The Ubiquitin-protein ligase E3A, UBE3A, also known as E6-associated protein (E6-AP), is known to play an essential role in regulating the degradation of various proteins by transferring Ub from E2 Ub conjugating enzymes to the substrate proteins. Several studies indicate that UBE3A regulates the stabilities of key viral proteins in the virus-infected cells and, thereby, the infected virus-mediated diseases, even if it were reported that UBE3A participates in non-viral-related human diseases. Furthermore, mutations such as deletions and duplications in the maternally inherited gene in the brain cause human neurodevelopmental disorders such as Angelman syndrome (AS) and autism. It is also known that UBE3A functions as a transcriptional coactivator for the expression of steroid hormone receptors. These reports establish that UBE3A is distinguished by its multitudinous functions that are paramount to viral pathology and human diseases. This review is focused on molecular mechanisms for such intensive participation of UBE3A in disease formation and virus regulation. The sysUBE3A gene encodes a member of the HECT type E3 Ub ligase, UBE3A  of approximately 100 kDa . The encBesides viral diseases, mutations in the UBE3A gene cause human diseases, such as a rare neurodevelopmental disorder, Angelman syndrome (AS), and some cases of autism spectrum disorder . For exa2The UBE3A protein is the family of E3 ligase proteins, containing the active site of the Ub ligase at its C-terminal domain known as the HECT domain, which transfers Ub from the E2 conjugation enzyme to the substrate  Fig. 1AAFig. 1A.www.ncbi.nlm.nih.gov/refseq; GeneCards, The Human Gene Database, www.genecards.org). In mice, the transcript of UBE3A isoform 1 is truncated using an alternative polyadenylation site before the E3 ligase coding exon, while the N-terminus of isoform 1 is identical to isoform 3, equivalent to human isoform 3 , and for the progression of viral diseases caused by the infection of hepatitis C (HCV), human papilloma virus (HPV), and human immunodeficiency virus type 1 (HIV-1), as described below. Collectively, these reports indicate that UBE3A function is multifarious and imperative to regulating various human and viral diseases.3AS is a rare neuro-genetic disorder characterized by severe developmental delay, motor dysfunction, penetrant epilepsy, severe intellectual deficit, speech impairment, etc., , and theIn addition to genotype-phenotype correlations between UBE3A and AS, the essentiality of UBE3A has been further proven. To elaborate, the downregulation of UBE3A expression using its specific shRNA inhibited apical dendrite outgrowth and impaired dendrite polarity. However, in mice, these impacts were obliterated by co-expression of UBE3A isoform 2 but not 1 or 3 . Further44.1Hepacivirus of the family Flaviviridae. Chronic infection of HCV develops hepatitis, cirrhosis, and hepatocellular carcinoma (HCC), which has increased dramatically in recent years  DNA virus belonging to the e family . It is re family , and thee family . Of these family  which cae family . The β ge family  and is ae family . The reme family . In addie family . Among te family .cis-acting regulatory sequences that control viral replication, transcription, and post-transcriptional control through the late regulatory element (LRE)  region encoding at least six regulatory proteins , the late (L) region expressing two structural proteins (L1 and L2), and the long control region ) containing the viral nt (LRE)   . The encnt (LRE) .Fig. 4Ro+/H+ exchanger regulatory factor 1 (NHERF1), and this type-specific interaction provides vital information for aiming to interfere with the activity of the UBE3A/E6 complex.UBE3A interacts with the E6 oncoprotein of HPV16, which causes anogenital cancers, most notably cancer of the uterine cervix, via the HECT domain , while E5UBE3A gene and, thereby, loss of UBR3A function and could be involved in the regulation of the progression of viral diseases caused by HCV, HIV-1, and HPV by interacting with the viral protein of each virus. Molecular mechanisms on UBE-associated AS and HPV-medicated cancers are relatively well addressed. However, the roles of UBE3A in HCV- and HIV-1-triggered hepatocellular carcinoma and AIDS, respectively, are as yet unknown, and thus treatment of these diseases is still at its incipient stage.As reviewed above, the UBE3A function is multifarious and critical in regulating of various human and viral diseases. It is responsible for severe neurodevelopmental disorders, such as AS and ASD, due to the mutation of the Since the essentiality of UBE3A for AS is proven from the finding that deletion of neuron-specific UBE3A is proficient in the induction of AS-like phenotypes in mice, a leading approach for developing therapeutics is focused on reinstatement of the gene through gene therapy techniques or nonsilencing paternal UBE3A expression. Specifically, adeno-associated virus- (AAV-) mediated transfer of the functional UBE3A has been successfully reinstated for the functional, long-lasting UBE3A expression in neurons , yieldinIn the virus-infected cells, UBE3A interacts specifically with viral proteins and degrades the interacting viral proteins in HCV and HIV-1 or recruits and degrades tumor suppressor molecule, p53, in HPV-infected cells, as described above. Recently, targeted protein degradation (TPD) has emerged as a therapeutic modality to tackle critical pathogenic proteins, using proteolysis-targeting chimera (PROTAC) protein degraders, also known as molecular glues . The PROThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Protein domains are the active subunits that provide proteins with specific functions through precise three-dimensional structures. Such domains facilitate most protein functions, including molecular interactions and signal transduction. Currently, these protein domains are described and analyzed as invariable molecular building blocks with fixed functions. Here, I show that most human protein domains exist as multiple distinct variants termed ‘domain isotypes’. Domain isotypes are used in a cell, tissue and disease-specific manner and have surprisingly different 3D structures. Accordingly, domain isotypes, compared to each other, modulate or abolish the functionality of protein domains. These results challenge the current view of protein domains as invariable building blocks and have significant implications for both wet- and dry-lab workflows. The extensive use of protein domain isotypes within protein isoforms adds to the literature indicating we need to transition to an isoform-centric research paradigm. Most proteins have, throughout evolution, been created using protein domains as molecular building blocks . The impWhile protein domains are defined, described and analyzed as protein subunits with fixed functionality ,5 that mHere, I term this phenomenon ‘domain isotypes’ and show that in humans, they are ubiquitous, used in a cell, tissue and disease-specific manner, have distinct 3D structures and can modify the biological function of a protein domain.P-values smaller than 0.05.All analysis was done in R 4.0.4 unless explicitly stated. Significance is in all analyses defined as Benjamini-Hochberg (BH)   and a query sequence (seq). Specifically the hmm alignment start and end coordinates (hmm_start and hmm_end), the corresponding sequencing alignment start and end coordinates (seq_start and seq_end) as well as the reference domain length (hmm_length). Truncation sizes for each end of the domain are calculated as respectively hmm_start -1 and hmm_end - hmm_length, and the largest value is used as the truncation size. The truncation fraction is calculated by dividing the truncation size by the hmm_length.Indel sizes are extracted by comparing the length of the aligned sequence with the reference model. Specifically, the length of the aligned reference model is calculated as hmm_end – hmm_start + 1, and the length of the aligned sequence is calculated as seq_end – seq_start + 1. The indel size is calculated by subtracting the aligned sequence length from the aligned model length, and the indel fraction is calculated