dataesr/scientific-paragraphs-categorization huggingface dataset:
Yade (Yet another dynamic engine) is an open-source software tool based on the discrete element method (DEM), which uses objectoriented programming techniques  [32]. The software program is written in C++ and has a Python interface for high flexibility and performance. This makes the simulation of millions of particles interacting with each other possible. Yade runs on Linux operating systems and is available from the official repositories of common distributions such as Ubuntu. The source code is freely available on GitHub and is licensed under the GPL2 open-source license  [22]. (Figure  7 ) depicts the results of a simulation of ball mill grindings forces by showing the evolution of contact forces between the balls and the liner, for three different levels of filling rate F r. By analyzing the curve, we can observe that the average value of the applied forces increases with the filling rate.
whose characteristics were taken from the work of  [4]  and collected in Table  2 , was use as a reference model. The simulations were performed according to the experimental de sign represented in Table  4 , using Yade software. Yade (Yet another dynamic engine) i an open-source software tool based on the discrete element method (DEM), which use object-oriented programming techniques  [32]. The software program is written in C++ and has a Python interface for high flexibility and performance. This makes the simulation o millions of particles interacting with each other possible. Yade runs on Linux operatin systems and is available from the official repositories of common distributions such a Ubuntu. The source code is freely available on GitHub and is licensed under the GPL open-source license  [22].
The latter is dependent on temperature. F mag is the corresponding attraction magnetic force over the soft magnetic material at location z. This approximation of the magnetic force is illustrated in Figure  6  for a model considering the parameters listed in Table  3. It is shown the non-linearity of the magnetic force as function of the distance z. From Equation (  8 ), the saturation magnetization M s of the soft magnetic alloy is dependent on the temperature, as shown in Figure  5. Thus, it may be apparent from Equation (  12 ) that the magnetic force is a function of: the distance z separation between the magnets and the soft magnetic alloy, the magnetization and the temperature θ. F mag (z) was computed numerically using Matlab. The results are shown in Figure  7. The parameters considered for obtaining these results are summarized in Table  4.
The coupled system between the mechanical vibration and the electrical domain is as follows: F mag = m d 2 z dt 2 + C dz dt + k(z 0 -z) -αV piezo i = α dz dt -C 0 dV piezo dt (15) This equation system is iteratively solved using Matlab (ver. R2017a, Natick, MA, USA). More details are presented on the next section.
Generator Dynamic Behavior In order to investigate the dynamic performance of the initial design of generator, we conducted a transient simulation of the coupled system solving iteratively the equations aforementioned. We opted for implementing this mathematical model in Matlab (ver. R2017a, Natick, MA, USA) for solving the coupled system iteratively with a time step of 200 µs to check the mechanical vibration of the beam during its commutations. First, the temperature response of the system was explored, as shown in Figure  11. At the initial time of simulation, the electrical current is applied and maintained constant up to the half of the time simulation end. The whole system is at ambient temperature 20 • C, thus at its closed position. Once the simulation time reaches the half period, the current is turned off and simultaneously the fan is turned on to get a forced convection over the generator.
Two consecutive competitions, namely AMA-I and -II (Antibody Modelling Assessment), were initiated in the 2010s in order to assess the antibody structure prediction methodologies  [17, 18]. Their evaluations of state-of-the-art software like Accelrys  [19] , PIGS  [20]  and the Rosetta Antibody modelling suite  [21]  underlined the difficulty in properly predicting the conformations of the six CDRs (three from heavy chains and three from light chains)  [17, 18]. Interestingly, some animals have developed immunoglobulins with slightly different architectures that often encompass only one chain. The first one comes from cartilaginous fishes such as sharks. They have a special antibody called the "New Antigen Receptor antibody" (IgNAR). The IgNAR is longer than human IgG heavy chains, with dissimilar sequences  [22].
