The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and understanding of PPIs. Despite recent progress, in silico methods remain inadequate in modeling the natural PPI hierarchy. Here we present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In this model, we create a hierarchical graph, in which a node in the PPI network (top outside-of-protein view) is a protein graph (bottom inside-of-protein view). In the bottom view, a group of chemically relevant descriptors, instead of the protein sequences, are used to better capture the structure-function relationship of the protein. HIGH-PPI examines both outside-of-protein and inside-of-protein of the human interactome to establish a robust machine understanding of PPIs. This model demonstrates high accuracy and robustness in predicting PPIs. Moreover, HIGH-PPI can interpret the modes of action of PPIs by identifying important binding and catalytic sites precisely. Overall, “HIGH-PPI [ Despite recent progress, machine learning methods remain inadequate in modeling the natural protein-protein interaction (PPI) hierarchy for PPI prediction. Here, the authors present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In biology, hierarchy information has been widely exploited to gain in-depth information about phenotypes of interest, for example, in disease biology5, proteomics8, and neurobiology11. Naturally, PPIs encapsulate a two-view hierarchy: on the top view, proteins interact with each other; on the bottom view, key amino acids or residues assemble to form important local domains. Following this logic, biologists often take hierarchical approaches to understand PPIs13. Experimentally, scientists often employ high-throughput mapping16 to pre-build the PPI network at scale, and use bioinformatics clustering methods to identify functional modules of the network (top view). On the individual protein level, isolation methods, such as co-immunoprecipitation17, pull-down18, and crosslinking19 are used to establish the structures of individual proteins, so that surficial ‘hotspots’ can be located and analyzed. In short, hierarchy knowledge of structure information is important to understand the molecular details of PPIs.Biological functions are accomplished by interactions and chemical reactions among biomolecules. Among them, protein–protein interactions (PPIs) are arguably one of the most important molecular events in the human body and are an important source of therapeutic interventions against diseases. A comprehensive dictionary of PPIs can help connect the dots in complicated biological pathways and expedite the development of therapeutic21 and inferring potential PPIs by link predictions23. The former focuses on extracting structural information using protein sequences. In particular, Convolutional Neural Networks (CNNs)25 and Recurrent Neural Networks (RNNs)28 have demonstrated high generalization and fast inference speed to capture key sequence fragments for PPIs29. 3D CNNs31 have shown to be better at extracting 3D structural features of proteins and thus capturing the spatial-biological arrangements of residues32 that are important to PPI predictions. However, 3D CNN suffers from high computational burdens and limited resolution that is prone to quantization errors29. The latter aspect of DL in PPI predictions focuses on the PPI network structures, which involves developing link prediction methods to identify missing interactions within the known network topology. Link prediction methods based on common neighbor (CN)33 assign high probabilities of PPI to protein pairs that are known to share common PPI partners. CN can be generalized to consider neighbors from a greater path length (L3)22, which captures the structural and evolutionary forces that govern biological networks such as the interactome. Additionally, distance-based methods measure the possible distances between protein pairs, such as Euclidean commute time (ECT)34 and random walk with restart (RWR)35. Most methods of traditional link prediction focus on known interactions but tend to overlook important network properties such as node degrees and community partitions.More recently, the massive growth in the demand and the cost of experimentally validating PPIs make it impossible to characterize most unknown PPIs in wet laboratories. To map out the human interactome efficiently and inexpensively, computational methods are increasingly being used to predict PPIs automatically. Over the past decade, as one of the most revolutionary tools in computation, Deep Learning (DL) methods, have been applied to study PPIs. Development in this field has been mostly focused on two aspects, learning appropriate protein representations37 to represent protein and network structures, respectively. In this way, the limitations of 3D CNN and link prediction methods mentioned above can be circumvented. First, GNNs can learn the protein 3D structures on more efficient graph representations, even when facing high-resolution requirements for structure processing. Second, due to the propagation mechanism, GNNs are capable of recovering network properties such as node degrees and community partitions. In short, this hierarchical graph approach aims at modeling the natural PPI hierarchy with more effective and efficient structure perceptions.More importantly, these methods perceive only one of the two views of outside-of-protein and inside-of-protein. Few can model the natural PPI hierarchy by connecting both views. To address this issue, we present a hierarchical graph that applies two Graph Neural Networks (GNNs)38 and compare it with leading DL methods. We demonstrate the superiority of HIGH-PPI with higher prediction accuracy and better interpretability. We also show examples that HIGH-PPI can identify binding and catalytic sites with high precision automatically.Here we describe a generic DL platform tailored for predicting PPIs, Hierarchical Graph Neural Networks for Protein–Protein Interactions (HIGH-PPI). HIGH-PPI models the structural protein representations with the bottom inside-of-protein view GNNs (BGNN) and the PPI network with the top outside-of-protein view GNNs (TGNN). In the bottom view, HIGH-PPI constructs protein graphs by treating amino acid residues as nodes and physical adjacencies as edges. Thus, BGNN integrates the information of protein 3D structures and residue-level properties in a synergistic fashion. In the top view, HIGH-PPI constructs the PPI graph by taking protein graphs (the bottom view) as nodes and interactions as edges and learns protein–protein relationships with TGNN. In an end-to-end training paradigm, HIGH-PPI gains mutual benefits from both views. On the one hand, the bottom view feeds protein representations to the top view to learn accurate protein relationships. On the other hand, protein relationships learned by the top view provide insights to further optimize the bottom view to establish better protein representations. HIGH-PPI outputs the probabilities of interactions for given protein pairs and predicts key “contact” sites for such interactions by calculating residue importance. We show the effectiveness of HIGH-PPI on the human interactome from the STRING databaseAlthough deep learning (DL) models for Protein–Protein Interaction (PPI) prediction have been studied extensively, it has not yet been developed for simulating the natural PPI hierarchy. Here, we suggest HIGH-PPI, a hierarchical graph neural network, for accurate and interpretable PPI prediction. HIGH-PPI works like biologists in a hierarchical manner as it contains the bottom inside-of-protein view and top outside-of-protein view 37 layer, ReLU activation function and a BN layer. Node features of the PPI graph are updated with recursive neighborhood aggregations of three GIN blocks. Two arbitrary protein embeddings are combined by concatenation operations, and a Multi-Layer Perceptron (MLP) is then applied as a classifier for prediction. Moreover, we also consider graph attention (GAT) and arbitrarily deploy two of the three GNN layers  on BGNN and TGNN. The performance of HIGH-PPI with various GNN layers is shown in Supplementary Fig. In the proposed end-to-end model, the initial stage is to create protein graphs for learning appropriate protein representation. An adjacency matrix of a protein graph is derived from a contact map connecting physically close residues (See Section 4.1 in “Methods” for details). Node attributes are defined with residue-level features for expressing the physicochemical properties of proteins (See Section 4.1 in “Methods” for details). To produce a protein graph representation, Graph Convolutional Network (GCN)38, which contains a critical assessment and integration of PPIs. SHS27k26, a homo sapiens subset from STRING38 that comprises 1,690 proteins and 7,624 PPIs, is used to train and evaluate the HIGH-PPI unless otherwise noted. However, a small fraction of proteins (∼ 8%) sometimes need to be removed because of the lack of their native structures in the PDB database. While evaluating the prediction performance for multi-type PPIs, we consider the prediction for each PPI type as a one-vs-all binary classification problem, for which two metrics, F1 score and area under the precision-recall curve (AUPR) are used for predicting the presence or absence of the corresponding PPI class. The overall performance of