ADORA2A, BDNF, and NTRK2 gene polymorphisms on the risk of childhood epilepsy and their associations with predisposition to epileptic comorbidities. A total of 444 children were enrolled in this study, and three single nucleotide polymorphisms, including ADORA2A rs2298383, BDNF rs6265, and NTRK2 rs1778929, were genotyped. The frequency distribution of genotypes was compared not only between CE patients and healthy children but also between CE patients with and without comorbidities. The results indicated that the carriers of ADORA2A rs2298383 TT genotype tended to have a lower risk of epilepsy , while the CT genotype was related to a higher risk . The ADORA2A rs2298383 CC genotype predisposed CE patients to comorbid neurologic disorders . Genetic variations in BDNF rs6265 and NTRK2 rs1778929 had no significant association with CE and its comorbidities. Fourteen ADORA2A target genes related to epilepsy were identified by the protein–protein interaction analysis, which were mainly involved in the biological processes of “negative regulation of neuron death” and “purine nucleoside biosynthetic process” through the gene functional enrichment analysis. Our study revealed that the genetic polymorphism of ADORA2A rs2298383 was associated with CE risk and predisposition to neurologic comorbidity in children with epilepsy, and the involved mechanism might be related to the regulation of neuron death and purine nucleoside biosynthesis.Epilepsy, a common disorder of the brain, exhibits a high morbidity rate in children. Childhood epilepsy (CE) is frequently comorbid with neurologic and developmental disorders, sharing underlying genetic factors. This study aimed to investigate the impact of  As a brain disorder, epilepsy is characterized by an enduring predisposition to generate epileptic seizures with neurobiological, cognitive, psychological, and social consequences , 2014. WADORA2A gene encodes a member of the guanine nucleotide-binding protein (G protein)-coupled receptor (GPCR) superfamily and uses adenosine as the preferred endogenous agonist to increase intracellular cAMP levels. ADORA2A competes with adenosine A1 receptor (ADORA1) in various neural functions and enhances excitatory neurotransmitter release for synaptic transmission  exists. BDNF, essential in the regulation of neuronal survival and differentiation, synaptic transmission, and plasticity, plays a pivotal role in the pathogenesis of epilepsy and central comorbid conditions associated with epilepsy . BDNF acADORA2A, BDNF, and NTRK2 genes of RK2 genes to invesn = 200) were further grouped into four categories based on clinical diagnosis: CE only (n = 105), CE with neurologic comorbidity (NC) (n = 49), CE with developmental comorbidity (DC) (n = 82), and CE with both neurologic and developmental comorbidities (NC/DC) (n = 36).A total of 200 patients with CE and 244 healthy children were recruited at Baoan Women’s and Children’s Health Hospital of Jinan University in China between October 2016 and December 2019. All the CE patients were diagnosed and interviewed by two independent neurologic clinicians according to the latest ILAE Commission’s classification criterion . ChildreThe neurologic comorbidities included cerebral palsy, hydrocephalus, cerebral injury, encephalopathy, encephalitis, meningitis, hydrocephalus, microcephaly, schizencephaly, pachygyria, and intracerebral hemorrhage. Developmental disorders were characterized by developmental impairment in reciprocal social interaction and communication skills and the presence of stereotyped behavior, interests, and activities. The qualitative impairments that defined the above-mentioned conditions were distinctly deviant relative to the individual’s developmental level or mental age. The developmental comorbidities in this study included intellectual and developmental disabilities, ADHD, early language disorder, autism, delayed psychomotor development, and mental and motor retardation. For CE with NC/DC group, the patients who presented CE and neurologic or developmental disorders simultaneously were included.ADORA2A rs2298383, BDNF rs6265, and NTRK2 rs1778929 were specifically designed in this study . The MassArray Typer 4.0 software was used for data acquisition and analyses.Genomic DNA was extracted from 1.5 ml of peripheral blood samples collected in ethylenediaminetetraacetic acid anticoagulation tubes according to previously reported methods . Polymer1, a public database containing interactions between known and predicted proteins  as cutoff criteria. The maximum number of interactors selected was not more than 20 (1st shell not more than 20). The PPI network was further visualized by Cytoscape v3.6.0.Protein–protein interaction (PPI) elucidates the function of regulation among proteins and thus is helpful to studying protein function . To furtproteins , was use2 and GeneCards database3. DisGeNET, a discovery platform for the dynamical exploration of human diseases and their associated genes, is considered as a good tool for investigating the molecular mechanisms underlying diseases of genetic origin  were calculated and used for filtering. Terms with a p-value < 0.01, a minimum count of 3, and an enrichment factor > 1.5 were collected and grouped into clusters based on their membership similarities. Kappa scores were used as the similarity metric when performing hierarchical clustering on the enriched terms. A subset of representative terms was then selected from the cluster and converted into a network layout. Each term was presented with a circle node, and terms with a similarity score > 0.3 were linked by an edge. The most statistically significant term within a cluster was chosen to represent the cluster. The network was visualized with Cytoscape (v3.6.0).To further analyze the signaling pathways and the functions of potential target genes, pathway and process enrichment analysis for common genes of resource . Metascat-test for continuous variables and chi-square test for categorical data. The chi-square test was used to assess the deviation from Hardy–Weinberg equilibrium. The differences in frequency distributions of genotypes between controls and cases and between CE patients with and without neurologic and developmental comorbidities were assessed by chi-square test or Fisher exact test with Yate corrections. The odds ratio (OR), together with the 95% confidence interval (CI), was calculated for multiple tests in this study. P-values less than 0.05 were considered to be statistically significant.The data were expressed as mean ± standard deviation (SD). Differences in the demographic characteristics between the two groups were analyzed by independent-samples ADORA2A rs2298383 from controls (n = 244) and cases (n = 200).The minimum sample size was calculated with a default alpha value (0.05) and power (0.8) by PASS sample size software (version 2020). The power of our study was computed for verification based on alpha (0.05), calculated effect size, and actual sample size in the present study using R Programming Language (version 3.6.3). Power equal to 0.8 is considered a high probability to obtain a statistically significant result. The results indicated the power > 0.9 based on the frequency distribution of three genotypes p = 0.47). Among 200 CE patients, the average ages of seizure onset and diagnosis of CE were 2.99 ± 3.09 and 3.43 ± 3.14 years, respectively. Compared with healthy individuals, most of the CE patients experienced febrile seizure (22%), status epilepticus (10%), abnormal pregnancy at perinatal stage (11.5%), abnormal delivery or delayed discharge from the newborn intensive care unit (NICU) (11.5%), and had a family history of epilepsy (15.5%). The main neurologic comorbidities in CE patients were cerebral injury (23.3%), structural brain lesions (21.9%), cerebral palsy (19.2%), and encephalopathy (13.7%). Intellectual disability (30.5%) and dysgenopathy (23.8%) were the two main developmental comorbidities in CE patients. CE patients with the comorbidities tended to be younger at first diagnosis of epilepsy than those without comorbidities, most of whom were diagnosed as epileptic at ages from 2 months to 1 year old. Additionally, the proportion of cases with a history of status epilepticus, abnormal pregnancy, abnormal delivery, or delayed discharge from NICU in the three CE comorbidity groups was significantly higher than that in the CE group, suggesting that these risk factors might be related to the epileptic comorbidities.The clinical characteristics of the participants shown in ADORA2A rs2298383, BDNF rs6265, and NTRK2 rs1778929 genotypes in controls and CE patients is shown in ADORA2A rs2298383, the distribution of CC, CT, and TT genotypes was 26.5%, 57%, and 16.5%, respectively, in the case group compared to 25%, 45.9%, and 29.1% in the control group (p = 0.0055). T allele was less frequent in CE patients than in healthy children  as evidenced by the allele analysis. Carriers of at least one polymorphic ADORA2A rs2298383 C allele tended to have a higher epilepsy risk for children , while TT genotype was associated with a lower risk . Homozygous carriers including mutant and wild genotypes (CC and TT) exhibited a lower susceptibility than heterozygous CT . For the polymorphism of BDNF rs6265 and NTRK2 rs1778929 gene, there was no significant difference in the frequencies of the genotype distribution between control and CE patients. Carriers of at least one polymorphic BDNF rs6265 T allele tended to have a relatively higher epilepsy risk for children .The frequency distribution of ADORA2A rs2298383, BDNF rs6265, and NTRK2 rs1778929 genotypes in CE patients with and without neurologic and developmental disorders is shown in ADORA2A rs2298383 genotype distribution was observed between CE patients with and without neurologic disorders. The allele analysis indicated a significant increase in the frequency of C allele for CE patients with NC compared to those without NC . The mutant homozygous TT and heterozygous CT genotypes were associated with a lower risk of neurologic comorbidity for CE patients, while homozygous CC genotype was related to a higher risk . However, the polymorphism of ADORA2A rs2298383 was not associated with CE comorbid developmental disorders as evidenced by the lack of significant difference between CE patients with and without DC. In the present study, BDNF rs6265 and NTRK2 rs1778929 gene polymorphisms were not found to be significantly related to the NC and DC for CE patients.The frequency distribution of ADORA2A rs2298383 was more frequent in CE comorbid NC/DC patients than that in CE patients without comorbidities compared with T allele as evidenced by the allele analysis . Homozygous CC genotype was associated with a higher risk of combined neurologic and developmental comorbidities for CE patients than mutant homozygous TT or heterozygous CT genotypes .Furthermore, the association between three investigated SNPs and the risk of epilepsy with two comorbidities was explored in CE patients with NC/DC. The results showed that the C allele of ADORA2A rs2298383 was further conducted by selected variables in CE patients. As shown in ADORA2A rs2298383 genotypes was observed in CE patients with and without NC or NC/DC in the subgroup of no febrile seizure . The CE patients without abnormal delivery or delayed discharge from NICU also exhibited a significant difference in genotype distribution of ADORA2A rs2298383 between CE patients with and without NC (p = 0.033).Additionally, a stratification analysis of ADORA2A were determined in the STRING database. The PPI network comprising 21 nodes and 103 edges was generated  signaling events,” “aging,” and “neurotransmitter transport” which were the top eight clusters by p-values(n = 9), followed by “G alpha (q) signaling events” and “aging”  to exert its effects . A high epilepsy . BDNF anterature .ADORA2A in epilepsy were further explored by bioinformatics analysis. Fourteen epilepsy-associated genes were identified as potential targets of ADORA2A. It suggested that ADORA2A might directly regulate or be regulated by these genes to exert its effect on epilepsy. Further functional