Specific sequences of V H Hs (e. g. , by using next-generation sequencing) targeting a given antigen have to be determined, and the best proper clone(s) need to be expressed. In this aim, different methods have been developed  [66]  that use, for instance, phage display libraries or even plants  [67] [68] [69]. For one particular antigen, a large number of experiments can be required to identify a lead V H H with a pertinent affinity. As for classical Abs, the understanding of the interaction between V H H and its partner of interest is better achieved when the 3D structure of the complex is available  [70]. Indeed, it would allow designing new V H H sequences with improved affinity or specificity or, in the context of biotechnology, grafting other partners (including V H Hs) onto it. Despite the increasing number of V H H structures available in the Protein Data Bank (PDB)  [71]  in recent years, the structures of a large number of V H H sequences are far from being fully solved.
This conclusion gives rise to the following principle: "structure is more conserved than sequence". These homology or comparative modelling approaches have been developed from the 1990s onwards  [83]  with the famous software called "Modeller"  [84]. Nonetheless, the main pitfall of this approach lies in the threshold of the sequence identity between the query and the template sequences. When it is too low, it precludes the use of this kind of method. A solution can be found by looking for the compatibility between known folds and very distant sequences, namely the threading approach. These techniques had some nice results in the late 1990s, e. g. , GenThreader  [85] , or, more recently, ORION  [86, 87] , despite a higher computational cost. In parallel, approaches that combine small fragments from very distant proteins and optimise tens of thousands of 3D models are being developed and are reaching maturity with Rosetta  [88, 89]  and I-Tasser  [90].
Modeller and ModWeb Modeller  [84]  is probably the most important software of comparative/homology modelling. Developed in 1993, it is still maintained (https://salilab. org/modeller/, last accessed date: 7 February 2022), free for academics and included in commercial packages. Modeller automatically produces a model encompassing all nonhydrogen atoms based on (multiple) sequence alignment for a sequence (provided by the user) to be modelled with known related structures  [106]. It takes as the input an alignment file of the sequence query with one or several sequences of structural template(s). Different refinement options exist. After multiple cycles of building and model evaluations, the chosen number of structural models is produced. The user must choose the best one associated with the lowest DOPE score  [107].
Nonetheless, the high sequence variability at the CDRs makes it a difficult challenge. Nowadays, Modeller is the most used software to model V H H structural models in theoretical studies. In 2010, our team modelled the first V H H  [111]. This V H H was optimised against human DARC/ACKR1, which is the entry point of a malarial parasite, Plasmodium vivax, into red blood cells. The structure of this anti-DARC V H H (named CA52) was built using two templates (PDB IDs: 1OP9 and 1JTO), selected using PSI-BLAST against PDB. The sequence query is 124 amino acids long and has an extra disulphide bond between CDR1 and CDR3 and was constrained as found in the second template. Two hundred models were generated using Modeller. Comparisons of the models were done by superimposition; the model with the median RMSD was selected.
The two V H Hs were built with Modeller using multiple templates from four different structures (PDB IDs: 4FZE, 4JVP, 2KH2 and 3P0G). RAMPAGE software, a tool based on the local protein geometry, was used to validate the generated models  [114]. The best models ranked were used for docking using ClusPro  [115]  with its human target TRNF1. This last step revealed the different binding sites of the two V H Hs. The authors used it to design bispecific V H Hs and tested them experimentally  [113]. Other examples were done using the single structural templates for the design of V H H focusing on several targets: vascular endothelial growth factor receptor 2 (VEGFR2) for antiangiogenic strategies in cancer therapy  [116] , bone morphogenic factor 4 (BMP4, implicated in carcinogenesis and tumour progression  [117] ), CD47 molecule, a promising cancer biomarker  [118]  and Vascular Endothelial Growth Factor 165 implicated in tumour angiogenesis and metastasis  [119].
ABGEN ABGEN software was made available in 1996. ABGEN is a homology-based algorithm that models an antibody and has some strong similarities with Modeller. It consists of two modules called ABalign and ABbuild  [125]. ABalign finds templates for the heavy and light chains and provides an identity score for each template identified. Based on the best template selected, ABbuild constructs a rigid model for all chains; then, the loops and side chains are optimised. Finally, ABGEN includes dedicated mechanistic approaches with XPLOR  [126]  and GROMOS  [127]  to refine the obtained models. ABGEN is no longer accessible and has not been used on V H Hs but was the first to specifically optimise the CDR loops.
