any voronoi vertices from a pocket and any ligand atom distance is inferior or equal to 1 angstrom, then the pockets are labelled as 'true'. All other pockets in the PDB file are by default categorized as 'false'. PickPocket: Pocket binding prediction for specific ligand families using neural networks. To train the predictive model PickPocket offers the possibility to use random forest, support vector machine algorithms or neural networks implemented in Python. The neural network is configured to test multiple architectures and automatically select the model with the best accuracy.
The Python program included the following libraries: scikit-learn(41), numpy (42) and pandas (43). Once the desired model it can be saved and used to make predictions on unseen data. A file containing all the Protein Data Bank pockets ( all pockets from all structures ) can be found in the data attached to the software. PickPocket: Pocket binding prediction for specific ligand families using neural networks. PickPocket: Pocket binding prediction for specific ligand families using neural networks. PickPocket: Pocket binding prediction for specific ligand families using neural networks.
The possible role played by individual attributes, sociodemographic characteristics and/or ecological pressures in the interaction between animals and the development of social relationships between them is of great interest in animal ecology and evolutionary biology. Social network Analysis is an ideal tool to study these types of questions. the Animal network toolkit Software (Ants) R package was specifically developed to provide all the different social network analysis techniques currently used in the study of animal social networks. This global package enables users to (1) compute global, polyadic and nodal network measures; (2) perform data randomisation: data stream and network (node and link) permutations; (3) perform statistical permutation tests for static or temporal network analyses, and (4) visualise networks.
Given the inherent non-independence of data, the development of different network permutation approaches has permitted the creation of Null Models against which statistical hypotheses can be tested  15. Although this progress has resulted in a tremendous diversity of SNA software, very few of these software propose an all-in-one toolbox for animal research. Even the most complete software programs, such as UCINET  17  and SOCPROG  18  , do not provide the most commonly used analysis procedures. Whilst the former lacks data stream permutations, the latter cannot be used for node label permutations and neither software offers time-aggregated SNA or has the high data flexibility of the R environment. R packages, on the other hand, offer high data flexibility, but there is no one package offering an all-in-one toolbox for animal research.
For instance, 'igraph'  19  and 'sna'  20  are among the most used R packages. These are generalist packages (used for all types of network analyses) and therefore do not manage procedures used in animal social network research such as permuted statistical tests. Other packages, such as asnipe  21  , do integrate some data stream permutations but do not allow users to compute SNA measures or run statistical tests. Finally, netTS package allows users to run analyses on time-aggregated networks but does not include the standard procedure for analysing animal networks (node label and data stream permutations). The use of different R packages requires data to be manipulated and/or formatted in specific ways, which requires a certain level of programming knowledge. This switching between the different software packages and the absence of guidelines may cause users to make errors.
Hence, these limitations may preclude the use of existing animal social network tools. The creation of the Animal Network Toolkit software (ANTs) is an attempt to manage these limitations. Whilst ANTs may be of use for all scientists working on social network analyses, it was developed specifically for animal behaviourists and ecologists who want to apply SNA to their research questions with analytical protocols that have been thought, designed and applied for more than 20 years in these specific research areas. This was the main reason behind the publication of this paper and the instructions to use the package (analyses and indices implemented in the packages). This R package has been developed for researchers who are studying animal social networks (usually small networks of up to 1,000 nodes, although the package can be used for larger networks) and have specific observational data protocols.
It provides them with a unique software to facilitate the processing of raw data and help researchers to select the most appropriate network measure and permutation approach for their data type, research question and run permuted statistical analyses to perform static and/ or temporal (time-aggregated) network analyses. All these steps have been tailored in generic functions based on standard analytical procedures to offer an all-in-one toolbox that is similar to UCINET and SOCPROG, but has the flexibility of the R environment, thus allowing biologist non-expert in graph theory to follow specific analytical protocols, whilst expert users can use ANTs functions to speed up their specific analytical protocols (Fig. 2 ). Furthermore, ANTs computing speed outperforms 'igraph' for the SNA measures that are common to both packages and 'asnipe' for data stream permutations (see benchmarks in ESM Appendix 1, Table  1  to Table  6 ).
Methods Data input. ANTs can process two types of data: (1) data representing the directed interactions of individuals (e. g. grooming) or their associations (e. g. proximity), and (2) data representing individual attributes (sex, age, dominance rank, etc. ). Interactions and association data can be input in the form of a matrix or a data frame(s). The data frame structure depends on the type of protocol the user wants to follow. For network permutations, data frames must be in an edge list format with at least two columns, one of which indicates the actor and the other the receiver. An additional column may indicate weights of interactions. These data frames allow the user to directly input data collected in the field. For the data stream permutation approach, data can be presented in data frame format.
