To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot's alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets. A new technique known as image-based phenotyping has potential to improve this subjective process [Changing climate, increasing population, and demand for high-quality food will challenge producers to grow more food with greater efficiency. A major pillar of the efforts to address this challenge is the selective breeding of higher-yield crop varieties with greater tolerance to extreme environments. Breeding routinely involves assessment of plants from candidate genetic lines to quantify desirable phenotypes. Current methods for assessing plant phenotypes are time-consuming, relying on laborious manual qualitative assessment, and often only consider a subsampling of the plants grown. For in-field trials, where a field area is subdivided into many  process , 2.Image-based phenotyping uses machine learning and image processing techniques to obtain quantitative measurements of the structural and functional properties of plants. For in-field trials, growth, health, and physical traits can be quantified from overhead images acquired from cameras mounted on tractors or unmanned aerial vehicles (UAV). Aerial imaging has potential for faster and more accurate assessment of a field , 3. A caMicroplots may or may not be regularly shaped or aligned due to inaccuracy during planting/seeding. Even if they are aligned, for example, in a grid-like arrangement (which is common), the orthomosaic may be warped due to small, cumulative errors in the stitching process or inaccuracies in geolocation information, which makes microplot localization more challenging. For example, such warping can make it difficult or impossible to rely on an expectation that microplot boundaries are perfectly straight lines.Microplots can theoretically be extracted from UAV orthomosaics based on GPS data alone. Usually, this means combining GPS data derived from the orthomosaic with preestablished GPS-based microplot shapefiles. However, both GPS sources are not always present. Or depending on the precision of GPS systems, the two GPS sources could disagree, sometimes in a nonlinear way, by 10 meters or more . FinallyAutomated detection and segmentation of microplots would enable automatic monitoring and quantification of microplot phenotypes, allowing a faster selection process that requires much fewer person-hours than manual assessment.There are three general approaches used to extract microplots from orthomosaic images such as manual, semiautomatic, and automatic methods.The first method is the manual method that localizes a microplot using the geolocation information on the drone to generate bounding boxes generated geometrically. Haghighattalab et al. implemented a simple grid-based method to extract microplots . As thisK-means classification algorithm to classify image pixels into “tree” and “nontree” groups. The limitations of both approaches are that they assume ideal separation between plots and sometimes merge multiple microplots into a single microplot. Khan and Miklavic proposed a semiautomated approach to extract microplots from wheat field orthomosaic images [The semiautomatic approach partially automates plot localization but still requires some manual intervention. Hearst and Cherkauer proposed a framework to extract microplots based on their map coordinates . This apc images . They usc images . They wec images . The sofc images . The proThe third approach is automatic methods, which are fully automatic without any manual intervention required. Parraga et al. proposed a segmentation methodology based on image processing techniques using UAV imagery. The proposed segmentation method had four steps: preprocessing, filtering, ROI map, and validation. This method can automatically segment the wheat microplots during the whole growth stage. However, it needs to tune several variables to work for all images during the whole wheat cycle, which results in a long time run. Ahmed et al. proposed an algorithm to detect and segment lentil microplots from multispectral aerial images . One of To the best of our knowledge, all of the published works on microplot extraction have been developed using learning-based and image-based analysis approaches. Although some models are automatic, all models have been designed and tested with orthomosaics having a relatively small number of microplots and used midseason orthomosaic images where there are no canopy closure and relatively uniform appearance of vegetation within microplots and clear gaps in between microplots. Some methods require a tool to manually create a cellular grid . Among tIn this paper, we propose a three-step image-based optimization approach where a map of the known microplot layout is overlaid and positioned as optimally as possible. In the next step, the position of individual microplots is optimized within the constraints of the overall layout. Compared to existing methods, ours adds robustness to challenging situations where there are a lack of visible microplot spacing and a lack of no canopy closure. Our approach also only requires a to-scale microplot layout map as input. The three-stage optimization procedure proceeds in a course-to-fine manner from overall field blocks to groups of microplot columns and finally to individual microplots to mitigate the effects of orthomosaic warping.The rest of the paper is organized as follows. In The datasets used in this paper were obtained from two wheat and canola breeding trials. To capture canola images, a Draganfly X4P quad-copter  carrying a MicaSense RedEdge camera  was used to take images in the summer of 2017. This camera captures images with five spectral channels: red, blue, green, near-infrared, and red-edge. Images were taken at a height of 15 meters or 20 meters depending on the day for canola datasets. The canola orthomosaic images are about 8100 × 9000 pixels in size. The canola trial has 39 rows and 6 columns of microplots in three aligned blocks of two columns. The microplot dimensions were 5 × 20 ft with an intermicroplot gap of 1 ft between microplots in the same block. A Sentera double 4K NDVI camera was used to take images of wheat at a height of about 19 meters in the spring of 2018. This camera captures NDVI images. The wheat orthomosaic images are about 12000 × 14000 pixels and contain 47 rows and 12 columns of microplots in three aligned blocks of 15, 15, and 17 rows. The wheat microplot dimensions are 3.5 × 13.5 ft with an intermicroplot gap of 2 ft between microplots in the same block. A high-resolution orthomosaic image of each field was produced by stitching images with Agisoft Metashape . Microplot ground truth bounding boxes were manually annotated in the orthomosaic images by a lab technician. A grid of equal-size rectangles corresponding to the known microplot layout was overlaid on the orthomosaic image so that each microplot is near its correct position. A lab technician used LabelImg to create the rectangles around the microplots. The bounding boxes were created by clicking two opposite corner points. Once done, the annotations were saved as xml in pascal voc format.The orthomosaic image in which microplots are to be localizedThe horizontal and vertical scale of the orthomosaic image in pixels per metermicroplot map of the microplot layout in the field in the form of a list of lists of coordinates of corner points of the microplot bounding boxes in units of meters relative to an arbitrary originA to-scale Our method requires the following inputs:The scale of the orthomosaic image is determined by the commercial software used to stitch the orthomosaics. The microplot map is easy to produce from the known field layout. It is usually convenient to use the top-left-most microplot corner as . The list of lists of microplot coordinates is ordered so that microplots can be easily associated with their microplot IDs in the field trial design. In the case of our regular-grid field layouts, the microplots are ordered in a column-major fashion.RGB orthomosaic image inputs are converted to a grayscale vegetation index image using an index that produces larger values for vegetation than soil. There are a few vegetation indices that satisfy this requirement such as NDVI, Excess Green (ExG), or Excess Green minus Excess Red (ExG-ExR) used by , 17. IndThe input scales of the orthomosaic image are used to scale the microplot map so that it is the correct size within the orthomosaic image's coordinate system by multiplying map's horizontal and vertical coordinates by the image's horizontal and vertical scale in pixels per meter, respectively.blocks and find a translation for the initial alignment of each block independently. In general, let B be a set of minimum bounding boxes around blocks of microplots, b + t be the translation of some b ∈ B by t = , and veg(b) be the sum of the vegetation index image within a rectangle b. The objective function to be maximized isb + t is the rectangle b translated by t and area(B) is the number of pixels enclosed by all rectangles in B. As long as the vegetation index ranges between 0.0 and 1.0, ϕveg will has a value in the same range.The next step of the algorithm is to find a translation of the scaled microplot map that maximizes the amount of vegetation within the microplot areas. Since the convex hulls of our microplot maps are rectangular, we searched for a translation that maximizes the sum of the vegetation index within the entire microplot map's bounding rectangle. For more irregularly shaped microplot maps or orthomosaics where stitching-induced warping would make successful alignment of the entire microplot map difficult, one could divide the microplot map into a small number of t that optimizes this objective function is found using differential evolution (DE) [f(x): IRn⟶IR without requiring its derivative. Since this objective function and objective functions in subsequent algorithm steps are composed of nondifferentiable functions, DE was an ideal optimization algorithm for our task. The ion (DE) . It is aAs can be seen in the magnified areas of the orthomosaics in B be the bounding box for the entire column and optimize equation , if the bounding box of c overlaps that of the column to its right, c′, then c is translated to the left so that it is positioned s pixels to the left of c′ where s is the number of pixels between c and c′ in the initial scaled microplot map. The per-column optimization process is described in For each column, we let equation  using DEB1, B2, ⋯, Bn and their bounding boxes or convex hulls with varying degrees of ease depending on the complexity of the spatial relationships between the subsets of microplots. For subsets with rectangular bounding boxes in a grid-like arrangement, the generalization is fairly easy. Moreover, this step could equivalently be performed as a per-row optimization.For microplot maps which are not organized into equal length columns, this step could be generalized and be applied to any set of subsets of microplots While the per-column optimization step somewhat mitigates the orthomosaic warping problem, individual microplots in a column can still be positioned poorly especially if the warping causes vertical displacement.The per-microplot optimization method requires three steps. The complete steps are summarized in (1) Update Initial Points. Individual microplot positions are optimized in a column-major order, starting from the top-left microplot, then working down the column, and starting at the top of the next column to the right when the current column has been completed. The differential evolution (DE) method is a population-based stochastic method that needs two important parameters for optimization, initial points and bounding research. In our