SWISS-MODEL SWISS-MODEL is a famous online protein homology modelling server that was firstly published in 2003  [132]. It has been frequently updated with both interface ergonomic and methodological improvements  [133, 134] , but the basis is rooted in the principles of the Modeller algorithm (see above). The research of compatible sequences is made by PSI-BLAST  [108] , combined with delicate HHblits  [135]. Then, the user can select which template(s) to use. An interesting point is the different measures provided to assess the qualities of the several models built, i. e. , the Global Model Quality Estimate (GMQE)  [133]. Building of the structural model is done with locally developed OpenStructure, i. e. , an integrated software framework for computational structural biology  [136]. In a similar way, they also developed measures to assess the quality of the models, i.
MoFvAb MoFvAb was published in 2015  [150]  and focuses on "Modelling the Fv region of Antibodies". In the first step, it annotates FRs and CDRs using the WolfGuy numbering system  [151]. Based on the segments, templates are identified for heavy and light chains. For all chains, rigid models are produced, except for CDR-H3. The latter undergoes de novo building. In the next step, the CDRs are relaxed, and a side chain refinement is performed based on the neighbourhood algorithm. The final steps involve VH/VL orientation prediction and refinement, rotamer optimisation and CHARMM minimisation  [150]. The methodology was tested on the AMA-I and -II datasets with good results but was never applied on V H H. It might be adapted for V H H, but the approach is not currently available.
Phyre2 Phyre  [165]  and its successor Phyre2  [166]  are generic approaches very similar to techniques such as SWISS-MODEL that combine evolutionary information with a dedicated analysis of the protein structure dataset. It is an available online web server (http://www. sbg. bio. ic. ac. uk/~phyre2/html/page. cgi?id=index, last accessed date: 9 February 2022). Phyre2 has two modes: a normal and an intensive mode. The normal mode consists of three steps: 1. Related sequences (with or without structures) are detected by HHblits  [135]  and HHsearch  [167]  to produce multiple-sequence alignment and to search for adequate templates for the input sequence. Once the templates are identified, the models are constructed but roughly only with the backbone.
Three-dimensional structural models of these proteins were built using RaptorX  [192]. The loop conformations were obtained using the dedicated FALC loop modelling web server  [193]. The best models were further validated using various algorithms available on the NIH SAVE server. Then, the best resulting models were wellrefined and optimised by energy minimisation performed with Gromacs software  [194]. Afterwards, in order to predict their mode of interaction, the V H H models were docked on the target CyaA-RTX domains with the ClusPro web server  [115]. Interestingly, all the nanobodies were found to interact with the target, especially in the linker region between the two domains CyaA and RTX, i. e. , in excellent accordance with the experimental data.
Rosetta, Robetta, Rosetta Antibody and V H H Modelling Application Developed under the guidance of David Baker, the Rosetta story began in the last century. Originally, it mainly consisted in an ab initio approach  [195] , but progressively, it evolved towards a de novo strategy that combines small structural fragments obtained from a sequence search in a dataset. The addition of evolution information and local protein constraints improved the quality of the results  [89, 196, 197]. A last round of various improvements led to erasing non-native contacts. Finally, an intensive production of alternative structural models and the clustering of models coming from homologous sequences contribute to make Rosetta a great successful tool in the proposition of structural models  [198, 199]. Rosetta development was impressive and can be analysed through different prims.
RosettaCM, dedicated to optimise comparative modelling steps, competed with the best similar approaches  [199]. A web server named Robetta is freely available (https://robetta. bakerlab. org/, last accessed date: 10 February 2022)  [200, 201]. This powerful tool is also freely downloadable for academics and can be installed locally. Rosetta software has evolved a lot since its beginning, being highly scriptable and customisable  [93]. Besides 3D predictions, it encompasses extremely valuable services, e. g. , protein design  [202]  or protein docking  [203, 204] , which are of particular interest in the context of V H H exploration. Rosetta was applied for the study of an anti-glycoprotein (cAbAn33), anti-lysozyme (cAbLys3) and anti-prostate-specific antigen (cAbPSA-N7). The contribution to the stability of these proteins of an extra-disulphide bond (between CDR1 and CDR3) present in camels and llamas was more precisely explored.