' family. computing network measures. Three types of network measures can be identified depending on the level of organisation: global measures, polyadic measures, and node measures. In ANTs, all these measures are grouped under the function family 'met'. All the node measures available in ANTs are synthesised in Table  1. The measures we proposed in the package ANTs are the ones commonly used in Animal Social Network Analyses  6, 22, [26] [27] [28]. Global measures (e. g. network diameter) are used to study the overall network and obtain valuable information regarding network efficiency, resilience, clusterisation, etc. Polyadic measures (e. g. assortativity) allow the study of interaction patterns between individuals. These measures provide information about how individuals interact according to their attributes.
To date, ANTs is the only software permitting the use of these approaches in an all-in-one environment and their application for the analysis of multiple networks. For more details on the different permutations and their applications according to the data collection protocol, the type of behavioural data collected and the research question, see Bejder, et al. 30  , Whitehead  27  , Whitehead, et al. 32  , Croft, et al. 28  , Farine  16  ,Momigliano, et al. 33   1. Once the permutation test has been performed, the function 'ant' , allows the user to obtain the statistical results from any output object of any function 'stat'. The 'ant' function returns a data frame with statistics specific to the type of statistical test run. However, some of these statistics are common to all tests, namely the P-values on the right or left of the distribution and the two-side p-values.
2. Measures of the 'effect size' of the posterior distribution according to the statistics of interest: 95% confidence interval and the mean of the distribution a posteriori (see Farine and Whitehead  34  ). The histograms of the post-distribution of the statistics of interest obtained from the permutations. Network visualization. ANTs allows network visualisation with a data frame containing node information and a matrix of interactions/associations. Nodes and links can be parametrised to modify their size and colour and highlight differences (e. g. females showing higher eigenvectors than males). Network layouts are currently based on Barnes Hut repulsion, Hierarchical Repulsion and Force Atlas 2. For more details on network visualisation, see ANTs function 'net. vis' in the package instructions document.
Discussion ANTs provides researchers with an all-in-one software containing the most commonly used analytical tools in research on animal social networks. In order to offer tailored functions, ANTs provides what we call analytical protocols that represent standard procedures developed in animal social network analysis over the past decade, namely (1) node label permutations, (2) data stream permutations (for group follow and focal sampling). These two analytical protocols can be used on single networks or time-aggregated networks, allowing unexperienced users to run complex analyses on social networks. Besides, ANTs is also a flexible tool that enables users to conduct each step independently, thus providing users who have programming and analytical skills with an ideal tool to run specific analyses according to their requirements.
) Several statistical tests (correlation test, t-test, General Linear Model, General Linear Mixed Model) with the calculation of permuted p-values, mean and CI posterior distribution. (6) The possibility to analyse static or temporal networks/comparative analyses for the comparison of different datasets (groups or species). (7) Network visualisation. (8) A fast computing process. ANTs is currently available in an open beta test version on the following GitHub web page: https ://githu b. com/Sebas tianS osa/ANTs (for the stable release) https ://githu b. com/ants-proje ct/ANTs (for the version under development).
Context. In the course of a close approach to planets or stars, the morphological and dynamical properties of rubble-pile small bodies can be dramatically modified, and some may catastrophically break up, as in the case of comet Shoemaker-Levy 9. This phenomenon is of particular interest for the understanding of the evolution and population of small bodies, and for making predictions regarding the outcomes of future encounters. Previous numerical explorations have typically used methods that do not adequately represent the nature of rubble piles. The encounter outcomes and influence factors are still poorly constrained. Aims. Based on recent advances in modeling rubble-pile physics, we aim to provide a better understanding of the tidal encounter processes of rubble piles through soft-sphere discrete element modeling (SSDEM) and to establish a database of encounter outcomes and their dependencies on encounter conditions and rubble-pile properties.
Methods. We performed thousands of numerical simulations using the SSDEM implemented in the N-body code pkdgrav to study the dynamical evolution of rubble piles during close encounters with the Earth. The effects of encounter conditions, material strength, arrangement, and resolution of constituent particles are explored. Results. Three typical tidal encounter outcomes are classified, namely: deformation, mass shedding, and disruption, ranging from mild modifications to severe damages of the progenitor. The outcome is highly dependent on the encounter conditions and on the structure and strength of the involved rubble pile. The encounter speed and distance required for causing disruption events are much smaller than those predicted by previous studies, indicating a smaller creation rate of tidally disrupted small body populations.