micro-F1 and AUPR scores for multi-type PPI prediction is averaged across all PPI types.We train and evaluate HIGH-PPI on multi-type human PPIs from the STRING databaseTo validate the predictive power of our model, we compare HIGH-PPI with leading methods from four perspectives, including (1) the overall performance under a random data split, (2) the robustness of HIGH-PPI against random interaction perturbation, (3) model generalization for predicting PPI pairs containing unknown proteins, (4) evaluations in terms of AUPR on five separate PPI types. For each method, all the proposed modules and strategies are involved to get the best performance.24), one sequence-based method , one 2D CNN-based method  and one machine learning (ML) method based on random forest . GNN-PPI applies a GNN module to learn the PPI network topology and 1D CNN to learn protein representations by taking pre-trained residue embeddings as inputs. PIPR, an end-to-end framework based on recurrent neural networks (RNN), represents proteins with only pre-trained residue embeddings. DrugVQA applies a visual question-answering mode to learn from protein contact maps with a 2D CNN model and extract semantic features with a sequential model. Supplementary Data File First, we compare the overall performance of HIGH-PPI with leading baselines in Fig. Second, to evaluate the robustness of HIGH-PPI, we analyze the model tolerance against interaction data perturbation including random addition or removal of known interactions. This simulates scenarios where PPI datasets always omit undiscovered interactions and may introduce mislabeled ones. Based on the perturbated PPI network, we split the training and test sets at an 8:2 ratio. We observe in Fig. 24. For PIPR, DrugVQA and RF-PPI, we visualize their best performances among all OOD cases using dotted lines, to demonstrate the absolute dominance of HIGH-PPI and GNN-PPI. Furthermore, we observe that HIGH-PPI consistently outperforms GNN-PPI, the second-best method, with large margins in all five scenarios. BFS typically produces worse performance than DFS, because BFS creates a more challenging and realistic mode where unknown proteins exist in cluster forms. ML method (RF-PPI) exhibits poor generalization. Furthermore, we follow Park and Marcotte42 to explore the differences in model performance on 3 kinds of PPI pairs with different degrees of OOD. Specifically, 42. There is a noticeable difference in model test performance across the 3 distinct classes of test pairs. Particularly, on Class 1 test pairs, both models (HIGH-PPI and GNN-PPI) perform the best, on Class 2 test pairs they are the second best, and on Class 3 test pairs they are the poorest. Furthermore, we find that for each model, the class proportion  scenarios where unknown proteins arrive in the test sets with different probabilities see Fig. . For exaFinally, for each of the five PPI types, we offer a separate performance analysis in terms of AUPR. In all five types, HIGH-PPI consistently beats other baselines with high significance as shown in Fig. We investigate the role of the bottom inside-of-protein view from four perspectives, including (1) the effectiveness of graph representations and backbones with native protein structures, (2) the model tolerance with low-quality protein structures, (3) the capability to predict motifs  in a protein, (4) the overall and type-specific feature importance.21, a deep learning framework for identifying interfaces of PPIs. For GNN, we learn from protein graphs in which the adjacency matrix is determined by First, we explore the effectiveness of backbones including RF, RNN, CNN and GNN in Fig. 43). We evaluate the quality of the input protein structure by calculating the root-mean-square deviation (RMSD) of the native one and the input. Native protein structures (RMSD = 0) are retrieved from the PDB database at the highest resolutions. We compute the best-F1 scores (box plots) of our method on a set of AlphaFold structures with various RMSDs , and show the average result of second-best method (GNN-PPI) in a blue dotted line. As can be seen, our model performance is always better than GNN-PPI, even with RMSD up to 8. The comparison with 3D CNN model21 further proves the denoising ability of the hierarchical graph for protein structure errors  the importance of degree and community recovery for predicting network structures, (2) comparison results of TGNN and other leading link prediction methods, (3) a real-life example to show the shortcomings of the leading link prediction methods.Recently, various works have demonstrated the usefulness of structure properties  of networks for predicting missing links. HIGH-PPI is inspired to efficiently recover the degree and community partitions of the PPI network by utilizing the network topology. We show an empirical study in Fig. 33, Katz index (Katz)50, Adamic-Adar (AA)51, preferential attachment (PA)52, SimRank (SR)53 and paths of length three (L3)22. MLP_IP, a DL approach, learns node representations using a multilayer perceptron (MLP) and identifies the node similarity via inner product (IP) operation. We calculate the MAE and NMI values of recovered networks and highlight those with a high capacity for recovery (NMI ≥ 0.7 and MAE ≤ 0.35) in orange. Results show that link prediction methods that are more adept at recovering network properties typically perform better. This gain validates our findings in Fig. Second, we evaluate the performance of TGNN and leading link prediction methods using PPI network structure as input. Our method (TGNN) takes interactions as edges and node degrees as node features. We compare HIGH-PPI with six heuristic methods and one DL-based method. Heuristic methods, the simple yet effective ones utilizing the heuristic node similarities as the link likelihoods, include common neighbors (CN)54 to the recovered structure, it cannot produce an accurate community partition as the abundant inter-community interactions disrupt the original community structure. To examine degree recovery ability, we randomly select 50% of interactions as inputs and show each method’s degree recovery result for node KIF22 in Fig. We provide an example on an SHS27k sub-network. As can be seen, there exist two distinct communities connected by two inter-community edges. We use the original sub-network as inputs and find that non-TGNN link prediction methods  tend to give high scores for intercommunity interactions. As an interesting observation, when we apply the Louvain community detection algorithmTypically, functional sites are spatially clustered sets of residues. They control protein functions and are thus important for PPI prediction. As our proposed model has the capacity to capture spatial-biological arrangements of residues in the bottom view, this characteristic can be used to explain the model’s decision. It is meaningful to notice that HIGH-PPI can automatically learn the residue importance without any residue-level annotations. In this section, we provide (1) a case study of predicting residue importance for the binding surface, (2) two cases of estimating residue importance for catalytic sites, and (3) an explainable ability comparison of precision in predicting binding sites.55, which is colored in red in Fig. First, a binding example between the query protein (PDB id: 2B6H-A) and its partner (PDB id: 2REY-A) is investigated. The ground truth binding surface is retrieved from the PDBePISA database56 (CSA), a database for catalytic residue annotation for enzymes. We calculate the residue importance of catalytic sites for query proteins . As seen in Fig. Second, in order to evaluate the prediction of catalytic sites for PPIs, we utilize the same GNN explanation approach in our model. The ground truth catalytic site is retrieved from the Catalytic Site Atlas24 and 3D CNN module in DeepRank21, respectively. We apply grad-CAM57 and 3Dgrad-CAM44 approaches to determine residue importance for CNN and 3D CNN models, correspondingly. We use the binding type PPIs from the STRING dataset as the training set, and randomly select 20 binding type PPIs as the test set. We use the ground truth from PDBePISA for each query protein and treat its residues with importance >0 as surface compositions. To gauge the precision of the surface prediction, intersection over union (IoU) is used, and the box plots of the IoU score distributions are shown in Fig. Additionally, we compare the model interpretability of the CNN, 3D CNN and HIGI-PPI models. We employ the CNN module in GNN-PPIProtein functional site prediction sheds light on the model decisions and how to carry out additional experimental validations for PPI investigation. Excellent model interpretability also shows that our approach can accurately describe biological evidence for proteins.In this paper, we study the PPI problem from a hierarchical graph perspective and develop a hierarchical graph learning model named HIGH-PPI to predict PPIs. Empirically, HIGH-PPI for PPI prediction outperforms leading methods by a significant margin. The hierarchical graph exhibits high generalization for recognizing unknown proteins and robustness against protein structure errors and PPI network perturbations.Even without explicit supervision from binding site information, HIGH-PPI demonstrates its ability to capture residue importance for PPI with the aid