Fortunately, cA-bAn33 and cAbLys3 have available protein structures that were used for creating models of mutants in which the extra cysteines CDR1 and CDR3 were replaced. ESyPred3D  [200] , i. e. , a methodology based on Modeller and Robetta  [200] , was used to generate the mutant models. The authors mentioned that the models from ESyPred correspond to those generated by Robetta (data were not shown). Hence, molecular modelling in this study has been useful in understanding the structural conservation of mutants in terms of fold. The models of the mutants predicted had very few differences compared to their parent V H H structures  [205]. From the generalist Rosetta software, a specific antibody development was done, namely RosettaAntibody  [206]. This tool is available online and can be used through Rosie (https://rosie.
3. CDRH3 de novo loop modelling completes the model prediction, along with the optimisation of the VH-VL interface. As the Rosetta modelling suite also incorporates a docking approach, this last was optimised for antibodies  [209]. However, RosettaAntibody appeared less efficient with single-chain antibodies and, so, with V H Hs. Accordingly, a specific adaptation in the loop definition was done  [210]  to better take account of the specificity of the V H H CDRs, especially CDR3  [211]. Until 2021, it was the only specific development and assessment dedicated to V H Hs. This study underlines the difficulty in V H H modelling and, specifically, the fact that an approach for IgG is not optimal for V H H. For a while, the specific script for V H H was not available in the new versions of RosettaAntibody, but it is now usable again.
AbPredict2 AbPredict is implemented in the Rosetta modelling suite through two versions: ABPredict 1  [213]  and ABPredict 2  [214]. It can be downloaded (http://abpredict. weizmann. ac. il/bin/steps, last accessed date: 14 February 2022) and is also free for academia. In short, AbPredict exploits known antibody 3D structures and optimally combines backbone conformations with a low-energy Monte Carlo search. A few details are given in the next section. AbPredict methodology is based on five independent databases comprising backbone torsion angles of the segments of VH, VL, CDRH3, CDRL3 and rigid body orientation of heavy and light chains. The initialisation starts by combining random segments from the five databases. A biased simulated annealing Monte Carlo (SA-MC) sampling is performed, with thousands of independent trajectories.
MOE/Antibody Pipeline MOE belongs to the Chemical Computing Groups Company, located at Montreal, QC, Canada, and has a pipeline for antibody and biologics designs  [217]. The tool works for classical antibodies and V H H. For a V H H sequence, MOE searches for templates in two databases: in-house-built and PDB databases. In the in-house-built database searching process, the template will be kept if the sequence similarity with the target is more than 50%. From the in-house-built and PDB databases, the top 10 and top five hits will be retained, respectively. The templates are then constructed by grafting the frameworks and the CDRs. Multiple models are built, and then, an energy minimisation is performed. From this group of models, a consensus model is established, and different optimised final models are proposed.
Macromoltek's SmrtMolAntibody The Macromoltek Company, located in Austin, TX, USA, created SmrtMolAntibody. The first step of this algorithm is to search an antibody database to detect templates for the VH and VL query sequences with the BLAST approach  [219]. Then, a similar search is done on a loop database to find more adequate templates for all six CDRs. Based on the identified templates, assembling the frameworks and grafting the CDRs allow building an initial antibody model. A specific modelling step is done for the CDR(H)3 loop. The top 50 models with the best (energy) CDRH3 loop conformations are retained. For each of these 50 models, the side chains are repacked, and their torsions are minimised. The abovementioned final steps are subject to multiple evaluations based on an energy score with modified and softened Lennard-Jones associated with side-chain rotamer frequencies  [219].