by dividing by the model length (hmm_length).The overall domain variation is defined as the largest absolute fraction when comparing the truncation fraction and indel fraction. For the cluster analysis, I for each domain analyzed the lengths using the Modes function from the LaplacesDemon R package modifying the ‘min.size’ argument as indicated in the figure (0.1–0.3). The Modes function finds the number of local maxima in the density distribution of the variables while ensuring a mode corresponds to a least the fraction of observations indicated by the ‘min.size’ argument (meaing at least 10–30% of observation in each cluster).https://bioconductor.org/packages/pfamAnalyzeR/), which currently has three functionalities: (i) it reads the fixed-with-file (fwf) format produced by pfam_scan.pl or the Pfam webserver into R taking know format problems and data versions into account; (ii) domain data is augmented with the domain truncation/indel calculations described above; (iii) each domain is assigned one of five mutually exclusive isotype categories. First, a domain is defined as having a truncation if the truncation fraction ≥0.1  and ‘non-reference’ (#2-#5 from above).et al. , I made all pairwise comparisons of max 9 randomly selected reference isotypes domains and max 21 randomly selected non-reference isotype domains. The selected reference isotype domains were also compared to each other to estimate the reference variability.et al.  . The interval of unrelated RMSD/TM values was defined as the 5–95 percentile of the 11 026 unrelated comparisons described above.I only analyzed comparisons where both of the aligned sequences were > 20 amino acids and where the length of the non-reference aligned sub-sequence corresponded to >80% of the original sequence length ( 48 369) . To obtahttps://github.com/schrodinger/pymol-open-source) was used to visualize the aligned structures by saving them with pymol.pdbs and afterward opening it in PyMOL. The catalytic residues were manually annotated by comparison to figure 3 in Rigden et al.  and 2 tn et al.  using thhttps://3did.irbbarcelona.org). DNA and RNA binding domains are defined as domains returned by searching the Pfam website (https://pfam.xfam.org/) for, respectively, ‘DNA binding’ and ‘RNA binding’ keywords. See Protein domains interacting in solved 3D structures were downloaded from the newest version (2020_01) of the 3DID database  of the same type (hmm_name), and the domain isotype was transferred from the pdb data to the DMI data.For the domain-motif interaction (DMI), I downloaded the 3DID DMI  must be made to make the motifs similar to any motif outside the cluster. Considering that the median motif consists of six amino acids, this results in clusters with very different motifs.For each domain (hmm_name), I clustered motifs using the distance matrix produced by stringdistmatrix from the stringDist R package  as inputTo identify domains where isotypes were associated with distinct motifs clusters, I only considered motif clusters where for each domain (hmm_name), at least one domain isotype was identified at least three times. A motif cluster was defined as isotype-specific if 80% of motifs within the cluster interacted with either reference or non-reference domain isotypes. Of the 98 domains (hmm_name) with at least two motif clusters, I found 10 where reference and non-reference isotypes were specific to distinct motif clusters. A Fisher test was used to test the statistical significance of the Trypsin example using the 4 × 2 contingency table of observed interaction counts where ‘4’ indicate the 4 motif clusters and ‘2’ is reference vs. non-reference groups.n = 6638) and how many genes had protein domains (same hmm_name) that overlapped in genomic coordinates but were classified as different domain isotypes by pfamAnalyzeR (as described above).To determine changes in the presence, absence, or isotype of interaction domains, I used Pfam to identify protein domains (as described above) in the GENCODE v38  annotatiP-values were corrected for multiple FDR testing, and significant associations were defined as FDR <0.05.I used enrichment analysis to associate specific domains (given by their hmm_name) with biological functions. First, I extracted gene sets from MSigDB ,30 expliP-values were corrected for multiple testing using FDR, and significant associations were defined as FDR <0.05. The gene sets associated with each domain (and isotype skew) are available in For each gene set significantly associated with a domain, I tested if the same gene set was also significantly associated with either the reference or the non-reference isotype of that domain as follows. For each gene and domain (hmm_name), I calculated the average overall domain variation , where larger values mean larger deviation from the reference isotype. For each domain, I then extracted the subset of genes with that domain and ranked them according to the average overall domain variation in decreasing order. I then used fgsea  to test et al.  . I re-anSTK4 gene  found in three cancer types: thyroid, kidney and colorectal  . CompareIn summary, I find that alternative splicing and evolution frequently creates domain isotypes.https://bioconductor.org/packages/pfamAnalyzeR/). pfamAnalyzeR uses a highly stringent approach to detect domain changes  . I find Thus, domain isotypes, created by alternative splicing, are not an exception but the norm and seem to be used in a cell and tissue-specific manner thereby contributing to cell-type and tissue specialization.8’) aided by a few other residues, including ‘His108’ , the reference and truncated isotypes were strikingly different  . Surprisingly specific categories of domain isotypes are associated with larger structural differences that approach what is expected when comparing random domains   requires a specific 3D structure, the structural differences of domain isoforms could modify such interactions (both positively and negatively). In support of this, I find that in SwissProt non-reference protein domain isotypes are highly enriched for protein domains that facilitate interactions with DNA, RNA and proteins  Figure (Suppleme) Figure . Indeed,) Figure . Thus, p) Figure .This is further emphasized since domain isotypes are also created through splicing. Indeed, the GENCODE v38  annotatiThe structure is function axiom also suggests that protein domain isotypes could have distinct biological functions. We, therefore, analyzed if individual SwissProt protein domains were overrepresented in the genes annotated to specific pathways or go-terms. I found a significant association between 552 protein domains and 6018 gene sets   ,33. I fi) Figure . ImportaI show that protein domain isotypes are created through both alternative splicing and gene evolution . These domain isotypes are ubiquitous and modulate or disrupt the functionality of protein domains in a cell, tissue and disease-specific manner. Despite the natural dichotomy between alternative splicing and evolution, I here present a joint analysis of both mechanisms to comprehensively examine domain isotypes. Surprisingly these results highlight the synergy of considering both mechanisms since both show domain isotypes are prevalent and functionally important.However, long-read technology consistently demonstrates that the current databases underrepresent protein diversity ,48 meaniThis article challenges the current perception of protein domains as building blocks with a fixed function. Instead, my findings suggest that protein domain isotypes are via alternative splicing or through evolution used to modulate domain function, thereby increasing the flexibility of protein adaptation. These findings also suggest caution when transferring concussions about a protein domain from one setting to another  as it is likely that different domain isotypes are used, whereby functional differences are expected. This is especially important when using protein domains for enzyme discovery  or drug The widespread use of protein domain isoforms also highlights, along with many other considerations ,16,52,53lqad084_supplemental_filesClick here for additional data file.