of a hierarchical graph, which is a good indicator of excellent interpretability. Suppose HIGH-PPI predicts the presence of a catalytic interaction for a protein pair but identifies important sites unrelated to catalysis, we will hardly trust the model’s decision. Moreover, interpretability provides trusted guides for subsequent wet experimental validations. For example, if HIGH-PPI thinks a catalytic site is important, experiments may be designed by targeting the specific site for validation.In conclusion, interpretable, end-to-end learning with a hierarchical graph revealing the PPI nature can pave the way to map out human interactome and deepen our understanding of PPI mechanisms.58. Due to huge semantic gaps between hierarchical views, humans always use a multi-view learning strategy to deepen the understanding of one view from the other one. Given rich hierarchical information, recent machine intelligence methods can effectively learn knowledge in each separate view but are not experts in gaining mutual benefits from both views. This is the challenge that our hierarchical world presents to machine intelligence. Here we connect both views by employing the forward and backward propagation of DL models. The forward propagation benefits the learning for the PPI network in the top view. In turn, the backward propagation optimizes the PPI-appropriate protein representations in the bottom view.We describe our intuitions in the hierarchical graph learning for PPIs. The world is hierarchical. Humans tend to solve problems or learn knowledge by conceptualizing the world from a hierarchical view59) and computational methods29 for protein function prediction. Some annotations may speed up learning PPIs. For example, two proteins with low scores of the “protein binding” function term hardly interact with each other. We suggest that future work may consider leveraging function annotations to enhance the expressiveness of protein representations. Inspired by the contrastive learning principle, a potentially feasible solution is to enhance the consistency in protein representations and functions. (2) Protein domain information may be beneficial for hierarchical models. We clarify the core ideas here and provide a detailed description in Supplementary Method We describe two main limitations of HIGH-PPI and outline potential solutions in future work. (1) We did not explore in depth how to use protein-level annotations. Annotations for protein functions are becoming more available due to the recent growth of protein function databases  The PPI network structure determines the adjacency matrix (1) The adjacency matrix We use the bottom view graph neural networks (BGNN) to learn protein representations. Graph convolutional networks (GCNs) have shown great effectiveness for relational data and are suitable for learning graph-structured protein representations. Thus, we propose BGNN based on GCNs.Given the adjacency matrix 36:Formally, we update residue representations with the neighbor aggregations based on the work of Kipf and WellingWith the learnable weight matrix 39 and average aggregation to obtain the entire graph representation of a fixed size, Finally, we perform the readout operation with a self-attention graph pooling layer37), which has the superb expressive power to capture graph structures. Formally, we are given the PPI graph We use the top view graph neural networks (TGNN) to learn PPI network information. We are inspired by graph isomorphism network (GINAfter three GIN blocks, TGNN produces representations for all proteins. For an arbitrary query pair containing the Given a training set We determine all the hyper-parameters through a grid search based on a 5-fold cross-validation. For BGNN, we set the output dimension 62 to generate explanations for HIGH-PPI. By taking the well-trained GNN model and its predictions as inputs, GNNExplainer returns the most important subgraph by maximizing the mutual information We employ the method called GNNExplainerGiven protein graphs z-scores of final residue importance for standardization:z-score importance after standardization.Assume that all nodes in the subgraph t-test with P-value < 0.05 considered significant.As indicated in figure legends, data in bar charts are represented as mean Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Supplementary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


IQSEC2) gene, located at Xp11. 2, are associated with nervous system diseases, such as epilepsy, autism, and intellectual disabilities. Gender-related differences in the severity of phenotype severity have been described previously. Here, we report the details of seven male children with IQSEC2 mutations from different families. During this investigation, we explored the relationship between the genotype and phenotype of IQSEC2 mutations; to do so, we recruited seven children with pathogenic/likely pathogenic IQSEC2 mutations who were diagnosed with global developmental delay and/or epilepsy. Their clinical features were assessed, and Trio-based whole-exome sequencing (trio WES) was conducted in seven pedigrees. A variety of algorithms and computational tools were used to calculate the pathogenicity, protein stability, conservation, side chain properties, and protein-protein interactions of mutated proteins. The seven patients ranged in age from 18 months to 5 years. Among them, six children were found to have both developmental delay and epilepsy, and one child only exhibited developmental delay. Four novel mutations  were newly reported. Two patients did not have truncated aberrant proteins caused by missense mutations. Still, they did have severe phenotypes, such as early-onset epilepsy in infancy, because the mutations were located in domains like the pleckstrin homology and IQ calmodulin-binding motif domains. The bioinformatics analysis also proved that missense mutations may be located in the functional region, which affects protein stability and is harmful. In summary, severe phenotypes, such as early-onset epilepsy in infancy, occur in male patients with a missense mutation in specific domains . Some female individuals with IQSEC2 mutations may be asymptomatic because of the skewed inactivation of the X chromosome.The isoleucine–glutamine (IQ) motif and Sec7 domain-containing protein 2 ( IQSEC2) gene is associated with neurologic disorders, including intellectual disability (ID), epilepsy, and autism  for the adenosine diphosphate ribosylation factor (ARF) family of GTP-binding proteins  motif and Sec7 domain-containing protein 2 , and magnetic resonance imaging (MRI) findings. Genomic DNA was extracted from the peripheral blood leukocytes of patients and their parents for trio-based whole-exome sequencing (Trio-WES). Each patient's guardians provided informed consent, and the study was approved by the ethics committee of the First Medical Center of the PLA General Hospital.Seven patients with confirmed https://software.broadinstitute.org/gatk/) was applied to detect single-nucleotide variants (SNVs) and indels (<50 bp). The CNVkit was used to detect the copy number variations (CNVs). The 1,000 Genome Project, Genome Aggregation Database, and Exome Aggregation Consortium were employed to annotate the frequency of the variants in the population. Additionally, the Online Mendelian Inheritance in Man (OMIM), Human Gene Mutations database (HGMD), and ClinVar database were employed to annotate related diseases. All variants were evaluated for possible side effects on protein function by three widely used prediction tools, Polymorphism Phenotyping version 2 (PolyPhen-2), Sorting Intolerant from Tolerant (SIFT), and MutationTaster, SpliceAI. Two missense mutations  were further analyzed by the mean of bioinformatics for the pathogenicity. The stability, conservation, side chain properties and protein-protein interactions of IQSEC2 protein were studied. The process will be described in detail in the third part of Methods. The candidate pathogenic variants associated with disease phenotypes were obtained according to American College of Medical Genetics and Genomics (ACMG) guidelines.Trio-WES was performed for probands and their parents. Briefly, venous blood was extracted from all probands and their parents into EDTA anticoagulant tubes. Next, genomic DNA was extracted from venous blood samples using a RelaxGene Blood DNA system . The libraries for WES were constructed using a NanoPrep DNA Library Preparation Module (for MGI), with 96 reactions. Following this, the libraries were sequenced on a BGI MGISEQ-2000 sequencer in 2 × 150-bp paired-end reads at a minimum of 150 × coverage. After obtaining the raw reads, read alignment was performed using the Burrows–Wheeler Aligner tool (version 0.7.17) with default parameters against the human genome assembly hg19 (GRCh37). The generated bam file was sorted and deduplicated using SAMtools and Picard. Next, the Genome Analysis Toolkit . Products were separated with 0.8% agarose gel electrophoresis and purified using a QIAquick gel extraction kit . Subsequently, the effects were sequenced using the 3730 x l DNA analyzer sequencing standards, BigDye™ Terminator version 3.1 , and 3730 x l DNA analyzer (Thermo Fisher Scientific).https://www.ncbi.nlm.nih.gov/homologene). IQSEC2 protein has no complete protein structure data in Protein Data Bank (PDB) format (protein