A transformer, called Evoformer, processes all of these inputs. An important innovation point about this latter is that it allows information exchange between the MSA and residue pairing blocks. The authors emphasised that this Evoformer block helps in establishing a rational between the spatial and the evolutionary information of residues. After passing through Evoformer, the resultant information is processed by structural blocks that output the coordinates directly. One particularity is that the intermediate MSA, residue pairing information and the predicted structure are reinjected into the Evoformer blocks. By default, these intermediate results are reintroduced in the network three times, which improves the predictions. AlphaFold 2 can be downloaded from a GitHub repository (https://github.
While preparing the submission of this review, DeepAb was published  [252]. It can be considered as the natural evolution of RosettaAntibody with deep learning RoseTTAfold. The algorithm can be divided into two main steps. The first part has a deep residual convolutional network, which consists of 1D ResNet, a Bi-Long short-term memory (Bi-LSTM) repertoire encoder and 2D ResNet. The 1D ResNet and Bi-LSTM encoder processes structural and evolutionary features. The 2D ResNet will output inter-residue distances and orientations. In the second step, Rosetta minimisation is used with distance and angle constraints. Concerning V H H modelling, Ruffolo et al. observed that CDR3 resembles that of CDRH3  [252]. Nevertheless, they suggest that the methodology should be refined with a database dedicated to V H Hs.
NanoNet NanoNet was proposed to the scientific community in August 2021  [253]. It is truly the first optimised V H H approach. This deep learning approach was trained with classical antibodies and V H Hs, as a large amount of data is needed to train the neural network and obtain relevant results. Its architecture is made by a convolutional neural network (CNN) with two 1D residual neural networks (ResNet). The first ResNet learns the frameworks and CDR hypervariable loops, while the second will apprehend inter-residue interactions. After a dropout layer to avoid overfitting, the last layer outputs C-alpha coordinates for a given input V H H sequence. Full backbone and side chains can then be constructed using Pulchra  [169]. This program is available online (https://github. com/dina-lab3D/NanoNet, last accessed date: 13 February 2022).
Discussion In the previous sections, we listed the different methodologies, provided their specificities and gave examples of V H H structural modelling. They are all very different and mainly used for docking purposes. However, it is highly difficult to precisely know the quality of each of these approaches/results. Indeed, they are all well-established approaches, but no real benchmark was performed until now, and the evaluations are limited to specific and extremely rare cases. A few examples are detailed hereafter. (i) The dedicated development of V H H scripts for the RosettaAntibody modelling suite is as expected associated with a specific evaluation  [211]  but mainly focuses on classical RosettaAntibody modelling than another tool  [207]. (ii) More recently, as Modeller  [84]  remains the most used comparative modelling approach, we evaluated the Modeller quality in V H H modelling  [74].
To better understand this complexity, we decided to evaluate and show a rather classical example of V H H modelling. We selected a structure of V H H that binds with lanthanides (PDB ID: 6XYF)  [257]  deposited in the PDB recent enough to never have been used in any of the previously cited approaches. 6XYF was obtained by X-ray crystallography with a very good resolution of 1. 11 Å. Please note that this case is a "classical" case of structural prediction, i. e. , representative of V H H modelling, as others were also tested. Eight different methods were carried out with six different software and online tools: Modeller with single-template and multi-template  [84] , ModWeb  [258, 259] , SwissModel with the best sequence identity template and a second time with the best GMQE score  [134] , AlphaFold 2  [91] , RoseTTAfold  [251]  and NanoNet  [253].
At first, Modeller was used with the simplest approach, i. e. , a single template. A template was selected with the highest sequence identity, i. e. , a V H H domain (PDB ID: 5LMW chain A) used as a crystallisation chaperone for different constructs associated with the type IX secretion system from Porphyromonas gingivalis  [260]. PDB 5LMW had a sequence identity of 89. 9% with the target sequence. In addition, Modeller was used in a multiple-template approach with three templates. We decided to use this method because of its remarkable efficiency, as demonstrated in Reference  [74]. Two new V H H structures were selected: anti-HIV V H H (PDB ID: 3R0M chain B)  [261]  and anti-HIV-1 gp120 V H H (PDB ID: 2XA3 chain A)  [262] , in addition to the 5LMW template. They also shared high sequence identity with 6XYF V H H: 89.