However, the mechanical behavior of the dual-phase lattice structure under dynamic compression and the enhancement mechanism of the reinforcement phase have not been widely studied with the increase in compression speed. Based on the design requirements of dual-phase lattice materials, this paper combined octet-truss cell structures with different porosities, and the dual-density hybrid lattice specimens were fabricated via the fused deposition modeling technique. Under quasi-static and dynamic compressive loadings, the stress–strain behavior, energy absorption capacity, and deformation mechanism of the dual-density hybrid lattice structure were studied. The results showed that the quasi-static-specific energy absorption of the dual-density hybrid lattice structure was significantly higher than that of the single-density Octet lattice, and with the increase in compression strain rate, the effective specific energy absorption of the dual-density hybrid lattice structure also increased. The deformation mechanism of the dual-density hybrid lattice was also analyzed, and the deformation mode changed from an inclined deformation band to a horizontal deformation band when the strain rate changed from 10 With the development of aerospace and deep-sea exploration, biomedicine, and other fields, as well as the increasingly extreme working environment, the demand for high-performance, lightweight materials and structures has increased ,3,4,5,6.The mechanical properties of lattice structures are mainly determined by the geometric characteristics of cell structures. Ashby et al.  designedAccording to the special internal combination of natural materials, such as bamboo , a dual-phase lattice structure composed of the matrix phase (MP) and the reinforcement phase (RP) was proposed. Yin et al.  designedIn this paper, a dual-phase lattice structure was selected to carry out compression tests under quasi-static and dynamic conditions, and its deformation modes at different strain rates under dynamic conditions were compared and analyzed accordingly.A double-density hybrid lattice (DDHL), inspired by the dual-phase mechanical metamaterial composites , was dev3, and the size of each Octet cell structure was 10 × 10 × 10 mm3. To ensure the largest contact area between Octet-RP and Octet-MP cell structures in the DDHL, the matrix phase and the reinforcement phase were arranged one by one; that is, all six surfaces of each Octet-RP were in contact with the Octet-MP, as shown in Considering that the diameter of the strut rod in the Octet cell structure will be very small when its porosity is too large, resulting in structure loss during 3D printing, in the process of sample preparation, the porosities of Octet-MP and Octet-RP cell structures were set to 65% and 75%, respectively, corresponding to strut diameters of 1.9 mm and 1.56 mm. Further considering the reliability of 3D printing and the stability of the hybrid lattice structure, the DDHL structure was composed of 5 × 5 × 5 cell units along the three-dimensional orthogonal direction. The overall size of the Octet structure was 50 × 50 × 50 mmDue to the complexity of the designed hybrid lattice, additive manufacturing technology was used to process the test specimens required for the experiments, and the fabrication process is shown in −3 s−1. The interfaces between a lattice sample and two compressing platens were lubricated with grease to minimize friction. At the same time, the deformation process and failure mode of the specimen were recorded with a gray point camera. The experimental device is shown in To characterize the overall engineering quasi-static stress–strain responses and energy absorption characteristics of DDHL structures, a MTS810 universal testing machine, which was manufactured by MTS Systems Corporation , was employed for uniaxial compression tests. During compression, the lattice specimens were sandwiched between two platens, where the lower platen moved upward at a constant speed of 0.05 mm/s, and the upper platen was fixed. Thus, the corresponding global engineering strain rate was controlled at 10ε = 0.45 smoothly to the densification stage, which was because the Octet lattice collapsed layer by layer. Thus, the densification turning point of the Octet lattice occurred when the last layer completely collapsed. The DDHL exhibited a different deformation mode without an apparent layer-by-layer collapse.The quasi-static response of the DDHL was compared with that of the Octet lattices to investigate the energy absorption performance and deformation mechanism of the DDHL. The engineering stress–strain curves of the Octet lattice and DDHL specimens are shown in ε = 0.2, a linear collapse shear band was initially formed, due to the deformation of the matrix phase, as shown in c in ε = 0.3, it was no longer a single shear band but several shear bands that were connected, as shown in As the engineering stress–strain response of the DDHL differed markedly from that of the Octet lattice, the deformation patterns of the DDHL and the Octet lattice were recorded by a high-speed camera and compared with each other, as shown in Q was used to measure its energy absorption and performance, that is, the energy absorbed per unit mass , except for the initial peak stress [When the specimen was fully compacted, the stress increased sharply, and the energy absorption of the specimen also increased sharply. However, the energy absorption was useless when the specimen was compacted, since the compressive stress was very large. The effective energy absorption capacity of the specimen is usually determined by the effective compression strain, also known as the densification strain εciency η ,39,40, wk stress .η was the densification strain εD, with Octet-RP as an example, as shown in Dε. Therefore, the effective specific energy absorption Qe can be expressed as:With the increase in compression strain, the compression strain corresponding to the maximum energy absorption efficiency D of dual-phasic lattices [n is the total number of cells, k is the number of reinforcement phase cells, However, for dual-phase lattice materials, the traditional method of calculating the densification strain by determining the energy absorption efficiency was not applicable  because lattices  accordinAccording to the above equation, the effective specific absorption energy of homogeneous Octet-RP and Octet-MP structures and DDHL structures can be easily calculated, as shown in −1, 10 s−1, and 100 s−1 were carried out using the Amsler HTM5020 high-speed tensile machine , which enabled a constant compression/tension at loading speeds from 0.001 m/s to 20 m/s. A Photron Fastcam SA1 high-speed camera was used to record the deformation process at 4000 frames per second, with 512 × 512 pixels. During the experiment, the lower compression platform was fixed, and the upper compression platform moved downward at speeds of 50 mm/s, 500 mm/s, and 5 m/s. When the strain rate was less than or equal to 10 s−1, the high-speed camera was triggered by the synchronization box to ensure the synchronization of the deformation figure and the compressive data. When the strain rate was 100 s−1, the loading rate was fast, making it difficult to successfully trigger the synchronization box. Therefore, the manual trigger method was used to start the recording of the high-speed camera. The loading platform and the specimen placement are shown in To investigate the mechanical behavior and deformation mode of the two-phase structure under dynamic compressions, dynamic compression experiments with strain rates of 1 s−1, 10 s−1, and 100 s−1 were about 3.45 MPa, 3.67 MPa, and 4.4 MPa, respectively. In the plateau stage, compared with the quasi-static compression, the stress–strain curve showed obvious fluctuations. The inflection point from the platform segment to the compacted segment cannot be observed in the figure because the fluctuation from the platform segment to the compacted segment is very small.The engineering stress–strain curves of the dynamic compression tests of the DDHL structure are shown in Hence, the effective specific absorption energy of the DDHL structure under different strain rates can be calculated according to Equation (4), and its change trend with the increase in strain rate is shown in −1 and 10 s−1 had no change in essence, compared with the quasi-static deformation mode, forming a deformation zone caused by the initial crushing deformation of the matrix phase, followed by the deformation of the reinforcement phase, as shown in −1, the deformation zone formed by the matrix phase deformation was not as clear as the deformation zone formed when the strain-rates were 10−3 s−1 and 1 s−1 and even showed a trend of layer-by-layer deformation, as shown in −1, it was difficult to clearly observe the inclined deformation zone formed by the deformation of the matrix phase. The deformation of the DDHL under a strain rate of 100 s−1 was mainly manifested as the deformation mode of layer-by-layer crushing. This was because, with the strengthening of the inertial effect, the weaker matrix phase had no time to deform, so it deformed together with the indenter and the reinforcement phase, as shown in According to the dynamic deformation patterns of the DDHL in Compared with the Octet lattice, the specific energy absorption of the DDHL structure was significantly improved because the interfacial sliding between the matrix phase and the reinforcement phase brought additional energy dissipation.With the increase in the compressive strain rate, the effective specific absorption energy of the DDHL was enhanced, due to the gradual emergence of inertial effects.−1, the deformation mode of the DDHL exhibited a layer-by-layer collapse.Under dynamic compressive loading, the deformation band initially formed by the matrix phase deformation of the DDHL structure was not obvious. Under a compressive strain rate of 100 sIn order to study the deformation mode and dynamic behavior of dual-phase hybrid lattice structures, the paper mixed two Octet lattices with different relative densities to realize the design of the dual-density hybrid lattice (DDHL) and carried out quasi-static and dynamic experiments with MTS 810 and Amsler HTM5020 high-speed tensile machines. The deformation modes of the DDHL structure under different strain-rates were observed, and the engineering stress–strain curves were obtained. The following conclusions were drawn from the analysis:


With the emergence of additive manufacturing technology, patient-specific cranial implants using 3D printing have massively influenced the field. These implants offer improved surgical outcomes and aesthetic preservation. However, as additive manufacturing in cranial implants is still emerging, ongoing research is investigating their reliability and sustainability. The long-term biomechanical performance of these implants is critically influenced by factors such as implant material, anticipated loads, implant-skull interface geometry, and structural constraints, among others. The efficacy of cranial implants involves an intricate interplay of these factors, with fixation playing a pivotal role. This study addresses two critical concerns: determining the ideal number of fixation points for cranial implants and the optimal curvilinear distance between those points, thereby establishing a minimum threshold. Employing finite element analysis, the research incorporates variables such as implant shapes, sizes, materials, the number of fixation points, and their relative positions. The study reveals that the optimal number of fixation points ranges from four to five, accounting for defect size and shape. Moreover, the optimal curvilinear distance between two screws is approximately 40 mm for smaller implants and 60 mm for larger implants. Optimal fixation placement away from the center mitigates higher deflection due to overhangs. Notably, a symmetric screw orientation reduces deflection, enhancing implant stability. The findings offer crucial insights into optimizing fixation strategies for cranial implants, thereby aiding surgical decision-making guidelines. Utilizing additive manufacturing (AM), commonly known as 3D printing, in designing patient-specific implants is now gaining traction, offering a promising alternative to conventional manufacturing techniques and methods ,2,3,4. IWhile bone grafts have stood as the gold standard for cranial defect reconstruction, the paradigm is shifting with the recent technological advancements in additive manufacturing. Novel materials such as titanium alloy Ti6Al4V), polyether ether ketone (PEEK), and polymethyl methacrylate (PMMA) have emerged as viable contenders . The incAl4V, polThe strategic design of fixation devices is vital to ensure seamless integration with the skull. This optimizes mechanical stability and mitigates the risk of infections, specifically those triggered by biofilm formation ,13. The During an external impact, the load transfer between the implant and the skull must occur through the skull-implant interface to ensure optimal load distribution while minimizing the occurrence of local stress concentrations in the screws and plates . This caOptimal fixation strategies should ideally produce minimal stress concentration, distributing external loads uniformly to the adjacent bone. Using fewer screws than the optimal number could lead to excessive deformation and the potential for gap formation. Hence, the principal aim is to attain optimal deformation, ensuring no gaps under external loading conditions while utilizing the minimum possible number of fixation points. In order to achieve this, it is essential to determine the critical parameters that have the most dominant effect on the deformation of the implant.In this study, the deformation effects of implants in response to varying material properties, shapes, and sizes of defects/implants, external loads, and intracranial pressure (ICP) were examined. A systematic methodology was adopted to delineate the relationship between fixation and deformation, focusing on implants with diverse geometries and materials tailored for additive manufacturing. Three implant materials—PEEK, PMMA, and Ti6Al4V—were considered. A comparative analysis centered on the number of fixation screws was undertaken to ascertain the optimal count. Furthermore, geometric configurations, including circular, elliptical, and square shapes, were evaluated to understand their influence on deformation in relation to the number of screws. 2. Notably, the mean cranial defect size in adults spans between 2500 mm2 and 10,000 mm2 [2 and a ‘larger defect’ approaching 8500 mm2. Three distinct geometric configurations—circular, square, and elliptical, each maintaining a consistent implant area—were examined for both defect dimensions. The assumed thickness of the skull was set at 6 mm, mirroring the average thickness observed in human skulls [A CT scan data (DICOM format) from a patient served as a foundation from which a 3D stereolithography (STL) model of a human skull was generated. The STL model was then converted to an object file (.obj) and imported into open-source software AutoDesk Meshmixer  to model cranial defects. The generated defect was approximated by a convex ellipse with a major axis of 72 mm, a minor axis of 58 mm, and a perimeter of 210 mm, translating to a defect area of approximately 3480 mm,000 mm2 . Based on skulls .The study emphasizes the significance of the number of fixation points, a critical variable in implant attachment. Typically, the required holes are drilled, and fixation plates, commonly with two holes, are utilized to secure the implant to the skull. The complete skull–implant–fixation assembly was not modeled. Instead, fixations were represented as through-holes with dimensions of 2 mm in diameter and 6 mm in height, reflecting the skull’s average thickness. Clinically, at least three fixation points are used to secure the implant to the skull. This approach is often based on the principle that a triangular placement offers the highest stability . MicromoFor enhanced stability, screws were positioned 41.5 mm away from the center of the larger square-shaped implants, spaced evenly in terms of angular distance. Two distinct orientations for the fixation points were examined, as depicted in The square-shaped implant, characterized by its four corners with an interior angle of 90 degrees, would logically suggest the need for four fixation points. For the purpose of studying this effect, only the larger square-shaped implants were considered. This choice was due to their pronounced edge direction changes, providing a valuable context for evaluating mechanical performance relative to the implant’s periphery. Four fixation points, one at each corner, were incorporated into the design.The positions of these fixation points were progressively adjusted, moving them inward from the outer edge towards the center see . Using aTo understand the influence of the curvilinear distance between consecutive screws on deformation trends, implants of varying sizes and shapes, combined with different screw counts, were assessed. Given the use of geometrically shaped implants and the placement of fixation points at angular equidistance, the curvilinear distance between the screws can be determined by dividing the perimeter of the circle offset from the outer edge (which accommodates the fixation screws) by the total count of these screws refer to . ThroughFor finite element method (FEM) simulation, the skull’s geometry was imported into SpaceClaim  and subsequently meshed using ANSYS 2021 . The simulations employed 3D 20-noded solid elements. 2 and 8500 mm2. The intracranial pressure (ICP) developed within the skull balances part of this external impact (The analysis focused solely on static loadings. An imposed load of 50 N, representing the approximate reactive force when resting on a level surface , was appl impact . For aduDeformation contour plots under a 50 N external load and 15 mm Hg ICP are illustrated in To quantify these effects, percentage reduction in deformation, 2, moving from three to four screws reduces deformation by 60% for circular, 80% for elliptical, and 50% for square implants. For circular and elliptical implants, deformation reduction beyond five fixations is marginal, suggesting an optimal fixation range of four–five points. This raises the question of whether this trend persists across varying defect sizes. For larger defects of 8500 mm2, using four screws instead of three reduces deformation by nearly 50% for circular and 75% for elliptical shapes. Square implants show approximately 50% reduction, which further drops with five to six fixations. However, the deformation reduction is under 20% for circular and elliptical types. For smaller implants with more screws, the percentage of deformation reduction dips below 10% when going from seven to eight screws for the circular and elliptical designs. Due to the square implant’s geometric symmetry and the positioning of eight screws, deformation reduces by over 30% for smaller implants and is below 10% for larger implants. Subsequent analyses for PEEK and Ti6Al4V implants of varying shapes demonstrate similar deformation trends to PMMA counterparts across shapes.The metric Square-shaped implants exhibit distinct deformation characteristics in comparison to their circular and elliptical counterparts. Specifically, when the screw count in square implants is increased from four to five, there is a notable 50% decrease in deformation (see Due to the inherent double symmetry of a square, one might assume that four or eight screws would be ideal. However, this assumption necessitates closer scrutiny, especially in terms of screw orientation. As illustrated in This analysis aims to identify the optimal curvilinear distance between consecutive screws to minimize deformation. For circular and square implants, the segment of the guiding circle’s perimeter between two neighboring screws defines the curvilinear distance. Likewise, for elliptical implants, the segment of the guiding ellipse’s perimeter (as shown in Across all materials and geometric shapes, deformation consistently reduces as the curvilinear distance between adjacent screws diminishes, resulting in enhanced constraint. For larger defects, the deformation changes negligibly when the screw separation is below 50 mm, indicating that this range is optimal (as depicted in Ti6Al4V exhibits markedly lower deformation under