Understanding cross-domain traffic scenarios from multicamera surveillance network is important for environmental perception. Most of existing methods select the source domain which is most similar to the target domain by comparing entire domains for cross-domain similarity and then transferring the motion model learned in the source domain to the target domain. The cross-domain similarity between overall different scenarios with similar local layouts is usually not utilized to improve any automatic surveillance tasks. However, these local commonalities, which may be shared across multiple traffic scenarios, can be transferred across scenarios as prior knowledge. To address these issues, we present a novel framework for cross-domain traffic scene understanding by integrating deep learning and topic model. This framework leverages the labeled samples with activity attribute labels from the source domain to annotate the target domain, where each label represents the local activity of some objects in the scene. When labeling the activity attributes of the target domain, there is no need to select the source domain, which avoids the phenomenon of performance degradation or even negative transfer due to wrong source domain selection. The effectiveness of the proposed framework is verified by extensive experiments carried out using public road traffic data. The tremendous development of mobile Internet of Things (IoT) has led to the widespread deployment of cooperative vehicle–infrastructure systems (CVISs) based on Internet of Vehicle (IoV) networks, promoting the development of intelligent transportation . VehicleTo address this problem, several approaches have been proposed –12. The Human beings have outstanding performance in identifying unseen objects through the attribute knowledge of empirical objects , 14. ForIn this study, we develop a new framework for cross-domain traffic scene understanding illustrated in We formulate a new idea to solve the cross-domain traffic scene understanding problem, which is to transform this problem into a multilabel classification problem with activity attributes  for solving.We introduce a scheme for transferring the local prior knowledge obtained from source domain scenarios to a new target domain scene, avoiding the phenomenon of performance degradation or even negative transfer due to wrong source domain selection.We employ a topic-word vector matrix transformation strategy, which is executed before the topic-word vector matrix of the target domain scene is input into TextCNN for multilabel classification to reduce the cross-scene variance.The main contributions of this study are as follows:The remaining of this paper is organized as follows: the related work is discussed in Existing behavior analysis methods for scene understanding typically includes two key steps: establishing behavior models from training samples and matching test samples with established models , 19, wheActivity modeling and anomalous event detection in multicamera networks is extremely challenging –29, regaFew methods have attempted to exploit the correlation between scenes without topological relationships –12, 30. CNN-based models perform extremely well in scene recognition tasks , 31, 32.We consider combining the Word2Vec word vector with the LDA topic vector to represent the text feature matrix at both word granularity and text granularity levels. Word2Vec mines the meaning of words at the word granularity level for fine-grained semantic representation of text. LDA constructs the topic distribution of text from the text granularity level by probabilistic model, focusing on the overall semantic representation of text.Given a surveillance scene, we firstly learn activities in the scene using Latent Dirichlet Allocation (LDA) . SimilarNa × Nb small cells and the motion vector of objects into Nm fixed directions. Then, the size of the codebook is Nv=Na × Nb × Nm.Under our framework, a traffic video sample can be segmented into nonoverlapping clips with equal frame length. Video clips are treated as documents and the trajectory points (positions and moving directions) of moving objects are treated as motion words. At this point, we treat a trajectory within a video clip as a sentence. Each video clip contains multiple trajectories. Therefore, each video clip is mapped to a motion word bag or a collection of sentences and embodies one or more motion patterns. The positions and moving directions of trajectory points are computed as motion words which are quantized according to a codebook. The codebook uniformly quantizes the video frame into s is divided into M-segment video clips/documents. Each video clip is represented as a random mixture of K topics, where K represents the number of topics. Each topic is modeled as a polynomial distribution φk=, i.e., a mixture ratio φ ~ Dirichlet(β) of various motion words in the codebook.The graphical representation of LDA model is shown in s is to discover the K topics (activities), expressed as polynomial parameters φks. For video clip j, the polynomial distribution θj= over the K topics is generated by the Dirichlet distribution Dirichlet(θj|α). For each motion word wji in video clip j, the topic zji=k is determined by probability parameter θjk; φzji determines the generation of motion word wji.Learning LDA for scene α and β, the joint distribution of topic mixture parameter θ, motion word mixture parameter φ, topics zj={zji}, and motion words wj={wji} isNj is the number of motion words in the video clip j.Given θ and the topic-word distribution matrix φ are generated. Each video clip j is now represented as a topic profile θjs, and scene s can be represented by its constituent activities φks.The Gibbs Sampling algorithm  is emploIn this work, we encode the motion words using Skip-Gram training model of Word2Vec  to form When modeling at the text granularity level, instead of directly applying the LDA model to map documents to the dimension of topics, documents are extended with features based on the LDA topic probability maximization principle to construct a document representation model that integrates word vectors with LDA. Using the maximum text length of documents in the document set as the reference, the document-topic distribution matrix is used to find the topic with the highest probability corresponding to the document. Then, after finding the word probability distribution under this topic through the topic-word distribution matrix, words are mapped into word vectors according to their probabilities from high to low, and the word vector matrix is filled in order until the number of rows of the constructed word vector matrix is equal to the maximum text length.d. In order to make the text feature extraction depth of TextCNN model wider and the classification effect more accurate, two convolutional layers are superimposed on the basic TextCNN model [An overview of the proposed framework for cross-domain traffic scene understanding is depicted in NN model , 39.When extracting the local features of the topic-word vector matrix in the first convolutional layer, we use three filter region sizes: 3, 4, and 5 and set the number of feature maps for each region size to 100 . Then, tIn the pooling layer, we perform 1-max pooling over each deep feature map, generating a feature vector of length 1 for each filter . That isL| neurons as the output layer. The sigmoid function is adopted as the output function. To avoid overfitting the training set, we use the dropout method proposed by Srivastava et al. [O(∑l=1LWlHlDl2Zl), which is the accumulation of the time complexity of the two convolutional layers, where l is the lth convolutional layer of TextCNN, L is the number of convolutional layers, W is the length of the input motion word sequence, H is the height of each filter, D is the dimensionality of the topic-word vectors, Z the number of sizes of filters, and the size of filters (feature maps) is also D. The space complexity is O O(∑l=1LWlDlZl), and the key lies in the feature maps output from each convolutional layer.The feature vectors cascaded after the pooling layer are fully connected with the label set |a et al. . Set a cφk of various motion words in the codebook. This is derived from the intuition that two activities which appear semantically similar to humans should have a similar distribution of motion words. From the transferability measurement perspective alone, an obvious alternative is to find rigid similarity transformations of each activity in the target domain scene to the source domain scene separately as a basis for activity attribute labels propagation. However, this would be overfitting, because, in this case, any activity model can be easily transformed into the same target activity model by the transformation matrix, which makes it difficult to choose the appropriate activity attribute labels for the target domain scene to be labeled. In contrast, our proposed method is tractable and free from overfiltering since it takes into account the full distribution of activities in the scene and feeds the transformed topic-word vector matrix of the target domain scene into the aforementioned TextCNN for multilabel classification.We are ultimately interested in cross-domain migration of local prior knowledge, facilitating the cross-domain scene understanding by mapping object's activity attribute labels. After the above topics/activities learning using LDA, each topic/activity is modeled as a polynomial distribution Ks topics/activities {φkss}ks=1Ks in source domain scene s and Kt topics/activities {φktt}kt=1Kt in target domain scene t, the distance between topic/activity φkss and topic/activity φktt is defined as DKL inWe next address how to align two scenarios (source domain scene and target domain scene) and transform the topic-word vector matrix of target domain scene. To quantify the similarity between probabilistically represented activities in source domain scene and target domain scene, we exploit the Kullback-Leibler Divergence  (KLD) toThen, we convert the problem of correspondence between the activities within the source domain scene and target domain scene into the weighted bipartite graph matching problem and solve it using the Kuhn-Munkres (KM) algorithm. By giving each vertex a top mark, the algorithm transforms the maximum weight matching into a continuous searching for an augmentation path to