Also, the proposed method is available as a web application from https://cbph.ir/tools/kludo.Though proposing algorithmic approaches for protein domain decomposition has been of high interest, the inherent ambiguity to the problem makes it still an active area of research. Besides, accurate automated methods are in high demand as the number of solved structures for complex proteins is on the rise. While majority of the previous efforts for decomposition of 3D structures are centered on the developing clustering algorithms, employing enhanced measures of proximity between the amino acids has remained rather uncharted. If there exists a kernel function that in its reproducing kernel Hilbert space, structural domains of proteins become well separated, then protein structures can be parsed into domains without the need to use a complex clustering algorithm. Inspired by this idea, we developed a protein domain decomposition method based on diffusion kernels on protein graphs. We examined all combinations of four graph node kernels and two clustering algorithms to investigate their capability to decompose protein structures. The proposed method is tested on five of the most commonly used benchmark datasets for protein domain assignment plus a comprehensive non-redundant dataset. The results show a competitive performance of the method utilizing one of the diffusion kernels compared to four of the best automatic methods. Our method is also able to offer alternative partitionings for the same structure which is in line with the subjective definition of protein domain. With a competitive accuracy and balanced performance for the simple and complex structures despite relying on a relatively naive criterion to choose optimal decomposition, the proposed method revealed that diffusion kernels on graphs in particular, and kernel functions in general are promising measures to facilitate parsing proteins into domains and performing different structural analysis on proteins. The size and interconnectedness of the protein graphs make them promising targets for diffusion kernels as measures of affinity between amino acids. The versatility of our method allows the implementation of future kernels with higher performance. The source code of the proposed method is accessible at The online version contains supplementary material available at 10.1186/s12859-022-04902-9. The first step in understanding protein function, evolution, and three-dimensional organization is typically to partition its structure into more elementary units called domains. Protein domains are compact and recurring units of proteins that are able to fold and function independently. Though it has been almost half a century since the introduction of the concept in 1973 by Wetlaufer –4: (1) tThough there exist several methods that attempt to predict domain boundaries from amino acid sequences, experimentally determined 3D structures of proteins provides rich information of atom coordinates that makes it a better starting point for delineation of the chains with readily available spatial structure. Generally, there are three types of approaches for the systematic identification of structural domains from 3D structure : manual,Exponential growth in the number of solved protein structures in the last two decades overwhelms human expert inspection and thus favors fully automated approaches . In factDespite the rich history of domain assignment techniques, agreement among the different algorithms over a dataset rarely exceeds 80 percent of the consisting structures  as it isFrom the viewpoint of computer science, protein domain assignment is a clustering problem, therefore as a matter of course, the main focus of the previous methods was the clustering algorithms they employed for protein partitioning. While most authors have tried to develop complex clustering algorithms, but designing a measure of affinity (proximity) between the amino acids that eases the clustering problem has remained rather uncharted.k appears as the inner product k we are able to obtain affinity between each pair of points in the unknown space Given a set of is space . Heretofis space , 49.K on a graph is a symmetric positive semi-definite matrix, with entries Graph node kernels express affinity between each pair of nodes in a graph. A kernel matrix terature . The corStructural domains of proteins are so entangled that, in most cases, clustering of the amino acid residues using the Euclidean distance measure would not lead to appropriate results . In pracIf there exists a kernel function that in its reproducing kernel Hilbert space (RKHS), structural domains of proteins become well separated, then protein structures can be parsed into domains without the need to use a complex clustering algorithm. Inspired by this idea, we developed a protein domain decomposition method based on diffusion kernels on protein graphs. We examined all combinations of four graph node kernels and two clustering algorithms to investigate their ability to decompose protein structures into structural domains.The performance of our method is tested on five of the most widely used benchmark datasets plus a set of protein chains with less than 40% homology based on SCOPe v2.07 . The resIn this section we present our proposed method: Diffusion Kernel-based Graph Node Clustering for Protein Domain Assignment (KluDo). For a protein chain this method consists of 6 steps: 1) collecting structural information, (2) graph construction, (3) single/multi-domain classification, (4) kernel matrix calculation, (5) obtaining candidate clusterings, and (6) determining the number of domains. Figure  collectiIn the first step, required structural information about the protein structure are obtained. Secondary structure and accessible surface area information are extracted from the protein structure using the DSSP tool , 53. RelA weighted undirected graph is constructed based on the protein structure such that each node represents an amino acid residue, and each pair of nodes is connected through an edge if there exists at least one atomic contact between the two amino acids. In our case, it is assumed that two atoms are in contact if their Euclidean distance is equal to or less than 4 Å. The number of atomic contacts between each pair of residues is considered as the weight of the edge between the two corresponding nodes. Further, to evaluate the plausibility of our approach for constructing the protein graphs a set of randomization tests were performed  classifier is used to categorize the input protein structure into either single or multi-domain classes. The classifier consists of a set of weak binary classifiers each trained on a balanced bootstrap