identical external loading when compared with PMMA and PEEK. Regardless of implant size or shape, deformation in PMMA and PEEK implants is approximately 40 and 30 times higher, respectively, than in Ti6Al4V. This might suggest the superiority of Ti6Al4V due to its minimal deformation. However, Ti6Al4V’s Young’s modulus is almost sevenfold higher than bone, thereby inducing stress-shielding . FurtherIn this study, the impact of different variables such as implant materials, shapes, and defect sizes on the deformation of cranial implants was examined. Three different materials commonly used in additively manufactured cranial implants were investigated. Alloplastic cranioplasty, that is, skull reconstruction with these various synthetic cranial implants, is accompanied by a high risk of complications such as implant exposure, chronic pain, infections, and cosmetic deformity. Although patient factors can contribute to these complications, further studies are needed in order to delineate implant variables that may underly these issues. The evaluation focused on features of the implants that may differ based on the implant material, as well as patient-dependent variables such as defect size and shape ,27. Our Our findings suggest an optimum fixation screw count of four to five, regardless of defect size. When feasible, a symmetric orientation of these fixation screws yielded superior deformation outcomes. Moreover, the study underscores the importance of maintaining a curvilinear distance under 40 mm between two screws, further solidifying the notion that a minimum of four fixations is vital for optimal deformation resistance. Intriguingly, as fixations shift inward along the diagonal, the curvilinear distance between screws decreases for the same screw count, signifying a dependence not only on the number of fixations but also on their relative position to the outer periphery.However, our observations also indicate a threshold concerning how close the fixations can be positioned to the center; surpassing this threshold can lead to a sharp increase in deformation. This intricate interplay between the analyzed geometric parameters suggests they cannot be isolated in their effects and must be considered holistically when determining the optimal number of fixations.Future directions for this research may incorporate machine learning approaches ,29,30 to


Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures. Multi-domain and complex structure assembly through domain segmentation and single domain modeling algorithms method outperforms existing state-of-the-art approaches. Meanwhile, structure-based drug design often relies on the interaction of different domains. Capturing the domain-domain orientation accurately is crucial to elucidate their functions and facilitate drug discovery3. However, multi-domain proteins and complexes are often more flexible than single domains, with a high degree of freedom in the linker or interaction region connecting the domain structure4. This flexibility of inter-domain orientation is a challenge both for experimental and computational methods.Most proteins in nature are composed of multiple domains that represent compact and independent folding units within the protein structure. Appropriate inter-domain interactions are essential to facilitate the implementation of multiple functions in a cooperative way8. In particular, the end-to-end protein structure prediction framework, AlphaFold29, built on the attention-based equivariant transformer network, was able to predict unknown structures of proteins with an unprecedented accuracy as witnessed in the CASP14 experiment10. With AlphaFold2, achieving a global distance test (GDT) score of 90 or higher for most targets is within reach, approaching experimental accuracy. Nonetheless, it is worth noting that several of the CASP14 targets, especially multi-domain targets, were not predicted to a 90 GDT score suggesting that there are further improvements in multi-domain prediction3. In fact, just about a third of proteins in the Protein Data Bank11 (PDB) contain multi-domain structures12. The lack of multi-domain structures makes the PDB database biased toward proteins that are easy to crystallize and single domain structures. This means that AlphaFold2 is more biased toward single domain prediction as it is trained on the PDB. AlphaFold2 structure predictions for multi-domain proteins are thus less accurate as on the domain level3.Recently, great progress has been made in protein structure prediction due to the introduction of deep learning techniques4. This approach may further improve the accuracy of multi-domain protein modeling by focusing on capturing inter-domain interactions on the basis of high precision single domain models, and to some extent lower the threshold of computing hardware compared to end-to-end methods.Fortunately, the single domain prediction is largely solved by AlphaFold2, which provides a feasible approach to multi-domain protein structure modeling, that is, based on a divide-and-conquer strategy, splitting the sequence into domains, generating models for each individual domain, and finally assembling each domain model into a full-length model13 and AIDA14, primarily focus on the construction of linker models by exploring the conformational space, with domain orientations loosely constrained by physical potential from generic hydrophobic interactions. Meanwhile, domain assembly can also be perceived as a docking problem. The previously proposed DEMO1 and SADA15 assemble the single domain structure by rigid body docking, which are essentially a template-based method that guides domain assembly by detecting available templates. However, structural alignment is limited by the number of multi-domain proteins in PDB, and the difficulty of capturing the orientation between domains from the template may increase as the number of domains increases15. The inter-residue distance predicted by deep learning improves the problem of insufficient number of multi-domain protein templates to a certain extent. But given the fact that it is currently relatively easy to obtain high-precision single domain structures, it may be more urgent and important to pay more attention to the capture of inter-domain interactions in deep learning.Often, studies in modeling multi-domain proteins by assembling domain structures can be divided into two categories, linker-based domain assembly and inter-domain rigid body docking. Linker-based methods, such as Rosetta17. The same physical forces that drive protein folding are also responsible for protein-protein associations18. Thereby, it is very likely that the deep learning model built by learning the inter-domain interactions in the existing PDB monomer structures have already learned the representations necessary to model protein complexes consisting of multiple single-chain proteins19.In addition to multi-domain targets, could protein complex structure prediction be achieved by domain assembly? Considering that intra-protein domain-domain interactions are not physically different from inter-protein interactions; their structure or function may be viewed essentially as the embodiment of inter-domain interactions. We hypothesize that inter-domain interactions learned from intra-protein could be used to predict the protein complex structure. In practice, many proteins that form complexes in prokaryotes are fused into long, single-chain, multi-domain proteins in eukaryotes22 and template-based methods25, which are limited by force-field accuracy and experimentally resolved multimeric structures, respectively. Recent methods incorporate coevolution-based contact/distance prediction and deep learning techniques to predict complex structures. The three-track network of the end-to-end version of RoseTTAFold5 generates protein- protein complex structure models by combining features from discontinuous crops of the protein sequence. Meanwhile, studies have been carried out whereby AlphaFold2 is adapted to predict the protein complex structure26. They demonstrate that the same network models from AlphaFold2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining16. Alternatively, an AlphaFold2 model, AlphaFold-Multimer27, trained specifically on multimeric inputs, significantly increases the accuracy of predicted multimeric interfaces while maintaining high intra-chain accuracy. In fact, these methods are performed by feeding the combined protein sequences into the deep learning models, for some large targets, most available GPU memory may have difficulty meeting the requirements of its inference. Therefore, it is highly desirable to develop a more lightweight approach, such as predicting the structure of protein complexes through domain assembly using domains in each chain as assembly units.Until now, there is no study for predicting the structure of protein complexes by specifically learning inter-domain interactions and using the modeling approach of domain assembly. Conventional approaches for predicting the protein complex structure include docking28 (AlphaFold database). Furthermore, we use the same network model trained on monomeric multi-domain proteins to predict protein-protein complex structures with domain assembly. It demonstrates that the proposed method, DeepAssembly, can be applied to protein complex structure prediction by using inter-domain interactions learned from intra-protein, and we provide evidence of such. Meanwhile, it also provides a more lightweight approach to assemble protein structures using domains as units, possibly reducing the GPU memory requirements to some extent.In this work, we proposed a framework, DeepAssembly, to assemble multi-domain proteins through inter-domain interactions specifically predicted by deep learning. Different from DEMO and SADA, as a data-driven deep learning method, DeepAssembly avoids the time-consuming template alignment process and does not depend on templates entirely, which pay more attention to the capture of inter-domain interactions to improve the prediction accuracy of inter-domain orientations. Experimental results indicate that DeepAssembly builds models with higher accuracy than AlphaFold2 and improves the inter-domain orientations of those multi-domain