find a perfect matching , 42. TheDKL(φkss‖ φktt) is calculated as the weight of the edge connected by the left and right vertex. Through weighted bipartite graph matching, the correspondence between topics/activities in the two scenarios is obtained. This is necessary because the correspondence cannot be estimated unless the activities in the two scenarios are aligned.Topics/activities in the source domain scene and the target domain scene are regarded as the left vertexes and right vertexes of bipartite graph respectively. The number of left vertexes is the number of topics/activities in the source domain scene, and the number of right vertexes is the number of topics/activities in the target domain scene. The Kullback-Leibler Divergence H assx, sy) and  are the scaling and translation parameters, respectively. Considering that rotation transformation may change the activity attribute labels of the output, we did not consider rotation parameters in the geometric transformation.For wide-area surveillance, semantically equivalent activities differ only in the geometry of their views but are equivalent under the geometric similarity transformation H (a 3 × 3 matrix). We denote the transformation matrix φktt to φktt ′ and to minimize the sum of the weights of the edges while keeping the corresponding relationship of the activities in the two scenarios unchanged. Then, we apply an objective function to find H asφkt′t)′ is transformed from φkt′t by matrix H. φkt′t is the activity in target domain scene t that corresponds to activity φkss in source domain scene s.To avoid overfitting, we consider that activities in the same scene share the same geometric transformation. Therefore, our goal is to find H which transforms φktt is a Na × Nb × Nm matrix and (φktt)′ is transformed from φktt through transformation matrix H. We first need to estimate the size Na′ × Nb′ × Nm of transformed topic/activity (φktt)′ by Na′=Na × sx and Nb′=Nb × sy. In order to obtain the value of each motion word in (φktt)′, we need to trace back to the value of the corresponding motion word in the original topic/activity φktt. It is worth noting that since the rotation parameter is not taken into account in the geometric transformation, we consider that the motion direction remains unchanged throughout the procedure of transformation. Some common interpolation methods can be used for this scaling task, including nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation. In our present work, bicubic interpolation method is adopted.This transformation is applied to topics/activities in a similar way to image transformation. That is to say, assume that x′, y′) is calculated exactly. However, since this transformation involves translation, the transformed topic/activity may extend beyond the original topic/activity boundary. That is, the scaled scene is beyond the boundary of the original scene and the codebook is inconsistent. To ensure that all topics/activities maintain the same codebook for comparability, for the transformed (φktt)′, we keep only those parts that lie within the Na × Nb range of the original topic/activity φktt. After the above operation, the codebook of the transformed target domain scene and the original target domain scene remains unchanged. Meanwhile, the size of the transformed topic/activity (φktt)′ is the same as that of the original topic/activity φktt, Na × Nb × Nm. Finally, we obtain the transformed topic-word vector matrix , as shown in Set the model parameters In order to evaluate the performance of our proposed T-LDA-TextCNN method, we conducted two experiments: single-source domain transfer and multisource domains transfer. We compare the proposed T-LDA-TextCNN with the eight state-of-the-art baseline methods: (1) domain adaptation support vector machine (DASVM) ; (2) mulIn this subsection, we evaluate the capability of our framework to transfer active attribute labels in the single-source domain case. We does not fix Junction dataset, Roundabout dataset, and Junction 2 dataset in turn as source domain datasets because transferring different activity attributes between different scenarios does not have reasonable feasibility in real applications. Therefore, we only use Junction dataset as source domain dataset because it has contained almost all the traffic patterns of both Roundabout dataset and Junction 2 dataset. We compare T-LDA-TextCNN with various kinds of single-source domain adaptation methods, including DASVM , TKL 4545, STL , and TNNTwo different comparative experiments are set up to verify the validity of the proposed method. In one case, video data of the target domain does not participate in the training at all, so as to illustrate the performance of T-LDA-TextCNN method under unsupervised learning. Precision is used as evaluation criterion for activity attribute annotation in target domain datasets (Roundabout dataset and Junction 2 dataset). Figures In the second comparison experiment, some labeled target domain video data are needed to assist in training the classifier, so as to illustrate the performance of T-LDA-TextCNN method under a small amount of labeled target domain video data training. The performance has been measured by Marco-F1 which is the mean of F1-measure of all activity attributes. Figures In practical situations, there are often multiple source domain scenarios related to the target domain scene. They have more or less knowledge (active attributes) related to the target domain scene, which can be utilized to improve the performance of the target classifier. In the experiments in this subsection, the source domain scenarios are no longer single, but multiple source domain scenarios related to the target domain scene. Roundabout dataset and Junction 2 dataset are fixed as the target domain dataset in turn. Junction dataset and Junction 2 dataset serve as the source domain datasets for Roundabout dataset, while Junction dataset and Roundabout dataset serve as the source domain datasets for Junction 2 dataset. The experimental setup and performance evaluation criteria are the same as those in As shown in Figures In this subsection, we evaluate the impact of SGD , AdaGradIt can be seen from Tables DASVM method can only handle the case where the source and target domain features are isomorphic. After the source-domain samples are used to initialize the discriminant function for the target domain problem, DASVM method iteratively removes the source-domain samples and gradually adjusts the discriminant function to fit the target domain instances. Each iteration of DASVM takes a time equivalent to the time required for supervised SVM learning. TKL method is a data-dependent spectral learning method. However, TKL only adapts the edge distribution, ignoring the alignment of the conditional probability distribution. Therefore, more in-depth research on the joint distribution adaptation based on spectral learning is needed. However, when solving the multisource domains transfer problem, they all need to involve new learnable parameters, and the contribution of each source domain cannot be quantified.STL and TNNAR methods are designed to solve cross-domain activity recognition (CDAR). STL exploits the intra-affinity of classes to iteratively perform intraclass knowledge transfer. TNNAR performs knowledge transfer for activity recognition through multisource domain selection and deep neural network. The source and target domain data of CDAR have the same dimensions, the same labels, and even the data distribution which can be the same. However, the scene is not reasonably feasible for practical cross-domain traffic scene understanding applications.MDA-HS method trains the respective independent SVM classifiers based on the training data of each source domain. The final prediction for each target domain sample is then obtained by averaging the predictions of all classifiers. The computational complexity is dominated by SVM classifiers training. The samples of different source domains of the problem solved by MDA-HS method are represented by different types of features, while the target domain samples have all types of features. DDA method simultaneously performs classifier adaptation, distribution alignment, and distinguished embedding. Data from different domains have different feature representations. MFSAN method uses multiple domain-invariant representations to train multiple domain-specific classifiers and align the target sample outputs of the domain-specific classifiers. The training process fine-tunes all convolutional and pooling layers and trains classifier layers by backpropagation, which is computationally expensive. CWAN is a deep learning model for the multisource heterogeneous domain adaptation (MHDA) problem. Not only the importance of different source domains is quantified, but also the conditional distribution between source and target domains is used for knowledge transfer. However, when more source domains are involved, they also need to solve new learnable parameters, which will lead to complex optimization problems.In this work, we focus on addressing the cross-domain traffic scene understanding problem, which substantially is distinguished from other cross-domain transfer methods on the following grounds.In this paper, we propose a novel framework for cross-domain traffic scene understanding by integrating deep learning and topic model. It transforms the cross-domain traffic scene understanding problem into a multilabel classification problem with activity attributes for solving. No source domain selection is required, avoiding performance degradation or even negative migration due to incorrect source domain selection. The Word2Vec word vector model and the LDA topic model are combined in the construction of the topic-word vector matrix, which takes into account the representation of both word granularity level features and overall semantic features at the text granularity level. The proposed scene alignment method is tractable and free from overfiltering due to the consideration of the full distribution of activities in the scene and feeding the transformed topic-word vector matrix of the target domain scene into the aforementioned TextCNN for multilabel classification. In future work, we aim to further investigate fully incremental transfer learning method in an online manner and apply this framework to environmental perception.