project, the initial points are the upper-left corner and the lower-right corner of each bounding box. To update the initial points, the location of the optimized microplots from the previous column and the location of the previous microplot in the same column are needed. Let  and  be the upper-left corner and the lower-right corner of all microplot rectangles, respectively. ghm be the height of microplot and the gd shows the difference between two microplots in the same row but in a different column that can show the amount of warping from the previous column. By observing the warping between two neighbouring microplots in the same row, the amount of displacement due to warping is equal to or less than ghm/2. Then, initial points are updated when gd < = (ghm)/2. As can be shown in B.2 are ((microplotB.1(r′1) + gd), c′1) and ((microplotB.1(r′2) + gd), c′2). If gd > (ghm)/2, it is more likely that the optimized microplot from the previous column was aligned more closely or exactly to its neighbouring microplot. Therefore, the initial points are not updated. In addition, the initial points of microplots in the first column and the first row of microplots in the microplot map are not updated because there is no information from their previous microplots.Let (2) Optimize the Location of Microplot. In this stage of our algorithm, the position of individual microplots in the microplot map is adjusted by optimizing equation  is a translation vector, B is the bounding box of the microplot whose position is to be optimized, w0, w1, ⋯, w5 are weighting terms , and tht that minimizes this function. The search space of the optimization is bounded such that Δx is between Δxmin and Δxmax, and Δy is between Δymin and Δymax. In our implementations, we use bounds dependent on g, the known gap size in pixels between adjacent microplots in the initial scaled mircoplot map.Individual microplot positions are optimized in a column-major order, starting from the top-left microplot, then working down the column, and starting at the top of the next column to the right when the current column has been completed. The microplot position is optimized using DE to find the ϕveg . It is minimized when the vertical gap between a microplot and its previously optimized neighbour equals the expected gap The even spacing cost (EC) is then defined asy is bounded by the expected vertical gap between microplots g so that the optimizer can move the bounding box at most g pixels up or down. Therefore, we normalized ∣gk − g∣ based on the difference between the maximum possible gaps between the positions of two estimated bounding boxes. We apply an exponential function to this to get ϕEC(gk). An exponential cost was selected instead of a linear cost to obtain a higher cost value for small normalized gaps (errors) in spacing.The search space for Δr1, c1) and  be the center of the previously processed microplot rectangle above it. Then, the alignment cost (AC) isThe third term is the alignment cost (AC). It is minimized when the centers of the microplots in a column are horizontally aligned. Let the starting center of the current microplot rectangle be  = . This is desirable since we can be reasonably certain at this phase of the algorithm that microplots in the microplot map are already quite close to their correct position. Translation cost is defined as the sum of the normalized Manhattan distance of the shift. The fourth term is the translation cost (TC) which encourages translation vectors with shorter length. It is minimized when M be the subimage of the vegetation index orthomosaic enclosed by the microplot's bounding box with width w pixels and height h pixels. The edge cost is minimized when the sum of the smallest row sum and smallest column sum of M is maximized. Let Mij be the vegetation index of the pixel at the i-th row and j-th column of the subimage M. The smallest row and column sums areh and 1/w are normalizing factors. In the example in c5 and r2 are the minimum column and row sums, respectively. The edge cost to be minimized isw4 and w5 are the remaining weighting terms omitted from equation (The last term in the objective function is the edge cost (EdC). This term encourages microplots to be nudged away from positions where the very edge of the microplot area overlaps nonmicroplot areas in the orthomosaic. It is illustrated in equation .(3) Find Overlapped Microplot and Omit It -1.2 cm. In this stage of per-microplot optimization algorithm, the overlapped microplot in the field is found by using information from the previous optimized microplot. The reason for adding this stage to the algorithm is that initial points for the first column and first row of individual microplots in the microplot map are not updated. Since the initial points and research areas play a vital role in optimizing the location of microplots, there is a high possibility of overlapping between microplots in the first column. Two kinds of overlapping may occur after optimizing the location of microplot. First, the current optimized microplot's location is overlapped with the previous microplot; second, the previously optimized microplot is overlapped with the currently optimized microplot. O is defined as the height of the overlapped area between two microplots. Microplot A and microplot B are two previously optimized neighbours. If the distance d is equal or less than expected gap g based on the initial map, it means that the current microplot overlapped with its previous neighbour. Then, to omit the overlapping problem, microplotC(r′1) + ΔO and microplotC(r′2) + ΔO. When the distance d is more than the expected gap g, it means that the previously optimized microplot overlapped with the current microplot. Then, to omit the overlapping problem, microplotC(r′1) − ΔO and microplotC(r′2) − ΔO.ΔThe complete algorithm is summarized in w0, w1, ⋯, w5 to accommodate seasonal variations in the microplot orthomosaic. We used DE for the hyperparameter search. For each dataset, we randomly selected one early-, one mid-, and one late-season image on which to perform the hyperparameter search. The objective function for this DE optimization was the mean DSC of the three images after segmentation with our algorithm, and w0, w1, ⋯, w5 are the variables being optimized. For the DE algorithm, the minimum and maximum bounds on the variables were set to 0.0 and 1.0.We performed a hyperparameter tuning step that optimizes The values of the hyperparameters used for evaluation are reported in We assessed the algorithm's ability to detect microplots and its ability to segment microplots accurately.detected if the overlap between the microplot and its ground truth is acceptably high. We quantified this overlap using the Dice Similarity Coefficient (DSC) [A is the set of pixels in a ground truth microplot and B is the set of pixels in the corresponding automatically segmented microplot, then the DSC isA microplot is considered nt (DSC) . If A isdetected if its DSC exceeds some threshold TDSC. DSC ranges between 0.0  and 1.0 (perfect overlap).Thus, a microplot can be considered displacement error for each microplot, defined as the Euclidean distance between the center point of the segmented rectangle and its corresponding ground truth bounding box. This metric is used by [To evaluate the accuracy of the segmentation, we determined the DSC for each microplot (including microplots not deemed to be detected). We also determined the  used by .True Positive Pixels (TP): number of pixels correctly labeled as belonging to the correct microplotTrue Negative Pixels (TN): number of pixels incorrectly labeled as belonging to the correct microplotFalse Positive Pixels (FP): number of pixels correctly labeled as background (not belonging to the correct microplot)False Negative Pixels (FN): number of pixels incorrectly labeled as backgroundIn order to facilitate comparison with future studies, we also quantified the segmentation accuracy in terms of sensitivity-specificity and precision-recall. Sensitivity, specificity, precision, and recall are quantities that are computed from the following counts:Sensitivity and specificity are complementary measures. Sensitivity is the proportion of pixels that are part of a microplot that are correctly labeled as such:Specificity is the proportion of pixels that are not part of a microplot that are correctly labeled as such:The segmentation accuracy is higher when both sensitivity and specificity are close to 1.0. When sensitivity is higher, it means that more of the foreground pixels are being included in the segmentation. When specificity is higher, it means fewer background pixels are being included in the foreground. Both conditions are required for excellent segmentation.Precision and recall are similar complementary measures in that better segmentations are characterized by both measures being closer to 1.0. Recall is defined in the same way as sensitivity (see above). Precision is the proportion of pixels labeled as microplot that actually do belong to microplots:The main difference between sensitivity-specificity and precision-recall is that the latter does not take into account TN pixels in the segmentation. Precision-recall therefore does not account for correctly labeled nonmicroplot pixels. However, precision-recall is a good alternative to sensitivity-specificity when the number of background (nonmicroplot) pixels is large relative to the number of foreground (microplot) pixels in an image, which can inflate the specificity score because TN pixels vastly outnumber FP pixels. Thus, we report both pairs of measures.Our algorithm was evaluated on the ten canola and seven wheat orthomosaic images described in For the canola dataset, the per-block optimization step was performed using a single block for the entire field. For wheat, the warping was severe enough to require dividing the microplots into three blocks for this step. The blocks were formed from the three obvious subregions in the field with the larger spaces between them as seen in t for the DE optimizer in the per-block optimization step were set to Δxmin = Δymin = 0, Δxmax = gw, and Δymax = gh, where gh is the height of orthomosaic image minus the height of all microplots and the expected distance between them and gw is the width of the orthomosaic image minus the width of all microplots and the expected distance between columns taken from the initial microplot map for both canola and wheat orthomosaics.The search bounds on t for the DE optimizer in the per-column optimization step were set to Δxmin = −gi, Δymin = −g and Δxmax = gi, Δymax = g, where g is the expected distance between immediately adjacent microplots and gi is the expected distance between immediately adjacent columns taken from the initial microplot map for both canola and wheat orthomosaics.The search bounds on t for the DE optimizer in the per-microplot optimization step were set to Δxmin = Δymin = −g and Δxmax = Δymax = g for canola orthomosaics. Due to severe warping in the wheat dataset, two search bounds were set. Δxmin = Δymin = −g and Δxmax = Δymax = g for microplots in the first row and the first column of microplots in the microplot map in the orthomosaics and Δxmin = −g, Δymin = −g/2 and Δxmax = g, Δymax = g/2 for the rest of microplots in the microplot map in the orthomosaics where g is the expected distance between immediately adjacent microplots taken from the initial microplot map.The search bounds on w0 = 0.976, w1 = 0.873, w2 = 0.975, w3 = 0.0918, w4 = 0.421, w5 = 0.822 and w0 = 0.639, w1 = 0.627, w2 = 0.520, w3 = 0.0118, w4 = 0.0138, w5 = 0.624, respectively (rounded to three significant digits).Hyperparameters used for both wheat and canola datasets were ϕ(t) over all microplots.Because the differential evolution (DE) method is a population-based stochastic