sample of the training set (described below). This way, balanced sets are generated (by resampling) while avoiding to neglect any part of data (by multiple bootstrapping).To provide training data for the classifier, we subtracted ASTRAL40 v2.07) and five well-known protein domain assignment benchmark datasets , a grid search over the training set using 5-fold cross-validation was performed. As a result, the combination of 190 decision trees and SMOTE resampling algorithm , 58 was The input features for the single/multi-domain classifier can be grouped into three (overlapping) types: 1) biological features, 2) network-based features, and (3) clusterability assessment features. Figure  network- biologicTo calculate 42 of the features, three network centrality measures are used: degree, closeness  and betwSince the constructed network in the previous step is weighted, in most cases there are two weighted and unweighted versions for the network-based features. Also as a variance can be calculated in two weighted and unweighted manners, a set of biological and network-based values are considered as weights when calculating weighted variances  is computed as:L is the Laplacian matrix of the network. The value of the parameter L is defined as D and A are degree and adjacency matrices, respectively. In the case of a weighted network, D is a diagonal matrix with the degree of the vertex i for i. The exponential for the matrix p between each pair of nodes while the denominator p! normalizes path counts by path lengths. With L as a symmetric matrix, In fact the term roved in .Markov diffusion kernel (MD) defines a discrete-time counterpart of diffusion distance between the nodes of a graph in a diffusion model [t as time (bandwidth parameter) and P as transition-probability matrix for the Markov chain process that is defined as on model , 66. Witon model . MD kernMarkov exponential diffusion kernel (MED) was introduced to balance the similarity measure in LED with respect to degree of vertices [M is defined as I as identity matrix and n as the number of vertices in the graph. Here, n is in fact the maximum possible degree  was first designated in the context of regularization operators as a kernel that counts all the paths between a couple of nodes in a graph, regardless of the path length [h length  , the radius of gyration  of a promentclass2pt{minimmentclass2pt{minimm, as an input parameter. In our method, starting from m to the extent that is feasible .Given a kernel matrix obtained in the previous step, several clustering algorithms (desirably kernel-based) can be used to parse the protein structures. In this study we incorporated two clustering algorithms: kernel k-means  and specThe kernel k-means algorithm works like the regular k-means algorithm in the feature space corresponding to a kernel function. Since cluster centers are not obtainable from the kernel matrix, the kernel k-mean algorithm uses a kernel trick to implicitly calculate the distance of the points to cluster centers. To reduce the sensitivity to random initial partitioning, we repeat the algorithm for 100 times and then select the best output in terms of within-cluster sum-of-squares.Also, spectral clustering operates on a similarity matrix . It first forms a similarity graph based on the similarity matrix, then uses spectral decomposition of the graph Laplacian to obtain a spectral embedding of the points. Finally it employs a clustering algorithm to cluster the points based on their spectral embedding. For this purpose we chose the standard k-means algorithm. Similar to using the kernel k-means algorithm, here the k-means algorithm is also repeated for 100 times and the best partitioning is selected.n can be shown by a list of cluster labels: ith residue in the protein chain. Following each clustering, to avoid splitting alpha-helices among multiple domains, each divided alpha-helix of size less than or equal to the parameter maximum alpha-Helix size to merge (MHS) is reassigned to the domain that comprises the major fraction of its residues (L is updated).Due to nature of the protein structures, some post-processing procedures are necessary after clustering of the amino acids. Figure L, the protein chain can be sliced into segments such that each segment consists of a set of consecutive residues that belong to the same cluster and no two consecutive segments are members of the same cluster. If we denote the number of segments by t it is obvious that Given the list Minimum segment size (MSS) is a main parameter of our algorithm that implies there should not be any segment with the size less than this value. As a result of clustering, short segments (with the size less than MSS) may be generated, which are removed in a greedy manner: starting with the shortest, the segment is merged with the adjacent ones. The procedure is repeated until no short segment is left. The maximum segment count to domain count ratio (SDR) serves as another input parameter of our algorithm. To meet this condition, the process of removing short segments is continued until the given ratio is fulfilled  parameter  is the mean distance between the ith amino acid and all other amino acids in the same cluster and b(i) is the smallest mean distance of the ith amino acid to all amino acids in any other cluster, of which ith residue is not a member. Finally, the number of domains, is chosen such that the silhouette score is maximized:Given a set of candidate clusterings (obtained from the previous step) and a matrix of pairwise distances between the residues  a certain threshold can be used to consider a predicted assignment as true. We used SCOP (SCOPe)  and CATHA and P in Fig. n, one can form an overlap table in which each element i and j in the predicted and true decompositions, respectively. After obtaining the optimal matching between the two decompositions, OL score is calculated as:This method is first presented by Jones et al. . To compM where the overlapping between the two partitionings is maximized:where Thus, an assignment is considered to be true if: (1) the number of domains complies with the target assignment, and (2) the overlapping between the identified and reference assignments is not less than a certain threshold. In this paper the threshold of 85% is used to report the main results.n and a pair of its domain decompositions (predicted and true decompositions) a contingency table can be calculated in which each element i and j in the predicted and true decompositions, respectively. So the main diagonal of the contingency matrix shows the pairs of residues that are assigned to the same domains. According to Fig. By having ground-truth domain decomposition of the protein