A key step during the entry of enveloped viruses into cells is the merger of viral and cell lipid bilayers. This process is driven by a dedicated membrane fusion protein (MFP) present at the virion surface, which undergoes a membrane–fusogenic conformational change triggered by interactions with the target cell. Viral MFPs have been extensively studied structurally, and are divided into three classes depending on their three-dimensional fold. Because MFPs of the same class are found in otherwise unrelated viruses, their intra-class structural homology indicates horizontal gene exchange. We focus this review on the class II fusion machinery, which is composed of two glycoproteins that associate as heterodimers. They fold together in the ER of infected cells such that the MFP adopts a conformation primed to react to specific clues only upon contact with a target cell, avoiding premature fusion in the producer cell. We show that, despite having diverged in their 3D fold during evolution much more than the actual MFP, the class II accompanying proteins (AP) also derive from a distant common ancestor, displaying an invariant core formed by a β-ribbon and a C-terminal immunoglobulin-like domain playing different functional roles—heterotypic interactions with the MFP, and homotypic AP/AP contacts to form spikes, respectively. Our analysis shows that class II APs are easily identifiable with modern structural prediction algorithms, providing useful information in devising immunogens for vaccine design. Many of the human pathogenic viruses are enveloped with a lipid bilayer that must fuse with a cell membrane for infection. The required membrane fusion reaction is catalyzed by a specific viral transmembrane protein, termed “membrane fusion protein” (MFP), which is an important target for the development of antivirals  and vaccHere, we focus on class II MFPs, which are found in enveloped RNA viruses from multiple viral families  having uDespite an extensive literature on class-II MFPs, comparatively little is known about their APs, partly because their structures have remained elusive for many years. Here, we review what is known about the structural biology of the class II AP. To provide a broader perspective, we have used AlphaFold, a machine learning approach that has recently emerged as a breakthrough in the prediction of protein structures ,23. The Togaviridae family, as well as several families of the Bunyavirales order of positive-sense and negative-sense single-stranded viruses, respectively, share common structural features: a long β-ribbon immediately followed by an “s-type” domain of the immunoglobulin superfamily (IgSF) [We show that, like the MFPs, the class II APs of the y (IgSF) . This sty (IgSF) , than thTogaviridae family in the recently established Martellivirales order of positive-strand RNA viruses [Alphaviruses encompass the only genus of the  viruses . This ge viruses . Mature  viruses . The pro viruses . It has  viruses . The str viruses . Structu viruses ,33.19-XX-C22), and the “wings”, formed by the BC loop. Domains B and C are also β-sandwiches with an IgSF fold, but of the “s-type” [The structures of the immature p62-E1 heterodimer and mature E3–E2–E1 heterotrimers have been reported ,34, show“s-type” , charactb. The fusion loop is maintained in a groove between domains A and B, and is thus not exposed on infectious particles. In the acidic environment of the endosomes, a region located between the central arch of the β-ribbon and domain B, known as the “acid-sensitive region”, becomes disordered. In turn, domain B moves out and allows exposure of the fusion loop such that it can insert into the cell membrane. In the immature p62/E1 complex, the presence of a linker connecting E3 to domain A clamps the acid-sensitive region in place, and the heterodimer is more resistant to acid treatment. In the spikes, E2 domain C contacts E1 domain III near the viral membrane, and establishes the threefold contacts that stabilize the (E2/E1)3 trimer.In the mature alphavirus E2/E1 complex, the region of E1 making most of the contacts with E2 is the tip of domain II, including the fusion loop, but also E1 β-strand Orthohantavirus genus of the Hantaviridae family in the Bunyavirales order of segmented, negative-strand RNA viruses. This genus includes many important pathogens distributed worldwide. Their transmission to humans can cause two severe diseases: hemorrhagic fever with renal syndrome (HFRS), produced by Old-World hantaviruses, such as Hantaan, Puumala, and Dobrava viruses; and hantavirus cardiopulmonary syndrome (HPS), produced by New-World hantaviruses, such as Andes and Sin Nombre viruses [Hantaviruses are rodent-borne viruses belonging to the  viruses . The vir viruses ,36,37. G viruses ,38, cont viruses  associat viruses ,41 for p viruses  for Old- viruses  for New-H), has been reported both alone [B), was crystallized as a tetramer [cd fusion loop of Gc in the heterodimer. Domains B and C display an IgSF “s-type” fold [The structure of the N-terminal two-thirds of the Gn ectodomain, known as the Gn “head”  whose function remains unknown. The C-terminal end of the “η1 loop” makes a parallel β interaction with β-strand b of Gc. The interface between the two glycoproteins is further sealed by two conserved glycan chains of Gn, which embrace the tip of domain II. Removal of either of these glycosylation sites has been shown to give rise to defects in intracellular transport of the glycoprotein complex [Orthohantavirus genus) with the crystal structures of GnH/Gc and GnB [B, which forms an intertwined tetramer composed of domain C and the MPER at the base of the spike.The conformation of the isolated Gn complex . The fac complex ,46, as w complex  of the s and GnB  from AndPhenuiviridae family of arthropod-borne bunyaviruses contains 20 genera, with 137 species inventoried in the latest report of the ICTV [Phlebovirus and Bandavirus genera, which include, respectively, the Rift Valley fever virus (RVFV), a mosquito-borne virus that produces acute hepatitis and fetal malformations in several mammalian hosts, and the Dabie virus , which was previously known as the “severe fever with thrombocytopenia virus” (SFTSV), a tick-borne bunyavirus pathogenic for humans. The genome consists of three segments of negative-sense ssRNA, termed S, M, and L based on their electrophoretic mobility. In most species of the family, the M segment encodes two glycoproteins (Gn and Gc) as a precursor polyprotein that is subsequently processed by a signal peptidase in the ER. In RVFV, the M segment contains several translational start codons that are alternatively used to produce a polyprotein precursor to the mature proteins NSm, Gn, and Gc, or p78 and Gc, where p78 encompasses the NSm–Gn fusion, and has a determinant role in virus dissemination within mosquitoes [The the ICTV . The mossquitoes . The Gn/squitoes  displayisquitoes . These sH for “phlebovirus GnH”, so termed to distinguish it from hantavirus GnH, or hGnH)), has been reported both for RVFV and DABV [H. Although the structures of these domains are different from that of their hantavirus counterparts, for the sake of clarity, we also term them A and B , is followed by an extended linker of nine amino acids, a β-sandwich with the same “s-type” IgSF fold observed in domain C of alphavirus E2 and hantavirus Gn, followed by a C-terminal, and an amphipathic α-helical hairpin stabilized by two intramolecular disulfide bonds. The structure of the latter segment is very similar to the MPER present on hGn, and we, therefore, term it the phenuivirus Gn MPER , identified as critical for Gn stability [We used AlphaFold  to prediR pMPER, . As menttability , is in tH and Gc were docked individually into this map using a molecular dynamic fitting approach [The precise organization and specificity of Gn/Gc spike interactions have remained elusive because of an inherent malleability of the viral particles, which deviate from having perfect icosahedral symmetry. This feature has limited high-resolution cryoEM structural studies. To date, the cryo-EM map with the highest resolution reaches just beyond 8 Å. This map was obtained by applying a localized reconstruction procedure  on partiapproach  and resuTospoviridae family of plant bunyaviruses has a single Orthotospovirus genus containing 26 species reported by the ICTV [The the ICTV . They arthe ICTV . The prothe ICTV . To thisN-terminal residues of Gn were not resolved in the reported structure. The PD is about 60 residues and comprises a long β-ribbon formed by two contiguous β-strands (D and E) arching over a long β-strand H and running parallel to it. The turn between β-strands E and H contains a two-stranded β-sheet and one glycosylation site. Structurally and topologically, the β-ribbon present in the PD would be equivalent to that previously described in the class II APs of alphaviruses, hantaviruses, and phenuiviruses, and the two-stranded β-sheet inserted in the turn a remnant of domain B [302 in the TSWV numbering), which is responsible for the disulfide-mediated dimerization observed when the soluble Gn ectodomain is produced. However, 14 residues further down in the sequence, there is another cysteine (C316) that was absent in the cloned ectodomain terminating at residue 313, which could potentially form a disulfide bond with C302, and would form an α-helical hairpin similar to that observed in hantavirus Gn. To explore this idea, we used AlphaFold to predict the structure of the complete TSWV Gn protein . The model thus generated predicted the formation of a hairpin similar to that reported for hantaviruses, and suggests that the segment (310IYKQTACINFS320), which the UniProt database partially predicts as part of a TM segment (underlined) and contains an N-glycosylation sequon (in bold), is actually the C-terminal helix of the tospovirus Gn MPER , pincer (PD), and C-terminal (CTD) domains . The NTDdomain B . The CTDdomain B  stabiliz Gn MPER A.Peribunyaviridae family of arthropod-borne bunyaviruses features 4 genera and 112 species reported by the ICTV [Orthobunyavirus (OBV) genus are transmitted by infected mosquitoes or midges, and many of them cause severe disease in humans and farm animals. The prototype of this genus is Bunyamwera virus (BUNV), but the most medically relevant species are Oropuche virus (OROV), which causes acute febrile illness in some regions of South America [The the ICTV . The mem America , La Cros America , and Sch America , a rumin America . Despite America  on BUNV  America . This woWe have predicted the structure of the full length Gn (residues 14–299) of the LACV polyprotein precursor (Uniprot code: Q8JPR1) using AlphaFold , and obtNairoviridae family of tick-borne bunyaviruses contains 7 genera with 47 species reported to date by the ICTV [Orthonairovirus) is the most relevant nairovirus, and is