method that uses random initializations, we ran our algorithm 5 times for each orthomosaic image and retained the result with the smallest sum of We considered a microplot to be detected with an acceptable overlap if its DSC is 0.5 or greater. It must be understood that this DSC threshold is not used to measure whether a plot is segmented acceptably well, but rather as a measure of the detection rate, that is, the proportion of microplots for which we are able to establish a correspondence with one of the bounding boxes and associate it with a microplot ID. The position of such rectangles is adjusted in a later phase of the algorithm before assessing the quality of the segmentation of the microplot. A large proportion of microplots was detected using this criterion in both the wheat and canola datasets for early-, mid-, and late-season images. Tables In Figures We have developed a fully automatic method to extract microplots in orthomosaic images. Our proposed method can be applied to rectangular-shaped microplot layouts. These layouts include rows and columns arranged perpendicularly. Our method works for NDVI and ExG images and allows users to derive the different vegetation indices as input. The most important advantage of this method can be generalized to other crop types as long as the field layout map is provided and plants have a consistent size within a trial. The main difference of our approach with previous methods is combining prior knowledge (shapefile) with image analysis within a multiobjective optimization and evaluating the approach with a wide range of crop type and growth stage. We obtained a better result for the canola dataset compared to the wheat dataset. As shown in As shown in Tables In this project, initial points and research bounds are essential parameters for the optimization to get the best results. Due to severe warping in the microplot map, some initial points are closer to a neighbouring microplot in the orthomosaic than to the correct microplot in the orthomosaic. Then, the per-microplot optimization step aligns them more closely to the neighbouring microplot. Updating initial points could help to solve an overlap problem and decrease the number of unidentified microplots. To update initial points, the algorithm requires the information of previously optimized microplots. Because of this, the initial points in the first row and column of microplots in the microplot map in the orthomosaic could not be updated. Then, the stage of finding an overlapped microplot was added to the per-microplot optimization step.The method for extracting and localizing microplots in the field described in this study would benefit any applications that use a remotely sensed field trial, whether for lodging prediction, herbicide tolerance estimation, detecting crop rows, detecting plants, estimating height, or estimating yield –22. For k-fold cross validation on the whole dataset may result in hyperparameter values that better generalize unseen datasets. This could take the form of random splits of a dataset into “training” (for optimizing hyperparameters) and “testing”  sets.One limitation of per-microplot optimization used for microplot identification is that our algorithm used NDVI images or ExG indices that provided higher intensity value of vegetation than that of soil. A limitation of finding hyperparameters is that hyperparameter tuning on the whole dataset is quite time-consuming. As such, the hyperparameter tuning method used in this study has used only three images from the dataset to obtain an efficient weight vector. Using a technique such as In future work, we aim to develop an algorithm that accounts for the different physical properties of various crop types and different microplot sizes. This will be achieved by finding general optimization techniques and making modifications to the objective function that are well suited to manipulate images and vegetation properties. In addition, since the search bounds are important when optimizing the position of blocks, columns, and individual microplots, we suggest including the search bounds in the hyperparameter tuning model. Furthermore, since height information might be helpful for late-season imagery, we could add the height information as another channel to our algorithm. Also, EasyMPE is a semiautomatic approach to obtain microplot information for drone imagery of whole fields . The maiIn some cases, plant senescence or disease may cause microplots to no longer be green, in which case it will be difficult for our algorithm to detect it. Indeed, some examples of this can be seen in the lower block of microplots in We have developed an algorithm based on a novel application of image-based optimization techniques to extract and segment microplots within a field. Our proposed method for segmenting wheat and canola trials performs automatic initialization of the known field layout over the orthomosaic images in roughly the right position. The algorithm not only relieves the processing time bottleneck of identifying and segmenting microplots in high-throughput image-based phenotyping pipeline but also can simplify the tedious preanalysis task of identifying microplots in an orthomosaic image. Being able to segment and extract the exact location of microplots for high-throughput plant phenotyping scenarios, such pipelines will be critical in the future search for the higher quality of harvested crop needed to feed a growing population. As the three-stage optimization procedure proceeds in a course-to-fine manner from overall field blocks, it has the potential to be adapted for more specific purposes, such as extracting the different physical properties of various crop types and different microplot sizes.


Background: The novel severe acute respiratory syndrome related corona virus-2 (SARS-CoV-2) belongs to the “Coronaviridae” family and order “Nidovirales”, which has caused the pandemic coronavirus disease 2019 (COVID-19). SARS-CoV-2 has been spread in more than a 100 countries, and more than a million have lost their lives. Vaccination and immunization could be an effective strategy to combat fatal COVID-19.Methods: For identification of effective vaccine candidate against COVID-19, various immunoinformatics online tools and softwares were used to predict epitopes. Cytotoxic T cell epitopes, helper T cell epitopes, and B cell epitopes from three structural polyproteins  based on the binding affinity towards MHC, antigenicity, non-allergenicity, and non-toxicity) were identified for vaccine development. The multiepitope based vaccine was constructed linking two additional adjuvants human beta-defensin-3 and human beta-defensin-2 at N and C terminal, respectively.Results: The constructed vaccine sequence was found to be a good antigen and non-allergen for the human body. The constructed vaccine was docked with the TLR-3 receptor.  The docked complex was further taken for molecular dynamics simulations and RMSD was calculated, which showed stable binding of the complex. The codon adaptation index (CAI) of 0.92 and GC content of 55.5% forE. coli (K12 strain) suggested efficient expression of the predicted vaccine.Conclusion: The current study can be helpful in the reduction of time and cost for further experimental validations and could give a valuable contribution against this pandemic. The size of the RNA of coronavirus is the largest among the viruses (~30 kb)2. They have glycoprotein projections on the envelope, which gives the corona appearance. CoVs are pathogens mainly involved in respiratory and gastrointestinal diseases in a wide range of animals and humans1,2. CoVs are divided into four sub-categories, namely alpha, beta, gamma, and delta, out of which alpha and beta coronavirus are known to infect humans1,3. From alpha and beta, four strains are responsible for the common cold, and two strains were found to be responsible for severe acute respiratory syndrome (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)4,5. Recently in December 2019, a novel coronavirus SARS-CoV-2 was detected from patients of novel coronavirus disease 2019 (COVID-19) in the Wuhan, China8. The symptoms of COVID-19 infection include headache, fever, pneumonia, and asthenia9,10. A severe and fatal outbreak of this virus has taken many lives and has created enormous economic loss worldwide. The treatment and prevention from this infection is the need of the hour.Coronaviruses (CoVs) belong to the family of coronaviridae in the order Nidovirales, and have single-strand positive-sense RNA15. Here, an attempt has been given to construct an in-silico vaccine, which can be further validated through experimental assays and could play a major role in the management of this pandemic. In this study, three structural proteins form SARS-CoV-2, based on the antigenicity has been selected for the construction of the vaccine. The first structural protein is Spike (S) glycoprotein, which has been reported to be a crucial surface protein of SARS-CoV-2, which facilitates the entry of the virus inside the host cell. It has been reported that for the entry of the SARS-CoV-2, S protein first binds with Angiotensin-Converting Enzyme-2 (ACE-2) receptor, and then is primed by the host serine protease (TMPRSS2)16,17. This priming of the S protein lets it to fuse into the host cell membrane and entry inside the cell. The second crucial structural protein, which induces a strong immune response, is Membrane (M) glycoprotein. It plays a crucial role in virus morphogenesis and assembly by interacting with several other viral proteins18. The third structural protein chosen for vaccine construct is Nucleocapsid (N) phosphoprotein; it links the viral genome to the envelope. It consists of two domains, N terminal and C terminal, and both can bind to RNA. It has been reported that the C terminal domain facilitates the physical interaction of the RNA genome and envelope19,20. All these three structural proteins are predicted to be good antigens and could induce the immune response.COVID-19 has given a serious and tough challenge to biomedical research scientists and researchers all around the world. Much research is looking at repurposing antiviral drugs, and developing new drugs and vaccines against the SARS-CoV-221,22. Plasma cells lack receptors, but they produce a large number of antibodies against the antigen. Memory cells express membrane-bound antibody molecules, but they are functionally inactive unless they encounter the same antigen again25. Furthermore, T cell epitopes are recognized by Major Histocompatibility Class (MHC), a glycoprotein present on the variety of the cells, which display the antigen to T cells26,27. Antigen-presenting MHCs are divided into major two classes, MHC class-I are expressed on nucleated cells while MHC class-II are only expressed by antigen-presenting cells. Class II MHC interacts with T helper cells and activates B cells via cytokines, while Class-I MHC interacts with cytotoxic T cells, which kills virus-infected host cells28,29. Further to know the ability of a constructed vaccine for inducing innate as well as antigen-specific acquired immunity, the constructed vaccine must dock with Toll-Like Receptor-3 (TLR-3). TLRs are mainly expressed on various leukocytes such as dendritic cells, natural killer cells, and cells of adaptive immunity such as T cells and B cells30,31. Hence, in this study, an attempt has been made to construct the multiepitope vaccine consists of Helper T cells (HTLs), Cytotoxic T Cells (HTLs), and B cell epitopes, which could interact with TLR-3 and generate the immune response. This constructed multiepitope vaccine may induce both humoral as well as cell-mediated immune responses.In this pandemic situation, an immunoinformatics approach could be a fast, scientifically sound, and