structures, extrinsic methods of clustering evaluation are applicable to score predicted partitionings. A limitation of the OL score is that it requires equality between the number of domains in the predicted and target assignments whereas there exist several extrinsic clustering validity indices that do not have such a limitation. To evaluate our method, we chose the adjusted Rand index (ARI) –79 whichdex (RI)  for chanTo report the main results in this paper the threshold of 50% is used for the ARI measure. Since it is possible to calculate the ARI measure for any domain decomposition, in this study the performance in terms of mean and standard deviation of the ARI score over each dataset is also presented.Benchmark_1 is a set of 467 consensus chains between AUTHORS, CATH, and SCOP provided by Veretnik et al. [Benchmark_2 and Benchmark_3 are provided by Holland et al. with some rigorous criteria [Islam, and a frequently used benchmark of 55 proteins provided by Jones et al. [Jones. Further, we utilized the latest release of ASTRAL SCOPe (version 2.07) to build our most comprehensive non-redundant set by removing the chains with more than 40% sequence identity; here is referred to as ASTRAL40. After removing the entries with missing chain IDs, this resulted in a set of 11958 chains, which is also used here to report the performance of different methods based on the number of domains.Five of the most commonly used datasets for protein domain assignments plus a subset of latest release of ASTRAL SCOP were used as test sets. k et al.  via exclcriteria  that hers et al.  that herWe examined the capability of KluDo to assign protein domains in the case of using each pair of diffusion kernels  and clustering methods  utilizing the parameter values of Table In order to analyze the performance of the whole pipeline (including the single/multi-domain classifier), KluDo was executed on the five widely used benchmarks plus the ASTRAL40 dataset. According to Table Finally, we inspected the performances over four extracted subsets of ASTRAL40 based on the number of domains. The result of the benchmarking using the spectral clustering algorithm and the OL score (with the threshold of 85%) on each subset (based on the SCOP assignments) is summarized in Fig. DomainParser works based on recursively bipartitioning of a weighted graph (flow network) using the Ford-Fulkerson algorithm: after setting a pair of artificial source and sink nodes in the protein graph, the minimum set of edges that disconnects the source and sink nodes are removed which results in two sub-graphs. This procedure is then repeated on each sub-graph until its stopping criteria are satisfied. DomainParser tries to avoid splitting of alpha-helices and beta-sheets using its graph construction procedure and stopping criteria. PDP attempts to decompose protein structures into smaller fragments based on the assumptions of compactness. By means of a series of cuts, it tries to maximize the inter-domain to intra-domain contact ratio. Another objective of the method is optimizing the number of expected contacts for a domain based on its surface area. In PDP, alpha-helices and beta-sheets are more prone to be split into different domains, compared to DomainParser. Though proper domain boundaries occasionally fall within secondary structures, this feature, besides the compactness assumption, may lead to faulty divisions of proteins in the case of loose structures. Similar to PDP, the DDomain algorithm also divides protein structures by maximizing the intra-domain contacts. However, this method uses a pairwise statistical potential based on a normalized contact-based domain-domain interaction profile rather than mere contact count in PDP. DDomain limits each structural domain to a continuous segment, which is not a valid assumption for many protein chains. SWORD makes use of evolutionarily preserved substructures  against four well-known available methods: DomainParser , 23, PDP peeling ) to recoThe performance of each method along with KluDo (using the LED kernel and both clustering methods) based on the OL (85% threshold) and ARI scores (%50 threshold) are illustrated in Fig. Furthermore, Table Graph representation provides a rich context for processing heterogeneous biological data. Although the graph node kernels used in this study are powerful measures of node similarity, they suffer when applied to sparse graphs with a low number of links. Owing to their nature, which is based on the notion of heat diffusion, they are vulnerable to missing links in such graphs . In the n is the number nodes in the graph. The MD kernel computes t matrix multiplications, each one with a cost of Another limitation of such kernels is their time complexity, which scales more than quadratically in the number of nodes. More precisely, matrix exponentiation in the case of the LED and MED kernels has a computational complexity of lgorithm . The matlgorithm ) is wellAlso, to intuitively show whether diffusion kernels on protein graphs would lead to a better separation of the structural domains compared to the original 3D structures, the proteins can be visualized in both cases. To view projection of the protein structures in RKHS of the kernels, several methods are applicable from which we use kernel principal component analysis (kPCA) , 86. FigAs described in the “palm and thumb parts as a single domain. As it is evident in the heat map, which illustrates the kernel matrix described in the “According to Fig. In the case of egg-white lysozyme  and can offer even greater precision for protein delineation with the help of future progress in this field. Moreover, the capacity of KluDo to propose multiple solutions can tackle the problem of biased study of ambiguous structures that is caused by considering only a single valid domain decomposition for such proteins. The source code of this project (written in Python 3.7) is available on Github at Additional file 1. Supplementary document. This document includes additional information about data preparation, the single/multi-domain classifier, bandwidth determination for the kernel functions, randomized graph tests and software tools used for implementation.Additional file 2. Features extracted from the datasets for the single/multi-domain classifier. This file includes the extracted features for the single/multi-domain classifier from the datasets ASTRAL95, ASTRAL40, Benchmark_1, Benchmark_2, Benchmark_3, Islam and Jones. For each protein structure, 78 features were extracted. The last column indicates class label. Refer to Additional file Additional file 3. Results and performance evaluation of KluDo over the multi-domain structures of ASTRAL40, assuming