This static theory has led to an extensive database of conditions for the onset of disruption. ally, or how they reaccumulate, could not be assessed and still requires the explicit modeling of the dynamics of the encounter. Several pieces of evidence point to a rubble-pile structure for most small bodies  (Richardson et al. 2002) , which was later confirmed by a number of space missions (e. g. ,  Fujiwara et al. 2006; Lauretta et al. 2019; Watanabe et al. 2019). In fact, due to their small size (smaller than a few tens of km), most of them are expected to be formed from the collisional disruption of a parent body in the main belt, leading to their formation as aggregates as a result of gravitational reaccumulation of smaller pieces resulting from the disruption  (Michel et al. 2001; Schwartz et al.
The collisions between the particles were treated as hard-sphere collisions, with no friction (see  Richardson et al. 1998, for details). Their model, which they recognized to be crude, provided a first general understanding of tidal encounter processes of rubble-pile objects. Further numerical modeling of tidal encounters was then performed by  Walsh & Richardson (2006) , whose motivation was to determine whether such encounters could explain the formation of observed binary asteroids. Covering a range of encounter conditions, they performed with the gravitational parallel N-body tree code pkdgrav 110,500 simulations, using a rubble-pile progenitor consisting of ∼1000 rigid 150-m spheres bound to one another by gravity. They then gave to those progenitors one of four rotation rates, that is, 3-, 4-, 6-, or 12-h periods.
Their results show distributions of physical and orbital properties of the resulting binaries, which were compared to the observed ones at that time, showing that this process may not be the source of most binaries. The main difference with observed binaries was that the eccentricity of the secondary formed in their simulations is much higher than the one of actual binaries. The formation of small binaries was then found to originate from another mechanism, which is beyond the scope of the current paper (see  Walsh & Jacobson 2015 , for a review). To understand the properties and evolution of NEO families originated by tidal fragmentation, tidal disruption processes and outcomes were also numerically investigated by  Schunová et al. (2014). Using the same code pkdgrav, they carried out N-body simulations of the tidal disruptions of km-sized spherical rubblepile progenitors on a range of Earth-crossing orbits.
Particles are taken to be in contact if and only if their surfaces are touching or mutually penetrating. The greater the extent of this penetration, the more repulsive the force that is generated. All contact forces are then computed, accounting for a static/dynamic tangential, as well as rolling and twisting frictions. Schwartz et al. (2012)  implemented the SSDEM in the code pkdgrav. Zhang et al. (2017)  updated this implementation by introducing the shape parameter β to represent a statistical measure of the contact area of realistic particle shapes, which allows mimicking the strong rotational resistance between irregular particles. In this study, we use the SSDEM to model tidal encounters, and investigate the influence of various encounter conditions and simulation parameters. In particular, as the material strength can significantly affect the mechanical properties and dynamical behaviors of self-gravitating rubble piles  (Zhang et al.
Tidal encounter setup We primarily consider close Earth encounters as examples to investigate tidal processes in this study. We note that the tidal forces can be scaled with the mass and density of the primary body and the density of the body performing the flyby. Furthermore, the encounter velocity can be normalized to the escape velocity of the primary body. Therefore, our simulation results also provide a database for predicting tidal encounter outcomes for different conditions. Figure  1  illustrates the close Earth encounter scenario. The simulated rubble pile approaches the Earth on different hyperbolic orbits, which are defined by the encounter velocity at infinity, V ∞ , and the perigee distance, q. The theoretical tidal failure limiting distance to the Earth (M ⊕ = 5. 97 × 10 24 kg, R ⊕ = 6378 km), d limit , for a cohesionless rubble pile with a friction angle of 18 • , a prograde rotation of 4.
For example, the study of  DeMartini et al. (2019) , where the same SSDEM code pkdgrav and an HCP configuration based on a radar shape model were used to simulate asteroid Apophis's close encounter with the Earth in 2029, shows that different encounter orientation can result in very different changes in the asteroid's spin period, from large spin up to spin down, or no effect at all. Combined with our above analyses, the sensitivity of encounter outcomes to the body orientation may be mainly caused by the asymmetric characteristic of the HCP model, rather than the shape irregularity. For the homogeneous RCP model, the particle arrangement orientation has little effect and the body is unlikely to be significantly spun up by the encounter. To eliminate the uncertainties due to the special HCP configuration, a more precise estimation on the encounter outcomes of Apophis and the effect of body orientation can be obtained by using a random packing, which is left for future studies.