reliable option for quicker vaccine development. These three proteins chosen for the present study were predicted to be good antigens, which gives the opportunity to predict B and T cell epitopes. When naive B cells interact with the antigenic B cell epitopes via its transmembrane bound antibody, they differentiate into two types of cells plasma and memory cellsNC045512.2) were retrieved from NCBI on the basis of their antigenicity. The spike (S) glycoprotein (YP_009724390.1), Membrane (M) glycoprotein (YP_009724393.1) and Nucleocapsid (N) phosphoprotein (YP_009724397.2) were retrieved in FASTA format. These three proteins together are referred to as SMN  polyprotein in this study.The complete sequence of all three structural polyproteins from SARS-CoV-2 reference sequence (Netctl 1.2 server32. Prediction of the epitopes depends on three major attributes: (1) binding affinity of MHC-1 class; (2) ability of the proteasome cleavage; and (3) TAP transport efficiency. The first two are predicted with the artificial neural network algorithm while third one using weight matrix. For the prediction of the epitopes threshold for epitopes, identification was chosen to be 0.75, weight on C terminal cleavage was set on 0.15, while the weight on TAP transport efficiency was set on 0.05. The predicted epitopes were ranked according to the combined score.First, the CTL epitopes for SMN polyproteins were predicted usingIEDB MHC II server was used33. The species/locus was selected as Human/HLA-DR, and a 7-allele HLA reference set was selected for the prediction. Further, 15 mer length of the epitopes were retrieved and ranked according to the percentile. The percentile rank is given after comparing the peptides score with five million 15 mers from the SWISSPROT database. The higher percentile value means a lower binding affinity of MHC-II. For further refinements of the HTL epitopes, these selected HTL epitopes were subjected to investigate whether they can induce IFN gamma immune response using theIFN epitope server34. For IFN gamma inducing epitopes selection, the Motif/SVM hybrid approach was chosen, and the model was set to be IFN gamma versus non-IFN gamma. Finally, the epitopes whose results were positive for the IFN gamma response were chosen for the in-silico vaccine development.For the prediction of HTL epitopes, theABCpred server35. This server predicts B cell epitopes using recurrent neural network algorithm. For the identification of the epitopes, the threshold was set on 0.51, while the window length for the prediction was chosen to be 16, keeping overlapping filter on. Top predicted epitopes having scored more than 0.9 was only chosen for the development of the candidate vaccine. Further, after the construction of the vaccine, linear as well as discontinuous conformational B cell epitopes were identified in the vaccine construct usingElliPro, an online server36. Elipro predicts the antibody epitopes taking protein antigen tertiary structure as input.B cell epitopes were predicted using the37, and only probable antigen epitopes were chosen for the construction of the vaccine. Further, the allergenicity was predicted using theAlgPred server38 and only non-allergenic epitopes were selected. Finally, all the epitopes were investigated for toxicity using theToxinPred server39 and non-toxic epitopes were selected. All the predicted epitopes had to cross all these barriers. The overall construct of the vaccine was also tested for these attributes.The important attributes such as the antigenicity, allergenicity and toxicity were predicted for all the predicted epitopes individually as well as after construction of the vaccine. First of all, the antigenicity was investigated using the VaxiJen 2.0 serverThe vaccine sequence was constructed using the best identified CTL, HTL, and B cell epitopes. For the construction of the sequence at the N terminal and C terminal, an adjuvant was added using EAAAK linkers. While HTL epitopes were linked using GPGPG, linkers and CTL epitopes were linked using AAY linkers. In the C terminal, HHHHHH was added for the easy purification of the vaccine.ProtParam40.The physicochemical properties such as molecular weight, PI, half-life, aliphatic index, and hydropathicity were predicted using online toolCFSSP41.Protein secondary structure prediction gives further opportunity to predict the tertiary structure as well as gives information about the activity and function of the protein. The secondary structure of the final multiepitope vaccine sequence was predicted by the free online web toolRosetta web tool42. Rosetta tool applies a deep neural network algorithm to predict the inter-residue distances as well as orientations. Then these orientations are converted to smooth inter-residue constraints followed by gradient descent energy minimization. Further, the coarse-grained models are generated, and full atom refinement is done. The validation of the model has been done through Ramachandran plot analysis usingVADAR web tool43. Further, the modelled structure was validated through theProSA web tool44, which gives the quality Z score of the modelled protein based on the already known similar size of the proteins crystal structures.The tertiary structure of the constructed vaccine was predicted using theGromacs software45. The structure was minimized using the steepest descent algorithm with 50000 steps, followed by NVT and NPT equilibration for 100 picoseconds, followed by MD simulations of 500 picoseconds. The last frame of the MD trajectory was taken for further analysis.To achieve a more stable structure, the predicted structure was further taken for molecular dynamics (MD) simulations usingID 1ZIW). The downloaded structure was prepared and processed for docking using dock prep toolUCSF Chimera software. For the docking, the vaccine construct and TLR-3 was uploaded topatchDock server46. Further, for refinement of the rigid body molecular docking solutions,FireDock server was used47. It gave the best 10 docked confirmation based on global energy and Van der Waal’s interactions.For molecular docking, the last frame from the MD simulations of the constructed vaccine was taken, and the TLR-3 structure was retrieved from Protein Data Bank 48. The codon optimization was performed forE. coli strain K12 as a host. Jcat gives the codon adaption index (CAI) and percentage GC content as output. The CAI gives the information of codon usage, generally score between 1 and 0.8, while GC contents should be between 40 % to 70%, values lying outside the given margin is suggested to be inefficient49.Finally, for expressing the constructed multiepitope, the vaccine needs to be expressed in the suitable vector inside the prokaryotic system. Hence, reverse translation and codon optimization were analysed using the Java codon adaptation web tool  structural proteins were retrieved from the NCBI database in fasta format. The proteins were investigated for antigenicity by Vaxijen web tool, and it was found that all the three chosen proteins could be good antigens. The default threshold of 0.4 was chosen as the criteria for the antigenicity in the Vaxijen tool. The spike protein showed a score of 0.46; membrane glycoprotein showed a score of 0.51; while nucleocapsid protein showed a score of 0.50. Hence, all three proteins were chosen for further predictions of B cell and T cell epitopes and the construction of the vaccine.CTL epitopes were predicted using Netctl 1.2 server for all the three selected proteins. A total of 38 epitopes was predicted from spike glycoprotein; 10 epitopes were predicted from membrane glycoprotein; while 9 were predicted from nucleocapsid protein. Out of all these predicted CTL epitopes, only 8 were selected for the construction of the vaccine, based on a high binding affinity towards MHC-I, antigenicity, non-allergenicity, and non-toxicity predictions, as shown inHTL epitopes were predicted using the IEDB MHC II server for all the three SMN structural proteins. Finally, 4 HTL epitopes were selected on the basis of binding affinity, antigenicity, non-allergenicity, and non-toxicity, as shown inFor the prediction of B cell epitopes, ABCpred server was used. Based on the binding score (>0.9), non-allergenicity and non-toxicity, a total of four B cell epitopes were finally selected, as shown inThe four B cell epitopes, four HTL epitopes and 8 CTL epitopes were selected for vaccine construction, which fulfilled all the criteria of binding affinity, antigenicity, non-toxicity and non-allergenicity. Besides these epitopes, two adjuvants were also added at the N terminal (human beta defensin-3) and at C terminal (human beta defensin-2) of the vaccine to increase the antigenicity. Adjuvant were linked via EAAAK linkers to the epitopes, HTL epitopes were linked via GPGPG linkers, while CTL epitopes were linked with AAY linkers, as shown inE. coli was more than 10 hours, and the instability index in the test tube was found to be stable. The aliphatic value of the vaccine sequence was 58.7 and the grand average of hydropathicity (GRAVY) was -0.348.The physiochemical parameters of the vaccine sequence were predicted by the ProtParam server. The molecular weight of the construct was predicted to be 38.8 KDa, and the theoretical PI value was 9.92. The predicted half-life inSecondary structure prediction was made using the CFSSP web tool. The result showed the presence of helix at 44.5%, sheet at 35.6%, and turns at 14 %.The 3D structure of the multiepitope predicted vaccine was predicted using the Rosetta web tool. It uses de-novo structure prediction using deep neural network algorithm to predict the inter-residue distances as well as orientations. Then these orientations are converted to smooth inter-residue constraints followed by gradient descent energy minimization. Further, coarse-grained models are generated, and full atom refinement is done. It gave five best-predicted models, and based on the TM score, one model was selected for further investigation, as shown inElipro predicts the antibody epitopes taking protein 3D structure as input. Linear B, as well as discontinuous conformational epitopes, were identified in the vaccine construct using ElliPro, an online server. A total of 8 linear epitopes were predicted, and the sequence of the top 3 epitopes have been reported in50. The simulation was done using the PatchDock server and further refined using FireDock. The best-docked complex had global energy of -14.91 Kcal/mol, and attractive Van der Waal’s energy was -18.1 Kcal/mol, which shows a decent binding affinity of the vaccine towards TLR-3. Further, the best binding pose was investigated for polar interactions using discovery studio visualizer51 between TLR-3 and vaccine, and it was found that GLN352, SER428, ILE370 of TLR-3 was making the hydrogen bond with TYR260, ARG321, and LYS166 of vaccine respectively  as a host organism. The CAI index of 0.92 and GC content of 55.6 % and half-life was already predicted to be more than 10 hours, suggests the efficient expression of recombinant protein inside theE. coli. This immunoinformatics study suggests that the predicted vaccine could generate specific adaptive immunity against SARS-CoV-2 and could provide a valuable contribution to the management of the COVID-19. This predicted vaccine candidate strongly warrant in-vitro and in-vivo study for the practical implications.The structural polyproteins from SARS-CoV-2 were selected for developing a multiepitope based vaccine. Initially, four structural proteins were chosen, namely spike, membrane, envelope, and nucleocapsid protein, based on their antigenicity prediction, role in facilitating the entry of the virus, and packaging inside the host cells. However, when CTL, HTL, and B cell epitopes were predicted, it was found that epitopes from envelope