name: 6FAE), and the prediction of full-length protein data as Alphafold was supported by the UniProt database (https://www.uniprot.org) (UniProt ID: Q5JU85).The amino acid sequence of the IQSEC2 protein in FASTA format was acquired from the National Center for Biotechnology Information (The following tools were used to predict the pathogenicity of missense mutations: Combined Annotation-Dependent Depletion (CADD), PolyPhen, SIFT, and MutPred. CADD is a computational tool designed to predict the deleteriousness of variants  was used to construct a sequence-based model and predict the stability change due to single-site mutations  is a bioinformatics tool that predicts protein stability changes combined with torsion angle potentials and structural environment–specific atom potentials  is a software program for predicting the stability change of mutated protein structures. DUET uses a support vector machine to estimate the value and direction of the free energy stability  to evaluate the effect of mutation on protein stability  is a web server that calculates the values of phylop and phastCons  that extracts information from UniProt clinvar, as well as gnomad, onto protein 3D structures in the PDB. It can evaluate whether missense mutations are pathogenic and provide annotations on the secondary structure of variant proteins, interactions with ligands, metals, DNA / RNA, or other proteins , a machine learning computational tool designed to predict the effects of missense mutations on protein-protein interaction binding affinity  treatment; however, his condition was not completely controlled. His MRI finding showed two noticeable abnormalities in de novo nonsense mutation  outside the domain. Patient 2 showed a frameshift mutation , inherited from his mother outside the domain. Patient 3 showed a frameshift mutation , inherited from his mother outside the domain. Patient 4 showed a de novo frameshift mutation  outside the domain. Patient 5 showed a de novo missense mutation  local to the Pleckstrin homology (PH) domain. Patient 6 showed a de novo missense mutation  local to the IQ calmodulin-binding motif domain. Finally, patient 7 showed a de novo nonsense mutation  outside of the domain  on the stability of the IQSEC2 protein varied in terms of prediction, but most outcomes supported the impact of amino acid substitutions on protein stability.When using I-STABLE, amino acid substitutions S1079P and R359C were recorded as increasing the stability of the IQSEC2 protein. In Positive phylop values (2.771 and 0.44) were found, respectively, and the PhastCons values were both 1, which indicated that Ser1079 and Arg359 were conservative. In Incomplete structure of IQSEC2 protein in PDB database, the amino acids sequence 729-1099 could be obtained, and the variant R359C was excluded. Ser in S1079p site corresponded to 728 in PDB. The predicted results showed that serine substitution for proline resulted in a decrease in binding affinity of 0.459kcal/mol. In in silico algorithms were investigated according to the clinical variant interpretation guidelines established by the ACMG.All mutations were explored within databases. Mutations in Patients 1, 2, 3, 4, and 7 led to protein truncation; therefore, they were likely pathogenic according to the ACMG. The other two mutations classified as pathogenic were assessed using mutation prediction tools. Subsequently, the clinical implications and considerations for evaluating IQSEC2 encodes GEFs for the ARF family of GTP-binding proteins  in IQSEC2 in both of their patients and the patients' unaffected mothers who had skewed X chromosome inactivation (XCI) (100:0). We evaluated the mother of patient 3. She showed an extreme skewness ratio of 100:0, with skewed inactivation of the X chromosome carrying the mutated IQSEC2 gene. These hypotheses require further clinical validation.A frameshift mutation in patient 3 was found, inherited from his mother. This mutation causes a truncated aberrant protein. Interestingly, no corresponding symptoms were found in the proband's mother. Wayhelova et al.  identifiIQSEC2 mutations in this paper. Severe phenotypes, such as seizures in early childhood, usually occur in male patients with IQSEC2 mutations, especially mutations in the PH and Sec domains. Some female IQSEC2 mutation carriers are asymptomatic, which is attributed to the skewed inactivation of the X chromosome. This conclusion suggests that a genetic diagnosis of IQSEC2 mutations is necessary for children with global developmental delay and seizures.We reported four novel The data that support the findings of this study are available from the big data center of Chinese People's Liberation Army General Hospital, but restrictions apply to the availability of these data and therefore are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Chinese People's Liberation Army General Hospital.This study was reviewed and approved by the ethics committee of the First Medical Center of the PLA General Hospital. Written informed consent was obtained from the minor(s)' legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.GY contributed to the conception and design of the study. LW and XL organized the database and wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This research was partially supported by the Natural Science Foundation of Beijing, China (No. 7222187), the Medical Big Data and Artificial Intelligence Research and Development Project of the Chinese PLA General Hospital (No. 2019MBD-004), the Epilepsy Research Fund of China Association Against Epilepsy (No. CU-B-2021-11), and the Nutrition and Care of Maternal & Child Research Fund Project of Guangzhou Biostime Institute of Nutrition & Care (No. 2021BINCMCF030).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The transmembrane glycoprotein CD93 has been identified as a potential new target to inhibit tumor angiogenesis. Recently, Multimerin-2 (MMRN2), a pan-endothelial extracellular matrix protein, has been identified as a ligand for CD93, but the interaction mechanism between these two proteins is yet to be studied. In this article, we aim to investigate the structural and functional effects of induced mutations on the binding domain of CD93 to MMRN2. Starting from experimental data, we assessed how specific mutations in the C-type lectin-like domain (CTLD) affect the binding interaction profile. We described a four-step workflow in order to predict the effects of variations on the inter-residue interaction network at the PPI, based on evolutionary information, complex network metrics, and energetic affinity. We showed that the application of computational approaches, combined with experimental data, allowed us to gain more in-depth molecular insights into the CD93–MMRN2 interaction, offering a platform for developing innovative therapeutics able to target these molecules and block their interaction. This comprehensive molecular insight might prove useful in drug design in cancer therapy. CD93 is a single-pass transmembrane glycoprotein belonging to the group XIV family of the C-type lectin-like domain (CTLD) superfamily , and it in vitro angiogenesis  article by using chimeric constructs containing the extracellular domains of CD93 fused to Myc, and the MMRN2 wild type fused to a His tag were generated as previously described . Figure in silico model structures and docking studies were performed to investigate the region of CD93 and MMRN2 involved in the interaction  with enough accuracy to determine the three-dimensional structure of the protein complexes; consequently, it can be used to screen the reliability of the selected poses . All resThe second step evaluated if the two selected poses fitted with experimental results derived from the study of As a third step, we mapped the gnomAD missense variants to the structure docking poses , obtainiIn silico and experimental procedures were used as a basis to determine the CD93 structure–function relationship. The CD93/MMRN2 complex was analyzed in vitro, dissecting interactions occurring in specific conditions. Protein docking procedures were used to predict the involvement of specific amino acid residues in the CD93/MMRN2 interaction. Structural analyses were conducted using bioinformatic tools for protein–protein interface regions, showing the key role of amino acid residues in the interaction. Here, we provide an approach to evaluate the best pose of protein–protein complex structures according to new experimental data. With the application of bioinformatic tools, we have described a four-step workflow to predict effects of variations on the inter-residue interaction network at the PPI, based on evolutionary information, complex network metrics, and energetic affinity; in addition, it allows us to map and explore regional intolerance to missense variation. These observations could provide a basis to strengthen the bioinformatics process involved in the development of new drugs.in silico and experimental procedures was effective for the determination of the structure–function relationship in a protein–protein interaction. However, it is important to emphasize that this exploratory study is based on only two docking poses; thus, it could be interesting to expand and further verify this four-step method on a larger case series to screen a higher number of potential docking poses (A comparison of ng poses .


Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Structural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution. PLOS Computational Biology Methods paper.This is a Identifying structural features associated with macromolecular dynamics is crucial to our understanding of the underlying physical behavior of proteins and their broader impact on biology and health. Structural and dynamical properties of proteins often serve as signatures of their functions and activities . Subtle Conventionally, the structural dynamics of proteins have been modeled using molecular dynamics (MD) simulations, which work by sampling structural ensembles from conformational landscapes. In infinite timescales, such structural ensembles are expected to represent all physical states such that their ensemble-averaged observables converge to true physical values and are thus physically meaningful. While MD simulations have provided key insights into the atomistic motions that underpin many protein functions , biologiA to protein B must be differentiable). There are many scenarios in protein dynamics where no such transformation is guaranteed because atomic features can be gained or lost during the evolution of the system  with the natural interpretation of providing relative evidence of association for each DEC statistic . From a classical hypothesis testing point-of-view, the null hypothesis for There are two key takeaways from this scaled formulation. First, the KLD is non-negative, and it equals zero if and only if the posterior distribution of β . In this setting, the term −log(|Σj−Λ−j|) + tr(Σj−Λj−) + (1 − p) remains relatively equal for each feature j and makes a negligible contribution to the entire sum. Thus, we simplify In practice, we use a few approximations to scale the otherwise computationally expensive steps in Λ. For a large number of topological features J, this calculation is expensive; however, it is only done once and and can be done with efficient matrix decomposition. The rate of change parameter j-th topological feature. This requires inverting a (J − 1) × (J − 1) matrix J times. Fortunately, we can reduce this computational burden by taking advantage of the fact that any j-th row and column from the precision matrix Λ. Therefore, given the partition in This approximation of the KLD still relies on the full precision matrix σj is the j-th column from the posterior covariance matrix Σ, and each j-th row and column from Ωj) operations which can be parallelized.Here, γ for each topological feature computed via reconstruction algorithm . A maximum of 100 represents the threshold at which the first atom of the protein is reconstructed, while 0 denotes the threshold when the last atom is reconstructed. Annotated are regions of interest (ROIs) according to literature sources that have previously suggested some level of structural association for each chemical change of interest, including: (i) the Ω-loop (residues 163–178) in TEM; (ii) the flap region (residues 47–55) in HIV-1 protease; and (iii) Domain 2 (residues 208–308) in EF-Tu.In these analyses, we compare the molecular dynamics (MD) trajectories of alternative states for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S6 Fig(i) Met290Val Abl1 and (ii) unbound Importin-β, respectively. We analyze Abl1 based on different fragments the protein. Specifically, we analyze (A) the whole protein structure, (B) residues 242–502, and (C) residues 242–315. Here, consistency between fragments within a protein type shows the robustness of SINATRA Pro to identify the same signal even when it does not have access to the full structure. The heatmaps highlight the atomic evidence potential on a scale from [0–100]. A maximum of 100 represents the threshold at which the first atom of the protein is reconstructed, while 0 denotes the threshold when the last atom is reconstructed. Annotated are regions of interest (ROIs) according to literature sources that have previously suggested some level of structural association for each chemical change of interest, including the DFG motif in Abl1. Note that, in the context of Importin-β, the superhelix includes the entire structure and so we do not include any additional annotations.In these analyses, we compare the molecular dynamics (MD) trajectories of the alternative states for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S7 Figβ-lactamase versus the Arg164Ser mutant  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) whole protein, (B) fragment 65–230, and (C) fragment 65–213. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the molecular dynamics (MD) trajectories of wild-type TEM r mutant , 22. Forysis see  with the(PNG)Click here for additional data file.S8 Figβ-lactamase versus the Arg164Ser mutant  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). This figure depicts results after applying SINATRA Pro using parameters {r = 6.0Å, c = 20, d = 8, θ = 0.80, l = 120} chosen via a grid search. We compare these results to the analyses with chains A and B presented in the main text. Here, the signal in the region of interest  persisted in the dimeric form, but is overshadowed by the noise in the other parts of the protein since the relative orientation between the two monomers causes each chain to be misaligned with itself. Highlighted are residues for regions of the protein corresponding to the fulcrum (brown), elbow (purple), flap (blue), cantilever (red), and I/V50 (yellow)  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) chain A and (B) chain B, respectively. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the molecular dynamics (MD) trajectories of wild-type HIV-1 protease versus the Ile50Val mutant  , 31, 32.ysis see  with (A)(PNG)Click here for additional data file.S11 FigtMD =  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). This figure depicts results after applying SINATRA Pro using parameters {r = 6.0Å, c = 20, d = 8, θ = 0.80, l = 120} chosen via a grid search. The heatmaps in panels (A)-(C) highlight residue evidence potential on a scale from [0–100]. A maximum of 100 represents the threshold at which the first residue of the protein is reconstructed, while 0 denotes the threshold when the last residue is reconstructed. Panel (A) shows residue-level evidence potential when applying SINATRA Pro to the whole protein, while panels (B) and (C) illustrate results when strictly applying the SINATRA Pro pipeline to atoms in residues 208–308 and 311–405, respectively. Panel (D) shows the association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (E) whole protein, (F) fragment 208–308, and (G) fragment 311–405.In this analysis, we compare the molecular dynamics (MD) trajectories of GTP-bound EF-Tu versus GDP-bound EF-Tu , 87, 88.ysis see . Here, t(PNG)Click here for additional data file.S13 FigtMD =  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) whole protein, (B) fragment 208–308, and (C) fragment 311–405. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the the molecular dynamics (MD) trajectories of GTP-bound EF-Tu versus GDP-bound EF-Tu , 87, 88.ysis see  with the(PNG)Click here for additional data file.S14 FigtMD =  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) whole protein, (B) fragment 242–502, and (C) fragment 242–315. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the the molecular dynamics (MD) trajectories of wild-type Abl1 kinase domain versus the Met290Val mutant , 89–91. ysis see  with the(PNG)Click here for additional data file.S16 FigtMD =  × 1.5 ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 3000 proteins in the study: 150 ns long interval × 15 different choices δ = {0.0, 0.1, 0.2, …, 1.4} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis  trajectories of IBB-bound Importin-portin-β –47. For ysis see . The ove(PNG)Click here for additional data file.S18 Figβ versus unbound Importin-β  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). This figure depicts results after applying SINATRA Pro using parameters {r = 6.0Å, c = 20, d = 8, θ = 0.80, l = 120} chosen via a grid search. The heatmap in panels (A) highlights residue evidence potential on a scale from [0–100]. A maximum of 100 represents the threshold at which the first residue of the protein is reconstructed, while 0 denotes the threshold when the last residue is reconstructed. Panel (B) plots the correlation between the SINATRA Pro association metrics and the root mean square fluctuation (RMSF) for all atoms with correspondences. Panel (C) shows the SINATRA Pro association metrics (and their corresponding standard errors) computed for each residue within the analysis  trajectories of IBB-bound Importin-portin-β –47. For (PNG)Click here for additional data file.S19 Figβ versus unbound Importin-β  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization [(A) shows the mean absolute coefficient of all atoms within each residue computed over each fragment-based analysis (see (B), we plot the correlation between the SINATRA Pro association metrics and the Elastic Net coefficients for all atoms with correspondences.In this analysis, we compare the molecular dynamics (MD) trajectories of IBB-bound Importin-portin-β –47. For rization  to assig(PNG)Click here for additional data file.S20 Fig(A) Arg164Ser mutant β-lactamase, (B) Ile50Val mutant HIV-1 protease, and (C) GDP-bound EF-Tu, respectively. We analyze datasets based on different fragments of each protein. Specifically, (A) in TEM β-lactamase, we analyze the whole protein structure, residues 65–230, and residues 65–213; (B) in HIV-1 protease, we analyze chain A and chain B; and, (C) in EF-Tu, we analyze the whole protein structure, residues 220–310, and residues 311–405. The y-axis denotes the absolute difference (or Δ-change) in RMSF between wild-type and mutants classes.In these analyses, we compare the molecular dynamics (MD) trajectories of the alternative state for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S21 Fig(A) Met290Val Abl1 and (B) unbound Importin-β, respectively. We analyze Abl1 based on different fragments the protein. Specifically, we analyze the whole protein structure, residues 242–502, and residues 242–315. Note that, in the context of Importin-β, the superhelix includes the entire structure and so we do include any additional sub-fragment analyses. The y-axis on denotes the absolute difference (or Δ-change) in RMSF between wild-type and mutants classes.In these analyses, we compare the molecular dynamics (MD) trajectories of the alternative state for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S22 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) the whole protein, (B) fragment 65–230, and (C) fragment 65–213. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S23 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) chain A and (B) chain B. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S24 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) whole protein, (B) fragment 208–308, and (C) fragment 311–405. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S25 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) the whole protein, (B) fragment 242–502, and (C) fragment 242–315. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S26 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S27 Figimplicitly normalize the 3D protein structures by rotationally aligning their topological summary statistics. To carry out this alignment procedure, we first take each pair of protein structures and superimpose the center of mass of the backbone alpha-carbons (Cα) atoms to the same origin. Next, we compute topological summary statistics over the mesh representation of each structure in m = 500 spherically uniformly distributed directions (see the R) between the association metrics from SINATRA Pro calculated from the structures aligned using Euler characteristic (EC) transform and structures pre-aligned using RMSD for β-lactamase: (A) the whole protein, (B) fragment 65–230, and (C) fragment 65–213; monomers of HIV-1 protease: (D) chain A and (E) chain B; and fragments of the elongation factor EF-Tu: (F) the whole protein, (G) fragment 220–310 (Domain 2), and (H) fragment 311–405 (Domain 3). Correlations near one symbolize high agreement between the two alignment schemes.Here, we perform sequence-independent structural alignment where we (PNG)Click here for additional data file.S28 Figimplicitly normalize the 3D protein structures by rotationally aligning their topological summary statistics. To carry out this alignment procedure, we first take each pair of protein structures and superimpose the center of mass of the backbone alpha-carbons (Cα) atoms to the same origin. Next, we compute topological summary statistics over the mesh representation of each structure in m = 500 spherically uniformly distributed directions (see the R) between the association metrics from SINATRA Pro calculated from the structures aligned using ECT and structures pre-aligned using RMSD for fragments of Abl1 Tyrosine protein kinase: (A) the whole protein, (B) fragment 242–502, and (C) fragment 242–315; and (D) unbound Importin-β. Correlations near one symbolize high agreement between the two alignment schemes.Here, we perform sequence-independent structural alignment where we (PNG)Click here for additional data file.S1 Table(i) the total number of proteins analyzed N = 50, (ii) the number of cones of directions c = {15, 20}, (iii) the number of directions within each cone d = {4, 8}, and (iv) the number of sublevel sets  used to compute the Euler characteristic (EC) along a given direction l = {25, 50}. We simulate 10 different datasets for each combination of parameter values. Values appearing after the ± symbol are the standard deviations of these estimated times across the different runs. Each analysis was performed using simulated protein structures with ∼2700 atoms and all runtimes were computed using a central processing unit (CPU) with 8 cores and 128 gigabytes (GB) of RAM.Each entry represents the time (in seconds) it takes to run each step of the SINATRA Pro algorithm based on: (PDF)Click here for additional data file.S2 Table(i) the total number of proteins analyzed N = 100, (ii) the number of cones of directions c = {15, 20}, (iii) the number of directions within each cone d = {4, 8}, and (iv) the number of sublevel sets  used to compute the Euler characteristic (EC) along a given direction l = {25, 50}. We simulate 10 different datasets for each combination of parameter values. Values appearing after the ± symbol are the standard deviations of these estimated times across the different runs. Each analysis was performed using simulated protein structures with ∼2700 atoms and all runtimes were computed using a central processing unit (CPU) with 8 cores and 128 gigabytes (GB) of RAM.Each entry represents the time (in seconds) it takes to run each step of the SINATRA Pro algorithm based on: (PDF)Click here for additional data file.S3 Table(i) the total number of proteins analyzed N = 200, (ii) the number of cones of directions c = {15, 20}, (iii) the number of directions within each cone d = {4, 8}, and (iv) the number of sublevel sets  used to compute the Euler characteristic (EC) along a given direction l = {25, 50}. We simulate 10 different datasets for each combination of parameter values. Values appearing after the ± symbol are the standard deviations of these estimated times across the different runs. Each analysis was performed using simulated protein structures with ∼2700 atoms and all runtimes were computed using a central processing unit (CPU) with 8 cores and 128 gigabytes (GB) of RAM.Each entry represents the time (in seconds) it takes to run each step of the SINATRA Pro algorithm based on: (PDF)Click here for additional data file.


Recent advances in the field of protein structure determination using liquid-state NMR enable the elucidation of multi-state protein conformations that can provide insight into correlated and non-correlated protein dynamics at atomic resolution. So far, NMR-derived multi-state structures were typically evaluated by means of visual inspection of structure superpositions, target function values that quantify the violation of experimented restraints and root-mean-square deviations that quantify similarity between conformers. As an alternative or complementary approach, we present here the use of a recently introduced structural correlation measure, PDBcor, that quantifies the clustering of protein states as an additional measure for multi-state protein structure analysis. It can be used for various assays including the validation of experimental distance restraints, optimization of the number of protein states, estimation of protein state populations, identification of key distance restraints, NOE network analysis and semiquantitative analysis of the protein correlation network. We present applications for the final quality analysis stages of typical multi-state protein structure calculations.The online version contains supplementary material available at 10.1007/s10858-022-00392-2. Biological functions of proteins covering target recognition, signal transduction and protein–protein interactions rely on protein dynamics and motion , experimental lower limits (c = l), and steric lower limits (c = v), Ic is subset of restraints of type c that are currently violated, and Conventional NMR structures are calculated following standard procedures using for example the software CYANA  clustered in 2 clusters by a Gaussian Mixture Model algorithm based on the distances from a selected residue to all other protein residues. This yields a set of residue-specific conformer clusterings. These clusterings are then compared to each by calculating their mutual information, measured in bits,A can be averaged to a single number, the structural correlation parameter μ, that will be used extensively in this paper:μ shows how much, on average, the state-identity of the conformers calculated from the distances collected from one residue tells us about the state-identity of the conformers calculated from the distances collected from another residue. The structural correlation parameter The recently introduced PDBcor algorithm  does not favor it over the structure bundle lacking it (V = 6.43 Å2). In contrast to the target function values, the average structural correlation values clearly favor the calculation with this distance restraint (μ = 0.552) over the calculation without it (μ = 0.276). Therefore, the use of structural correlations from PDBcor is considered advantageous in the refinement stage of multi-state structure calculation.Structural correlations N-state system, structural correlations are expected to increase approaching the N states with increasing model complexity that is able to properly fit the experimental data and counter underfitting. In addition, if less than the existing number of states are included in the structure calculation, violations of the experimental restraints occur . Together they constitute a powerful tool for the validation of NMR-based multi-state structures.https://github.com/dzmitryashkinadze/PDBcor) Below is the link to the electronic supplementary material.