protein structures with lower accuracy in AlphaFold Protein Structure Database29. Meanwhile, the input sequence is split into single-domain sequences by a domain boundary predictor, and then the structure for each domain is generated by a single-domain structure predictor (remote template-enhanced AlphaFold2). Subsequently, features extracted from MSAs, templates and domain boundary information are fed into a deep neural network (AffineNet) with self-attention to predict inter-domain interactions. Finally, DeepAssembly performs the creation of the initial full-length structure using the single-domain structures, followed by iterative population-based rotation angle optimization32. The domain assembly simulation is driven by the atomic coordinate deviation potential transformed from predicted inter-domain interactions, where the best model by our in-house model quality assessment33 is selected as the final output structure.DeepAssembly is designed to automatically construct multi-domain protein or complex structure through inter-domain interactions from deep learning. Figure 34 (TM-score) to evaluate the accuracy of the built models. Here, the TM-score is a metric defined to evaluate the topological similarity between protein structures ”. Even if the domains are replaced by AlphaFold2 predictions, DeepAssembly still shows improvement compared with AlphaFold2  AlphaFold database is a very nice resource to have. However, some issues are likely to emerge in the futureH. sapiens proteome in AlphaFold database with the TM-scores < 0.8 for the corresponding AlphaFold2 structures and the experimental structures in PDB contain more than 85% of solved residues (see Methods). We predict these multi-domain proteins using DeepAssembly and compare them with structures deposited in AlphaFold database  (PDB ID: 3WK4_A), a candidate target for therapies for hypertension or inflammation3 Å Fig. . In addi38. In the AlphaFold2 model, almost all domains are predicted accurately (average TM-score is 0.961), however, there is an obvious difference in orientation between the serine protease (SP) domain and the other domains relative to the experimental structure , the central protease of the complement system of immune defense, consisting of five domainsure Fig. . As visiure Fig. . The Deeure Fig. . These rWe apply DeepAssembly to CASP14 and CASP15 targets to further evaluate its performance. We selected a total of 30 multi-domain targets from CASP14 and 15 as test proteins according to the “Domain Definition” on the CASP official website, of which 17 are from CASP14 and 13 are from CASP15. These targets all have known PDB structures that been released. We assembled full-length models by DeepAssembly on these CASP targets with single-domains generated by PAthreader, and compared with AlphaFold2  is relatively difficult to predict. As illustrated in Supplementary Fig. It is well known that domains, as independent folding and functional units, reappear across species in protein structure. For some specific inter-domain interactions, they are not only present in monomeric proteins, but also implicit in protein complexes. We find an example of the Cdc42/Cdc42GAP/ALF3 complex (PDB ID: 1GRN), consisting of two protein chains , is better than RoseTTAFold for 66% cases, and generates more high-quality models  in 32.4% of cases  in paired MSAs on the outcome. It is obvious that MSA Neff has a large impact on the performance, the proportion of proteins that are correctly predicted to interface increases with deeper MSA Fig. . Too sha16. An interesting successful assembly is obtained modelling chains A and B from the complex with PDB ID 3ALZ  and the heterotetramer NuA4 core complex (PDB ID: 5J9T). As illustrated in Supplementary Fig. 43. Therefore, the structural determination of membrane proteins is critical to advancing our understanding of their function as well as the drug design. Here, we apply DeepAssembly to transmembrane protein structure prediction.Membrane proteins are involved in a variety of essential cellular functions including molecular transporters, ion channels and signal receptors, which constitute about half of current drug targets44. The model built by DeepAssembly had the correct inter-domain orientation, yielded a TM-score of 0.882 , DeepAssembly correctly predicted the structures of its three channels (average TM-score = 0.985), as well as their interfaces (DockQ = 0.591)  Fig. . Figure 91) Fig. . These eHowever, there is still room for further improvement. Since our deep learning model is only trained on the data set of mainly soluble multi-domain proteins, which are different from the structural characteristics of membrane proteins, our method still has some limitations for the assembly of some membrane proteins that are large or flexible across transmembrane regions. For example, for the target of acid-sensing ion channel (PDB ID: 2QTS) with three chains, there is a deviation between experimental structure and predicted model (DockQ = 0.402), especially in the transmembrane region Fig. . This ma43. To address the challenge, some statistical and transfer learning-based methods may be promising, such as DeepTMP43, which predicts the transmembrane protein inter-chain contact by transferring the knowledge learned from the initial model trained on a large dataset of soluble protein complexes. This approach alleviates the limitation of training deep learning model directly on few membrane proteins, and simultaneously provides insights into how we further improve the complex structure prediction by leveraging multi-domain protein data.Moreover, the number of membrane proteins is rather limited compared with a great deal of soluble protein structures resolved in PDB, as their experimental determination is subject to the complicated membrane environment. This poses a major obstacle to training deep learning model directly on such a small dataset of membrane proteinsIn order to rigorously test the model performance of DeepAssembly, we assemble multi-domain proteins with experimental domains to exclude the influence of single-domains on the final structure. In this experiment, DeepAssembly is compared with SADA, DEMO and AIDA  between predicted atomic coordinate deviation (pACD) and true atomic coordinate deviation (tACD) on 2,190 models (10 models for each protein) assembled by DeepAssembly using experimental domain structures. Where, pACD represents the coordinate deviation under the predicted inter-domain interactions, and tACD is for the true interactions from experimental structure. Furthermore, in Supplementary Fig. A key component of DeepAssembly is the deep neural network-based inter-domain interactions prediction, which is used to guide the domain assembly procedure. Our predicted inter-domain interactions are represented by affine transformations between inter-domain residues, which contain rotations and translations between domains. Here, we devise an affine transformation-based potential called atomic coordinate deviation (ACD), which is minimized in domain assembly simulations to make the domain relative positions of the conformation gradually satisfy the predicted inter-domain interactions (see details in Methods section). Figure 46 searching Uniclust3047 and BFD48 databases, and only the distance between inter-domain residues is considered when the error is calculated. On average, the inter-domain distance error of DeepAssembly is 0.560 Å, which is 22.7% less than AlphaFold2 (0.724 Å) and 51.3% less than RoseTTAFold (1.151 Å). In addition, the data list in Supplementary Table Predicted inter-domain interactions include inter-residue distance between domains. We compare the DeepAssembly with AlphaFold2 and RoseTTAFold according to the predicted inter-domain distances   to develop the pipeline. This dataset contains a total of 10,593 multi-domain proteins, with each protein chain having 40 to 1,000 residues, sharing <40% sequence identity and having a resolution within 3.0 Å. These multi-domain proteins contain between 2-9 domains, determined by DomainParser49 or confirmed by domain information for the corresponding entries in CATH50 and SCOPe52. Specifically, there are 8,399 (79.3%) two-domain proteins, 1,681 (15.9%) three-domain proteins and 513 (4.8%) proteins with four domains or more. In order to train a deep learning model for predicting inter-domain interactions, 1,0064 (95%) multi-domain proteins in this dataset are randomly selected as the training set, and the remaining 529 (5%) are used as the validation set.We collected a multi-domain protein dataset from our previously developed multi-domain protein structure database (MPDB) , the PDB structure for each entry is directly downloaded from the PDB at https://www.rcsb.org/. Of these 23,391 proteins, the protein that has any of the following features is removed: (i) without experimental structure in the PDB; (ii) is confirmed as containing only one domain by DomainParser or corresponding entries in CATH and SCOPe database; (iii) having redundancy between each other for the UniProt primary accession; (iv) with a TM-score > 0.8 for the corresponding structure in AlphaFold database; (v) with less than 85% of solved residues for the experimental structure in the PDB, leading to a final set of 164 multi-domain proteins.To evaluate the performance of DeepAssembly in improving the inter-domain orientation, we further construct an independent test set of 164 multi-domain proteins. All entries come from 23,391 41 to test the performance of DeepAssembly on protein complex prediction. These complexes share <30% sequence identity and have a resolution between 1-5 Å. The dataset consists of 56% eukaryotic proteins, 30% bacterial, 4% archaea, 2% virus and 8% from mixed kingdoms. For eukaryotic proteins, there are 36% from H. sapiens.Further, we used 247 heterodimeric protein complexes with known interfaces from a previous study29, to search for high-quality templates for inter-domain interactions prediction. PAthreader finds an optimal alignment between the query sequence and template sequence by a three-track alignment algorithm . Specifically, the distance profiles are represented as the probability distribution of pairwise residue distances predicted by our in-house inter-residue distance