protein were not able to satisfy the criteria of non-allergenicity and antigenicity simultaneously, and hence envelope protein was not considered further in the study. While epitopes selected from SMN structural polyprotein were satisfying, all the criteria such as antigenicity, non-allergenicity, non-toxicity, and high binding affinity towards MHC and also HTL epitopes were able to induce IFN gamma immune response. The constructed multiepitope vaccine from the selected epitopes from all the three SMN polyproteins was again investigated for antigenicity, and it was found that vaccine construct predicted to be a potent antigen with score 0.60 (predicted by Vaxijen). Further, the vaccine construct was looked for its allergenicity, and it was found that the vaccine was a non-allergen with a score of -0.59 . Further physicochemical parameters were analysed for vaccine sequence, and it was predicted to have a molecular weight of 38.8 KDa, PI of 9.92, and half-life inside theE. coli strain (K12). This immunoinformatics study may reduce the expenditure and time for vaccine research and may give a significant value in the management of COVID-19. This in silico prediction warrants the in-vitro and in-vivo study to test the practical implications of the predicted vaccine.In this study, a multiepitope  vaccine construct has been predicted and modelled through immunoinformatics techniques. The predictions suggest that the constructed vaccine could generate both humoral and cell-based adaptive immunity towards SARS-CoV-2. Further, it was also predicted that it may easily be expressed inside thehttps://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2/NCBI: Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome, Accession number NC045512.2:https://www.ncbi.nlm.nih.gov/protein/YP_009724390.1/NCIB: surface glycoprotein [Severe acute respiratory syndrome coronavirus 2], Accession number YP_009724390.1:https://www.ncbi.nlm.nih.gov/protein/YP_009724393.1NCBI: membrane glycoprotein [Severe acute respiratory syndrome coronavirus 2], Accession number YP_009724393.1:https://www.ncbi.nlm.nih.gov/protein/YP_009724397.2NCBI: nucleocapsid phosphoprotein [Severe acute respiratory syndrome coronavirus 2], Accession number YP_009724397.2:http://doi.org/10.2210/pdb1ZIW/pdbProtein Data Bank: Human Toll-like Receptor 3 extracellular domain structure, Accession number 1ZIW: et al., utilizedin silico Reverse vaccinology approach to develop multiepitope vaccine against SARS-CoV-2. Overall, the manuscript was well written and is a valuable contribution at this time of the pandemic.The current research article by Kumarin silico approach is a reliable and fast option for quicker vaccine development. The authors have utilized various immunoinformatic online tools to predict T-cell and B-cell epitopes from three structural polyproteins and constructed vaccine sequence.  Given the pandemic situation, the in vitro / in vivo studies, the immunoinformatic approach is helpful to reduce the cost and time for further experimental validation and can be a valuable contribution during the pandemic. Although the current study lacks theIs the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Molecular genetics, ImmunologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. in-silico.Kumar et al. in the manuscript attempted in-silico vaccine development against SARS-CoV-2 using publicly available SARS-CoV2-Sequence. They used three different crucial structural proteins (glycoproteins) from Helper T cell, Cytotoxic T cell, and B cell epitope. The epitope was predicted using publicly available tools. After predicting the epitope, they attempted an in-silico vaccine construction by applying various conditions including adding linked adjuvants to predict the efficiency of the vaccine. This immunoinformatics approach is very interesting given the SARS-CoV-2 evolution of new mutant strains, help to predict effective vaccineinvitro experiment to confirm any of the predictions as proposed in the manuscript. However, the in-silico finding from the study will be immensely beneficial to the researcher who studies the SARS-Co-V2. As the author mentioned in the manuscript it lacks anyIs the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Genomics, Single-cell Technology, and Cancer BiologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Brassicaceae, which lacked SBE1, but had a duplicated SBE2. Among our findings after a comprehensive and critical review of published data, was that as apple, banana, and tomato fruits ripens, the ratio of the highly digestible amylopectin component of starch increases relative to the more digestion-resistant amylose fraction, with parallel increases in SBE2 transcription, fruit sugar content, and decreases in starch. It is tempting to speculate that during the ripening of these fruit when starch degradation occurs, there are rearrangements made to the structure of starch possibly via branching enzymes to increase starch digestibility to sugars. We propose that based on the known action of SBEs, and these observations, SBEs may affect produce quality, and shelf-life directly through starch accumulation, and indirectly, by altering sugar availability. Further studies where SBE activity is fine-tuned in these crops, can enrich our understanding of the role of starch across species and may improve horticulture postharvest quality.Starch branching enzymes (SBEs) are key determinants of the structure and amount of the starch in plant organs, and as such, they have the capacity to influence plant growth, developmental, and fitness processes, and in addition, the industrial end-use of starch. However, little is known about the role of SBEs in determining starch structure-function relations in economically important horticultural crops such as fruit and leafy greens, many of which accumulate starch transiently. Further, a full understanding of the biological function of these types of starches is lacking. Because of this gap in knowledge, this minireview aims to provide an overview of SBEs in horticultural crops, to investigate the potential role of starch in determining postharvest quality. A systematic examination of SBE sequences in 43 diverse horticultural species, identified SBE1, 2 and 3 isoforms in all species examined except apple, olive, and The online version contains supplementary material available at 10.1186/s12870-021-03253-6. Horticulture likely originated 20,000 years ago . There aStarch is critical to human society given its versatile uses . Starch Starch is almost ubiquitous in higher plants , 15, incThe widely accepted view is that starch accumulates either in a transitory state, or for long-term storage starch . TransitStarch accumulates as semi-crystalline, water insoluble granules that vary in diameter from 1 to 100 μm depending on species . Starch Amylose and amylopectin are synthesized by the coordinate action of a group of four key enzymes . The corTwo major classes of SBEs are biofunctionally known: SBE1 and SBE2  and one putative class 3 SBE , and European olive (Olea), but these species all have two class 2 SBE isoforms  belong to the α-amylase family of enzymes, specifically the glycoside hydrolase family 13 superfamily , 66, wit.4.1.18 bnus Fig.  68, 69], 692- teArabidopsis [Arabidopsis [The SBE NH2-terminus contains two conserved domains: a chloroplast transit peptide for plastid-targeting, and a CBM48 (carbohydrate-binding module 48) domain for binding to starch . The C-tbidopsis , 72, butbidopsis . Howeverbidopsis , the GBSbidopsis  or the Sbidopsis , renderiFour conserved regions critical for catalysis, named Regions 1-4 Fig. , are fouStarch metabolism is tightly regulated by plants’ internal clock and the external day-night shifts, especially in photosynthetic organs where transitory starch turnover occurs on a daily basis . The traThe amylose-to-amylopectin ratio influences the textural, cooking, and nutritional properties of starchy foods, and the functionality of starch-derived biomaterials , 88–92. Starch, or the proportion of the amylose fraction of starch, is used as a common ripening biomarker for apple , banana Starch can also influence the quality of fruit juice. Although starch is degraded to sugars when fruit ripens, this conversion is not complete. Ripe fruit processed for juice therefore contains starch, which is treated with amylases for clarification . FurtherArabidopsis, the expression of SBEs and the changes of amylopectin and amylose (AP/AM) show a similar trend, but there is variation in when SBE transcripts peak. Although there is no information on SBE transcriptional levels in spinach during the diel, there may be some similarities with Arabidopsis because the pattern of leaf starch accumulation is comparable in spinach and Arabidopsis [Prepackaged leafy greens are convenient and healthy, and are popular options for salads in western countries , 100. Mebidopsis –108.Fig.The preharvest starch reserve may alter the postharvest quality of leafy greens. Harvested green produce are stored in optimized packaging under limited light exposure conditions which restricts new energy and carbon input from photosynthesis Fig.  111–113113. HoweArabidopsis influences flowering time and reproductive growth, key markers of development, and fitness [The amylose-to-amylopectin ratio in  fitness , 125. WhPotato, sweet potato, and cassava are generally considered as high glycemic index (GI) foods because the starch in their storage organs is easily digested to sugars when consumed, leading to a rapid increase in blood sugar level . It is eStVInv-silenced, CIS-resistant tubers, SBEs transcriptional level were suppressed [In addition, potato tubers suffer from a postharvest disorder: cold-induced sweetening (CIS). Potato tubers are stored at low temperatures (< 11 °C) to extend shelf-life and to meet year-round demand . Howeverppressed . Naturalppressed . TherefoStarch is a major component of the dry mass of fruits at commercial harvesting time. Starch is transiently synthesized and stored in unripe fruits with a peak just before ripening . Starch Apart from climacteric characteristics, after the onset of ripening, starch content plummets sharply accompanied by starch decomposition into soluble sugars, and the total soluble sugar content continues to rise proportionally Fig. . This dyAccompanying the starch-sugar dynamics, amylopectin-to-amylose ratio (AP/AM), also changes interactively Fig. . The difStarch, in general, plays an essential role in balancing the plant’s carbon budget as a reserve of glucose that is tightly related to sucrose metabolism and sugar signaling pathways . Starch Arabidopsis, but little research has been conducted on postharvest leafy greens. Quality metrics such as shelf-life, flavor, color, firmness, and texture are of consumers’ choice, and they are related to the limited pools of storage compounds in detached leaves, which cells rely on to maintain basic cellular activities [Arabidopsis [Arabidopsis given the dissimilar numbers of their isoforms and domain features . Figure S2. Starch branching enzyme (SBE) gene structure in select horticultural crops. Figure S3. Protein domain of starch branching enzymes (SBEs) in select horticultural crops and Arabidopsis thaliana. Figure S4. Predicted cis-elements of the 2 Kb upstream region of the SBE coding sequences. Figure S5. Predicted protein-protein interaction ‘STRING’ networks of selected SBE proteins. Figure S6. Correlation between starch content and respiration in diverse ripening produce.Additional file 2: Table S1. SBE expression, starch and sugar content, relative levels of amylopectin (AP) and amylose (AM) in various species. Table S2. STRING analyses of potential SBE-protein interactions. Table S3. Starch content and respiration data in various ripening produce.