enrichment analysis indicated that these genes were principally involved in “purine nucleoside biosynthetic process” and “negative regulation of neuron death” biological processes. Neuronal death is found to be associated with the development of epilepsy. Preventing seizure-induced neuronal death in chronic epilepsy and status epilepticus will be a valuable strategy for future seizure management. The purine nucleoside adenosine, as an intercellular signaling molecule, is neuroprotective and plays a neuromodulatory role in the brain. Purinergic signaling involved in cell-to-cell communication through activation of adenosine receptor A2A-R (encoded by ADORA2A), as well as NTRK2 receptors and BDNF, is associated with neuroprotective signaling (Based on the association study, the underlying mechanisms of ignaling . Furtherignaling . ADA andignaling . Additioignaling . NT5E reignaling . The chaignaling . These sADORA2A rs2298383 genotypes in CE patients with and without comorbidities is hard for a meaningful difference to be obtained. Our ongoing study will increase the sample size and validate the role of ADORA2A, BDNF, and NTRK2 in epilepsy. The data from bioinformatics analysis lack experimental validation including in vitro and in vivo studies, which will further be performed to elucidate the underlying mechanisms of ADORA2A on epilepsy. Additionally, considering that gene polymorphism may possibly alter gene transcription, ADORA2A mRNA expression, ADORA2A protein expression, and intracellular cAMP level in different genotypes of ADORA2A rs2298383 will be investigated to provide insights into the molecular mechanisms of ADORA2A on CE and the comorbidities.Due to the relatively small sample size in the present study, further analysis between subgroups with different types of epilepsy is not feasible, and the stratification analysis of ADORA2A rs2298383 was associated with CE and its comorbidities. Carriers of ADORA2A rs2298383 TT genotype tended to have a lower epilepsy risk in children, while the CT genotype was related to a higher risk. ADORA2A rs2298383 CC genotype was predisposed to comorbid neurologic disorders. Fourteen epilepsy-associated genes were identified as potential targets of ADORA2A in epilepsy, and four key genes including NT5E, ENTPD1, ADA, and ADK were mainly involved in the “negative regulation of neuron death” and “purine nucleoside biosynthetic process” biological processes. The results suggest that rs2298383 mutation of ADORA2A gene deserves attention in CE. Our ongoing study will further validate the role of ADORA2A in epilepsy and provide insights into the molecular mechanisms of neurologic and developmental comorbidities.The present study showed that epileptic children with a younger age, history of status epilepticus, abnormal pregnancy, abnormal delivery, or delayed discharge from the NICU tended to suffer neurologic or developmental disorders. Gene polymorphism of The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Baoan Women’s and Children’s Hospital Ethics Committee. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.XF, BL, and XX designed the study and wrote the protocol. YC and WL managed the literature searches and collected the clinical data. HX and YY as neurologic clinicians, diagnosed diseases and enrolled the participants of the case–control study. YC, HG, and SX collected the blood samples. WL conducted the bioinformatics analysis. CX undertook the statistical analysis. XF and BL wrote the first draft of the manuscript. All the authors have contributed to and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bacillus thuringiensis (Bt) is a natural pathogen of insects and some other groups of invertebrates that produces three-domain Cry (3d-Cry) toxins, which are highly host-specific pesticidal proteins. These proteins represent the most commonly used bioinsecticides in the world and are used for commercial purposes on the market of insecticides, being convergent with the paradigm of sustainable growth and ecological development. Emerging resistance to known toxins in pests stresses the need to expand the list of known toxins to broaden the horizons of insecticidal approaches. For this purpose, we have elaborated a fast and user-friendly tool called CryProcessor, which allows productive and precise mining of 3d-Cry toxins. The only existing tool for mining Cry toxins, called a BtToxin_scanner, has significant limitations such as limited query size, lack of accuracy and an outdated database. In order to find a proper solution to these problems, we have developed a robust pipeline, capable of precise 3d-Cry toxin mining. The unique feature of the pipeline is the ability to search for Cry toxins sequences directly on assembly graphs, providing an opportunity to analyze raw sequencing data and overcoming the problem of fragmented assemblies. Moreover, CryProcessor is able to predict precisely the domain layout in arbitrary sequences, allowing the retrieval of sequences of definite domains beyond the bounds of a limited number of toxins presented in CryGetter. Our algorithm has shown efficiency in all its work modes and outperformed its analogues on large amounts of data. Here, we describe its main features and provide information on its benchmarking against existing analogues. CryProcessor is a novel, fast, convenient, open source (https://github.com/lab7arriam/cry_processor), platform-independent, and precise instrument with a console version and elaborated web interface (https://lab7.arriam.ru/tools/cry_processor). Its major merits could make it possible to carry out massive screening for novel 3d-Cry toxins and obtain sequences of specific domains for further comprehensive in silico experiments in constructing artificial toxins. Bacillus thuringiensis (Bt) produces a broad range of proteinaceous toxins, which are highly specific and could act as an enormously powerful instrument in the arsenal of insecticidal approaches. Exclusive toxicity against target species makes Bt toxins applicable in agriculture and may prevent contagious diseases spread by insects, such as dengue and malaria [Bt toxin group is Cry , and the toxins are produced on the transition to the sporulation stage and show specificity predominantly against species of Lepidoptera, Diptera and Coleoptera orders [Crop losses caused by insects continue to be one of the major challenges in agriculture. Exploiting chemical insecticides appears to be losing ground due to the potential toxicity for humans, the detrimental impact on the environment, and the increasing pest resistance to existing chemicals ,2. Thank malaria . The mosa orders .Among all Cry toxins, three-domain Cry (3d-Cry) toxins represent the largest group. Despite considerable differences in amino acid sequences, these toxins share a conservative structure . Three dBt toxins as an insecticide dates back to 1938 [Bt or isolated toxin solutions have been put into practice but also transgenic plants bearing cry genes. By 2018, approximately 191 million hectares of biotech crops have been cultivated all over the world [Bt-cotton, resulting in savings up to US$30 per hectare and an increase in crop yields of more than 80–87% [Bt-derived insecticides have occupied more than 50% of the global biopesticide market [The first commercial usage of  to 1938 . Since the world . For insBt crops [Bt toxins from sets of biological sequences but, unfortunately, it has several limitations that prevent it from being used for large-scale analysis. A web version of BtToxin_scanner allows the processing of limited amounts of data at a time (16 Mb). The tool allows the extraction of Cry toxin sequences directly from Illumina reads only in the standalone version, whose algorithm is poorly described. Furthermore, BtToxin_scanner is not capable of predicting domain structures of identified Cry toxins. Another strategy of expanding the diversity of Cry proteins is to construct artificial toxins by a combination of existing domains and parts of toxins. It requires efficient and precise prediction of domain structures of proteins. For now, there is no specific instrument capable of generating domain structures among the sequences of 3d-Cry toxins. The only tool, which provides data for domain boundaries of Cry toxins, is CryGetter [Nonetheless, emerging resistance of insects to Cry toxins threatens sustainable implementation of biopesticides, including Bt crops . One strBt crops . It alloryGetter . Nonethecry genes are located at different contigs, they are combined by PathRacer, thus enabling the extraction of full sequences.We tested our tool, CryProcessor, in all possible modes and compared it with the existing tool BtToxin_scanner . CryProcBt genome assemblies from National Center Biotechnology Information (NCBI) Assembly database  in multi-thread mode utilizing 72 CPU threads in parallel. The modes “find domains” and “domains only” were compared to the BtToxin_scanner standalone version using a dataset comprising protein sets from 511  service , in the Both “find domains” and “domains only” modes of CryProcessor appear to outperform BtToxin_scanner  in runtiBt genome assembly   using eiDepending on the mode chosen in the previous step, either performing a domain mapping over found sequences and truncating accessory sequences (“find domains” mode) or filtering the sequences comprising all the three domains in the right sequential order (“domains only” mode);Bt nomenclature database [Comparing found proteins to database  using Didatabase  with 100Annotating the observed toxins against the Bt-related identical protein group (IPG) database entries.We developed a hidden Markov model (HMM)-based algorithm called CryProcessor for robust and effective Cry toxin mining from FASTA files or directly from Illumina FASTQ reads using PathRacer  . CryProcFor HMM model compilation, sequences from CryGetter   technicaCryProcessor supports two search modes: the “domains only” mode and the “find domains” mode. The “find domains” mode implies the aforementioned screening strategy on four HMM-based models . The “domains only” mode implicates running hmmsearch with the domain models directly on the query without preceding filtration. Thus, in the case of the “domains only” mode, hmmsearch is performed on the full query within each searching iteration, while in the “find domains” mode, domain mining is executed on the sequences that passed the previous HMM-based filtration with full toxin models. When extracting 3d toxins, the sequence passes the filter only if the domain indices increase monotonously, indicating the right domain order, and the domain intervals do not overlap.The presence of Genbank accession numbers in the entry IDs allows CryProcessor to perform the annotation using efetch. The output represents a tab-separated (TSV) table with the accession numbers of the respective Strain, Assembly, and Nucleotide entries.CryProcessor can be launched on Illumina reads in FASTQ format. SPAdes   or metaSFor the sake of convenience, we also elaborated a web interface, comprising several key features of CryProcessor with some limitations. The web server includes the “domains only” mode. The IPG-annotation step in the online version is depreciated. Users can upload protein FASTA-files or Illumina reads in FASTQ-format. The uploading files are limited to 400 Mb. After the successful performance of tasks, the results of processing are sent to the user’s e-mail. https://github.com/lab7arriam/cry_processor. An automatically updating version of CryProcessor is also presented in Docker Hub, called lab7arriam/cry_processor. The web server is free for academic use and available upon registration at https://lab7.arriam.ru/tools/cry_processor.CryProcessor 1.0 is a Linux command-line Python3.7 application. The software is licensed under the GNU General Public License v3.0, and its source code is free and available at