the prototype of the family. It is transmitted to humans upon a bite by an infected tick, or by contact with blood or tissues of infected livestock. CCHFV is not pathogenic in most vertebrates, but, in humans, it causes hemorrhagic fever with a case fatality rate up to 30% [The the ICTV . Crimeanp to 30% . The genp to 30% , and botp to 30% .There are few structural studies on nairoviruses, partly because CCHFV has to be handled in bio-safety level 4 (BSL-4) laboratories. Hazara virus (HAZV), a non-pathogenic nairovirus that belongs to the same serogroup as CCHFV, is used as a model for nairovirus studies . HAZV haWe used AlphaFold-2  to prediOne of the defining characteristics of viral class II MFPs is that to be functional, they must fold as a heterodimer with an accompanying protein, which then multimerizes to create a surface lattice during particle morphogenesis. The resulting complex regulates where and when the MFP is released to undergo its fusogenic conformational change for entry into a target cell. In addition, at least in some alphaviruses and hantaviruses, the AP is directly involved in the recognition of a cellular receptor to induce the required endocytosis step ,43. DespThe β-ribbon moiety, together with domains A and B , mediates the heterodimeric interaction with the fusion protein, whereas domain C directs spike formation, essentially, via homo-oligomeric interactions. On top of this conserved motif, there are two accessory domains characteristic of each viral family: an N-terminal domain A and a central domain B at the turn of the β-ribbon. Contrary to domain C, these domains are exposed at the top of the spike, where they are highly exposed to the humoral immune system and display the most variability. Domain A displays a “C-type” Ig-like fold in alphaviruses and hantaviruses, is reduced to a single β-sheet in tospoviruses and OBVs, and, in nairoviruses, is a large and cleavable protein, termed gp38, containing a β-sandwich that partially retains the topology of domain A seen in alphaviruses and hantaviruses. Domain B is a six-stranded β-sandwich with identical topology in alphaviruses and hantaviruses, but is a mixed α/β structure in phenuiviruses, a simple β-hairpin in tospoviruses and nairoviruses, and is altogether absent in OBVs.The long β-ribbon appears to be used to accompany the long domain II of the MFP, with the fusion loop at the distal end. It has evolved alternative domains A and B to mask the conserved fusion loop while providing the required variability to avoid the antibody response of the vertebrate host. The limited extent of domain B in tospoviruses could be related to the lack of an antibody response in plants and in the arthropod vectors. Likewise, its absence in OBVs, which, like the tospoviruses having a small domain A, is likely related to the large and highly variable N-terminal domain of the MFP of these viruses, which was shown to carry the main antigenic determinants .In addition to the invariant β-ribbon followed by domain C displaying an identical topological arrangement of the β-strands, the bunyavirus class II APs have additional features in common, highlighting a closer evolutionary relationship. Domain C always contains a cysteine residue located at the N-terminal end of β-strand A, which either forms a disulfide bridge with β-strand F  or with the linker that connects to the β-ribbon (phenuiviruses). Furthermore, a membrane-proximal external region (MPER) in the form of an α-helical hairpin stabilized by one or two disulfide bonds is present between domain C and the transmembrane segments of Gn in all the bunyaviruses for which we examined the structure.As a whole, the combination of experimental data and AlphaFold predictions point to a common origin for the class II membrane fusion machinery, and not just the MFP. If the homology in the AP has not been evident up to now, it is because the two proteins that make up the complex are not under the same evolutionary pressure, and the Aps have evolved much faster. Our observations point to an ancestral fusion machinery with an AP containing the β-ribbon, domain C, as well as domains A and B, with a topological arrangement of β-strands as observed in the hantaviruses, as suggested by the similarity in their 3D fold observed with the very distant alphaviruses. In this scenario, these domains would have been replaced in the case of the phenuiviruses, nairoviruses, and tospoviruses, and lost during evolution in the case of the OBVs. Alternatively, the original AP could have been the minimal version as seen in OBVs and tospoviruses, and a more recent horizontal exchange event could have taken place from hantaviruses to alphaviruses. It is impossible, with the available data, to provide a timing of events.https://talk.ictvonline.org/taxonomy/, accessed on: 20 November 2021): alphaviruses in the Martellivirales; Rubella virus in the Hepevirales; flaviviruses in the Amarillovirales; and hantaviruses, phenuiviruses, nairoviruses, tospoviruses, and peribunyaviruses in the Bunyavirales. This classification is informative about the evolutionary pathway of viruses in general. The various orders of RNA viruses classified by the ICTV are based mainly on the polymerase gene. The fact that they all independently acquired ortholog genes for particle assembly and the subsequent cell entry step is a further illustration of the mosaic nature of viral genomes, which are composed of genes of different origins assembled together. This is further stressed by the fact that different members within the above-mentioned orders have unrelated genes for their MFPs , and some include enveloped viruses, as well as non-enveloped viruses, which have genes for a capsid protein and no MFP . This observation is in line with the enveloped herpes viruses being related to non-enveloped tailed bacteriophages, which have acquired very different machineries to invade cells, whereas their sets of core genes for their DNA-genome replication and encapsidation are related [Our observations support the notion that the class II membrane fusion machinery has evolved from a common ancestor, despite its presence in viruses belonging to four different orders of RNA viruses according to the current taxonomy . Upon release into the extracellular milieu, they must be able to fuse when up-taken by another cell into an early endosome, where the pH is also mildly acidic. To avoid triggering fusion in the TGN during exit of the producer cell, and still release virions that are fusogenic at mildly acidic pH during entry, flaviviruses require a more complex assembly pathway that involves budding of non-fusogenic immature particles, which are transformed in the TGN via proteolysis by furin. Flaviviruses thus appear to have adapted their fusion machinery to their particular life style, which led, during evolution, to the loss of the canonical protein accompanying the MFP, and its replacement by a protein of yet a different origin. Some bunyaviruses have been reported to also bud in the ER of the infected cell , others Chlamydomonas reinhardtii [Chlamydomonas gametes, was shown in pull-down experiments to be associated with HAP2 in its pre-fusion form [Several class II MFPs have been also found in eukaryotic cells, where they participate in the fusion of somatic cells , or in tnhardtii . The traion form . These aThe lack of sequence similarity and structural information has been an obstacle to establishing parallels between the different families of viruses, to infer functional properties, and, given that in many cases the AP is the main target of neutralizing antibodies ,85,86,87


The replication origin firing factor Treslin/TICRR comprises an essential Sld3-like core that requires the flanking Ku70-like N-terminal and the C-terminal domains for efficient origin firing. Faithful genome duplication requires appropriately controlled replication origin firing. The metazoan origin firing regulation hub Treslin/TICRR and its yeast orthologue Sld3 share the Sld3-Treslin domain and the adjacent TopBP1/Dpb11 interaction domain. We report a revised domain architecture model of Treslin/TICRR. Protein sequence analyses uncovered a conserved Ku70-homologous β-barrel fold in the Treslin/TICRR middle domain (M domain) and in Sld3. Thus, the Sld3-homologous Treslin/TICRR core comprises its three central domains, M domain, Sld3-Treslin domain, and TopBP1/Dpb11 interaction domain, flanked by non-conserved terminal domains, the CIT (conserved in Treslins) and the C terminus. The CIT includes a von Willebrand factor type A domain. Unexpectedly, MTBP, Treslin/TICRR, and Ku70/80 share the same N-terminal domain architecture, von Willebrand factor type A and Ku70-like β-barrels, suggesting a common ancestry. Binding experiments using mutants and the Sld3–Sld7 dimer structure suggest that the Treslin/Sld3 and MTBP/Sld7 β-barrels engage in homotypic interactions, reminiscent of Ku70-Ku80 dimerization. Cells expressing Treslin/TICRR domain mutants indicate that all Sld3-core domains and the non-conserved terminal domains fulfil important functions during origin firing in human cells. Thus, metazoa-specific and widely conserved molecular processes cooperate during metazoan origin firing. Accurate and complete DNA replication guarantees faithful genetic inheritance. It requires complex regulation of replication origin firing to ensure (1) efficient firing to avoid non-replicated gaps, and (2) appropriately controlled firing in space and time to facilitate the metazoan genome replication program and coordinate replication with other chromatin processes like transcription .Replication initiation is a two-step process in eukaryotes. The first step, origin licensing, in G1 phase is the formation of pre-replicative complex (pre-RC), the loading of the Mcm2-7 replicative helicase onto double-stranded DNA . In pre-The main regulation step of origin firing is pre-IC formation. In yeast, a dimer of Sld3 and Sld7  and Sld3 , thereby identifying Ku70-like β-barrels in both the Sld3 binding domain of yeast Sld7 and the Sld7-binding domain of Sld3 (PDB-ID: 3X37_B) (PDB-ID: 3X37_A)   (Itou et 3X37_A) . The Sld 3X37_A) . It is n3HRZ_D)   or convergent evolution . To distinguish these possibilities, we used iterative profile-to-sequence (HMMer) and profile-to-profile comparisons (HHpred) . HHpred 3HRZ_D)   . The sec of 97%)  Fig S7)3HRZ_D) (5Y58_E)  (5Y58_F) (1JEY_B), and human XRCC6 (X-ray repair cross-complementing protein 6) (PDB-ID: 1JEY_A)    . In furt(5Y58_F) , human X 1JEY_A) . Both se 1JEY_A)  and S8. We next tested whether Treslin/TICRR and MTBP may indeed interact via a homotypic Ku70/Ku80-type β-barrel-dependent interaction. Previous biochemical and structural studies had shown that MTBP/Sld7 regions, now established here as part of their β-barrels, interact with Treslin/Sld3 .We showed previously that deleting two