European Journal of Behavior Analysis (EJOBA) both started. This article not only extends the information on EABA and EJOBA from a previous publication  and the  Behavior analysis started with enthusiasm in Europe  during the second half of the 1960s and into the 1970s. Essential for the development of behavior analysis in Europe was B. F. Skinner’s many visits—for example, to the United Kingdom, Sweden, and France. It is worth mentioning that Skinner presented at the 13th International Conference of Psychology, in 1951 in Stockholm. Several well-known psychologists, such as Jean Piaget, attended the conference. Skinner’s presentation was on “The Experimental Analysis of Behavior,” where he talked about fixed-interval and fixed-ratio schedules and how the different lengths of discriminative stimuli influenced behavior on these schedules.1European Journal of Behavior Analysis (EJOBA). We will emphasize a view on the situation of behavior analysis in Europe and the role that an organization should have in fostering and sustaining collaborations between behavior analysts from the varied European regions.However, we will argue that behavior analysis did not continue to expand in the way that we might have wanted in all its domains , an informal organization with no membership record, was founded in 1963 in the United Kingdom, possibly instigated by Harry Hurwitz, then at Birkbeck College London before moving to the United States, and was perhaps the first organized group of people who aspired to put together and expand behavior analysis in Britain and, by extension, to the rest of Europe , and then followed successively by Christopher M. Bradshaw (then at Manchester University), Julian C. Leslie (from Ulster University), and the late C. Fergus Lowe . Since then, the EABG has been run from Bangor University, with J. Carl Hughes taking the leading role, and the conferences are now held before Easter every 2nd year (opposite years of the EABA conferences). These last decades have seen a shift in the EABG from focusing largely on operant conditioning in nonhuman animals to focusing on studies of human operant behavior and applied behavior analysis, with an emphasis on requirements for professional training. The EABG’s audiences have become, therefore, much larger and more varied—nearly 200 attended the most recent meetings, with conferences now lasting 3 days and run at University College London. The participants are drawn from a wide range of countries within and beyond Europe, which was also happening in the early days of the EABG.From its early inception in Europe through the United Kingdom, behavior analysis expanded to different countries in the 1970s; particularly interested were the Behaviour Analysis in Ireland (BAI) group , which later became the Division of Behaviour Analysis of the Psychological Society of Ireland  held its first conference within Europe in Venice, Italy, in 2001, which was preceded by one of the series of International Congresses on Behaviorism and the Sciences of Behavior, organized in Seville, Spain, in 1998, which greatly informed discussions regarding the constitution of the EABA. Thus, by the early 2000s, behavior analysis appeared to have established a secure presence within a broad and diverse international framework beyond the United States and countries such as Mexico and Brazil, particularly in Europe. The series of international ABAI conferences has recurrently been held in Europe: Oslo (Norway) in 2009, Granada (Spain) in 2011, Paris (France) in 2017, and Stockholm (Sweden) in 2019; they will continue in 2022 with a conference in Dublin (Ireland), which was postponed from 2021 due to the coronavirus pandemic affecting the world at this moment. Similarly, the series of International Congresses on Behaviorism and the Sciences of Behavior (assembled by Peter Harzem together with Emilio Ribes) were also frequently held in Europe: Palermo  in 1994, Seville (Spain) in 1998 (as mentioned before), and Santiago de Compostela (Spain) in 2006. These were continued under the name International Congresses on Behavior Studies (and under the leadership of Paolo Moderato) in Rethymno  in 2010, Lisbon  in 2012, and Milan  in 2014.Acta Comportamentalia, published in Mexico, which hosts articles in Spanish, French, Italian, and Portuguese.Another series of conferences that intertwined with the aforementioned and contributed to spreading behavior analysis in Latin European countries was the international congresses on behavioral studies called Latini Dies that connected Spain, France, Mexico, Portugal, and Italy. Those who met at these conferences influenced the creation of, in 1992, the common scientific journal Finally, verbal relations were the main topic at three international meetings. The first was held in Kreuznach, Germany, in 1986, and the others in Mexico in 1988 and in Brazil in 1989. The presentations for the first meeting in Germany were edited by Hayes and Chase .https://www.europeanaba.org/index.php/about-us/eaba-history shows important events in the history of the EABA. Further information on the EABA can be found on its official website.2The EABA was officially founded at the 2001 ABAI international conference in Venice, after a number of discussions that took place at meetings in London , Dublin and Amiens , and particularly Seville in 1998 at the first focused meeting during the Fourth International Congress on Behaviourism and the Sciences of Behaviour. Arntzen et al.  gave detBeyond the EABA, there are several national organizations in Europe , Giulio Lancioni , Julian Leslie , Maria Malott , Jerry Shook , and Richard Shull .This conference was at the University of Crete, Greece. The scientific program included 37 events: 20 paper sessions, 11 symposia, and 6 keynote addresses. In addition, 25 posters were exhibited, and a presidential address served as the opening of the conference. Invited presenters were Gina Green , Chris Bradshaw , Bill Heward , Peter Killeen (The Five Causes of ADHD), Robert Mellon , Paolo Moderato , and Gabríela Sigurðardóttir .The conference venue was at both the University of Lusíada and the Cultural Centre of Belem, Portugal. Altogether, the scientific program included 37 events: 12 symposia, 18 paper sessions, and 7 keynote addresses. In addition, a regular poster session with 65 posters and 8 Expo posters was held, and a presidential address served as the opening of the conference. Invited speakers were Jennifer Austin , Mecca Chiesa , Camille Ferond , Iver H. Iversen , John C. Moore , and Niklas Törneke . A presidential address served as the opening of the conference.The conference venue was at Stockholm University, Sweden. The scientific program included 35 events: 16 paper sessions, 14 symposia, and 6 keynote addresses. In addition, a poster session with 49 posters and 14 Expo posters was held. Invited speakers were Shahla Alai-Rosales (Technology-Assisted Interventions to Teach Work Skills to Adults With Autism Spectrum Disorders), Lanny Fields , Wayne Fuqua (Improving the Effectiveness and Accountability of ABA Service Delivery Through Evidence-Based Practice Strategies), Ricardo Pellón , and Alan Poling .The conference was hosted at Kore University of Enna, Sicily, Italy. Altogether, the scientific program included 55 events: 25 symposia, 24 paper sessions, 1 panel discussion, and 5 keynote addresses. In addition, a poster session, opening session, and presidential address were a part of the program. The invited speakers were Angelika Anderson , Nancy Marchand-Martella , Carol Pilgrim , James Todd , Maurice Feldman , and Mark Mattaini .The conference was co-organized by the Faculty of Applied Social Sciences and School of Social Work at the University of Applied Sciences, Würzburg, Germany. The conference’s scientific program included 48 events: 22 paper sessions, 18 symposia, 2 panel discussions, and 6 keynote addresses (a couple of the keynotes had to be canceled on short notice for different reasons). In addition, the conference included one poster session with 26 regular posters and one Expo session with 5 posters, as well as an opening session and a presidential address. The invited speakers were Sigurður Óli Sigurðsson , the target article was written by Tonneau . The conOrganizers of symposia or meetings other than EABG and EABA have contacted EJOBA to ask for possible publication of special issues or sections. Special sections have been published based on meetings in Sarasota, Florida, and from two International Summits on Higher Education, Autism, and Behavior Analysis, the first in Texas in 2010 , with Fergus Lowe (Bangor University) and John Wearden (then at Manchester University) as contributors from the United Kingdom. Figure Behaviour Analysis Letters along with the appointed editors and the first editorial board; they were behavior analysts mainly from the United Kingdom and the United States, as was the case for each one of the two editors.The increasing momentum of behavior analysis in Europe during the 1980s (see the previous discussion) can be seen as well in terms of the foundation of the first behavior-analytic journal within a European domain. This was Behaviour Analysis Letters was subsumed in 1984 into Behavioural Processes, another Elsevier journal that was first published in July 1976. Behavioural Processes nowadays, as stated on its webpage , is dedicated to the publication of research on animal behavior from any theoretical perspective—including the behavior-analytic, cognitive, ethological, ecological, and evolutionary points of view—and considers publications investigating basic behavioral phenomena and behavioral studies of more applied significance. The inclusion of the behavior-analytic perspective was due to the incorporation of the policies that came from Behaviour Analysis Letters, and indeed John E. R. Staddon  was appointed editor of the journal in 1984, together with Georges Thinés (Catholic University of Louvain), who had been acting as the sole editor since the inception of the journal in 1976. Behavioural Processes still preserves that double editorship, with corresponding associate editors from the two broad approaches of cognitive neuroscience and behavior analysis.Behavioural Pharmacology was published; the first editors were Paul Wilner , Michael Emmet-Oglesby , and David Sanger , for years allied with the European Behavioural Pharmacology Society, established a few years earlier, in 1986:Behavioural Pharmacology was clear in its aims:The first editorial of Behavioural Pharmacology is to provide a forum in which behaviour and pharmacology are equally prominent. , with Paul Willner still involved as one of its associate editors. International Journal of Psychology and Psychological Therapy / Revista Internacional de Psicología y Terapia Psicológica, published in Spain and accepting contributions both in Spanish and in English, was established in 2001. It is mainly run from the University of Almería and is an interdisciplinary journal that publishes empirical and theoretical contributions in any area of psychology, with a stronger emphasis on applied behavior analysis and applied psychology. Behavioral Psychology / Psicología Conductual is a journal established in 1993 that is operated from the University of Granada, covering behavioral studies broadly understood, with a particular emphasis on clinical psychology. Finally, Conductual is a free-access electronic journal established in 2013 and run by a mixture of Spanish and American (predominantly Mexican) behavior analysts; it was initially more in the interbehaviorism orbit, but that slowly shifted to a more conventional behavior-analytic tradition, linked in different ways to the Sociedad para el Avance del Estudio Científico del Comportamiento since 2018, a Spanish behavioristic society, as well as to the Seminario Internacional sobre Comportamiento y Aplicaciones, an organization based in Mexico. Conductual publishes conceptual, basic, and applied articles in Spanish, Portuguese, and English, all with an emphasis on the study of the behavior of individuals based on empirical evidence or shared philosophical postulates to natural sciences. Similarly, as mentioned previously, Acta Comportamentalia holds close objectives.In Europe, other behavior-analytic journals have been successful. The Figure It is also essential to mention the types of teaching of behavior analysts that occur over more intensive periods—for example, summer schools in Oslo after the ABAI conference in 2009 and three summer schools arranged by the EABA in Rethymno  in 2015, Cádiz (Spain) in 2017, and Pelči (Latvia) in 2019. In general, the scientific program at these different summer schools has included scholars from different parts of Europe and the United States and students  interested in behavior analysis.Sigurdardottir  discussehttps://www.bacb.com/wp-content/uploads/2020/06/Recent-Changes-to-International-Focus_200520.pdf). As noted by Roll-Pettersson, Gena, et al. . The BACB provided an argument for the change: “The rationale behind changing our international focus is multifaceted and was shaped over a multiyear period following extensive research, training from the certification industry, and consultation with certification and testing experts”  spread across different countries. Furthermore, the outlets we presented in the paper are important for students, junior researchers, and senior researchers, and clinicians, so they may publish their research and interventions in a variety of peer-reviewed journals. We would argue that these peer-reviewed journals are essential methods of dissemination of behavior analysis in Europe. The two main behavior-analytic conferences in Europe, the EABG’s and EABA’s, are as important as the journals for the dissemination of behavior analysis in Europe, with the EABA conferences serious about including all the domains of behavior analysis in their scientific programs.