all structures as multi-domain. This file contains KluDo results as well as accuracy and mean and standard deviation of the ARI score over the multi-domain structures of ASTRAL40, assuming all structures as multi-domain. The accuracy is calculated based on the OL and ARI scores considering the thresholds from 5% to 100% with the interval 5%. From the ASTRAL40 dataset, 2208 out of 11958 protein chains that were recognized as multi-domain by both SCOP and CATH were considered. All these structures were assumed as multi-domain by KluDo (the single/multi-domain classifier was not employed). The results and performance for all kernel function-clustering method combinations with the default parameter values are provided. KK and SP stand for kernel k-means and spectral clustering, respectively.Additional file 4. Results of KluDo along with the other methods over the test datasets. This file encompasses the results of KluDo along with the other methods over the datasets Benchmark_1, Benchmark_2, Benchmark_3, Islam, Jones and ASTRAL40. KluDo’s results are presented for all combinations of kernel functions and clustering methods using the default parameter values. KK and SP stand for kernel k-means and spectral clustering, respectively.Additional file 5. KluDo's performance along with the other methods in terms of accuracy over the test datasets. This file comprises the accuracies over the results in Additional file Additional file 6. Kludo's performance along with the other methods in terms of ARI mean and standard deviation over the test datasets. This file includes mean and standard deviation of the ARI score over the results in Additional file Additional file 7. KluDo's performance based on the number of domains over ASTRAL40. This file consists of the accuracies over the results in Additional file


The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses.The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (  Enzymes are powerful natural biocatalysts that have evolved through billions of years to perform the complex chemical reactions required to sustain life. Improving different properties of such biocatalysts has been one of the main objectives of protein engineering, and both directed evolution  and compde novo loop design, where the extent of backbone constraints was arbitrarily set to the first three proximal amino acids of each flanking secondary structure , a web-based tool aimed to cover this gap in a protein engineering toolbox. Specifically, the web server automates the identification of regular SSEs in the input protein structure, allowing the user to fine-tune the limits of such elements. Loops and their geometries are calculated to provide the user with an easy way to compare the elements on the scaffold and insert proteins to be exchanged. LoopGrafter is designed to help researchers to identify regions of interest in their scaffold protein according to their dynamic behaviour: rigid, flexible or hinge regions connecting parts of the protein with different flexibility properties . Furthermore, motion cross-correlations between the selected loops to be grafted and other loops in the protein are assessed to suggest additional regions to be engineered. LoopGrafter calculates all possible combinations of the selected loops and, for each loop, every recombination point on the two flanking SSEs that generate a different grafted protein sequence. This exhaustive exploration of the recombination sequence space allows LoopGrafter not to require the sequence of the final chimeric protein as input, as it happens in available loop remodelling tools that can be used for loop grafting  are averaged to obtain the flexibility values of such segments. Coordinated motions in the protein are predicted using ANM residue-to-residue cross-correlation values. Such values are used to calculate protein segment cross-correlation values as previously described (The 3D structures of the two input proteins can be uploaded from the Protein Data Bank (PDB)  or uploaan (GNM)  and anisan (GNM) ,27 elastan (GNM)  to obtaiescribed .The input protein structures are superimposed using combinatorial extension (CE) . From suFor each loop region to be grafted (understood as the super-secondary structure formed by the coil segment and its flanking regular secondary structures), all the different residue doublets on the flanking SSEs represent a potential boundary for the grafting experiment. Thus, different lengths of the flanking SSEs might be preserved from the scaffold, but the resulting graft design will contain the whole coil region from the insert protein. A boundary is defined on a given position only if such a position can be matched by the previously described sequence pairing. If such position corresponds to an unpaired residue (be it on the scaffold or on the insert proteins), the new boundary is defined by pairing the unaligned residue with the closest flanking residue from the sequence where the gap is. A particular pair of boundaries  is only accepted for further evaluation if the grafted variant provides any sequential variation on the scaffold protein or any other accepted design.3D models for each of the constructed grafted variants to be evaluated are built using MODELLER ,31. The However, only the insert input protein should inform the grafted coil segment and only the scaffold one the rest of the model. To meet these requirements, the coil parts from the 3D template of the scaffold protein are removed, and the 3D templates for the insert protein correspond to the coil region with flanking secondary structures. To align these insert templates to the target grafted sequence, the 3D coordinates of each insert fragment to be grafted are superimposed again on the scaffold 3D structure using CE . In compMODELLER provides a 3D model of the grafted design and a discrete optimized protein energy (DOPE)  value to123IVHMESVVDVIESWDEWPDIEEDIALIK154 and 206WPREIPIKGDGPEDV220 were removed from the structure (note a 11-position shift in comparison to the deposited PDB structure). This structure and the sequence corresponding to the chimeric protein carrying the luciferase sequence for these regions (Supplementary Data) were given as input to the ‘Advanced’ mode of use of DaReUS-Loop  will be removed and the insert from which such loop(s) will be transplanted. These structures can either be uploaded by the user from their local file system or be retrieved from RCSB PDB  secondary structure definition; (ii) loop exploration; (iii) flexibility evaluation; (iv) correlation evaluation; (v) loop pairing; and (vi) loop grafting Figure . The useSecondary structure definition: As the proteins are uploaded, the secondary structure assignment can be