predictor, DeepDisPRE. The structure profiles are histogram distributions of pairwise residue distances, which are extracted from PAcluster80, a master structure database constructed by clustering PDB and AlphaFold database. The templates are eventually ranked by “rankScore” calculated with linear weighting of alignment score and predicted DMscore by a trained deep learning model. During training, in order to enhance the generalization ability of the network model and avoid overfitting, we restrict the available templates to up to 10 with the highest “rankScore” to increase the diversity of the template features used for training. At inference time, considering the influence of the template quality on the final structure, we provide at most the top 5 templates with the highest “rankScore” to the trained model according to the following criteria: (i) the template with “rankScore” >0.6; (ii) “rankScore” difference between the previous selected <0.03; (iii) the length of template more than 30% of the query sequence.We use our recently developed remote template recognition method, PAthreader53 by clustering the spatially close residues according to the inter-residue distance using a domain-residue clustering algorithm. Then we segment the input sequence according to the determined domain boundary to form several single-domain sequences. Next, the structural model of each domain is generated using an extended version of AlphaFold2 updated by replacing the template search component (HHsearch46) of AlphaFold2 with PAthreader. Specifically, remote templates for the sequence of each domain are searched separately through PAthreader with the same parameters used in template recognition for full-length sequence. For the sequence of each domain, we feed the top 4 templates identified by PAthreader into AlphaFold2 model and run the program with default parameters, selecting the first ranked model output as the final single-domain structural model. For single-domain with disconnected sequence, we first concatenate its disconnected sequences into a continuous sequence, and then input this sequence into the PAthreader model to generate the single-domain structure  and the Big Fantastic Database48 (BFD) using HHblits46 (version 3.2.0) with default parameters and e-value of 1e-3. The template features are derived directly from the top Ntempl remote template structures searched by PAthreader (Ntempl = 10 for training and 5 for inference). For each template, the local coordinate system for each residue is first establish, then the six elements  is constructed to guide the domain assembly, defined as:entclass1pt{minimaFastRelax protocol with ref2015 energy function in Rosetta55.We assemble the full-length protein model in the domain assembly module. The overall architecture consists of three stages: first initial model creation stage, followed by iterative model update stages where a pool of multi-structures is maintained during the population-based rotation angle optimization, and finally full atom-based refinement. At the initialization stage, 1,000 initial full-length models are generated where the rotation angles mentclasspt{minima33, a graph coupled network based on embeddings of protein language model to assess the quality of the model built by DeepAssembly. GraphCPLMQA utilizes both the sequence and structure embeddings generated by the protein language model, with various protein model structure features. They are input to the encoder-decoder module of the graph coupling network, and the mapping relationship among sequence, structure and quality is obtained to predict the quality of protein models.We use a developed model quality assessment tool, GraphCPLMQA34 between the predicted multi-domain protein model and the corresponding experimental structure calculated by TM-score tool downloaded at https://zhanggroup.org/TM-score/. The second type is one that measures the interface quality of predicted protein complex model. In this regard, we report DockQ score39 calculated by public tool . Besides the above metrics, we also reported the inter-domain distance error to evaluate the predicted inter-domain distance precision. The detailed descriptions of above metrics are given in Supplementary Note We use three types of metrics to evaluate the quality of the multi-domain protein and complex model built by DeepAssembly, and the predicted inter-domain distance precision. The first type of metrics is one that measure the closeness between the built model and the experimental structure in PDB. In this respect, we adopt the RMSD and TM-scoreAll data were carefully collected and analyzed using standard statistical methods. Comprehensive information on the statistical analyses used is included in various places, including the figures, figure legends and results.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting SummaryPeer Review File


A review of studies on the electroplastic effect on the deformation process in various conductive materials and alloys for the last decade has been carried out. Aspects, such as the mode and regimes of electric current, the practical methods of its introduction into materials with different deformation schemes, features of deformation behavior accompanied by a pulsed current of different materials, structural changes caused by the combined action of deformation and current, the influence of structural features on the electroplastic effect, changes in the physical, mechanical, and technological properties of materials subjected to plastic deformation under current, possible mechanisms and methods of physical and computer modeling of the electroplastic effect, and potential and practical applications of the electroplastic effect are considered. The growing research interest in the manifestation of the electroplastic effect in such new modern materials as shape-memory alloys and ultrafine-grained and nanostructured alloys is shown. Various methods of modeling the mechanisms of electroplasticity, especially at the microlevel, are becoming the most realistic approach for the prediction of the deformation behavior and physical and mechanical properties of various materials. Original examples of the practical application of electropulse methods in the processes of drawing, microstamping, and others are given. The physical phenomena that occur when a pulsed current is applied to solid metal materials are well known. These include the thermal effect, the appearance of a magnetic field , vibration, etc. . Often, The EPD is based on all the above mechanisms in interaction with plastic deformation , which can be enhanced, leading to increased deformability and carried out at lower stresses due to the occurrence of the electroplastic effect (EPE) . EPE is Unlike many other reviews ,7 relateExternal and internal factors affecting the deformation behavior and mechanical properties of various materials, the effect of EPD on the microstructure and properties and vice versa, and the effect of microstructure features on EPE are considered in this discussion. In particular, attention is paid to the effect of grain size in the nanoscale, dynamic recrystallization , crystalInformation on modeling and the most accepted EPE mechanisms is given ,13. The Examples are given on the application of pulsed electric current to traditional metal forming processes such as rolling, drawing, stamping, and dimensional processing , as wellThe benefit from the use of EPE in this area is primarily associated with an increase in deformability , a decreOverall, the review considers external and internal factors affecting the deformation behavior, microstructure, and properties of different materials. Information on modeling, the most accepted EPE mechanisms, and application examples are also provided. For the study or application of EPE, it is important to understand and regulate the influence of many external factors. Such factors should include the source (generator) of current pulses, the current mode and current regimes, and methods of current supply.Today, three main modes of electric current are known—direct, alternating, and pulsed. Below, we will consider the factors related to pulse current, since the first two modes are less effective for the practical application of EPE. A feature of the pulse current is the variety of adjustable parameters. These include direction, density, frequency, duration, duty cycle, and pulse shape. The passage of pulsed current is accompanied by various phenomena: thermal effect, electro- and magnetoplastic effects, electric and magnetic fields, and vibration. These phenomena are well studied and find practical applications. One of them, namely EPE, is considered an alternative to heat treatment, as well as deformation methods of structure materials .o, the period T (or the frequency ν), and the minimum pulse duration tp  . A multiple increase in the duty cycle Q >> 1 actually means the transition of the pulse-current mode to the single pulse mode with minimal thermal effect of the current or even the absence of it. Accordingly, a decrease in the duty cycle up to Q = 1 means the action of a multi-pulse current, which should contribute to an increase in the deformation temperature from room to high. Another parameter of the pulse current is the duty cycle Q = T/tStudies on the effect of pulse-current duty cycle on EPE are extremely rare and are devoted to applied issues of application to high-speed rolling  or to th2 using tension at a temperature of 78 K and a current density of 400 A/mm2 , or even be oriented at an angle (bending and stamping) . It is a00 A/mm2 . It is acr when EPE occurs . A. A20]. A2 was observed in the Ti-7Al alloy, which is uniquely suited for uncoupling Joule heating and EPE . A . A 5]. A and EPE . The aut49.3Ni50.7 alloy, which is austenitic at room temperature, the stress jumps are directed upward at the pseudo-yielding stage and are caused by the direct A → M transformation. On the contrary, the work-hardening stage showed the downward stress jumps that are directly related to EPE. In the Ti50Ni50 alloy, which is martensitic at room temperature, the stress jumps are directed downward at the stage of martensite reorientation and are caused by typical EPE. An increase in deformation leads to the reverse M → A transformation and, accordingly, to the shape-memory effect (SME). The difference in the stress jumps amplitude at the same current regime is associated with the morphology of the A and M phases, which are equiaxial and lamellar, respectively.The original and traditionally investigated EPE in structurally stable metals and alloys manifests itself in the form of downward stress jumps in tensile curves. However, in metastable shape-memory alloys (SMAs) that undergo strain-induced transformations, single-current pulses at different tension stages can lead to the opposite direction of stress jumps. For example, in coarse-grained (CG) TiNi alloys, the sequence of upward and downward jumps changes depending on the phase composition  5]. In . In 5]. 