The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database ( TogethGiven this, a number of meta-resources dedicated to data integration in the protein network context have been developed. These resources aim to collect and critically assess protein–protein functional linkage data, integrate it, connect it to previous knowledge, and allow users to browse, compare and retrieve organism-wide protein networks. Among frequently used and actively maintained frameworks are ConsensusPathDB , FunCoupResearchers employ protein network meta-resources for a wide variety of purposes, broadly falling into three categories: (i) facilitating individual, small-scale molecular discoveries, (ii) facilitating large-scale data analysis and (iii) contributing to new methodologies and workflows. With respect to STRING, it has, for example, proven useful in interpreting and reducing newly acquired genetic screening datasets—these are sometimes noisy and unwieldy, and STRING can be used to distill such data into more manageable sets of observations and hypotheses. As a case in point, consider three recent screens for SARS-CoV-2 human host factors . All thrHere, we provide an update on the current features of the STRING database and describe some novel developments in more detail. The latter include a complete redesign of the co-expression based interaction prediction pipeline, newly exposed sub-scores for experimental dataset confidence, as well as novel facilities allowing users to upload and analyze any newly sequenced genome of interest.The scope of protein–protein links in STRING is that of a ‘functional association’ —proteinsAll protein–protein association evidence in STRING is assessed and quantified, and its correspondence to the above definition is benchmarked against common membership in KEGG pathway maps . The result of this benchmarking/calibration is a STRING ‘confidence score’ for each association; confidence scores are scaled between zero and one, and correspond to the estimated likelihood of a given association being true, given the underlying evidence. Confidence scores are first computed separately per evidence type see  for an eAll confidence scores in STRING are pre-computed and freely available for download, under a Creative Commons Attribution license (CC BY 4.0). The various evidence types are grouped into seven distinct ‘evidence channels’, with separate sub-scores available for each channel. These channels can also be individually viewed on the STRING user interface, together with their underlying evidence, and can be enabled or disabled separately as desired. Of the seven channels, the first three  are dealing with association evidence that can be gleaned from genome sequences alone. These so-called ‘genomic context’ channels  are The deep learning-based relation extraction text mining model has received significant upgrades in the current version. Specifically, the language representation model that we use has changed from BioBERT  to a bioAll protein–protein associations assembled for the STRING database are then transferred across organisms, where applicable, based on orthology relationships with the assumption that orthologs of associated proteins are likewise associated . For thOne of the unique features of STRING has always been its support for a large selection of non-model organisms: the current version of the database contains protein–protein interaction networks  for >10 000 distinct genomes. When selecting which genomes to include in a STRING update, key factors are the organism's research prominence, genome quality, and completeness. Subsequently, STRING utilizes genomes from authoritative sources only, including Ensembl , UniProtHowever, new genomes are sequenced and assembled daily, and existing genomes are re-sequenced or re-annotated; overall, the number of taxonomically distinct species that are being sequenced has been doubling roughly every 3 years . The UniTo improve this situation, STRING now allows its users to upload any fully sequenced proteome , enabling them to browse and query the predicted protein–protein interactions in an identical manner to the genomes already natively covered by the STRING database. This includes access to the evidence viewers, homology viewer, network clustering methods, gene set enrichment analysis, bulk download, and REST API access. The outline of the proteome annotation pipeline is shown in Figure The network prediction based on orthology is then performed as described previously . In paraThe degree of co-expression between RNAs (or between proteins) across different conditions provides an essential insight into the functional protein–protein interaction network of a cell . STRING The expression data tends to be sparse, high-dimensional and highly redundant. These attributes decrease the performance of correlations using Pearson Correlation Coefficient. For previous versions of STRING we have reduced the redundancy by removing highly correlated expression matrices before correlating the gene expression which, in turn, increased the recovery of the functional associations derived from these matrices. However, this did not fully address all of the challenges of such datasets.To address that, in the latest version we have utilized a novel method called FAVA   to buildRNA-Seq datasets have increased sensitivity for genes exhibiting low levels of expression ,70. ThisFor the experiments channel, STRING integrates pairwise experimental interaction evidence from BioGRID, IntAct, MINT, and others , see Fig. Each reFor interactions detected by HT experiments, benchmarking against KEGG molecular pathway maps and scoring is performed separately for each experiment: each pair is assigned a raw score based on the number of shared and non-shared interactors for both proteins within this experiment. The more shared and the fewer non-shared interactors, the higher the raw quality score of the protein pair from this experiment. Based on the raw score, all pairs detected in the experiment are ranked and benchmarked against KEGG. A typical STRING calibration function is derived for each HT experiment, which is then used to assign a functional association score to each protein pair identified in the experiment .https://www.ebi.ac.uk/ols/ontologies/mi, . SpecifThe highest number of experimentally detected associations has been derived by affinity chromatography technologies (40%), followed by genetic inference (34%) and transcriptional complementation assays (11%). Out of these, 82% came from HT experiments. The associations derived from these three HT experiments, on average, place below or around a confidence score of 0.25 Figure , while, In STRING’s ‘experimental’ evidence viewer, the user can now better appreciate the reliability of each experimental prediction, as the individual confidence for every HT and LT dataset is now communicated to the user in a form of three-tier confidence grade .Version 12.0 of STRING covers a phylogenetically diverse collection of 12 535 high-quality genomes. Beyond these, the system will record which genomes are frequently submitted by users—and these will then be prioritized for inclusion into subsequent releases. In addition, the results of an ongoing online user survey will be analyzed . This way, STRING will keep concentrating its resources on those areas that are of most interest to its users.STRING is freely available, under a Creative Commons Attribution license (CC BY 4.0).