Animal Models and Experimental Medicine (AMEM). We are intensely proud of the publication of each paper, because we recognize that all our efforts are worthwhile to provide a platform for communication and sharing of outstanding scientific results and reinforce the discipline position of animal models and experimental medicine.The year 2022 marks the fifth year of the publication of AMEM is jointly published by the Chinese Society of Experimental Animals and the Institute of Medical Laboratory Animals of the Chinese Academy of Medical Sciences & Peking Union Medical College, in collaboration with Wiley, a renowned international publishing group. Since its inception in 2018, AMEM has received 333 manuscripts from 45 countries and regions, including Mainland China, the United States, India, Nigeria, Brazil, Bangladesh, Australia, Japan, Spain, and Israel. It has become an important international exchange platform for innovative research results in the fields of basic medicine and comparative medicine, and it has successively been included in domestic and foreign scientific databases such as Chinese Science Citation Database (CSCD), Directory of Open Access Journals (DOAJ), PubMed Central, and World Journal Clout Index (WJCI). In 2021, it was successively included in the MEDLINE and Clarivate Analytics Emerging Source Citation Index (ESCI) databases. Being included by ESCI signifies that AMEM's academic level and international influence in the field of laboratory animal science have been acknowledged internationally and it has laid a solid foundation for the journal to become a world‐class journal.AMEM are an important part of this integration process. It has played an irreplaceable role in Coronavirus Disease 2019 (COVID‐19) scientific research as well as medicine and vaccine research and development. With a high degree of academic acumen and a sense of national mission, the editorial team of AMEM took the lead in publishing the research results of the new coronary pneumonia model of the rhesus monkey in the first issue in March 2020. At present, this article has been cited 134 times, setting the highest citation rate since AMEM was published, significantly improving AMEM's international dissemination power and also reflecting the social responsibility of the journal's expert team and manuscript authors in public health emergencies. The research results of the rhesus monkey’s new coronary pneumonia model by the research team of the editor‐in‐chief were successfully selected as the “Top Ten Progresses in Life Sciences in China” in 2020.At present, life sciences, especially the intersection and integration of medicine and engineering science, materials science, and information science are accelerating. The fields of experimental zoology and comparative medicine covered by AMEM changed from a quarterly publication to a bimonthly publication. AMEM will continue to uphold its original aspirations, adapt to the needs of the times to accelerate the construction of international frontier hotspots columns, and enhance its international influence by collecting contributions from top international experts. We will focus on the current frontier hotspots of international basic medical research, such as cardiovascular and cerebrovascular diseases, neurological disease, natural drugs, etc., and publish the summaries and research articles related to them. At the same time, we will increase our online promotion efforts and develop together online and offline to promote the international dissemination and promotion of the results of basic medicine. High‐quality academic papers are the soul of the journal, while members of the editorial board are the guardians of the soul. In the face of new opportunities and challenges, AMEM has added more than 60 editorial members, covering basic medicine and clinical research as well as life science research. We warmly welcome more domestic and foreign experts with international perspectives to join us to promote the integration and cooperation in all aspects of basic medicine and simultaneously inject new forces into the development of experimental medicine.At the beginning of the new year, AMEM looks forward to more exchanges and cooperation with domestic and international counterparts.We thank the executive editor and other experts of the editorial board for their hard work. I deeply wish the journal a promising future in its fifth year and wish you all a happy new year!


The vaccine 3D structure was established ab initio. Furthermore, the vaccine displayed excellent binding affinity for critical innate immune receptors: TLR2  and TLR3 . Vaccine binding with these receptors was dynamically analyzed in terms of complex stability and interaction energetics. Finally, we speculate the vaccine sequence reported here has excellent potential to evoke protective and specific immune responses subject to evaluation of downstream experimental analysis.Crimean-Congo hemorrhagic fever (CCHF) is a highly severe and virulent viral disease of zoonotic origin, caused by a tick-born CCHF virus (CCHFV). The virus is endemic in many countries and has a mortality rate between 10% and 40%. As there is no licensed vaccine or therapeutic options available to treat CCHF, the present study was designed to focus on application of modern computational approaches to propose a multi-epitope vaccine (MEV) expressing antigenic determinants prioritized from the CCHFV genome. Integrated computational analyses revealed the presence of 9 immunodominant epitopes from Nucleoprotein (N), RNA dependent RNA polymerase (RdRp), Glycoprotein N (Gn/G2), and Glycoprotein C (Gc/G1). Together these epitopes were observed to cover 99.74% of the world populations. The epitopes demonstrated excellent binding affinity for the B- and T-cell reference set of alleles, the high antigenic potential, non-allergenic nature, excellent solubility, zero percent toxicity and interferon-gamma induction potential. The epitopes were engineered into an MEV through suitable linkers and adjuvating with an appropriate adjuvant molecule. The recombinant vaccine sequence revealed all favorable physicochemical properties allowing the ease of experimental analysis  Hemorrhagic fevers are caused by several distinct families of viruses and referred as viral hemorrhagic fevers (VHFs) . HemorrhNairovirus from the family of Bunyaviridae, which transmit to humans through domestic livestock and wild organisms . The GP38 protein is key in interactions with the vertebrates/tick hosts and critical in cell tropism. It also has an additional key role in CCHFV associated immune response  based on the multiple CCHFV proteins is presented in The comprehensive via Immune Epitope Data Base (IEDB) server  of CCHFV from NCBI . The pre) server . Linear ) server , and tho) server  for the ) server . Virulen) server  using a ) server . AllerTo) server  was usedoxinPred . To evaloxinPred , and onlanalysis .ab initio modeling of the vaccine was implemented to build its 3D structure with the help of 3Dpro of SCRATCH protein server  Java Codon Adaptation Tool (https://www.snapgene.com/).AAY linkers were utilized to associate all processed epitopes to produce a MEV construct . To desin server . Later on server , and then server  of Galaxn server . The MEVion Tool , and expion Tool . The cDNThe MEV was subjected to C-ImmSim server  to compuAt this phase, molecular docking of designed MEV with human TLR2 and TLR3 (PDB ID: 5D3I and 1ZIW) was performed to assess the vaccine’s affinity to the said receptors. TLR3 is a pattern recognition receptor family transmembrane protein. RNA viruses’ infection is detected and responded by eliciting the expression of NF-kB (Nuclear Factor kappa-light-chain-enhancer) and interferon regulatory transcription factor (IRF3). In contrast to other TLRs, TLR3 recruits TIR-domain-adapter-inducing interferon-β (TRIF). The innate immune system is stimulated and activated due to IRF3, which increases type I interferons development, that substantially induces adaptive immunity , 56. Vac2 on the remaining system, 1000 steps of energy minimization of all atoms exception to 5 kcal/mol—Å2 restraint on alpha carbon atoms, and 300 steps energy minimization on non-heavy atoms with the restraint of 100 kcal/mol—Å2 on rest of the complex. Systems were later heated to 300K (NVT ensemble). Temperature on the systems was maintained through Langevin dynamics  or Poisson Boltzman (MMPBSA) equation. The solvation energy is further split as:whereas the vacuum phase energy can be described by the following equation,Prediction of potential antigenic epitopes was done based on the amino acid sequences of N, RdRp, Gn/G2, and Gc/G1 proteins of CCHFV and then engineered into a MEV. The predicted epitopes were prioritized using several steps, each ensured selection of potential epitopes that fulfil requirements of an effective MEV candidate. An ideal multi-epitope vaccine should contain B and T-cell epitopes to stimulate an extensive immune response network. Initially, the proteins were analyzed for B-cell epitopes, and total 10 epitopes were predicted from N, 6 from RdRp, 5 from Gn/G2, and 11 from Gc/G1 proteins. The predicted epitopes were 5 to 100 amino acids long. The B-cell epitopes then subsequently investigated for T-cell epitopes and only strong binders to both MHC-I and MHC-II were selected based on the lowest percentile score. The selected epitopes were then examined for common peptides. For selection of T-cell epitopes, only the reference set of MHC alleles was opted. Each epitope further go-through sequence similarity check with human proteome to discard epitopes homologous with the host. This is a pre-requisite, as any epitope similar to the host may lead to strong autoimmune reactions and instead of protection, the MEV may deteriorate the host health. To facilitate experimental testing of MEV before clinical trials, the epitopes sequence identity was also tested against the mouse proteome and results revealed no similarity. All the filtered epitopes were also found free from transmembrane helices, that might ease the cloning and expression analysis. One key factor which is essential for downward analysis is the epitopes’ binding ability to the host immune system products, which means their antigenicity potential. All the