visualized in a two-dimensional (2D) representation presented under the ‘Secondary structure’ caption, and their structures are shown in the 3D viewer situated at the upper left side of the web server page. At this step, the user is allowed to redefine such SSEs, by selecting regions on the 2D representation and changing its secondary structure assignment to sheet, helix or coil.Loop exploration: Super-secondary structures consisting of two consecutive regular SSEs and the coil segment linking them are automatically extracted from the input proteins. Their geometries are calculated as described in ArchDB (distance (D), and hoist (δ), packing (θ) and meridian (ρ) angles, which are the parameters describing the geometry of the loop  are shown in separated boxes according to the same scale. Each of such boxes contains a plus (+) sign that enables the selection of that particular loop as a candidate to be grafted. Any loop can be selected for grafting regardless of their assessed flexibility. Below the 3D viewer, the candidate loops to be grafted and the preserved ones are listed during the whole process. Finally, at the bottom of this section, the agreement between the different flexibility methods is presented in the form of correlation values  the cross-correlation of the selected loops (super-secondary structures) to any other loops in the protein, shown in light shade, and (ii) the cross-correlation of the coil segment of the selected loops to any SSE in the other loops of the protein. The first metric gives an idea of the overall accord of the motion of the selected loops with others. Except in the case of loops present in regions with an important dynamic gradient . It has to be noted that the paired loops need to be structurally equivalent. This means that the pair of loops needs to be reasonably overlaid in space after the CE superimposition  that generate different sequences are explored . After this initial exploration, the user is informed about the total number of different grafting variants the server will generate and their sequences can be downloaded in JSON format. Compared to available loop remodelling tools  in the ‘Grafting results’ box on the upper horizontal menu. The Results page presents an overview of all calculated grafting variants (‘Experiments’), their DOPE and Rosetta FastRelax scores, the number of inserts in the resulting grafting variant and its number of amino acids (length). The scores and the number of inserts can be used to sort the obtained variants, and any individual variant can be selected for 2D and 3D visualization, showing the scaffold and insert regions colour coded. Results can be downloaded in three different forms: (i) the 3D structure coordinates of any selected variant  a digest of all variants scores and sequences (‘Download score table’); and (iii) the compressed bundle of all results, including the 3D structure models generated by MODELLER and Rosetta for each grafting variant .Renilla luciferase  consists of two relatively similar conformers, so any of the chains can be inputted to the server . In contrast, the asymmetric unit of Renilla luciferase (PDB ID 2PSF) contains two different conformers, one corresponding to an open cap domain (chain A) and the other corresponding to a closed one (chain B). Since the closed cap domain (chain B) is more similar to the scaffold bifunctional ancestor, it should be chosen as input for the insert protein. The global similarity between the proteins should be noted: scaffold and insert only differ by 1.27 Å RMSD after CE superimposition and 2% in sequence length. Crucially, the similarity values recommended for using LoopGrafter are <8 Å RMSD and <20% difference in sequence length.The grafting approach reported there can be replicated using the LoopGrafter. First, in the ‘Protein upload’ step the bifunctional ancestor should be introduced as scaffold and The two input proteins result in different secondary structure assignments in the N-terminal region connecting the main and cap domains (residues 135–167 in the scaffold protein). Particularly, a small helical element is present in the scaffold (residues 151–153) but not in the insert protein. Since this region is important for the grafting experiment, the helical structure can be deleted from the scaffold protein in this step. Alternatively, a user-defined loop bypassing the small helical structure can be defined on the scaffold protein in the next step. In case such knowledge about the importance of the region is not available at this moment of the process, the steps in the web server can always be traced back and one of the two strategies can be used. In this example, the region 151–153 from the scaffold protein was selected and set to ‘coil’.After calculating loops and their geometries in the second step (‘Loop exploration’ step), the flexibility and dynamic properties of the scaffold protein can be assessed in the third one . From this analysis, the loop 6g75_A_135 can be identified as a key element connecting stationary and dynamic parts of the scaffold protein. This loop corresponds to loop 9 in  and is tR = 0.42). This loop was not considered in the original grafting effort on the bifunctional ancestral scaffold. However, because of being involved in the joint dynamic system of opening and closing of the active site pocket, its transplantation brought the bioluminescence efficiency levels of the scaffold to the level of optimized commercially available luciferase .Other regions in the scaffold protein that have coordinated movements with the selected loop(s) can be identified in the ‘Correlation evaluation’ step. In this example, the loop 6g75_A_210 strikingly has a notable cross-correlation with the motions of the coil segment of the previously selected loop , but not for the second one (6g75_A_210)  . The solrst loop  when comrst loop . Even whrst loop , LoopGrarst loop . SupplemLoopGrafter is a web-based application designed to provide guidance in the process of transplanting loops between homologous, structurally related proteins. The power of this technique in protein engineering has been shown in the past ,11. SurpWith the aim of assisting the non-expert in the mission of transplanting loops between structurally related proteins, the server provides a step-by-step procedure at the end of which the user will obtain a number of chimeric designs and structures. The approach successively involves calculating loops and their geometries in the two input proteins, assessing their similarities and dynamics using elastic network models, exploring correlated motions among loops and finally selecting a number of loops to be transplanted. The exploration of the loop dynamics and especially their cross-correlations is often key to explain the function of proteins . The wayWhen designing chimeric proteins, one of the most difficult questions is deciding the exact sequence points where to perform the excision. ProtLego is a recent tool designed to generate interdomain chimeras  that is Streptococcus pyogenes sortase A (Finally, we have showcased that the scoring systems from MODELLER and Rosetta help identifying loop-grafted chimeric designs that encompass structural and biological meaning. Among all generated chimeric sequences, based on the provided scores, the user can select the designs more likely to be successful in the lab. In the use case described here, experimentally successful loop-grafted chimeric proteins were favourably scored by both methods . The tooortase A . In thesortase A ,43 to feortase A  and imprhttps://loschmidt.chemi.muni.cz/loopgrafter.LoopGrafter is a web server available at gkac249_Supplemental_FileClick here for additional data file.


Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data.L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively.In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction. Proteins are the basic organic matter that constitutes all cells and tissues of the living body. Accurately and automatically annotation of protein function is one of the fundamental tasks of bioinformatics, and it has become very hot in recent years. Methods for experimentally determining protein function such as gene expression inhibition , targeteThe rapid developments of high-throughput technologies have generated huge amounts of high-quality, large-scale protein interaction data, which provide fundamental and abundant data for network-based approaches to deduce protein functions. Schwikowski et al.  found thGrAPFI Saccharom [GrAPFI .L of them to annotate the unknown protein. The flowchart for the PHN method is provided in Fig. The outline for the proposed PHN method includes 1) constructing a heterogeneous biological network by integrating the topology of PPI networks, protein-domain association data, and protein complex information, (2) running the propagation algorithm on the heterogeneous biological network to generate a functional similarity partners list for the given target protein, and (3) scoring and ranking functions from the partners list in descending, and picking out top  construcu, S1 and S2 denote the set of direct neighbours and level-2 neighbours of u, respectively. We classify all proteins except u into four categories: direct neighbours that are also level-2 neighbours (i.e. S1 ∩ S2), direct neighbours that are not level-2 neighbours (i.e. S1-S2), level-2 neighbours that are not direct neighbours (i.e. S2-S1) and protein that are not direct neighbours or level-2 neighbours , it is considered to have a necessary functional association with u and the functional similarity between them was set to 1. So, the functional similarity between a node in the sets S2-S1 and u is assigned 1 − α. Given a protein v in the network, the functional similarity between u and v based on the local topology of PPI networks can be defined as follow:u according to Eq. fs = α, fs = 1, fs = 1, fs = α, fs = 1 − α, fs = 0.It has been observed that more than 70% of proteins perform at least one function with their direct neighbours in networks . We makems denote the module similarity of the interaction between u and v. Then, the module similarity of interaction  is calculated using the following equations:Cu and Cv denote the set of protein complexes containing u and v, respectively. Cu ∩ Cv denotes the set of protein complexes containing both u and v. Finally, we construct a weighted protein–protein interaction network with high confidence by integrating the topology of PPI networks and protein complexes. The weight between protein u and v in the newly constructed protein–protein interaction network is the linear combination of their functional similarity and module similarity, and is defined as:Protein complexes are functional units of macro-molecular organization consisting of interacting proteins that perform cellular biological functions . Consideu contains domain d, u connects domain dn with an edge in the protein-domain association network and mpd = 1, otherwise, there is no edge between them and mpd = 0.Domain refers to the dense spherical region of protein subunit structure, which is composed of 100–200 amino acid residues, each with unique spatial conformation and different biological functions. If protein PL(dn) represents the list of proteins that contain the domain dn. We quantitatively analyse the association between protein u and PL(dn) of the domain dn according to weighted protein interaction data, which is expressed as follows:In this work, we evaluate the similarity between domains through their associated protein interaction information. The domain-domain similarity network is constructed based on the above constructed weighted protein–protein interaction network and the protein-domain association network. Let dni and domain dnj, the similarity between them can be calculated as follows:PL(dni) and PL(dnj) are the protein set containing domain dni and domain dnj, respectively and pnx and the set of protein Then, for a given pair of domain MPP, MPD and MDD is the adjacency matrix corresponding to the protein–protein interaction network, protein-domain association network and domain-domain similarity network, respectively. MPDT is a transport matrix of the matrix MPD.Based on the newly established protein–protein interaction network, protein-domain association network and domain-domain similarity network, a heterogeneous biological network is constructed and formally expressed by the adjacency matrix as follows:HBM_T based on the matrix MPD by normalized operation, which is formalized as follows:Restricted to the small-world and scale-free features of the protein–protein interaction network, existing distance measures such as shortest distance and Euclidean distance are not suitable for evaluating the functional similarity between proteins , 19. In pni to protein pnj is expressed as:The transition probability from protein dni to domain dnj is expressed as:The transition probability from domain pni to domain dnj is expressed as:The transition probability from protein dni to protein pnj is expressed as:The transition probability from domain u with other proteins by the following equation:The parameter λ is the moving probability of the movement from the weighted protein–protein interaction network to the domain-domain similarity network and is assigned as 0.2 . And theMPP and protein-domain association network. For a given protein pi, its initial functional similarity