Protein docking provides a structural basis for the design of drugs and vaccines. Among the processes of protein docking, quality assessment (QA) is utilized to pick near-native models from numerous protein docking candidate conformations, and it directly determines the final docking results. Although extensive efforts have been made to improve QA accuracy, it is still the bottleneck of current protein docking systems. In this paper, we presented a Deep Graph Attention Neural Network (DGANN) to evaluate and rank protein docking candidate models. DGANN learns inter-residue physio-chemical properties and structural fitness across the two protein monomers in a docking model and generates their probabilities of near-native models. On the ZDOCK decoy benchmark, our DGANN outperformed the ranking provided by ZDOCK in terms of ranking good models into the top selections. Furthermore, we conducted comparative experiments on an independent testing dataset, and the results also demonstrated the superiority and generalization of our proposed method. Docking sampling candidates can be naturally represented as graphs according to their 3D structures, in which residues are treated as nodes and residue-residue contacts as edges so that the QA process can be formulated as a graph classification problem. GNN as a graph-oriented deep learning architecture, can learn node latent representations across the global topology of a graph. Such an advantage provides an adaptive way to generate deep residue (node) representations considering neighboring residue interactions, energetic contributions and local graph topology. So that GNN can distinguish near-native models by examining these inter-residue representations at protein-protein interfaces.We formulated the protein docking model QA process as a binary classification problem, which takes a 3D structure of the candidate docking model as the input and outputs its probability of a near-native model. The framework of our proposed method is shown in In this paper, we proposed DGANN to implement it, which was adapted from Deep Graph Convolutional Neural Network (DGCNN)  as shownIn practice, we build the graph on the entire docking model with learn-deep representations of all residues by stacking GAT layers over the whole graph. By doing this, embedding of each node can model its interactions with surrounding residues. Especially the residues at the edge of the docking interface can aggregate information not only from neighbors inside the interface but also other residues outside the interface. Based on these residue (node) embeddings, we further generated protein-protein interface representations and conducted QA scoring in DGANN as Datasets Section), since ZDOCK can generate all docking models and their QA results using the ZDOCK scoring function. Evaluation Metrics Section) of good models at different top ranks from our ensemble DGANN results and ZDOCK results. In this experiment, we tried the mean and maximum scores from all meta-classifiers as ensemble results and checked their EFs at 0.01, 0.1, 0.5, 1, 5 and 10% top-scores as shown in For fair comparisons, we benchmarked on the docking models from ZDOCK  at different top ranks of DGANN and ZDOCK. max to remove the biases caused by different imbalanced degrees among different protein complexes, while the success rate only reported an overall ability to hit positives  embeddings may introduce redundant and noisy residue interactions and damage their discerning power.k residue (node) embeddings at the interface region to define the fixed size of graph-level representations for further QA scoring. Therefore, it is necessary to select a reasonable k to keep generalization and prevent information loss across all protein complexes. For the cases with fewer than k nodes, we padded the placeholders 0 to fix the output of top-k-pooling.In DGANN, the top-k-pooling module picks the best k, to show its influence on QA results. Another option to squeeze node-level features with various shapes to graph-level features with a fixed shape is the pooling operation. Here, we also attempted both global max pooling and global mean pooling over all nodes at the interface.k are shown in k equals 100, the top-k-pooling strategy slightly outperformed other k options and two global pooling approaches in terms of true positives, true negatives and EFs. According to k value implies that almost all the residues at the interface provide informative features and contribute to the selection of near-native models. Between the two global pooling methods, mean-pooling took the total information from all interfacial residues while max-pooling kept significant signals from interfacial residues. The top-k-pooling with an optimal k can make a good balance, so that it achieved relatively better performance.The confusion matrices and EFs of different Significant similarity between the training data and the test data may cause performance overestimation for a machine learning model due to overfitting. To investigate the effect of similar targets between the training set and test set on the performance, we went through different identity thresholds in CD-HIT  clusteriDOVE  and iScoThe success rates on the independent dataset were shown in As shown in In our work, we evaluated DGANN on a ZDOCK decoy set, which provided all docking models and their QA racking results. The dataset covered the docking sampling results from ZDOCK3.0.2 on 176 protein complexes from protein docking benchmark 4.0 , each ofIn addition to the ZDOCK benchmark 4.0 dataset, we also used benchmark 5.5 for the independent test set. The ZDock benchmark 5.5 includes 97 target complexes that are independent of the ZDOCK benchmark 4.0 dataset.Three indicators iRMSD (root mean square deviation of the interface residues), fnat (the fraction of receptor-ligand residue contacts in the native structure that is reproduced in the decoy) and lRMSD (root mean square deviation of the ligand residues)  were calWe took the protein docking models falling into this category as negative samples, and others as positive samples. However, some protein complexes in the ZDOCK decoy set did not contain any near-native docking models based on CAPRI criterion. Besides, lRMSD, fnat and iRMSD of some protein docking models could not be computed owing to the inconsistency of the sequences between decoys and native complexes . After fThe 3-D structure of each candidate docking model was transformed to a graph, where its nodes were all residues at the model and its edges were defined according to the distance between any atoms of two residues. If the distance is less than 5 Å, we assigned an edge to connect them. After obtaining the graph structure of each protein docking model, we encoded each node (residue) with physico-chemical properties and sequence conservation information. For residue physico-chemical properties, we employed five principal components from 237 AAIndex physical-chemical properties presented by Mathura  to derivhttps://github.com/coffee19850519/PPDocking/tree/master.Since we formulated the protein docking model QA as a graph classification problem, any graph neural network for graph classification can be used as a classifier. Here, we modified DGCNN  with GATGAT module stacks two GAT layers to learn node embeddings by aggregating their neighbors’ features under the style of self-attention. This type of layer can specify different weights to different neighboring nodes according to their node attributes. The attention coefficients i and its first-order neighbor j are calculated as follows:W is a learned weight matrix, i and node j. j to node i. And then i can be computed with an activation faction by the following equation:Finally, the embedding of node In our work, the activation function is tanh. Through these calculations, each 26-D input node encoding is mapped into the 32-D node features by using shared weights with the shape of 26 × 32. The node features implicitly include the interactions between two direct neighboring residues and local structural information. The upper GAT layer aggregates longer distanced neighbors’ node embeddings and maps them to a scalar by using another shared weights with the shape of 32 × 1 for each node, which is deemed as the importance of the node over the whole graph. We converted the entire docking model into a graph and learned embedding of all residues by stacking GAT layers over the whole graph.Top-k-pooling module was utilized to obtain a fixed size of graph-level representations for different protein docking models. Since the protein-protein interface is the most distinctive and informative region, we only pooled the residue (node) embeddings at protein-protein interface to a graph-level representation. At this stage, only the interface residues are used in the pooling operations. However, because the number of residues at interfaces varied among protein docking models, we needed to unify the size of graph-level representations for further prediction. We used the top-k-pooling operation proposed by DGCNN to sort the node embeddings according to their importance and keep the top k nodes. For the models with fewer than 100 interface residues, we padded the placeholders 0 to fix the output shape of top-k-pooling. And then the two GAT outputs of the selected k nodes are read out and concatenated to form the graph-level embeddings of protein-protein interfaces.QA scoring module supplied a 1-D convolutional layer and a fully connected layer to generate flatten feature vectors on the graph-level embeddings. Finally, a sigmoid function is employed to predict the probabilities of native-like models as QA scores.Pipeline of Our Method Section, we performed 5-fold cross-validation and trained our DGANN on 100 balanced bootstrapping subsets for ensemble learning. But for validating DGANN, all trained classifiers were tested on all imbalanced 3,600 candidate docking models of each protein complex.In the training stage, we adopted binary cross-entropy  as a losmax is the ratio of all docking models and native-like models, which indicates the degree of difficulty to select the positives from all docking models of a given protein-protein complex. EF shows the ability to enrich the number of native-like models at the top-scoring points compared to a random selection. Success rate is the percentage with at least one successful prediction when having a certain number of top ranks. In our experiments, each test protein complex’s metric values were averaged over outputs from the models in the 5-fold cross-validation as the final QA performance.To validate the performance of our method, we used two evaluation measures: Enrichment Factor (EF) and success rate. The EFx% was defined as the ratio of the number of near-native models at x% top ranks relative to the number of testing docking models from a given protein-protein complex as follows:In this study, we presented a deep graph neural network-based approach named DGANN for protein docking QA. We first formulated protein docking QA as a graph classification problem and converted 3D structures of protein docking models into graphs. In DGANN, we used GAT layers to aggregate the neighboring residue properties using the attention mechanism, which implicitly captured neighboring residue interactions and local topology information in node embeddings. Such multi-body interactions at residues were shown to be effective in selecting native-like protein structure models. Furthermore, a top-k-pooling strategy was employed to select a fixed size of residue (node) embeddings from protein docking interfaces to form graph-level representations for prediction. Our GNN model is significantly different from an earlier GNN model for docking evaluation . In the Our work demonstrated that graph neural network can naturally extract the multi-residue interaction and topology information from molecular structures. For future work, we plan to include more residue features, such as secondary structural descriptors, solvent accessibility and electrostatic effects, into the node attributes for further improving graph representations. In addition, we will explore constructing atomic level graphs of protein docking models, which may provide higher-resolution predictions. And more advanced graph classification GNN architectures should be explored in the field of protein docking QA in the future. Moreover, our work also indicates the power of ensemble learning to address the imbalanced problem. However, only simple linear combination strategies were implemented in this work. Nonlinear ensemble i.e., stacking strategies  may also