Structural information on the interactions of proteins with other molecules is plentiful, and for some proteins and protein families, there may be 100s of available structures. It can be very difficult for a scientist who is not trained in structural bioinformatics to access this information comprehensively. Previously, we developed the Protein Common Interface Database (ProtCID), which provided clusters of the interfaces of full-length protein chains as a means of identifying biological assemblies. Because proteins consist of domains that act as modular functional units, we have extended the analysis in ProtCID to the individual domain level. This has greatly increased the number of large protein-protein clusters in ProtCID, enabling the generation of hypotheses on the structures of biological assemblies of many systems. The analysis of domain families allows us to extend ProtCID to the interactions of domains with peptides, nucleic acids, and ligands. ProtCID provides complete annotations and coordinate sets for every cluster. The authors previously developed the Protein Common Interface Database (ProtCID), which compares and clusters the interfaces of pairs of full-length protein chains with defined Pfam domain architectures in different PDB entries to identify biological assemblies. Here the authors extend ProtCID to the clustering of domain-domain interactions that also allows analyzing domain interactions with peptides, nucleic acids, and ligands. This process is very challenging and time consuming even for scientists trained in bioinformatics.All proteins function via interactions with other molecules, including nucleic acids, small molecular ligands, ions, and other proteins—in the form of both homo-oligomers and hetero-oligomers. How such interactions occur and defining their role in protein function are the central goals of structural biology. For any protein system of interest, it is valuable to understand the structure in all known functional forms, including different conformations and oligomeric states and interactions with nucleic acids, ligands, and other proteins. To accomplish this, it is often necessary to examine 100s or even 1000s of available structures of a protein and its homologs in the Protein Data Bank (PDB)2. Various authors have estimated the accuracy of the biological assemblies in the PDB in the range of 80–90%6.Of central importance to the utility of experimental structures is the accuracy of annotations. Authors of crystal structures are required to deposit a biological assembly into the PDB, which is what they believe to be the biologically relevant oligomeric form present in the crystal. This is in contrast to the asymmetric unit, which is the set of coordinates used to model the crystal lattice. The author-deposited biological assembly is different from the asymmetric unit for about 40% of crystal structures in the PDB4.A common approach for identifying biological assemblies of molecules within protein crystals is to compare multiple crystal forms of the same or related proteins. Each crystal form will have different non-biological interfaces between proteins and with crystallization ligands, while in most cases the biological interactions will be shared between them. We have shown that if a homodimeric or heterodimeric interface is present in multiple crystal forms, especially when the proteins in the different crystals are homologous but not identical, then such interfaces are very likely to be part of biologically relevant assemblies7, which assigns protein domain families (as defined by Pfam8) to every protein sequence in the PDB, and the Protein Common Interface Database (ProtCID), which compares and clusters the interfaces of pairs of full-length protein chains with defined Pfam domain architectures in different entries in the PDB9. ProtCID provides clusters of homodimeric and heterodimeric chain–chain interactions across PDB entries whether they are present within asymmetric units, between asymmetric units, or between unit cells. Although ProtCID was limited to protein–protein interfaces of full-length chains, it has been very useful in identifying biologically relevant interfaces and assemblies within crystals13, including those that were not annotated in the PDB’s biological assemblies. ProtCID provides coordinates and PyMol scripts for visualizing interfaces in each cluster.To enable this form of analysis, we previously developed PDBfam18, and some are intended to predict which interfaces may be biologically relevant from conservation scores and physical features and using machine learning predictors20. Interactions with peptides, nucleic acids, and ligands have also been presented in several webservers and databases27. Very few of these resources provide clustering of structural information across the PDB, and very few provide coordinates for download.Accessing the full amount of structural information in the PDB for a particular problem, a database needs to perform several tasks: (1) dividing proteins into conserved domain families, since many functions are performed by different types of domains and combined in different ways in different proteins; (2) creating coordinates of all protein–protein interactions for each PDB entry using crystal symmetry operators, since many biologically relevant interactions are not present in the asymmetric unit or in the PDB biological assemblies; (3) with these coordinates in hand, clustering of homo- and heterodimeric protein–protein and domain–domain interactions, and interactions of proteins domains with peptides, nucleic acids, and other ligands; (4) automated download access to the Cartesian coordinates so that visualization and analysis can be performed. Several servers analyze interfaces in either the asymmetric units and/or the biological assemblies of PDB entriesIn this paper, we extend the ProtCID approach from clustering full-length protein chains to clustering domain–domain interactions within protein crystals. The inclusion of interactions between individual domains greatly extends our ability to generate hypotheses about the functional interactions of proteins. We show examples of domain-level ProtCID clusters for some experimentally validated, biologically relevant protein–protein interactions that were in some way challenging to identify in the biological literature. This is especially true of weaker interactions within homooligomers, which are very difficult to distinguish from crystallization-induced interactions.Analysis at the domain level has enabled new features in ProtCID: clustering of interactions of protein domains with peptides, nucleic acids, and small-molecule ligands. We have added access to ProtCID data at the level of protein superfamilies, which Pfam refers to as clans. It is often the case that there is no structure of a protein for a particular Pfam that contains biologically relevant information such as peptide, nucleic acid, or ligand binding. Thus, the existence in ProtCID of interactions for one protein family can be used to develop hypotheses for the structures of other protein families within the same superfamily. We show some examples of this approach. We have enabled a new search feature in ProtCID with which a user can identify possible domain–domain and domain/peptide interactions that are possible amongst a set of proteins (or between one hub protein and other proteins). This utility enables the identification of structural information on direct protein–protein interactions that might occur in large multi-subunit protein complexes.ProtCID provides clustered structures of the interactions of protein domains with other protein domains, peptide, nucleic acids, and ligands. Each cluster is highly annotated, including PDB ids and chain IDs, protein names, UniProt ids, species, protein domain family identifiers (Pfams), crystal forms, surface areas, and biological assembly annotations. A crucial feature of ProtCID is the ability to download single archive files that contain coordinates for each interaction and scripts for visualizing the structures.The ProtCID database contains information on four types of interactions: protein–protein interactions at the chain level, protein–protein interactions at the domain level, domain–peptide interactions, and the interactions of domains with nucleic acids and ligands. Domain–domain interactions can be between domains of the same Pfam or different Pfams, and can be interchain or intrachain. Chain–chain interactions are between chains with the same domain architecture  or between those with different architectures.7 containing 8636 Pfams observed within the PDB. Each PDB chain is annotated by a Pfam architecture as the ordered sequence of Pfams along the chain, e.g., (SH3)_(SH2)_(Pkinase). ProtCID contains 12,914 Pfam chain architectures from 42,336 proteins. Statistics on the number of the interactions, clusters, PDB entries, and Pfam domains are provided in Table Generating hypotheses for protein interactions by observing them in multiple crystals of homologous proteins requires grouping proteins in the PDB into homologous families at the domain and chain levels. To accomplish this, we utilize our database called PDBfamThe statistical advantage of analyzing protein–protein interactions at the domain level is evident in Table 28. Except for PDB ID input, all inputs result in a list of PDB structures containing the Pfams, providing a comprehensive overview of homologous structures for any given query. From one structure, a user can check the clusters for chain–chain, domain–domain, and domain–peptide interactions. Coordinates and sequences are downloadable for each cluster including scripts for visualizing the interactions in PyMOL.On the ProtCID web site, there are four types of inputs: (i) a PDB ID; (ii) one or two Pfam IDs or accession codes; (iii) one or two protein sequences; (iv) one or more UniProt IDs. A user can browse Pfam IDs, Clan IDs, Pfam–Pfam pairs, peptide-interacting Pfams, ligands, and Pfam–Pfam networks Fig. . Pfams aGrouping proteins in the PDB into homologous families at the domain level not only provides more clusters, but also provides more significant signals for developing hypotheses for biological interfaces. Domain-level clusters often have more crystal forms and more sequences than chain-level clusters for proteins in the same family. We provide several examples of both same-Pfam and different-Pfam domain–domain interactions.Aspartate kinase, Chorismate mutase, TyrA domains) are present in the sequences of many enzymes, and typically bind single amino acids at their dimer interface. The largest cluster of (ACT)/(ACT) domain-level interfaces 29. Inherited mutations at the domain–domain interface of the PAH ACT domain are associated with phenylketonuria30. We can hypothesize that the same ACT dimer is associated with activation in human tryptophan 5-hydroxylases 1 and 2 and tyrosine 3-hydroxylase, which are homologous to PAH and contain similar domain architectures. Human D-3-phosphoglycerate dehydrogenase (PHGDH) also contains an ACT domain that we hypothesize forms the same dimer; the ACT domain in the E. coli and M. tuberculosis PHGDH proteins are members of the (ACT)/(ACT) domain cluster. This (ACT) domain dimer commonly occurs in other Pfam domains in the same Pfam clan  is the core catalytic domain of phenylalanyl-tRNA synthetases (PheRS). While PheRSs vary significantly in both sequences and structures, the tetrameric quaternary structures are conserved due to the conservation of the catalytic domainers Fig. . The thrDomain clusters between different Pfams can be used to gather structural data on how some domains perform specific binding functions as modules within larger proteins. Ras domain proteins bind proteins from a number of different families, including proteins that contain domains in the (RA) Pfam (Ras-association) family. The human proteome contains 43 different genes that possess (RA) domains. In ProtCID, there is a domain–domain cluster of (Ras) and (RA) that contains structures in 8 crystal forms from 10 PDB entries, 8 Uniprot sequences, and 4 different Pfam chain architectures Fig. .32. ProtCID has two intra-chain interface clusters of (AMP-binding) and (AMP-binding_C) domains  has structures in both conformations (PDB: 5IFI [10.2210/pdb5IFI/pdb] and 5K85 [10.2210/pdb5K85/pdb]). The two clusters can be used to model these proteins in the two forms.In