2. In this case, the authors explained the effect by structure relaxation 28] the sThe structure refinement effect, when using a combination of rolling and current, was also shown in pure zirconium  and brasA number of papers are related to the study of the electrical current effect in the deformation process on the dissolution of secondary phases or particle formation and aging. Thus, paper  shows th2).The fracture nature of TC11 titanium alloy under tension was investigated by the authors in . It is nIn addition, the use of pulsed current improves the distribution of macro- and microdefects and, in some cases, can reduce or eliminate these defects in many metals and alloys .Note, that the concepts of “deformation with current—microstructure” are interdependent and affect each other. In particular, the variation of the initial phase composition, grain size, etc. will influence the deformation behavior and related effects. Thus, in , the infIt should also be noted that the authors, who do not set a task of heating with electrical current, do not observe significant structural changes, in particular grain growth under the simultaneous influence of current and deformation. That confirms the presence of an athermal component of the electroplastic effect .Thus, as a rule, the observed structural features during deformation with current are local in nature and relate, for example, to small differences in grain misorientation or to the morphology of the formation of twins . This faOne of the most important technological properties, which is influenced by deformation with current, is deformability. This parameter is primarily of interest to researchers studying EPE. Due to the increase in deformability, pressure treatment becomes possible without increasing the temperatures of brittle materials that are difficult to deform under normal conditions. A number of studies show that deformation with current can achieve better deformability of titanium alloys ,62, magn2) compared to tension without current in AA1050 aluminum alloy. In addition, a number of researchers are studying the increase in deformability with current using nonstandard test schemes, for example, shear deformation of the Ti64 alloy [A decrease in the flow stress under the pulsed current can lead to an increase in the relative elongation of the material . The pap64 alloy . In 45]2) comparAn increase in strength with a simultaneous increase in plasticity is achieved by a combination of deformation and current, usually due to more intensive refinement of the microstructure. Thus, in , an incrHowever, some researchers note a decrease in plasticity under the action of current, but these effects are found in alloys disposed to aging . The micThe effect of various combinations of current and drawing deformation on the mechanical properties of 308 L stainless steel was investigated in . It is sThe above work  also demIn , the autThe effect of current on fatigue properties is demonstrated in . It is s50.0 Ni50.0 alloy.In a number of papers, the functional properties of shape-memory alloys and their dependence on the introduction of current during deformation were studied ,77. TherThus, the simultaneous effect of deformation and current can have a significant impact not only on the technological properties (increase in deformability) but also be one of the methods to control the mechanical and functional properties  in alloys with different natures.Despite the growing interest in EPE-related problems, the nature of the effect remains completely unexplored, and the proposed mechanisms are theoretical. The formulation of a physical experiment explaining the essence of EPE is difficult . Therefore, a number of papers are related to modeling the accompanying EPE effects. A recent review of the current mechanisms of electroplasticity  confirmsThe authors of another review on the physical nature of electroplasticity , based oMathematical models often describe the behavior of a material during deformation with an electric current based only on the heating effect. The range of works considering other factors is quite limited.In , the tem2. A model based on localized Joule heating and the Hall–Petch ratio is presented in [In , a commoented in . They noented in , the effented in .In , the aut® program is described. It is shown that it is pertinent to model the electroplastic phenomenon as the superposition of rate-dependent and rate-independent components of flow-stress evolution. The important characteristics of electric-assisted deformation reported in the literature, such as an instantaneous stress drop, recovery during the removal of electric current, and long-range softening, are predicted successfully using the dislocation density-based constitutive model. The implemented model is shown to simulate the experimental data very well in both continuous and pulsed current conditions . At the same time, a distinctive feature of the studies in recent years is the emphasis on modeling the athermal mechanisms of EPE. However, a significant disadvantage of all modeling work is the practical lack of approaches that determine these effects in fine-grained and nanostructures, which today is one of the most important areas of research in physical materials science. The creation of EPE models at the nanoscale or atomic levels is relevant since the main proposed mechanisms of nonthermal nature take place at this scale. It should also be noted that, despite the variety of works on EPE modeling, there is a lack of views on the nature of the accompanying phenomena.Let us consider several areas of potential application of EPE. The main direction for use can be considered as a combination of electric pulsed current and metal forming for manufacturing of semifinished products of different shapes  from structural metals and alloys . The method of electroplastic cold rolling has become the most widespread. It was applied to materials based on titanium ,45, alumDrawing with current has demonstrated effects similar to rolling with current ,101. EleA potentially promising direction for using EPE could be the technology of obtaining microwires with a giant magnetic impedance ,103, wheIt is known that pulsed current is used in higher speed processes, such as stamping and bending , when noIn addition to metal forming processing or turning, other original technological solutions using pulsed current are known. These include cutting ,106,107,2 to a thickness of 0.3 mm at a speed of 0.5 m/s without intermediate annealing [Currently, several dozen machine tools and metalworking machines have been created on the basis of EPE in different countries. One example is a mill designed and created in Russia by the order of South Korea for rolling a strip of stainless steel with a cross section of 2 × 100 mmnnealing . Summarizing the above-mentioned applications in industry, we can note a wide range of studies and potential methods aimed at intensifying technologies and improving the quality of structural materials. However, there are relatively few really working technologies that are commercially in demand. This is due to the limitation of devices (generators) in terms of the maximum pulse-current density, and, consequently, the cross section of the processed products, as well as the forced low deformation rate in production processes.This review has been prepared for a better understanding of the progress that has been made in the field for the knowledge, understanding, and application of the phenomenon of electroplasticity in different materials. Over the past few years, it has become clear that the interest of scientists in the manifestation of electroplasticity has significantly increased. The number of published articles in the world on this topic is growing and contains more than a hundred per year.Among the studied external and internal factors that strongly influence the effects of current in conductive materials, new ones have appeared; for example, the pulse-current duty cycle and grain size in a wide range, which expand our understanding of the electroplastic effect’s nature. Studies of the EPE in materials with shape memory and the use of current modes that stimulate the relaxation phenomena without significant heating are of particular interest.The observed structural features during deformation with current are local in nature. There are small differences between microstructure after deformation with and without current. This fact leads to additional interest from researchers but also complicates the task of understanding and explaining the electroplastic effect’s nature, which remains open to this day.The simultaneous effect of deformation and current can have a significant impact on the technological properties (increase in deformability) and also can be one of the ways to control the physical, mechanical, and functional properties  of alloys of different natures.Due to the variety of emerging and interacting phenomena accompanying the combination of deformation with current, the necessity of creating a unified theory of electroplasticity becomes clearer. In this regard, modeling, especially at the micro-, nano-, and atomic levels, is an extremely important trend for fundamental and applied EPE research.As for the use of electroplasticity, we can note a relatively small number of such examples in real industry, compared with research. In most cases, this is due to the insufficiently high technical characteristics of pulse generators and low deformation rate.