large regions of the Treslin/TICRR M domain, amino acids 265–408 (M1) or 409–593 (M2), compromised MTBP binding . DeletinWe found that a region C-terminal to the Ku70-like β-barrel is also required for MTBP interaction. The N-terminal 557 amino acids of Treslin/TICRR, but not the N-terminal 486 amino acids, bound to MTBP . Small dTogether, our analysis of the N-terminal 600 amino acids of Treslin/TICRR revealed that the structurally conserved part with Sld3 includes the Ku70/80–like β-barrel in the M domain. Thus, the central part of the Treslin/TICRR protein including the M, STD, and TDIN domains constitutes a core that is homologous to Sld3, flanked by Treslin/TICRR-specific terminal domains. Moreover, Treslin/TICRR, MTBP, and Ku70/Ku80 share an N-terminal domain structure comprising a vWA domain followed by Ku70-like β-barrel domains .We next wanted to test whether the Sld3-like Treslin/TICRR core is sufficient to support replication in human cells or whether it requires the higher eukaryote-specific CIT and C-terminal domains. We performed BrdU-PI flow cytometry upon RNAi-replacement of Treslin/TICRR using mutants that lacked either the CIT , the C-terminal region , or both (Treslin/TICRR-core) . TreslinSurprisingly, the Treslin/TICRR-core mutant was inactive. BrdU incorporation in Treslin/TICRR-core cells was nearly as strongly compromised as in the non-replicating control lines . This inWe then tested which part of the C-terminal region cooperates with the CIT, and whether the cooperation depends on the described binding activities for Chk1 and BRD2/4. We successively truncated the C-terminal sequence in combination with CIT deletion. Neither truncating the Chk1- (Treslin/TICRR-ΔCIT/ΔC99)  nor the Treslin/TICRR-core did not support replication, as described above. To test whether the known core activities of Treslin/TICRR are intact in the Treslin/TICRR-core protein we tested association with MTBP and TopBP1. Treslin/TICRR-core and ΔC853 co-immunoprecipitated TopBP1 from 293T cell lysates similarly as Treslin/TICRR-ΔC651 (with or without CIT), suggesting that C-terminal deletion of the important amino acids 1057–1257 did not detectably compromise TopBP1 binding , which cWe therefore suggest that two higher eukaryote-specific Treslin/TICRR regions  have important functions in replication.Subtle particularities in cell cycle profiles of Treslin/TICRR-core cells suggested that this mutant may have other defects than cells lacking Treslin/TICRR function. For example, a delay in S phase entry in Treslin/TICRR-core cells could explain the occasionally observed decrease of the S phase sub-population . As usuaTogether, the Treslin/TICRR terminal regions that are specific to higher eukaryotes cooperate in parallel pathways towards an essential function in replication origin firing.We here present a characterization of a major origin-firing regulator, Treslin/TICRR, based on its domain structure. Our insight that Treslin/TICRR and Sld3 share similarity of the M domain (Treslin/TICRR) and the N terminus (Sld3), respectively, completes the view that the three central domains of Treslin/TICRR, M-domain, STD, and TDIN, constitute a Sld3-like core that is flanked by two Treslin/TICRR-specific terminal regions, the CIT and the C-terminal region . These tImportant molecular activities of the core domains are known. TDIN is essential for replication in Sld3 and Treslin/TICRR through CDK-mediated interaction with Dpb11 and TopBP1, respectively . The SldWe found that the Sld3-like core of Treslin/TICRR was insufficient to support replication and origin firing in U2OS cells, whereas individual deletions of the Treslin/TICRR-specific CIT and C terminus had only mild effects, if any , on Treslin/TICRR’s ability to support replication. We concluded that the CIT and the C-terminal region cooperate in parallel pathways to promote DNA replication origin firing. The simplest scenario is that CIT and the C-terminal region promote firing through functions in the molecular process of origin firing that have yet to be revealed. However, more indirect scenarios cannot be excluded. Our finding supports the idea of molecular processes and regulations that are specific to higher eukaryotes to facilitate faithful duplication of their extremely complex genomes. Previous publications had shown roles for higher eukaryote-specific protein domains of TopBP1  and MTBPThe molecular activities underlying the proposed origin firing functions of CIT and the C-terminal region remain unknown. Our mutants combining CIT-deletion and successive C-terminal truncation excluded significant contributions of the described Chk1- and BRD2/4-binding regions of the Treslin/TICRR C terminus . InsteadInterestingly, the CIT contains a vWA domain that is also shared by (1) Ku70/Ku80  and (2) Determining the molecular and cellular functions of the non-core Treslin/TICRR domains will help us better understand the specifics of origin firing in higher eukaryotes compared to yeast. Because Treslin/TICRR mediates origin firing regulation, understanding its non-core domains will likely be necessary to unravel how the complex higher eukaryotic cells coordinate origin firing with other cellular processes.2. Stable AcGFP-Flag-GFP-Treslin/TICRR-expressing U2OS cell clones were generated using a pIRES puro3-based vector system by random genome integration followed by selection on 0.3 μg/ml puromycin and picking of individual clones as described  and 293T  cells (both lines kind gift from The Crick institute tissue culture) were cultured in standard conditions in DMEM/high glucose , 10% FCS, penicillin/streptomycin in 5% COescribed , 2013.Endogenous Treslin/TICRR was replaced by siTreslin-resistant transgenes by transfecting U2OS cells twice with Treslin/TICRR siRNA (GAACAAAGGTTATCACAAA) using RNAiMax  as described . LuciferAntibodies against Treslin, MTBP, and TopBP1 were described . Anti-Br2 were added to the lysis buffers.293T cells were transfected using standard calcium phosphate precipitation. 72 h after transfection, cells were harvested and lysed in 5–10 times cell pellet volume using detergent in native lysis buffers and douncing. Lysis buffer for anti-GFP immunoprecipitations in http://www.pymol.org). Structure similarity searches and structural superpositions were performed using Dali (Multiple sequence alignments were generated with the program T-Coffee using default parameters , slightling Dali .The authors will comply with Life Science Alliance policies for the sharing of research materials and data.


Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties of proteins are frequently analyzed based upon whether they contain one of these domains. To date, however, knowledge of whether and how protein domains interact with ligands has been limited to domains that have been observed in co-crystal structures; this leaves approximately two-thirds of human protein domain families uncharacterized with respect to whether and how they bind DNA, RNA, small molecules, ions and peptides. To fill this gap, we introduce dSPRINT, a novel ensemble machine learning method for predicting whether a domain binds DNA, RNA, small molecules, ions or peptides, along with the positions within it that participate in these types of interactions. In stringent cross-validation testing, we demonstrate that dSPRINT has an excellent performance in uncovering ligand-binding positions and domains. We also apply dSPRINT to newly characterize the molecular functions of domains of unknown function. dSPRINT’s predictions can be transferred from domains to sequences, enabling predictions about the ligand-binding properties of 95% of human genes. The dSPRINT framework and its predictions for 6503 human protein domains are freely available at  Domains are recurring protein subunits that are grouped into families that share sequence, structure and evolutionary descent . ProteinWhile identifying the types of molecules with which a protein interacts has already proven to be an essential component of functional annotation pipelines , pinpoinIn this paper, we aim to develop methods to comprehensively identify, for all domains found in the human genome, which types of ligands they bind along with the positions within them that participate in these interactions. To date, computational methods to characterize domain–ligand interactions ,18–20 haFor the remaining domain families, current techniques fail to identify whether they are involved in mediating interactions, what types of ligands they bind, and the specific positions within them that participate in binding. A method that can identify a domain’s ligands and interaction positions without relying on structural information not only would be a great aid in characterizing domains of unknown function (DUFs) or other domains for which interaction information is unknown but also would enable further downstream analyses by identifying interaction sites within proteins that contain instances of these domains. Indeed, at present, the proteins for only ∼13% of human genes have co-complex structures with ligands and from which interaction sites can be derived see .domain Sequence-based PRediction of INTeraction-sites (dSPRINT), to predict DNA, RNA, ion, small molecule and peptide binding positions within all protein domains found in the human genome. While previous methods have predicted sites and regions within protein sequences that participate in interactions , dSP, dSPdomas  to predict which positions within domains bind five different types of ligands  and then utilizes a novel heterogeneous ensemble architecture that combines information across different ligand types. Using rigorous cross-validation testing, we empirically demonstrate excellent performance in identifying ligand-binding positions within domains and additionally show that dSPRINT’s per-position predictions can be utilized effectively to identify which types of ligands a domain is binding. Finally, we apply dSPRINT to 4,286 human domains for which ligand-binding information is not known , and newWe first curate a training set of human protein domains with reliably annotated ligand-binding positions. We begin by identifying all Pfam  domains Domain positions are labeled as binding RNA, DNA, ion, peptide and small-molecule based on InteracDome , a colleFor each protein domain position, we create feature vectors as follows. Features arise from aggregating information from instances of the domains in human genes. Instances of each domain are found within human proteins via HMMER. Then, for each protein domain position, as defined by Pfam HMM match states, we aggregate diverse information (as described below) for all corresponding protein sites . Details about the versions of the algorithms used, along with parameter settings and architectures are given in For each of our five ligands, we use the labeled domain positions