ProtCID, we cluster Pfam domain–domain interactions both between different protein chains and within chains. In some cases, the latter can be used to analyze changes of orientation of two domains within protein chains. For example, adenylation enzymes contain an N-terminal domain (Pfam: AMP-binding) and a smaller C-terminal domain (Pfam: AMP-binding_C) that undergo a relative rotation of 140° after the initial adenylate-forming stepins Fig. . These tA primary goal of ProtCID is to generate hypotheses of the structures of oligomeric protein assemblies that may not be readily obvious to authors of crystal structures. Many such structures are due to weakly interacting dimers that are facilitated by attachment to the membrane or by scaffolding by other proteins or nucleic acids. To demonstrate the utility of ProtCID, we present several examples of this phenomenon, both confirmed experimentally in the literature and new but provocative hypotheses Fig. .Fig. 3Hy33 that was present in two PDB structures at that time. Extensive experimentation indicated that this interface between the C-terminal domain of one monomer and the N-terminal domain of another monomer served to activate the latter. Later structures of ErbB234, ErbB435 and a heterodimer of EGFR and ErbB3 were also noted to contain the same dimer36. This asymmetric dimer was unexpected because most protein homodimers are symmetric or isologous2.In 2006, Kuriyan and colleagues discovered the biological relevance of an asymmetric dimer of the EGFR kinase domain37.The biological effect of this dimer serves as an example of how ProtCID might lead to such a hypothesis. In ProtCID, there is a domain-level cluster of the (Pkinase_Tyr) Pfam that contains the asymmetric dimer from 10 crystal forms and 98 PDB entries of EGFR, one CF and entry of ErbB2, one CF and 3 entries of an ErbB3/EGFR heterodimer, and 2 CFs and 2 entries of ErbB4 38 and were initially not recognized40; they were first identified by their occurrence in five different crystal forms of BRAF38.The observation of similar interfaces in crystals of homologous proteins can be used to utilize experimental data available on one protein to generate hypotheses for other members of the same family. ProtCID enables this kind of inference in an easily accessible way. One intriguing example is observed for the Pfam domain (Pkinase_Tyr). This Pfam includes tyrosine kinases and most of the tyrosine-kinase-like (TKL) family of kinases. In ProtCID, the TKL kinases BRAF and RAF1 dimers are well represented in the largest cluster for Pfam (Pkinase_Tyr) Fig. . The catArabidopsis CTR1, and Tyrosine kinases CSK and ITK  have been described as similar to the BRAF kinase41. Recent analytical ultracentrifugation, mass spectrometry, and mutational data on RIPK2 confirm the significance of RIPK2 in four crystal forms in this ProtCID cluster42.The cluster also includes five other kinases: TKL-family kinases RIPK2, MLKL, and 43. The cluster shows that MLKL can be dimerized in the same way as BRAF, CRAF, and RIPK2. In fact, all five structures of human MLKL kinase domains in the PDB  contain the BRAF-like dimer. The asymmetric unit of PDB entry 4M69 [10.2210/pdb4M69/pdb] is a mouse RIPK3-MLKL heterodimer44. In this crystal, RIPK3 also forms a BRAF-like dimer. It does not appear in the cluster because RIPK3 is slightly closer to the Pkinase Pfam than the Pkinase_Tyr tetramer. It does appear in a (Pkinase) cluster of 10 CFs and 65 entries. It is therefore possible to build a hypothetical heterotetramer of one RIPK3 dimer and one MLKL dimer which are integrated by the RIPK3-MLKL heterodimer interface  is a TKL-family pseudokinase that functions as a substrate of Receptor-interacting serine/threonine kinase 3 (RIPK3) in necroptosis45. In ProtCID, there is a large domain-based cluster of interfaces for the (Ras) Pfam comprising 16 crystal forms and 108 PDB entries. The cluster includes structures of HRAS , KRAS , NRAS , RAB-11B , and mouse Rnd3   of a beta dimer, which has been studied by Muratcioglu et al.46. By contrast, we do not find the α3–α4 dimer implicated in the same study in any PDB entry.HRAS, KRAS, and NRAS form oligomeric structures at the plasma membrane, where they are anchored by palmitolyation and farnesylation5E95 [10.2210/pdb5E95/pdb]), disrupted HRAS nanoclustering and signaling through RAF47. Most structures with GTP are considered active, while most structures with GDP are inactive. Of the 92 structures of HRAS in the cluster, 81 of them (88%) are bound with GTP or guanine triphosphate analogs, 9 of them with GDP, and two have no bound ligand , that was unrecognized by the authors of this structure50.Similarly, Ambrogio et al. very recently identified the α4–α5 dimer in their crystal of KRAS51. Indeed, several of the crystals that contain the α4–α5 dimer also contain Ras binding partners, and therefore consist of (Ras)/Ras-effector heterotetramers, including the (RBD) domain of RAF1 kinase, the (RasGEF) domain of RAS guanyl releasing protein, and the (RA) domains of Phospholipase C epsilon and Rassf5  of αZ, αA, αB, and αC. The largest chain-based cluster contains 17 crystal forms and 71 PDB entries. This head-to-tail symmetric dimer  family of bromodomain proteins. These proteins, BRD2, BRD3, BRD4, and BRDT, contain two tandem bromodomains, BD1 and BD2, followed by a small extra-terminal (ET) domain towards the C-terminus. In between BD2 and the ET domain there is a coiled-coil Motif B that is associated with homo- and heterodimerizationins Fig. , includi54. The same authors determined the crystal structure of BD1 of human BRD2, and identified a head-to-head symmetric dimer as the likely biological assembly, which was verified by mutation of residues in the interface. The BD1 head-to-head cluster is the fifth largest chain-based cluster of bromodomains, comprising six crystal forms and 25 PDB entries , which mimic hirudins57. The second large cluster is a large set of inhibitor peptides bound to the active sites of more than a dozen trypsin-like protease family members. The third cluster is entirely made of complexes of the heavy and light chains of some trypsin-like proteases, including thrombin, chymotrypsin, urokinase, acrosin, and matriptase. These complexes are created by internal cleavage by activating enzymes.As an example, Trypsin domains have three large peptide-interface clusters in ProtCID Fig. . The fir58. The last cluster includes fragments of the beta subunit of casein kinase bound to the N-terminal domain of the alpha subunit of casein kinases.Serine/Threonine kinases (Pkinase) have three peptide-interface clusters in ProtCID Fig. . The firThe human proteome contains many protein domain families that primarily function as peptide-binding modules, often within larger proteins that also contain other domains. If two proteins can be demonstrated to interact, for instance through high-throughput protein–protein interaction studies, and one of them contains a peptide-binding domain, then in many cases a reasonable hypothesis is that the peptide-binding domain of one protein may bind to an intrinsically disordered region of the other. We have therefore used ProtCID to compile a list of the more common peptide-binding domain families within the human proteome with available structural information in the PDB. We include only domains that primarily function as non-enzymatic peptide binders and are well represented in the PDB and the human proteome (see Methods section). We refer to these as Professional peptide binding domains (PPBDs). There are currently a total of 42 PPBD Pfam families with peptide-bound structures in the PDB  one ligand and its interacting Pfams; 2) one Pfam and its interacting ligands. Figure  one Pfamhttps://www.uniprot.org/uniprot/Q15633] and DHX9_HUMAN [https://www.uniprot.org/uniprot/Q08211]) have structures in the PDB with RNA. (DEAD) domains are a family of helicases that unwind nucleic acids, including both DNA and RNA. The interaction of (DEAD) domains and single-stranded DNA occurs in 19 entries and 6 UniProts in the PDB  all-against-all for the structural analysis of large protein complexes; (2) one-against-all for the structural analysis of hub proteins.Many proteins are part of large protein complexes or molecular machines with many different components and thus interact both directly with some partners and indirectly with many others. Large-scale studies of protein–protein interactions have also identified hub proteins that participate in a large number of interactions with other proteins62. The ProtCID Uniprot search of these four proteins returns an interaction network based on four Pfam domain-domain clusters  containing the (MOZ_SAS) domain of KAT7_HUMAN[https://www.uniprot.org/uniprot/O95251], and one structure (PDB: 4AFL [10.2210/pdb4AFL/pdb]) containing the (ING) domain of ING4_HUMAN[https://www.uniprot.org/uniprot/Q9UNL4]. We built homology models of JADE3_HUMAN[https://www.uniprot.org/uniprot/Q92613] and EAF6_HUMAN[https://www.uniprot.org/uniprot/Q9HAF1] with SwissModel63 using the yeast structures as templates, and then superposed these models, and the human proteins in PDB entries 5GK9 and 4AFLonto the homologous chains in the yeast HBO complex (PDB: 5J9Q [10.2210/pdb5J9Q/pdb]) to build a model of the human HBO1 complex , which lists Pfams that have structures with nucleic acids or peptide and Pfams do not. The numbers of Pfams, UniProts, and human UniProts for a clan and whether they interact with peptides and nucleic acids in the PDB are provided in Supplementary Table We have added access to ProtCID Pfam-based clusters at the Clan (superfamily) level. This is enabled through a page for each clan  and DND1_HUMAN[https://www.uniprot.org/uniprot/Q8IYX4] (Dead end protein homolog 1), among others. Pfam (Staufen_C) is found in human proteins STAU1, STAU2, PRKRA, and TARBP2.Similarly, we can use ProtCID to develop structural hypotheses for proteins in nucleic-acid binding Pfams that do not yet have nucleic-acid bound structures in the PDB. For example, the clan page for DSRM lists the 7 Pfams in this clan, including (dsrm) shown in Fig. The Protein Data Bank holds a tremendous amount of information on the interactions of molecules in biological systems, but it can be difficult to access and analyze rapidly. ProtCID is designed to accomplish this task with a unique combination of features that enable users to obtain the rich structural information available for any biological system quickly and easily. Some of these features are individually or jointly available in other databases but all of them are necessary to access the full range of structural information currently available. We discuss several of these features in turn.First, for crystallographic structures, we build the coordinates of set of 27 unit cells (3 × 3 × 3) and determine the entire set of unique protein–protein interfaces present within the crystal. These form the basis of all further analysis in ProtCID. Many databases that provide data on protein–protein interactions only analyze coordinates within the asymmetric unit and/or within the biological assemblies deposited in the PDB, missing interactions that are not annotated in the PDB.65. ProtCID helps to sort out which PDB entries contain which interfaces of these oligomers. By contrast, some databases lump all interactions of domains into one group, and do not distinguish between different modes of binding.Second, the clustering of interactions is crucial for providing evidence in favor of the biological relevance of any specific interaction analyzed in ProtCID. Some protein families have evolved different ways of forming oligomers in different branches of their phylogenetic trees. These interaction will appear in distinct ProtCID clusters with non-overlapping protein sets. In other cases, some proteins form larger oligomers that contain two or more distinct interfaces. Some proteins exist in different oligomers under different conditions with different functional properties4. As far as we are aware, providing the number of crystal forms is a unique feature of ProtCID among protein–protein interaction databases and servers.Third, for