filtered epitopes were predicted to be strongly antigenic. Moving forward, only epitopes were selected that have a high capacity of adhesion. The epitopes were also found non-allergic, thus reducing the chances of causing vaccine-associated allergic responses. Finally, careful evaluation resulted into 31 epitopes which were safe and non-allergic Table 1.The MEV construct was designed by combining filtered epitopes from all the target proteins. Designing of such vaccines is a promising technique, as it circumvents many limitations associated with the whole organism-based vaccines, as well as sub-unit vaccines. Moreover, due to limited antigenic capabilities of induvial epitope-based peptide vaccines, MEV designed from multiple conserved proteins strengthen immune stimulation and generate strong and specific immune response. To combine the selected epitopes, AAY linkers were employed. These linkers increased α-regions and reduced β-, turn-, and coil-regions, thus making the epitopes lesser flexible. To the N-terminus, EAAAK linker is rigid and keep the functional domain separated and allow its proper presentation to the host immune system. β-defensin was attached as adjuvant, which is a powerful immune adjunct as it significantly amplifies lymphokines secretion leading to the regulation of T cell-mediated immune response and the synthesis of antigen-specific immunoglobulin . MEV is “GIINTLCKYYCRVRGGRCCVCSCCPKEEQIGKCSTRGRKCCRRKKEAAAKPVSHEHVDWAAYRAQSAQIDTAAYRTNTETNNPAAYLFPDKFEDFAAYESSSILAFKAAYTQNVTTSEKAAYPVNAIDAEMAAYDLKARPTYGAAAYRLADRRIAEL”.in vitro was 30 h, and Escherichia coli in vivo >10 h, and yeast in vivo > 20 h. The net entropy observed was 17.01 and have no transmembrane helices ; hence, no difficulties can be anticipated in cloning and expression analysis. Next, the MEV construct was evaluated for antigenicity, immunogenicity, solubility, and allergenicity by using multiple tools to ensure the accuracy. The results revealed that designed MEV is highly antigenic (scores: 0.50 and 0.87), immunogenic, soluble (scores: 0.59 and 0.98), and non-allergic.The MEV is 157 amino acids long and has molecular weight of 17.44 7 kDa. Generally, vaccine of molecular weight < 110 kDa, is supposed to be feasible during purification process. Theoretical pI scores the vaccine was 8.90. This can aid in tracking the vaccine on a 2D gel. The aliphatic index score was 61.21, indicating its thermostable nature of the vaccine. The vaccine has 17 negatively charged, and 24 positively charges residues. Later, GRAVY score was predicted for the vaccine, and that was −0.477, indicating hydrophilicity. The half-life of the vaccine in reticulocytes of mammals, ab initio using 3Dpro predictor tool. The 3D structure of MEV is demonstrated in The secondary structure analyses revealed that 50.32%  residuesin silico cloning of MEV was achieved in pET28a(+) expression vector to assist genetic engineers and molecular biologist in cloning the MEV experimentally and get a high possible expression. Reverse translation and codon optimization of the MEV was done as per E. coli K12 system to optimize the expression , Arg14-Cys18 , Lys26-Gln29 , Lys44-Ala48 , Val57-Ala60 , Ala73-Glu79 , Tyr74-Tyr110 , Ala85-Phe88 , Leu87-Phe106 , Ala108-Asn113 , Ala126-Ala150 , Asp128-Met131 , Ala132-Ala146 , and Ala133-Gly143 . As these residues had highly unstable energy level of >1.34 kcal/mol and χ3 angle out of range (< −87 and > + 97). The mutated MEV 3D structure is shown in Immune simulation of the host immune system in response to the MEV revealed robust primary and secondary immune reactions. The host immune system response in terms of different antibodies titers and cytokines and interleuckins responses to the MEV are illustrated in Molecular docking of the designed MEV was carried out with specific immune response receptors, TLR2 and TLR3. Stable interactions with these receptors are essential to produce an effective immune reactions. TLR3 has a marked effect in recognition of virus linked molecular patterns leading to activation of NF-kappa B and type I interferons. For both receptors, 20 docked solutions were generated and ranked based on binding energy Table S3Molecular dynamics (MD) simulation was further performed on the docked MEV and TLRs complexes to investigate stability and affinity of the systems at thermobaric condition and time Figure 7The hydrogen bonds formation between MEV and TLRs were further studied in term of their radii distribution during simulation time. It was revealed that the binding of MEV to TLR2 is dominated by several interactions leading to strong stability of the complex. RDF plots were generated for some of the close intermolecular contacts and are presented in via MMGBSA and MMPBSA methods are tabulated in total) of −260.14 kcal/mol and MMPBSA energy of −309.77 kcal/mol. The system energy is dominated by electrostatic energy  and favorably supported by van der Waals energy . The system solvation energy is 853.46 kcal/mol in MMGBSA , and MMPBSA 803.83 kcal/mol . Further, the net energy of MEV-TLR2 system was decomposed into residues of TLR2 to underline residues that are hotspot in interactions with the MEV. The following are the hotspot residues for the MEV-TLR2 system; Ser33, Ser56, Lys55, Gln79, Gln152, Asn177, Val80, Gly344, Asn345, Arg422, Tyr483, Val503, Lys505, Arg507, Val556, Asp557, and Glu629.The atomic-level interaction energies were investigated using MMGB/PBSA. The MM-PBSA.py module of AMBER18 was used as an end state free energies computation approach as it is computationally easy to perform and more reliable than docking scoring functions . Bindingtotal) of −50.18 kcal/mol in MMGBPSA and −89.72 kcal/mol in MMPBSA. In MMGBSA, delta energy for MEV-TLR3, TLR3, and MEV is −70725.09 kcal/mol, −57301.02 kcal/mol, and −13373.87 kcal/mol, respectively. High contribution in MMPBSA was observed from MEV, followed by receptor TLR3, and MEV-TLR3 complex. Total electrostatic energy estimated for the system in both MMGBSA and MMPBSA was −987.49 kcal/mol and dominate the overall energy of the system. This energy can be split into, MEV electrostatic contribution , TLR3 , and MEV-TLR3 . Van der Waals energy is also favorable with net system energy is −144.20 kcal/mol . Total solvation free energy for the system was noticed unfavorable, i.e. MMGBSA  and MMPBSA  mainly because of polar energy . Non-polar solvation energy, on the other hand, seems favorable in both MMGBSA and MMPBSA is −1101.70 kcal/mol. List of hotspot residues in MMGBSA and MMPBSA is given in For TLR3, the total binding energy  were predicted and analyzed rigorously by employing various approaches. The most potent nine epitopes were chosen based on their immunogenic properties  for further analyses. The selected epitopes showed 99.74% global coverage. This result suggested that the MEV would be effective on majority of the world population round the globe. Next, the MEV sequence was designed by joining selected epitopes through linkers and adjuvant. An adjuvant (β-defensin) was added to the N-terminal of the MEV along with EAAAK linker, and epitopes were fused together through AAY linkers. β-defensin serves as an excellent adjuvant due to its antimicrobial and immunomodulatory properties, and it has been used in several previous studies  , 73. Alt studies , 25, 75, studies .in vitro and in vivo experiments, physicochemical properties of the vaccine were assessed. The designed MEV was found to be highly antigenic, immunogenic, soluble, thermostable, and non-allergenic, demonstrating the epitope vaccine’s ability to elicit robust immune responses without causing allergic reactions. Tertiary and secondary structures provide information about a protein’s function, interactions with other proteins/ligands, and dynamics (in silico cloning revealed that our designed MEV will be expressed at a high level in E. coli K12 system.To ease follow up experimental analyses of the MEV and allow successful setting of dynamics . The desdynamics . Disulfidynamics . RecombiTheoratically, since the MEV designed by joining multiple B derived T cell epitopes, it should elicit strong cellular and humoral immune responses. However, the immune system response may vary according to the different factors, mainly because of mechanism of pathogencity . Therefoin vitro and in vivo; however, the selection criteria for filtering epitopes and post analysis were quite stringent, which facilitate the designed MEV to be the potent candidate against CCHFV. In a nutshell, the presented MEV construct must be evaluated experimentally to uncover its real immunogenicity for practical applications.In the current study, we presented an integrated computational framework describing the design of an MEV by targeting major antigenic/conserved proteins of the CCHFV. The formulated MEV strongly elicit both primary and secondary immune responses, showed a good affinity of binding with innate immunity TLR2 and TLR3 receptors, thus providing the adaptive immunity to establish and counter the pathogen properly. The intermolecular affinity was validated by molecular dynamics simulations that predicted highly stable binding of the MEV with the receptors, which was evident by strong chemical interactions. Despite keeping high standards in the computational methodology for MEV design against CCHFV, there are still several limitations that can be improved and investigated in future studies. For example, the vaccine is immunogenic but the real extent of immune protection against the pathogen is something that needs to be explored experimentally. The current results are preliminary and still need to be uncovered The original contributions presented in the study are included in the article/MQ, SI, and SA: conceptualization, data curation, visualization, software, methodology, investigation, and writing—original draft preparation. MM, SA, and UA: validation and writing—reviewing and editing. L-LC: supervision, project administration, funding, and writing—reviewing and editing. All authors contributed to the article and approved the submitted version.This work was supported by the starting research grant for High-level Talents from Guangxi University, China, and Postdoctoral Project from Guangxi University, China.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.