and their feature vectors to train five separate binary classifiers to predict positions within domains that contact them: (i) logistic regression (LR), (ii) support vector machines (SVM) with the RBF kernel, (iii) random forest (RF), (iv) gradient boosting as implemented by the XGBoost algorithm  (XGB) ant’: all five base models trained on the target ligand including the original features, (ii) ‘M1L5Ft’: one base model, XGB, trained on all the ligands (and not just the target ligand) including the original features, (iii) ‘M5L5Ft’: all the base models trained on all the ligands including the original features , and (iv) ‘M5L5Ff’: all the base models trained on all the ligands but using just their outputs and not the original features. For the second layer model , we primarily use XGB since we have found that it is the best-performing model in the first layer across all the ligands. However, since XGB can be unstable when used with a small number of features, we use LR as the meta-model for the M5L5Ff architecture that does not use the original features. Altogether, for each of the four stacking architectures, we obtain five trained ensembles, where each predicts per-domain-position binding scores for one of the five different ligand types  identifying positions across all domains that bind the ligand of interest  and (ii) identifying binding positions within a domain known to bind a particular ligand (per-domain performance). For both tasks, we measure performance by computing the precision-recall (PR) curve, the receiver operator curve (ROC), the area under the precision-recall curve (AUPRC), and the area under the receiver operator curve (AUC). We use AUPRC as our primary performance metric as ROC curves can overestimate the performance of a classifier on imbalanced data. Since AUPRC is sensitive to test sets with different positive rates, we also consider AUPRC as compared to the expected performance by a classifier that predicts that each example is a positive or negative uniformly at random; that is, we divide the measured AUPRC by the fraction of binding positions for that ligand. We refer to this as the AUPRC fold-improvement (AUPRC-FI).For each ligand type, when we measure global performance, we consider binding and nonbinding positions for that ligand across all domains. When we measure per-domain performance, we consider binding and non-binding positions for each specific domain and ligand pair. These per-domain measures can be calculated only for domain–ligand pairs that have at least one binding (positive) position.For each model, we train it using each subset of four folds, and compute its AUPRC, AUC and AUPRC-FI on the fifth unseen fold; for our performance evaluations, we report the average of these values over the five test folds.Our models are tuned and selected based upon performance on the global prediction task as measured by AUPRC. Hyperparameters are optimized using the ‘random search’ approach  where hyWe use nested 5-fold cross-validation for tuning hyperparameters for all of our base models. That is, hyperparameter tuning is performed using validation sets consisting of folds within the training set only. See We train five additional XGB models that predict whether a domain as a whole binds each of the five considered ligand types. Here, a domain is considered to bind a ligand if it has at least one position that participates in an interaction with that ligand . This removes all the sequences for many domains and ultimately yields five domain families that bind DNA within seven proteins with 727 total labeled domain positions, seven domain families that bind peptide within eight proteins with 1522 total labeled domain positions, and two domain families that bind RNA within two proteins. Due to the small number of RNA-binding positions in this set, we only consider performance with respect to DNA- and peptide-binding positions. Additionally, we do not make any further adjustments to remove proteins overlapping with the DRNApred or SPPIDER training sets, as this removed too many sequences and domains.To create a valid benchmark test set that does not overlap the training sets of dSPRINT, and the other methods, we first construct a set of all protein domains with InteracDome labels that have at least two but at most 10 instances in human, as these are not used by dSPRINT for training. We note that due to the relatively small number of instances of these domains, dSPRINT may be disadvantaged as it relies on aggregating features from across domain instances. Next, we remove domains that are either similar to one another or similar to domains in dSPRINT’s training set . We note that for all five ligand types, the three stacked architectures that use the original features in addition to different combinations of the outputs of the base models  outperform all the base models. Moreover, for all ligands, these three ensembles outperform M5L5Ff, an ensemble that uses only the first-layer outputs, thereby demonstrating a clear advantage for using the original features. Nevertheless, the first layer predictions are by themselves very informative, as M5L5Ff performs similarly to the other ensembles for DNA and, to a lesser extent, ion.For four of five ligand types , the best performing ensemble includes base models trained on other ligands . For these ligands, knowledge about whether a position is predicted to participate in binding to other ligand types improves the prediction beyond what is obtained using the standard stacking approach of combining different models trained on the same prediction task  Figure . As expeWhen we consider only the highest scoring position for each domain, this top prediction is correct for between 40% and 67% of domains in each of the ligand types . When coWe next consider performance for each domain–ligand pair individually Figure  as measuThere are many domains for which our predictions lead to near perfect performance with AUCs close to 1, including the RNA-binding domains Pumilio (PF00271), RRM (PF00076), KH (PF00013) and Helicase (PF00271); the DNA-binding domains Homeobox (PF00046), HLH (PF00010), zf-C2H2 (PF13984) and Ets (PF00178); the ion-binding domains CAP (PF00188), EF_hand (PF13833) and Hemopexin (PF00045); the peptide-binding domains SH3 (PF00018), PDZ (PF00595), Arm (PF00514) and Proteasome (PF00227); and the small molecule-binding domains Hydrolase (PF00702), Exo_endo_phos (PF03372) and Sulfatase (PF008844). Indeed, for 45 domain–ligand type pairs, we achieve perfect ranking with AUCs equal to 1.0. For several ion- and small molecule-binding domains that are relatively long and yet have a small number of binding positives, our predictions obtain greater than a 100-fold improvement over what would be expected by a baseline method that ranks positions arbitrarily.For many domains, the ligands they bind (if any) are unknown. For this reason, we next train an XGB machine learning model that uses dSPRINT’s per-domain-position predictions of ligand binding to predict whether a domain as a whole binds DNA, RNA, peptides, small molecules or ions. We use 5-fold cross-validation on a training set of domains known to bind a particular ligand and show PR curves for each of the five ligands . Four of dSPRINT’s top 10 predictions of RNA-binding positions are found in DUF1754, which is the only domain found in FAM32A, a known RNA-binding protein ,53. For Our predictions also yield some interesting hypotheses about the functions of human proteins. For example, DUF1220  is found in NBPF proteins and is notable for its lineage-specific expansions in human, its correlation with brain size, and its putative association with neurological disorders such as autism . While tHere we have trained machine learning models to predict which positions within domains are involved in interactions with DNA, RNA, ion, peptides or small molecules. Additionally, these per-domain-position predictions are themselves used to predict whether a domain as a whole binds one of these ligands. Our new domain-centric approach enables the characterization of numerous human domains for which no proteins that contain them have solved co-complex structures. Since >95% of human proteins contain domains , by tranWe have designed dSPRINT using primarily non-linear classifiers, which excel at discovering higher-order interactions between features. Moreover, since different machine learning models can capture different aspects of a dataset, we have implemented a heterogeneous stacking architecture that not only combines predictions across models but also across different ligand types. We have empirically demonstrated that stacked models improve predictive power for all ligands Figure . NotablyBinding positions for some ligands appear to be more difficult to predict than for others. This can be due to either biological or technical challenges (or both). For example, ion-binding positions often consist of conserved amino acids of certain types , whereasBy examining the final machine learning models using feature importance techniques, it is possible to glean which features are most important for predicting different types of ligand-binding positions within domains. In preliminary analysis using the XGBoost trained models’ built-in feature importance , we obsin vitro. Further, a large fraction of RNA-binding proteins do not have a known RNA-binding domain (As with all supervised learning approaches, our approach relies upon a training set of positive and negative examples. We utilize labels that are extrapolated from curated co-complex structures. While interactions that are observed in these co-complex structures are highly reliable, structural data has inherent biases due to the experimental procedures . For insg domain . Examinig domain . While sg domain . Moreoveg domain . OverallWhile we have demonstrated the usefulness of our predictions for annotating DUF domains, our framework can be used for annotating many types of other protein domains, including assigning novel functions for already annotated domains. Moreover, knowledge of the specific sites within domains involved in interactions can help prioritize disease-causing mutations ,62 and mFuture applications might extend our approach beyond the human proteome. For instance, the ability to annotate ligand-binding properties of domains across the evolutionary spectrum may advance our understanding of the functional impact of the evolution of protein domain architectures ,65. Our To conclude, we expect that dSPRINT will be a great aid for expanding our knowledge of domain functions and molecular interactions, for annotating the molecular functions of proteins, and for performing numerous domain-based analyses.http://github.com/Singh-Lab/dSPRINT.dSPRINT source code and trained models are available at: gkab356_Supplemental_FileClick here for additional data file.