chain–chain and domain–domain interaction clusters, ProtCID provides the number of crystal forms. Larger numbers of crystal forms and higher percentages of all available crystal forms for an interaction type indicate that the interfaces in the crystal are likely to be biologicalFourth, for all clustered protein interactions, ProtCID provides a link to download all the coordinates in the cluster and scripts to visualize the interactions. Many webservers provide the coordinates of each structure for some type of interaction but downloading each structure requires a click. There can be 100s of structures for an interaction of interest. Some allow online visualization but no coordinate download; some do not provide coordinates at all. Without the coordinates, it is not possible to perform any further analysis of the complexes.https://www.uniprot.org/uniprot/P38398] instead of P38398), species, Pfam domain and chain architectures  instead of PF00071), and whether interactions are present in the biological assemblies deposited by authors and calculated by PISA. The last of these provides information on whether an interface is regarded as biological by the authors of structures, which then may be followed up in the papers describing the structures. Many online databases do not contain information on the identities of proteins in the complexes they list, instead providing only PDB identifiers and links to the PDB’s website. Clicking through 100s of structures this way is not viable.Finally, ProtCID provides complete annotations of the members of each cluster for protein-protein interactions (at the chain and domain levels) and for interactions of domains with peptides, ligands, and nucleic acids. These annotations include: PDBid and chain, crystal form, UniProt identifiers , and thus there will be no clusterable interfaces across crystal forms. ProtCID depends on Pfam, and there are some proteins that are poorly annotated by Pfam. Pfam misses some N or C terminal elements of secondary structure that may form a large portion of a homodimer or heterodimer interface. This compromises our ability to cluster some domain–domain interfaces. We compensate for this in part by continuing to provide the chain–chain clustering, since these include the entirety of each chain.In sum, we hope that with the extension of ProtCID to protein domains and further enhancements described in this paper, it will prove useful in helping users to access the vast amount of structural information that can be used to understand the properties of biological systems.7. Our protocol employs alignment of hidden Markov models (HMMs) of PDB protein sequences to Pfam HMMs as well as structure alignments for validating the assignments of weakly scoring hits. We carefully annotate split domains, which are protein domains with other domains inserted within them, and correctly identify a larger number of repeats in repeat-containing proteins than Pfam does. With these Pfam assignments, we define a Pfam architecture for each protein in the PDB, which is defined as the ordered list of Pfams along the chain along with their positions in the sequence. For example, a protein with one kinase domain and one SH2 domain in that order is annotated as (Pkinase)_(SH2). Supplementary Fig. To create ProtCID, we first assign Pfam domains to all unique sequences in the Protein Data Bank with a procedure we developed for this purposeN symmetry operators and the other is solved as an asymmetric unit dimer and a related space group with N/2 symmetry operators.Entries are assigned to the same crystal form if: (1) they have the same entry Pfam architectures; (2) the same space group; (3) the same asymmetric unit stoichiometry of protein chains; (4) crystal cell dimensions and angles within 1% variance. We then compare the interfaces of crystals with the same Pfam entry architectures and if at least two thirds of their interfaces are highly similar interfaces, then we merge the crystal forms into one crystal form. This can happen when two different structures are essentially the same crystal and contain all of the same interfaces, but one is solved as an asymmetric unit monomer and a space group with β atoms with distance ≤12 Å and at least one atomic contact ≤5 Å, or at least five atomic contacts ≤5 Å.Chain interfaces are generated from a collection of 27 unit cells arranged in a 3 × 3 × 3 lattice. The domain interfaces are generated from chain interfaces by the Pfam-defined start and stop positions within the full-length chains. Two domains (or chains) are considered to be interacting if and only if they have at least ten pairs of C66 metric, Q described by Xu et al.4, which is equal to a weighted count of the common contacting residue pairs in two interfaces divided by the total number of unique pairs in the two interfaces. A value of Q of 1 means two interfaces are interacting in an identical way. A value of Q of 0 means there are no common contacts. We cluster chain and domain interfaces with surface area >100 Å2 by a hierarchical average-linkage clustering algorithm. Initially each interface is initialized to be in its own cluster. At each step, the two clusters with the highest average Q-score between them are merged as long as their Qavg ≥ 0.30. When no two clusters can be merged with Qavg ≥ 0.30, then the algorithm is stopped.To measure the similarity of two interfaces, we must have a correspondence of the residues in one interface with homologous positions in the other, especially when the proteins are homologous and not identical sequences. Since we have an alignment of every structure to Pfams, we can use the Pfam HMM positions to identify homologous positions in two homologous proteins or protein domains (as long as the HMM covers 80% of the shorter domain). The similarity of interface pairs was calculated with a Jaccard-indexWe cluster interfaces of all pairs of individual Pfam domains from single and multi-domain proteins. This includes both same-Pfam pairs and different Pfam pairs. Same-Pfam pairs are mostly homodimers, but they also include some interchain homologous heterodimers and intrachain domain–domain interactions with the same Pfam. Different-Pfam pairs are heterodimers (domains with different sequences from two different protein chains) but they also include some intrachain domain–domain interactions. For example, we cluster all (Pkinase):(Pkinase), (SH2):(SH2), and (Pkinase):(SH2) interfaces, regardless of what single-domain or multi-domain architectures these domains come from. For each homodimeric or heterodimeric pair of Pfam chain architectures or Pfam domains, ProtCID reports clusters that contain the interface , as well as the PDB entries that contain them and the interface surface areas, and whether or not the interface is present in the author or PISA-defined biological assemblies for these entries. Links are provided to download the coordinates of the domain and chain dimers and PyMol scripts to visualize them.β–Cβ contacts with distance ≤12 Å, or ≥5 atomic contacts with distance ≤5 Å. If a peptide contacts several chains in a biological assembly, the interface with ≥75% atomic contacts is used as the peptide-interacting interface; otherwise, we keep all the interfaces. For any two Pfam-peptide interfaces in the same Pfam, the number of same-Pfam HMM positions interacting with peptides are counted as Nhmm. We use the pair_fit command in PyMOL to superpose coordinates of the domain structures via their common Pfam HMM positions. We calculate the minimum RMSD (RMSDpep) between the peptides by linear least-squares fit of all possible sequence alignments between them. We cluster Pfam–peptide interfaces using a hierarchical average-linkage clustering algorithm by Nhmm and RMSDpep. In this method, each interface is initialized to be a cluster. At each step, the two clusters with the largest number of common interacting Pfam HMM sites are merged, as long as the Nhmm ≥ 3 and RMSDpep ≤ 10 Å.Interactions with peptides are defined only for Pfam domains, not entire chains. This is because most peptide-binding domains are modular and are reused in different contexts with other domain types. A peptide is defined as any protein chain with length less than 30 amino acids in the PDB. The chain type is based on the attribute “Polymer Type” defined in the PDB mmCIF files; a protein or peptide chain has the Polymer Type defined as polypeptide. A domain–peptide interface is defined as an interaction with ≥10 CThe primary function of the domain must be peptide-binding in most proteins that contain the domain. Some domains primarily perform other functions such as binding DNA or other folded protein domains, and their functions are modified by peptide binding; these are excluded from PPBDs. In most cases, there is a common motif, often confined to one amino acid position, that demonstrates that peptide binding was a function of the common ancestor of proteins that contain the domain.There must be structures of at least three different peptide-bound complexes of the domain in the PDB , and the peptides must all bind in the same location and orientation on the surface of the domain.There must be at least three human proteins that contain the domainWe exclude repeat proteins  that have evolved the ability to bind peptide multiple times in a manner consistent with convergent evolution.We used several criteria to define professional peptide binding domains (PPBDs):We exclude domains for which peptide-binding includes catalytic modification of the peptide, which includes proteases and enzymes that add or remove post-translational modifications.Domain–ligand interactions are calculated from the asymmetric units in the PDB. We define all non-polymer molecules except water in the PDB as ligands. A ligand interaction refers to at least one atomic contact with distance ≤4.5 Å between a protein domain and a ligand. Nucleic acids are treated in the same way as other ligands. Domain–DNA/RNA interfaces are calculated from the biological assemblies in the PDB. The interface with the largest number of Pfam HMM positions in contact with DNA or RNA is selected.http://dunbrack2.fccc.edu/ProtCiD/Results/EntityPfamArchWithPfam.aspx?PfamId=PK).For each Pfam, we superpose all available domain structures based on their Pfam HMM positions and provide a script file to display the interactions in PyMOL. The domain structures and PyMOL script files are compressed as a single file, named after the Pfam ID . The ligands are clustered based on the number of common Pfam HMM positions that they contact. One PyMOL session contains the coordinates of all structures for a given Pfam domain, and all interacting ligands. Each ligand and each group of ligands are defined as selection objects with the names of ligands; users can turn on or off each selection by clicking it in the object list. There are three types of coordinates provided by ProtCID: (i) the best domain with least missing coordinates of a PDB entry, marked by pdb ; (ii) the best domain of a unique sequence, marked by unp ; (iii) the best domain of a crystal form, marked by cryst . The unp and cryst folders contain fewer domains, and these may be easier to open in PyMOL for big Pfam families. A user can download the gzipped tar files that contain the coordinate files of domain–ligand interactions along with PyMOL scripts from each Pfam page on the ProtCID web site . In this way, users can obtain and view all homologous protein structures interacting with a specific ligand in one click.Besides the data for a Pfam and its interacting ligands, ProtCID also provide data sets for a ligand and its interacting Pfams. For each ligand, ProtCID provides domain coordinates and PyMOL scripts for structures of the Pfam with that ligand. All PyMOL scripts and structures are compiled and compressed into a single archive file named after this ligand  and can be downloaded from the page for each ligand  to identify interactions among them (http://dunbrack2.fccc.edu/ProtCiD/Search/Uniprots.aspx). There are two ways to identify interactions: “First to All” and “All to All”. “First to All” refers to the interactions between the first protein in a list and all other proteins in the list (proteins 2 to N). These results may be of interest if the first protein is a hub protein with many protein interactors. “All to All” prompts the server to identify all structures that may occur among a list of input proteins, which is more useful for large protein complexes or complicated pathways with uncertain connections. A user can choose interface types to search for: either “Interfaces of Pfam domains” or “Interfaces of Input Sequences”. In both cases, the server produces a Cytoscape network (http://www.cytoscape.org/) where each node is one of the input Uniprot identifiers. Edges are drawn between the nodes if there is structural information available for that pair of nodes.A user can input a list of UniProt accession codes  and the number of PDB entries (denominator) of the biggest interface cluster. Clicking on an edge provides a list of structures that contain direct interactions of the two Uniprot sequences connected by the edge. These can be useful in determining whether the target sequences are actually in common entries in the PDB.Thermobifida fusca, ProtCID provides the interactions from structures of these specific proteins as well as and their Pfams , then an interaction is predicted and an edge is drawn. As an example, if we input the 5 Uniprot identifiers for the Cascade complex of the CRISPR-Cas system from https://www.uniprot.org/uniprot/P04637#interaction).Pfam assignments and interactions can be downloaded as text files. Like other queries, all coordinate files are downloadable for further study. Supplementary Fig. http://dunbrack2.fccc.edu/ProtCiD/IPDBfam/PfamNetwork.htm?Pfam=Pkinase) produces a Cytoscape network with the input Pfam at the center, and edges to all Pfams that interact with the Pfam in PDB structures; the number of available structures is shown. It is also possible to browse networks of interacting Pfams (http://dunbrack2.fccc.edu/ProtCiD/Browse/PfamBioNet.aspx).ProtCID also provides the ability to browse the interactions among Pfams without the need for input Uniprot sequence identifiers. A search for a single Pfam . Sequences are searched against the Pfam HMM database by running HMMER 3.1 (http://hmmer.org/). A simple greedy algorithm is used to assign Pfams to the sequences. The hit with the best E-value  and at least ≥80% coverage of the Pfam HMM is selected first. For each additional Pfam hit in order of E-value, if it does not overlap with any existing Pfam assignments by >10 residues on either end, then an assignment is made. After assigning Pfams, ProtCID returns interactions of Pfam architectures and individual Pfam domains.A user can input one or two amino acid sequences (instead of UniProt Identifiers) on the ProtCID web site to retrieve common interfaces between them or between homologs with the same Pfams (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


Salix L. spp.) is a promising bioenergy resource crop due to its high growth rates and superb regenerative ability. Sprouting capacity is influenced by many factors, such as parent tree species and size, which are important limiting factors for stump survival or sprout growth. In this study, we aimed to quantify the survival and regeneration performance of sprouts  from the stumps of two Salix species from three diameter classes . An attempt was made to explore why the stump size affects the regeneration of willows by analyzing the carbon and nitrogen proportion of stumps. Stump survival did not differ between the two Salix species. However, the sprout regeneration of S. triandra was much better than that of S. suchowensis. An increase in stump diameter caused increases in the number of sprouts produced per stump, the mean height and basal diameter of sprouts per stump, the leaf chlorophyll content, and the biomass of sprouts per stump. By contrast, stump diameter did not significantly affect stump survival. The results indicate that the larger stumps store more carbon and nitrogen than small-sized stumps, which may be one of the reasons why the larger willow stumps have a stronger resprouting ability. This study provides essential information regarding the sprout regeneration of short-rotation coppice willow plantations after harvest.Shrub willow ( Salix L. spp.) and poplar (Populus L. spp.) are the most common species currently used in commercial SRC systems. Shrub willows have several traits, including high yields, easy vegetative propagation, a short reproduction cycle, and the ability to regenerate after multiple harvests, making them ideal for SRC systems [The current main sources of liquid biofuels are traditional crops. These so-called first-generation biofuels have come under intense criticism for competing with food production . Lignoce systems ,6,7,8. TQuercus variabilis during the first year, and the effect vanished in the second and third years [Willows have a strong sprouting ability. Sprouts produced from the stump are the most important source of regeneration for willows after cutting. They have a large established root system, which usually supports rapid growth, multiple flushes from stored nutrients, and the increased ability to obtain resources ,10. The rd years . Effectsrd years . Severalrd years ,18. HoweCarbon and nitrogen storage play an important role in regulating tree growth ,20. PrevSalix suchowensis and Salix triandra are shrub willows with relatively different ecological habits, distributed in Central and Northern China, respectively. These two kinds of shrub willow are considered promising biomass crops due to their superb coppicing capacity [capacity . In thisS. suchowensis by crossing NF2 and XY12 with LS7 separately. A full-sib pedigree was established for S. triandra through a DB447 × DB134 cross. The cutting orchard for these pedigrees was maintained at Sihong Forest Farm in Jiangsu Province, China. In the spring of 2018, over 7000 cuttings were collected from 787 progenies of three willow families planted using a random block design. The planting density was 0.5 m × 0.5 m. At the end of 2018, the willow shoots at 5 cm above the ground in the two blocks were harvested with a branch shear. The stumps were classified using diameter into three groups, namely, 10–15, 16–19, and 20–30 mm. Two full-sib families were established for −1; total nitrogen, 1.3 g kg−1; available phosphorus, 11.5 mg kg−1; available potassium, 135.3 mg kg−1). The field test was implemented at Baima Forest Farm in Nanjing, Jiangsu Province, China (N 31° 60′ E 119° 17′). The average annual temperature is 15.4 °C and the average annual precipitation is 1009.7 mm. The soil on this site was yellow-brown loam  was used to measure the leaf areas of the fresh leaves from the bottom, middle, and top of canopies at the end of September 2019. The SPAD  value representing absorbance by chlorophyll was measured with a chlorophyll meter TYS-B . Three fresh leaves of each plant were selected for measurement at the end of September 2019. Moreover, 60 progenies were randomly picked from each willow family. The willow shoots at 5 cm above the ground were harvested with a branch shear at the end of November 2019. The harvested shoots were dried using a DHG-9140A electric heating air blast drying box  at 105 °C until constant weight. The dry weight of each shoot was measured using an electronic balance with a precision of 0.1 g.At the end of November 2018, 60 progenies were randomly picked and harvested from each willow family. A 5-cm sample was collected from the lower part of the stem. The stem samples with bark were dried using a DHG-9140A electric heating air blast drying box, then ground to a wood meal. We measured the nitrogen and carbon concentration of the stump samples using an Element analyzer  in 5 mg of the wood meal of the stumps, according to the manufacturer’s directions .ijky is the observed value of the response variable  of species i in block k, considering stump diameter level j; µ is the overall mean; iα, jβ, and kλ are the species, stump diameter, and block effects, respectively; ij(αβ) is the bifactorial interactions; and ijke is the random experimental error term. Species and stump diameter were considered as fixed effects, whereas block and error were considered as random effects. The linear mixed model was used to evaluate the significance of the effects of the factors and any interaction. Tukey’s honestly significant difference multiple comparison test was used to identify significant differences among basal diameter classes (p < 0.05).Growth trait data were analyzed using the linear mixed model on SPSS 23.0 statistical software . The model used was as follows:The results were represented as mean ± SE (standard error). The correlations of stump diameter with the growth traits of willows were tested using Pearson’s correlations. Boxplots were made using the ggplot2 package in R software (version 3.6.0).S. suchowensis and S. triandra, respectively. Among them, 1380, 1743, and 1557 stumps were included in three diameter classes of 10–15, 16–19, and 20–30 mm, respectively  (S. suchowensis (p < 0.01). A significant positive correlation was observed between the stump diameter and sprout number of S. triandra (p < 0.01). The sprout height and basal diameter regenerated from stumps of S. triandra were significantly higher than those of S. suchowensis (S. suchowensis (7.8 ± 0.1) was higher than that of S. triandra  . All grohowensis a,c. HoweS. suchowensis and S. triandra were correlated . The strongest correlation was between leaf area and leaf length (r = 0.88). A strong correlation was also found between leaf morphological variables  and dry biomass. Leaf chlorophyll content was significantly positively correlated with sprout height, basal diameter, sprout number, and carbon proportion of stumps, but was negatively correlated with leaf width, leaf length, leaf area, and dry weight. The relation detected between sprout number and dry biomass was insignificant. The correlation analysis for the growth variables is listed in S. suchowensis and S. triandra was over 96%. The survival rate of the stumps of these two shrub willows after the first-rotation harvest was much higher than that reported for oak coppice, at 84–86% [The survival of sprouted stumps appeared to be influenced by various factors, such as species , age of t 84–86% , or short 84–86% . This fit 84–86% ,33. A cot 84–86% ,18,34. Ht 84–86% ,31. Old t 84–86% , whereast 84–86% . In the S. suchowensis and S. triandra. This result was similar to that of Ashish et al. [The growth status of sprouts is an important indicator that determines the competitive success of stump sprouts with neighboring plants ,36. The h et al.  and Randh et al. . Howeverh et al.  observedh et al.  found thh et al. ,40. The h et al. ,42. Simih et al. ,41, our h et al. ,17.S. triandra was larger than that of S. suchowensis. This result provides evidence that S. triandra has a stronger regeneration ability after cutting than S. suchowensis. A positive correlation was observed between leaf morphological variables  and the dry biomass of sprouts per stump. Leaf area is closely related to biomass [S. triandra was significantly larger than that of S. suchowensis. This indicates that the photosynthetic capacity of S. triandra stump sprouts may be higher than that of S. suchowensis. No significant correlation between leaf area and stump diameter was observed in this study. However, the stump diameter had a significantly positive effect on the leaf chlorophyll content. Chlorophyll is a major determinant that reflects the plant’s photosynthetic ability [The biomass of sprouts on a stump increased with the increase in stump diameter in oak species . In the  biomass , which i biomass ,46. In t ability . The leaThe diameter of stumps has a strong positive influence on the regeneration ability of two shrub willows. The results also reveal that larger stumps store more carbon and nitrogen than small stumps, which may be one of the reasons why larger willow stumps have a stronger resprouting ability. This study provides fundamental information for the management of SRC plantations and the optimal cultivation patterns of willows.