The paper studies the properties of brass workpieces for antifriction rings under severe plastic deformation by high-pressure torsion. The evolution of microstructure and mechanical properties of deformed workpieces after six cycles of deformation by high-pressure torsion at 500 °C have been studied. All metallographic studies were performed using modern methods: transmission electron microscopy (TEM) and analysis electron back scatter diffraction patterns (EBSD). The deformation resulted in an ultrafine grained structure with a large number of large-angle boundaries. The strength properties of brass increased compared to the initial state almost by three times, the microhardness also increases by three times, i.e., increased from 820 MPa in the initial state to 2115 MPa after deformation. In this case, the greatest increase in strength properties occurs in the first two cycles of deformation. Antifriction materials are used to increase the durability of friction-exposed mechanisms and machine surfaces and to reduce friction losses. Antifriction rings are often used in technology because they interact with the support ring through an intermediate ring made of material with a higher tensile strength than the antifriction ring. Such rings are occasionally produced by casting. When cast, most metals and alloys have a coarse-grained structure d > 100 µm) 00 µm 1,,3. The f. The f3.Currently, there are a large number of SPD methods, the main ones being equal-channel angular pressing (ECAP) ,17,18 anBecause of its ability to strongly deform the sample (with a true strain of ≥ 10)  and to aThe geometric shape of the sample at HPT is designed so that the entire bulk of the material undergoes deformation under quasi-hydrostatic compression, which is ensured by the applied pressure of anvils and pressure from the outer layers of the sample. Due to this deformation scheme, the machined sample is not destroyed, even despite the high degree of applied deformation . In spitThis design allows the high-pressure torsion process to be realized by a straight motion of the striker relative to the frame. When the press frame is lowered, the upper striker moves linearly with the press frame. A stationary lower striker is fixed on the bottom plate of the press. When the lower striker contacts the upper one, the upper striker transmits to the die a torque. As a result of the contact friction forces directed at an inclined angle to the responsive part of the die, the straight motion is transformed into a torsional motion. This results in the HPT of the piston ring.The purpose of this work is to study the properties of antifriction rings obtained by severe plastic deformation by the HPT method in order to improve the service life of antifriction rings.Rings of face seals are usually made of deformable brass of LZhMts59-1-1 grade , so the laboratory experiment was carried out just on this material. The initial workpiece had a ring shape with a diameter of 55 mm, a width of 3 mm and a thickness of 3 mm. Brass grade LZhMts59-1-1 refers to manganese brass and has a high ductility and high strength. The alloy’s high ductility is due to the fine grain structure resulting from its alloying with iron. This brass is also highly corrosion resistant in seawater and atmospheric conditions, is quite ductile at high temperatures, so it withstands hot deformation well and is satisfactorily pressure treated at room temperature. It has good antifriction properties. Since this brass in the initial state has a two-phase structure α + β, which has low ductility, so in order to reduce the initial grain size, the ring workpieces were subjected to preliminary heat treatment. The preheating treatment consisted of annealing the brass at 600 °C and slow cooling together with the furnace.Experimental tooling for the HPT was made of 5XB2C steel on a CNC turn-milling machine . The components of the structure were subjected to a special heat treatment after manufacturing to increase the strength properties. After all elements of the structure were manufactured, they were assembled. Thus, the lower holder was installed on the base plate of the hydraulic press and secured with tie bolts through the retaining angles . In the The structure was assembled and the experiment itself was carried out in the laboratory on a single-column hot-stamping crank press model PB 6330-02 , the force of which is 1000 kN. Deformation was carried out at a temperature of 500 °C. Number of deformation cycles—6. The workpieces for deformation were heated in a Nabertherm resistance chamber furnace in a conventional  atmosphere. The heating temperature was set on the furnace panel and the holding time at the set temperature was set. After the end of heating operations, the workpiece was taken out of the furnace by pincers and fed into the working area of the die. The workpieces before and after deforming are shown in Polished specimen for metallographic studies were prepared according to the standard method for this purpose. The rings were examined in the middle plane of the sample, as well as in two cross-sections: transverse and longitudinal, to avoid the influence of peripheral areas.2 slices in 0.2 μm increments. Different misorientations between grains were established using a minimum resolution of 2° misorientation. Due to the experimental accuracy of EBSD method, all low-angle boundaries with a misorientation of less than 2° were excluded from consideration. All scanned reference points with a confidence index of ~0.1 were excluded from the sets provided to improve the overall accuracy of the images. The colors of the grains on the map correspond to the orientations indicated in the stereographic triangle. Thus, different colors in adjacent grains correspond to a misorientation between these two grains of more than 2°. Grain boundaries are indicated either by white lines corresponding to misorientations at small angles of 2–15° or by black lines corresponding to misorientations at large angles >15°. The fraction of indexed diffraction patterns was 98% of the total number of measured points. On all EBSD cards, all points that were unindexed were removed during the standard replacement (cleaning) procedure. The surface of workpieces was prepared by jet polishing on a Tenupol-3 device [The fine structure was examined on a JEM2100 transmission electron microscope (TEM)  at a magnification range of 1000 to 50,000 times. The TEM objects were prepared by polishing with a Tenupol-3 device at −28 °C and 20V. In order to have a more objective interpretation of the grain structure compared to TEM, the EBSD analysis was performed using a Philips XL-30 REM  with a field cathode. The accelerating voltage is 20 kV. To process the results, the laboratory software Tex SEM  was used. The misorientation was calculated between adjacent (contiguous) scanning points. The dimensions of the scanning stage were predetermined based on the measurement areas and the expected grain or sub-grain sizes. Scanning was performed on 50 × 50 μm3 device .Mechanical tests for uniaxial tension were performed at ambient temperature on Instron 5882 machine  with a strain rate of 1.0 mm/min . The tenThe microstructure of brass in the initial state  consists of α-phase a where αAs can be seen from the microstructure during deformation dynamic softening processes occur , but they do not lead to complete softening of the deformed metal, as there is an excessive density of dislocations in the structure. And with increasing deformation cycles, the structure becomes less dispersed and more homogeneous.To determine the crystal orientations and information about grain size, texture, and misorientation of the boundaries, EBSD analysis was performed. Orientation maps of brass LZhMts59-1-1 microstructure before and after 6 cycles of deformation by HPT method are shown in B), yield strength (σ0.2), percentage elongation (δ), and relative contraction (ψ). Tensile curves obtained as a result of tensioning of original samples and after two, four, and six cycles of deformation by HPT method are shown in After a detailed examination of microstructure evolution of brass rings, we see that severe plastic deformation by HPT method helps to significantly refine the microstructure, so the next stage of the study is to conduct mechanical tensile tests. Tensile tests determined the standard characteristics of strength and plastic properties: tensile strength (σAnalysis of the microstructure after deformation showed that after the second cycle of deformation by HPT method, the microstructure of the copper matrix is refined and iron particles take a flat lens-like shape, with inclusions looking flatter and more elongated in the longitudinal section and lens-like in the transverse one. Grain size is refined from 17 μm in the initial state to 7 μm after the second cycle of deformation, the structure is multigrain b. The hiBoth orientation maps shown in Based on TEM studies and EBSD analysis, it is clear that the type of structure obtained after six cycles of deformation by the HPT method is obtained by dynamic recrystallization.The results of microhardness studies in both longitudinal and transverse sections showed a fairly homogeneous microhardness across the entire section. After six cycles of deformation the microhardness in comparison with an initial condition increases almost in three times, i.e., has increased from 820 MPa in an initial state up to 2115 MPa after deformation. Thus, the greatest growth of microhardness falls at the first two passes.After annealing, the brass samples have an extended strain-hardening area , curve 1Thus, according to the results of microstructural studies, it can be concluded that HPT in the new die is an effective method for grinding the microstructure and obtaining the finished workpieces. With an increase in the number of deformation cycles the microstructure becomes more developed and homogeneous, which favorably affects the mechanical properties. And the greatest dispersion of the structure, as well as an increase in mechanical characteristics occurs during the first two cycles of deformation.–the most intensive grain refinement in brass of LZhMts59-1-1 grade is observed during the first two cycles of deformation, and during the subsequent cycles the rate of dispersion of grains strongly decreases. Thus, six cycles of deformation allowed to disperse the structure from 17 μm to 1 μm;–the fraction of large-angle boundaries is 13% for the original samples and 79% after six cycles of deformation, which suggests the formation of ultrafine-grained structure with the presence of a large number of large-angle boundaries without sharp texture. The portion of special boundaries revealed by EBSD analysis is minimal ~3%; and–deforming brass rings by repeated HPT processing leads to significant hardening, and therefore will increase their service life.Based on the results obtained from the conducted studies, the following main conclusions can be drawn, in particular, it was found that:


L. By contrast, thermal stress led to 5–7% loss of native contacts in all four domains, and simultaneous loss of >30% of native contacts in the VL-VH and CL-CH interfaces. This revealed divergent destabilising pathways under the two different stresses. The underlying cause of instability was probed using FoldX and Rosetta mutation analysis, and packing density calculations. These agreed that mutations in the CL domain, and CL-CH1 interface have the greatest potential for stabilisation of Fab A33. Several key salt bridge losses underpinned the conformational change in CL at low pH, whereas at high temperature, salt bridges became more dynamic, thus contributing to an overall destabilization. Lastly, the unfolding events at the two stress conditions exposed different predicted aggregation-prone regions (APR) to solvent, which would potentially lead to different aggregation mechanisms. Overall, our results identified the early stages of unfolding and stability-limiting regions of Fab A33, and the VH and CL domains as interesting future targets for engineering stability to both pH- and thermal-stresses simultaneously.Successful development of protein therapeutics depends critically on achieving stability under a range of conditions. A deeper understanding of the drivers of instability across different stress conditions, will enable the engineering of more robust protein scaffolds. We compared the impacts of low pH and high temperature stresses on the structure of a humanized antibody fragment (Fab) A33, using atomistic molecular dynamics simulations, using a recent 2.5 Å crystal structure. This revealed that low-pH induced the loss of native contacts in the domain C There is increasing evidence to suggest that under native conditions, aggregation takes place primarily from partially unfolded native-like states H-domain sites were potentially labile to aggregation. Their structural deformation increased the solvent-accessible surface area of the APRs in those regions. The unfolding process of an antibody Fab fragment was also studied using an elastic network model, to reveal that the constant regions were more flexible, and unfolded earlier, than the variable regions L-VH interface was found to precede the unfolding of the domain structures. In contrast to the studies on the Fab above, the VH domain of the scFv was found to be more thermally resistant than the VL domain.Molecular dynamics (MD) simulations have been extensively used to study protein stability Tm), and aggregation onset (Tagg) temperatures decreased at lower pH, while the aggregation rate (lnagg) also increased at low pH or high temperature (L domain that was correlated to increased aggregation kinetics at low pHWe previously characterised the stability of A33 Fab perature . A combiL or VH) and a constant (CL or CH1) domain. Each domain forms an immunoglobulin fold, having two layers of β-sheets, an inner β-sheet and an outer β-sheet. The variable domains contain the antigen binding site at their complementary determining regions (CDRs), formed by three loops in VL and three loops in CL. There are five disulfide bonds in Fab, four of them intra-domain and the last one between the light and heavy chains at the hinge region. Individual domains interact to form the variable region interface (VL-VH), and the constant region interface (CL-CH1). Interface contacts are shown in Fab is composed of one light and one heavy chain , each coL domain at low pH, compared to destabilization of all four domains in the high temperature conditions. While conformational deformations at low pH were closely linked to the disruption of glutamate-containing salt-bridges, the high temperature conditions led to a generally increased fluctuation and promiscuity of salt bridge contacts throughout the structure. We examined the impact of these structural fluctuations on the solvent exposure of predicted aggregation-prone regions (APR) under the two stress conditions. Finally, we used in-silico mutational prediction tools, and packing density calculations to also reveal the potential of the constant domain interface and CL domain, for engineering that could simultaneously improve stability under both low pH and high temperature conditions.We previously performed MD simulations on a homology model of A33 Fab to generate possible structures that best fit to pH-dependent SAXS data 22.1L, VH, CL and CH1) along the simulations, as changes in RMSD are indicative of a conformational change. Simulations in the unfolding trajectories  were compared to the simulations in the native trajectory (pH 7.0 at 300 K). For every condition of pH and temperature, six independent simulations were performed, and their average RMSD for each domain are shown in L-VH and CL-CH1), during the simulations using a soft-cutoff g) of the whole protein are also shown at every condition in g matches that observed previously by SAXS experiments, although each Rg determined by SAXS was fractionally higher than those from MD because solution X-ray scattering is also affected by the hydration shell around the protein To determine which domains of Fab A33 are more susceptible to unfolding under low pH and high temperature, we first followed the RMSD of each individually aligned domain  was observed throughout the simulation at pH 3.5, with 69.7 ± 3.5% of native contacts remaining at 100 ns  of Fab A33, at pH 3.5 and pH 7.0 . The VH domain retained much more structure at 340 K (2.46 Å RMSD at 100 ns) than at 380 K, implying it could still resist this more moderate temperature stress.The high temperature stress deformed all of the Fab domains, with the Vaffected  E-H. At H and CL domains losing the most over the simulation. At 100 ns, the VH domain could only preserve 0.875 ± 0.009 fraction of native contacts at 380 K, compared to a fraction of 0.950 ± 0.004 at 300 K. Both the VL and CL domains reserved fractions of 0.92–0.93 at 380 K, which is significantly lower than the fraction of more than 0.95 at 300 K. Apart from the VH domain at 380 K, the native contacts did not see any further reduction after 40 ns, suggesting a temporal equilibrium in the local dynamics on the timescale studied.The native contacts in each domain E-H reveaL, VH, CL domains, the absolute difference was still relatively minor , compared to that at pH 7  also lost native contacts at low pH , and several residues in VH, had fewer native contacts at pH 3.5. Meanwhile, beta-strand residues 34 and 48 in VL, and loop residues 315 and 320 in VH, had more native contacts at pH 3.5. In the constant interface  in the VL domain at pH 4.5, each showed a large variability between repeat trajectories. The variability of β-strand P could be associated with the increased flexibility in the loop 198–205  and CL β-strand M (16%) suffered the most β-strand loss at high temperature. The C, F, G, H, J groups in VH domain also lost 4–12% β-strand at 380 K, and this was partially seen for F, G, J groups at 340 K. Interestingly, the VH J strand was previously found to deform at high temperature in a different FabH1 strand N saw significant loss at 380 K, though not yet at 340 K.Upon thermal stress, the loss of β-strand became more persistent, with β-strand reductions in nearly all regions, starting at 340 K C, and thH B (residue 224–226) (L M (residue 159–163) (H1 N (residue 381–383)  may also drive the experimentally observed formation of protein aggregates and fibrils under these conditions (see the APR discussion below).The loss in β-strand content at high temperature was closely linked to the domain deformation (RMSD), elevated flexibility (RMSF), and loss of native contacts. The most evident losses of β-strands, V224–226) J, CL M (159–163) P and tha381–383) N, P, wer2.3To identify the ionizable residues that potentially drive the conformational changes at low pH and high temperature, a salt bridge analysis was performed. Salt bridges were identified over the simulation time for all the MD simulations carried out, using an O-N bond distance cutoff of 3.2 Å. From these, the occurrence (%) of each salt bridge during the simulation was calculated and averaged for the six independent repeats at each condition. The salt bridges with occurrence over 10% are listed L domain. As a result, the CL domain had the largest number of salt bridges, with four intra-domain ones, two paired with the CH1 domain and one paired with the VL domain  compared to at pH 7.0. Salt bridges Glu195-Lys149, Glu165-Lys103, Glu430-Lys428, Asp122-Lys126 remained the most popular at pH 4.5, but with their occurrence reduced by up to 18% compared to at pH 7.0. The Asp167-Lys169 salt bridge had the greatest decrease in occurrence at pH 4.5, falling to less than half that at pH 7.0, suggesting that it could have a critical impact under the low pH stress.The further decrease to pH 3.5, resulted in a significant loss of salt bridges and only five in total. All the salt bridges involving Glu were lost, including three of the most frequent at pH 7.0. Only pairings of Asp with Lys or Arg remained, consistent with the average pKa of 4.25 and 3.65 for Glu and Asp, respectively L domain, this domain was also most prone to the low pH stress  software, which calculates occluded surface and atomic packing ectively . The fewectively . While tectively . This coectively .Fig. 8Ca2.6G) between the Gibbs free energies (ΔG) of the protein carrying a simulated point mutation and the wild-type protein, to find those mutations that will most significantly reduce the free energy of the protein. These approaches are often also combined to find consensus predictions G from both FoldX and Rosetta, for all possible single-mutant variants when accessing all 19 substitutions across the 442 residue positions in Fab A33, totaling 8398 mutations. FoldX identified 1612 (19.2%) of these mutations as stabilizing, while Rosetta-ddG identified 1606 (19.1%). Of these, 852 (10.1%) were predicted by both algorithms. G values for stabilising mutations as predicted by both FoldX and Rosetta, highlighting 25 stabilizing mutations ranked at the top by one or both algorithms. The location of mutations involved in the interface contacts were also highlighted in the To explore the repacking of the domain interfaces, computational tools were applied to design stabilising mutations. Protein modelling software such as FoldX H1 domain . Loop residue S171 interacts with the switch loop connecting the VL and CL domains. Thus overall, the CL domain has a relatively high potential for stabilization, through repacking of the CL-CH1 interface (N137L/M), within the CL domain , or through improved interaction between CL and VL (S171F) (L at low pH and the dissociation of the CL-CH interface at high temperature.Other predicted mutations within C (S171F) C. These L domain, several other mutations were recommended . Seven APRs were found, namely residues 31–36, 47–51, 114–118 and 129–139 in the light chain and residues 261–265, 325–329 and 387–402 in the heavy chain. The presence of these APRs was confirmed using Amylpred2 Previously, the SAXS solution structures at pH 7 and pH 3.5 showed a 3% increase in SASA for APR 387–402, and yet few changes at other sites H domain, and its significant solvent exposure could be a source of instability to aggregation at both elevated temperature and decreased pH. The increased solvent exposure arose through the loss of beta-sheet content in VH, while the APR itself was in β-strand E, which did not see significant loss of secondary structure : R2 = 0.50) (SI), giving broadly similar result as by the homology model To validate the value of the full-length crystal structure , the Rosetta “cartesian_ddg” 4.2- added to neutralize the net charges, and NaCl added to an ionic strength of 50 mM for all simulations. The system was energy minimized using the steepest descent minimization integrator to achieve the maximum force less than 1000 kJ/mol/nm. The solvent and ions around the protein were equilibrated in position-restricted simulations for 100 ps under NVT ensemble to stabilize at the specified temperature, and then at 100 ps under NPT ensemble to stabilize at atmospheric pressure. Lastly, MD simulations were carried out for 100 ns in six replicates under the five conditions . Jobs were submitted to the UCL Myriad High-Performance Computing Facility. The time step of the simulations was set to 2 fs and trajectories were saved every 100 ps.Molecular dynamic (MD) simulations on the Fab A33 crystal model were conducted in Gromacs 2019.3 4.3L (1–108), VH (215–334), CL (109 to 214) and CH1 (335 to 429). Averages and SEM of six independent repeats are shown. Radius of gyration (Rg) was calculated using the Gromacs “gmx gyrate” tool. Native contacts over simulation time were calculated using MDAnalysis L-VH) were calculated between residues 1–108 (VL) and 215–334 (VH). Constant domain native contacts (CL-CH1) were calculated between residues 109–214 (CL) and 335–429 (CH1). Total contacts, including native and non-native ones, were calculated using the Gromacs “gmx mindist” tool (SI). RMSF of each domain was calculated using the Gromacs “gmx rmsf” tool. Both residue-level RMSF and native contact are projected on the protein structure . All-atom RMSD (side-chain included) of individual domains during the simulations were calculated using the Gromacs “gmx rms” tool, with corresponding domains as the reference. Domains were Vtructure .Secondary structure (SS) assignments of each residue along the trajectory were done using the DSSP module Lastly, salt bridges were calculated along the trajectories using VMD and a cutoff distance between O and N groups of 3.2 Å. From these, the occurrence (%) of each salt bridge during the simulation was calculated, and averaged for the six independent repeats at each condition.4.4The effect of mutations on the stability of Fab A33 was studied using FoldX4.5Occluded surface (OS) program was used to calculate the atomic packing of Fab A33 4.6Aggregation prone regions (APR) of Fab A33 were predicted using PASTA 2.0 4.7Fab homologous sequences were retrieved from the Protein Data Bank (PDB) Cheng Zhang: Investigation, Project administration. Nuria Codina: Investigation. Jiazhi Tang: . Haoran Yu: Supervision, Methodology. Nesrine Chakroun: Investigation. Frank Kozielski: . Paul A. Dalby: Conceptualization, Funding acquisition, Supervision, Writing - review & editing.