In a first research question, we examined whether the relations that are generally observed between the coherence of written autobiographical narratives and outcomes of mental health and social support, can be replicated for the coherence of oral narratives. Second, we studied whether the coherence of oral narratives is related to the coherence of written narratives.t-tests were calculated on data of two separate studies to examine the research questions.Pearson correlations and First, only thematic coherence of oral narratives was significantly, although moderately, negatively associated to symptoms of depression, anxiety and negative social interactions. Second, the coherence of oral narratives was higher than the coherence of written narratives. Only the thematic coherence of oral narratives was positively associated with thematic and total coherence of written narratives. Furthermore, correlations between written and oral narratives were stronger for negative narratives as compared to positive narratives.The ability to elaborate emotionally and make meaning out of important life events in oral narratives is, to a certain extent, related to better mental health and more social support. Furthermore, thematic coherence may be a relatively stable feature of individuals’ narrative styles that is reflected in narratives of different modalities. Nonetheless, these topics need to be further researched to overcome present limitations. On a daily basis we talk about the highs and lows of our lives to the people around us . These hHowever, the extent to which the social function is successfully fulfilled by sharing autobiographical memories and consequent support is received, is in part dependent on the distinct ways in which these memories are narrated e.g., . One of As coherence has been repeatedly designated as one of the necessary conditions of high-quality narration , it has For instance, With regards to mental health, multiple outcomes of psychological well-being and psychopathology have been investigated over the years. Possibly, one source of inconsistencies in the results might arise from these differences in measurement methods. For instance, in addition to differences in research samples, conceptualizations and coding schemes for coherence, also narratives of different event types and in different modalities have been used in the investigation of coherence e.g., . To dateParticularly with regards to social support, it would be crucial to compare relations to coherence of oral versus written narratives. To our knowledge, there has been no research that directly compares the relations between coherence in different modalities and outcomes of mental health or social support, within the same person. There is some work that does compare modalities of remembering in relation to well-being, but none of these include the fundamental characteristic of coherence. For instance, first research question, we want to examine if the associations to mental health and social support that have sometimes been observed for the coherence of written narratives, can be observed for the coherence of oral narratives. We hypothesize that coherence of oral narratives will be positively associated to psychological well-being and positive social interactions, and negatively to symptoms of depression, anxiety, stress and to negative social interactions. This hypothesis is based on a critical evaluation of the existing literature, which yields mixed results but is nonetheless still to a larger extent in support of the idea that narrative coherence might be beneficial for social and psychological adjustment, at least in older adolescents and adults , once developed e.g., . For inssecond research question involves whether coherence of oral narratives is related to coherence of written narratives. We hypothesize to observe a moderate association between oral and written narrative coherence, based on results of previous studies suggesting the combinatory impact of both stable and variable elements in the construction of coherent narratives  and 58 were women (85.3%), ranging in age between 17 and 20, r = 0.71  , Anxiety (A), and Stress (S) Scales DASS-21: . This inr sample . Exampler = 0.77; SSL-N: r = 0.56  and – Negative Interactions (SSL-N), which have also proven to have good construct validity, high internal reliability, SSL-I:0.90 ≤ Cronbach’s α ≤ 0.93; SSL-N:0.69 ≤ Cronbach’s α ≤ 0.81, and test–retest stability, SSL-I: r = 0.56 . Researcr = 0.56 . ExampleIn the narrative coherence writing tasks, participants were instructed to write for a minimum time of 10 min about an autobiographical high and low point in their life . Participants were encouraged to take this task very seriously, and to really take their time to think and write about impactful events, as no maximum time to do so was given. The specific instructions were as follows: “I would like for you to write about your most positive/negative experience of your life. This should be an extremely emotional event that has affected you and your life. You may include the facts of the event, as well as your deepest thoughts and feelings. All of your writing will be kept confidential. Do not worry about spelling, sentence structure, or grammar. There is no time limit on your writing; you may write about this event for as long as needed.”Participants were asked to narrate out loud about a turning point memory in their life for a minimum time of 5 min. The following instructions were presented on the computer screen during the entire narration process: “I would like you to think back over your life and identify an event that has changed your life or the kind of person you are. It could be something from any area of your life–your relationships with other people, your work and school, your outside interests, and so forth. Please identify a particular episode in your life story that you now see as a turning point in what your life is like or what you are like as a person. Please describe what happened, when it happened, who was involved, what you were thinking and feeling, why this experience is significant, and how it changed your life or you as a person.”The oral memories were first transcribed into written narratives. Afterward, all narratives were rated manually according to the Narrative Coherence Coding Scheme (NaCCS) . Using tICCcontext = 0.94; ICCchronology = 0.88; ICCtheme = 0.90. Afterward, all other oral data were rated by the first author.All written narratives were rated independently by three trained coders, upon which a consensus system was used to determine final scores . For the oral narratives, we used the standard approach in the domain to code narratives. Namely, we established good inter-rater reliability on 10% of the data, as was evident from a high interclass correlations (ICC) for all dimensions, The written measurement of coherence was conducted as part of a first-year psychology course “Methods of Research,” in which students are given the opportunity to contribute to science and gain experience in research practices. Participants were collectively informed about the aims and procedure of the study. Then, after signing agreement to the informed consent, they could individually and online complete the questionnaires and the writing tasks, which involved writing about a high point memory and a low point memory. Instructions were given to do this in a quiet space with no distractions and to respond to all questions as honestly possible. After participation, students were given further information about the specific aim of the study, contact details of the research team, professional help instances, a course credit and they were thanked for their effort and time. This study was conducted in accordance with ethical guidelines and approved by the Social and Societal Ethics Committee of the KU Leuven (G-2018 01 1067).The oral measurement took place 4 months after the written measurement, and was part of an experimental study, for which individuals who participated in the written measurement were invited via e-mail. In the experiment, participants were individually invited to the lab, where they were informed about the aims and procedure of the study. Then, after signing agreement to the informed consent, they could individually fill out the DASS-depression items on the computer. No other questionnaires were administered during this part of the study. Afterward, they were asked to narrate out loud about a turning point memory. Important to note here is that half of the participants in this study told the narrative in the presence of an experimenter with an evaluative attitude and in front of a camera, which was part of the experimental manipulation in that study. The other half of the participants presented their narrative into a microphone whilst being alone in the cubicle. In fact, all oral narratives were audio recorded and later transcribed to be rated on coherence. After participation, students were given further information about the specific aim of the study, contact details of the research team, professional help instances, a course credit and they were thanked for their effort and time. This study was conducted in accordance with ethical guidelines and approved by the Social and Societal Ethics Committee of the KU Leuven (G-2018 10 1376).3.Results were analyzed using the coherence scores on the individual dimensions , as well as total narrative coherence scores. For the written narratives, the mean of the dimensional and total scores on high point and low point narratives were used. For the oral narratives, the dimensional and total scores on the turning point narrative were used. Pearson’s correlations were calculated using SPSS version 26. An alpha level of 0.05 was set for all analyses. Anonymized data have been made publicly available at the Open Science Framework and can be accessedt(67) = 3.49, p = 0.001, thematic, t(67) = 5.70, p < 0.001, and total narrative coherence, t(67) = 4.17, p < 0.001, but not for chronological coherence, t(67) = 1.15, p = 0.25.Descriptive statistics were calculated for the dimensions of coherence as well as the total coherence score in written and oral narratives. The ranges, means and standard deviations are presented in We calculated Pearson correlations between the coherence of oral narratives and outcomes of mental health and social support, that were assessed either during the written measurement 4 months earlier or at the time of the oral measurement itself. Note that only depressive symptoms were measured at both times, whereas all other measures were collected during the written measurement.In In ∗∗ in Bonferroni corrections were applied since correlations were calculated for four measures of coherence . Data can thus be compared to an adjusted alpha level of 0.0125. For oral narratives, only the negative association between thematic coherence and negative social interactions, remained significant. For written narratives, multiple correlations remained significant when compared to the adjusted alpha  = −2.37, p = 0.21. More specifically, negative written narratives were generally more coherent than written positive narratives, Mpos = 5.32, SDpos = 2.43, Mneg = 5.90, SDneg = 2.32. Therefore, we investigated if the coherence of oral narratives was similarly related to the coherence in positive written versus negative written narratives. A paired-samples After applying Bonferroni corrections for testing coherence multiple times, α = 0.0125, only the associations of oral thematic coherence with written thematic and written total coherence remained significant, both for total narrative coherence scores, as well as for the negative narratives separately.first question concerned the associations of the coherence of oral narratives with mental health and social support. Results indicated that relations that are generally observed for written narrative coherence were also evident for oral narrative coherence, but for only the thematic dimension. Only thematic coherence in oral narratives was negatively associated with concurrent depressive symptoms, and retrospectively negatively associated with anxiety symptoms and perceived negative social interactions. After Bonferroni corrections, only the correlation between thematic coherence of oral narratives and earlier reported negative social interactions remained significant. For written narratives, a more extended pattern of associations was observed, with more measures of coherence being related to mental health and social support.In the present study, the Important to notice however, is that this more extended pattern of results for written narratives is likely dependent on the fact that outcome measures were collected concurrently for the written narratives, whereas 4 months before the oral narratives were collected. Our results indicate that coherence might be particularly relevant for concurrent adjustment, but that associations decrease in strength over time. This is also confirmed by our findings that the coherence of both oral and written narratives was significantly negatively associated with the concurrent measure of depressive symptoms, but not with the depressive symptoms measured either 4 months earlier or later.Overall, significant results were not observed for every measure of coherence and every outcome. Hence, these findings are still to be interpreted in the light of existing evidence, which is rather inconsistent. Even when significant associations were observed, the magnitude of the correlations was still very small. Additionally, our findings are merely correlational in nature and do not provide evidence on causes of effects. Like in any type of correlational research, the interference of possible third variables cannot be completely ruled out. For instance, it is possible that working memory capacity or extraversion may explain (part of) the relation between coherence and mental health or social support .second question was whether coherence of oral and written narratives was related. We observed that only thematic coherence in oral narratives did significantly relate to written narrative coherence, as well as to the dimensions of theme and context in the written narratives. Bonferroni corrections yielded only the association of thematic oral coherence to thematic written and total written coherence significant. Thus, we only found support for the idea that the way in which individuals elaborate emotionally on autobiographical experiences and create meaning out of them could be part of a moderately stable narrative style. Possibly, the dimensions of context and chronology are more dependent on the specific event or on the modality of the narrative, and hence less reflective of the individual’s narrative style, or the ability to cognitively emotionally process and elaborate on personal experiences. This is in line with what The Despite the fact that coherence was measured for different event types , in different contexts  and the fact that measures were taken 4 months apart, individuals’ level of thematic coherence is, at least to some extent, consistent in narratives of different modalities. Whilst in experimental research these differences in methods may be regarded as possible confounds, they are actually an advantage in correlational research. If associations happen to be found, and they are robust even when the circumstances are non-identical, the sturdiness of findings is only increased. For instance, it can be compared to the situation when the test–retest reliability is also interpreted as more significant and credible when measures stem from timepoints that are 4 months apart compared to taken only a week apart.r = 0.33, to an earlier observed correlation of two oral narrative coherence measurements over a 5-month time period, r = 0.48, , suggests that there are in fact also event- and modality-specific aspects in addition to the stable narrative style that can impact coherence to some extent. This is in line with earlier work  can be found below: Open Science Framework (10.17605/OSF.IO/9ZTR).The studies involving human participants were reviewed and approved by the Social and Societal Ethics Committee of the KU Leuven (G-2018 01 1067 and G-2018 10 1376). The patients/participants provided their written informed consent to participate in this study.LV, PB, and DH: conceivement, design of the studies, and manuscript revision. LV: data collection, data analysis, and manuscript writing. PB and DH: general supervision. All authors have conceived and designed the studies and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Complicated and sophisticated protein homeostasis (proteostasis) networks in the endoplasmic reticulum (ER), comprising disulfide catalysts, molecular chaperones, and their regulators, help to maintain cell viability. Newly synthesized proteins inserted into the ER need to fold and assemble into unique native structures to fulfill their physiological functions, and this is assisted by protein disulfide isomerase (PDI) family. Herein, we focus on recent advances in understanding the detailed mechanisms of PDI family members as guides for client folding and assembly to ensure the efficient production of secretory proteins. A typical mammalian cell expresses approximately 20,000 different proteins, more than 70% of which contain multiple domains . These pMeanwhile, approximately one-third of all newly synthesized proteins such as antibodies and insulin are inserted into the endoplasmic reticulum (ER). Disulfide bond formation can drive folding, inhibit unfolding ,7,8, anda, b, b’, and a’, which form a compact U-shape [a and a’ domains contain a Cys-X-X-Cys motif as a redox-active site essential for thiol-disulfide exchange reactions with substrate proteins. To recruit clients, the human PDI b’ domain provides the principal substrate-binding sites based primarily on a hydrophobic pocket including Phe240, Phe249, and Phe304 [b’ induces significant rearrangement of its N-terminal Trx-like domain, resulting in a more compact overall structure of PDI [b’ domain, preventing PDI from binding to other clients. In addition to conformational changes caused by substrate binding to the b’ domain, the redox state of the active site, especially in the PDI a’ domain, undergoes striking conformational changes that affect substrate selectivity [human revealed redox-dependent conformational switching [a, b, and b’ domains are arranged on the same plane, and only the a’ domain twists outward by ~45°  is identified as an enzyme that promotes oxidative folding ,19,20 an U-shape   was estimated to be 35.8 ± 0.2 Å and 37.2 ± 0.2 Å for the reduced and oxidized forms, respectively. The largest r values (Dmax) were estimated to be 126 Å for the reduced form and 138 Å for the oxidized form, indicating that the oxidized form has a more extended conformation than the reduced form in solution. Intriguingly, single-molecule observations using high-speed atomic force microscopy (HS-AFM) provided new insight into the redox-dependent conformational changes of human PDI. Oxidized PDI exists as two populations, with long-axis lengths of 82 ± 9 Å for the closed form and 111 ± 17 Å for the open form, while reduced PDI exists as a single population with long-axis lengths of 86 ± 12 Å for the closed form [human PDI is important for client binding/release.In agreement with structural information obtained from crystallographic analyses, our SAXS measurements revealed redox-dependent conformational changes for solution . The radsed form   to partially folded (~14 μM) and unfolded (~3 μM) states [The redox-regulated conformational switch in PDI appears to be beneficial for the binding/release of various clients with different structures along client folding pathways; oxidized PDI enables the introduction of disulfide bonds in unstructured substrates during the initial folding step, whereas reduced PDI presumably traps partially structured folding intermediates and rearranges non-native disulfide bonds. PDI has several binding sites in the central redox-inactive ) states . This su) states   states . Thus, uThe central hydrophobic cavity generated by PDI dimers is diverse in terms of lifetime and shape. In the presence of foldable reduced and denatured BPTI, assembly into dimers and disassembly into monomers are repeatedly observed with a period of ~1 s according to HS-AFM analysis . On the Regarding the restriction of cavity size and client capacity, multiple PDI dimers bind to large substrates, such as the 90 kDa plasminogen and the 87 kDa laminin-511, suggesting that PDI dimers bind to local conformations of large substrates to allow oxidative subunit/domain folding simultaneously at multiple sites . CollectHuman chorionic gonadotropin (hCG) is a placental hormone that stimulates secretion of the pregnancy-sustaining steroid progesterone, and promotes the maintenance of the corpus luteum during the beginning of pregnancy [PDI functions not only as a catalyst of oxidative folding, but also as an assistant of assembly A. Human regnancy . hCG is regnancy   is another PDI family member with a PDI-like U-shaped structure composed of a, b, b’, and a’ domains [a and a’ domains contain a Cys-X-X-Cys motif as a redox-active site that is essential for thiol-disulfide exchange reactions with substrate proteins. The human ERp57 b’ domain does not have a hydrophobic pocket  assembly (see 2-microguloblin (β2m) light chain containing one disulfide bond [It is reported that ERp57 assists the assembly of neonatal Fc receptor (FcRn), an MHC class-I-related receptor. FcRn transfers maternal immunoglobulin G (IgG) from mother to young via the neonatal intestine, and regulates serum IgG levels to establish neonatal immunity . Regardimbly see . Similaride bond   pathway. The reductive unfolding of misfolded proteins facilitates their retrograde translocation via the dislocon channel in the ER, and their subsequent degradation by the ubiquitin-proteasome system in the cytoplasm. The largest PDI family member, ERdj5 , reduces aberrant interchain disulfide bonds in the null Hong Kong (NHK) variant of α1-antitrypsin and the J chain of IgM, in concert with immunoglobulin heavy chain binding protein (BiP) and ER degradation-enhancing α-mannosidase-like protein 1 (EDEM1) . The cryIn this NHK ERAD pathway, disulfide-linked NHK oligomers are trapped by EDEM1, which associates with the C-terminal cluster of ERdj5, and aberrant disulfide bonds of NHK are reduced by the catalytic sites of the C-terminal cluster of ERdj5  Figure C. The reThe dynamics between the N- and C-terminal clusters of ERdj5 are likely involved in client transfer with EDEM1 and BiP along the ERAD pathway. Interestingly, the ERdj5 Ser557Pro mutant, fixed in form I and lacking cluster dynamics, displayed insulin reduction activity similar to that of the wild-type enzyme, but it was less efficient for intermolecular disulfide cleavage of the oligomeric J chain than the wild-type enzyme . TransieERdj5 is also reported to catalyze the oxidative folding of the low-density lipoprotein receptor (LDLR) . BecauseIn addition to ERdj5, other chaperones such as ERp57, ERp72, and CNX were shown to bind to LDLR as it passes through the ER ,76,77. Ma, b’, and a’ domains [human MTPα interacts with a hydrophobic pocket in the b′ domain of PDI. Tyr605 of MTPα plays a major role in this interaction (b’ domain accommodates Trp272 of a protruding hairpin region of human ER oxidoreductin-1α (Ero1α) [b’ domain. Besides the PDI b’ domain, the a domain interacts with the α-helical domain of MTPα, while the a’ domain contacts the lipid-binding domain of MTPα   and P4-H domains   ,84, a PD (Ero1α) ,86,87 (F of MTPα A, upper.ERp57 also participates in the assembly of PLC, an immune-related supramolecular complex, in the ER. PLC is a transient multisubunit membrane complex containing HLA, CRT, TAP, ERp57, and the chaperone tapasin  Figure A. The HLa and a’ domains of ERp57 gain access to the N-terminal domain of tapasin [a and a’ domains [The HLA heterodimer without antigenic peptides is recruited by CRT and becomes part of the transient supramolecular PLC, in which the tapasin chaperone stabilizes the HLA heterodimer . ERp57 i tapasin ,49, a fu domains  (Figure Recent analysis of ER proteostasis networks has revealed that PDI family members catalyze client folding and domain/subunit assembly through their distinct functions and structures . PDI effAlthough client recognition mechanisms of PDI family members can be inferred from the structures determined for MTP  and PLC