score between the target protein u is expressed by the weight of interaction between pi and u in the protein–protein interaction network, that is:The parameter t to 0.5 , 22. \dodj, its initial score is computed by the following formula:PL(di) is the protein set of domain di. In Eq.  protein interaction networks are widely used in the research of network-based function prediction algorithms as a gold standard data because of their completeness and convincement. Here, we apply PHN and seven competing methods on yeast interaction networks to test the performance of our new method. The original protein interaction data used in this work is downloaded from the BioGRID  database [The database , compiledatabase , which cdatabase . The GO database  databaseIn this work, the performance of PHN is evaluated by two cross validations, including leave-one-out cross validation and ten-fold cross validation. To measure the quality of predicted functions by our method, we adopt the ROC (Receiver-Operating Curve) , 28 as aOn the construction of the weighted protein–protein interaction network, we employ a user-defined parameter β to balance functional similarity and module similarity in Eq. GrAPFI by the average Precision, Recall and F-measure. Figure In this part, we use leave-one-out cross validation to evaluate the quality of the functions predicted by PHN and seven other competing methods. In each round, there is only one protein in the testing set and the rest in the training set. Firstly, we evaluate the comprehensive performance of PHN and seven other competing algorithms, such as NC, Song, DCS, DSCP, NPF, PON and GrAPFI, respectively. And then top L of candidate functions are picked out to annotate the target protein. For the three methods of Song, DCS and DSCP, the top M (M ≤ L) of proteins with the highest function similarity to the target protein are selected, and the top L of functions from these M proteins are selected as predicted functions. A more valuable comparison between algorithms is presented by plotting F-measure curves as the value of L (L ≤ 50) varies. Figure L in BP, MF and CC. From Fig. L in the interval [L.The choice of the number of functions predicted by various methods is an important factor affecting their performance. We try our best to select a unified candidate functions selection strategy for each method to comprehensively and objectively compare and analyse the performance of different methods. The predicted functions are arranged in descending order based on the functional similarity score values derived by PHN, NPF, NC, PON and interval , 5 is thK functions are picked out and put into positive data set as candidate functions, and then the remaining functions are stored in negative data set. The upper limit values of K in BP, MF and CC are 518, 219, and 174, respectively. With different values of K selected, the values of TPR  and FPR (True Positive Rates) are computed for each method, respectively. Then, the values of TPR and FPR are plotted in ROC curves with different cut-off values. The experimental results are illustrated in Fig. Moreover, we also employ Receiver-Operating Curve (ROC) curves and the corresponding areas under the ROC curve (AUROC) values to evaluate the overall performance of each method. Firstly, functions are ranked in descending order according to the functional similarity scores to the target proteins computed by each method. After that, the top GrAPFI, respectively. Compared with NC which has the best performance in ROC curves among seven competing methods, the AUROC of PHN for the MF and CC category is improved by 123.4% and 23.56%, respectively.For further comparison, we calculate the AUROC of all these methods. For the BP category, the AUROC of PHN is 154.03%, 75.33%, 75.91%, 40.29%, 28.06%, 548.92% and 209.91% larger than that of Song, DCS, DSCP, NPF, NC, PON and GrAPFI, respectively. In addition, we plot ROC curves of all methods for the MF, CC and BP category as shown in Fig. GrAPFI, respectively. As for the MF and CC category, PHN increases the AUROC by no less than 15% and 28%, respectively, compared with other competitive comparison methods.TO avoid the possible deviation caused by leave-one-out cross validation, we further evaluate the prediction performance of the PHN method using the ten-fold cross validation. The entire set of proteins is divided into ten equal sets randomly, nine of which are used for training and the remaining part is used for testing. The process is repeated 1000 times, each time using another testing set. The results of ten folds are averaged to generate the final performance. Table ProteinS are one of the most important and diverse macromolecules in cell life activities. Precise labelling of protein function information is important for promoting the research and development of protein mechanism analysis, disease mechanism analysis and control, new drug research and development, crop production promotion, bio-energy development and so on. The rapid developments of high-throughput technologies have generated large quantities of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Despite the effective improvement of these computational methods, building a suitable network model to integrate multiplex biological data remains a challenge due to the incomplete and error-prone raw PPI data. How to construct an effective network model that integrates multiplex biological data and network topology remains a challenge. Current methods aggregated multiple biological data into a single network, in which the role of different types of data is reflected in the form of setting weights or parameters. The choice of weighting parameters and the inherent properties of different biological data restrict the further development of these methods. In this work, we construct a heterogeneous biological network with two categories of nodes: protein and domain. To shake off the bound of the small-world and scale-free of PPI networks, innovatively, we use the propagation algorithm on the heterogeneous biological network and obtain a functional partners list with aggregated similarity to the target protein. Finally, we score and rank functions from the partners list in descending order. The number of candidate functions we selected is equal to the number of functions of the neighbour most similar to the target protein. To assess the overall performance of PHN, we use the leave-one-out cross validation and ten-fold cross validation. The F-measure and AUROC of our method improved by more than 17% and 15%, respectively, compared with other approaches. The experimental results also indicate that PHN is a specific and effective method that can predict protein function.


Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins. PepNN integrates an attention-based deep learning module with transfer learning to predict the peptide binding sites of input proteins and the binding ability of proteins lacking a known peptide ligand. Moreover, peptide ligands binding to extracellular receptors have important functions2. In total, it is estimated that there are roughly 104 human proteins that contain at least one PRM3 and that there are over 106 peptide motifs encoded in the human proteome1. Disruption of these interactions and their regulation can consequently result in disease; for instance, many proteins with PRMs harbour oncogenic mutations4. It has also been shown that viral proteins encode peptidic motifs that can potentially be used to hijack host machinery during infection5.Interactions between proteins and peptides are critical for a variety of biological processes. A large fraction of protein–protein interactions are mediated by the binding of intracellular peptide recognition modules (PRMs) to linear segments in other proteins6. One widely used peptide docking tool is FlexPepDock, a Rosetta protocol that refines coarse-grain peptide-protein conformations by sampling from the degrees of freedom within a peptide7. In general, benchmarking studies have shown that peptide docking approaches often fail to accurately identify the native complex conformation10, indicating that this problem remains unsolved; current approaches are limited by the high flexibility of peptides as well the inherent error of scoring heuristics6. Machine learning approaches provide potential alternatives to docking, as they can sidestep the issue of explicit enumeration of conformational space and can learn scoring metrics directly from the data.In the absence of ample experimental data including solved structures, gaining molecular insight into these interactions and their associated disease states is contingent on the ability to model peptide binding computationally. This has been a difficult problem that has traditionally been approached with peptide-protein docking16. More recently, a deep convolutional neural network was developed that takes as input a voxelized protein structure and outputs predicted binding sites on the protein, which achieved high accuracy17.A number of machine learning approaches have been applied to the problem of predicting the binding sites of peptides with varying amounts of success, including random forests and support vector machines (SVMs)18. These modules are effective at learning long-range dependencies in sequence inputs and have been successfully adapted to graph inputs19. Graph neural networks in general have had success on various related problems, including protein design20. Importantly, we build upon attention to develop reciprocal attention, a variant that updates two input encodings based on dependencies between the two encodings, while maintaining symmetry in the updates. This architecture updates the embedding of both residues participating in an interaction simultaneously, and reflects the fact that the conformation of a bound peptide depends on the interacting protein target21.Here, we present an alternative deep learning architecture that allows for both sequence and structure-based prediction of peptide binding sites and performs the latter in a rotationally and translationally invariant manner. In particular, we develop an architecture that is partially inspired by the Transformer, a model that primarily consists of repeated multi-head attention modules22. Complexes of protein fragments with receptors thus represent a natural source of data for model pre-training. In addition, the idea of pre-training contextualized language models has recently been adapted to protein biology for the purpose of generating meaningful representations of protein sequences24. The success of these approaches provides an opportunity to develop a strictly sequence-based peptide binding site predictor.One significant hurdle to the development of deep learning approaches for the modelling of peptide-protein complexes has been the paucity of available training data. To overcome this problem, we exploit available protein–protein complex information, thereby adding an order of magnitude more training data. The “hot segment” paradigm of protein-protein interaction suggests that the interaction between two proteins can be mediated by a linear segment in one protein that contributes to the majority of the interface energyIn this study, we integrate the use of contextualized-language models, available protein–protein complex data, and a task-specific attention-based architecture, to develop parallel models for both structure and sequence-based peptide binding site prediction: PepNN-Struct and PepNN-Seq. Comparison to existing approaches reveals that our models perform better in most cases. We also show that the developed models can make reasonable peptide-agnostic predictions, allowing for their use for the identification of novel peptide binding sites.19. PepNN-Struct makes use of graph attention layers to learn a contextual representation of an input protein structure . We found that PepNN-Struct consistently achieves higher performance than the Graph Transform, irrespective of whether pre-training is done or ProtBert embeddings are included Fig. . As reci26. For an unbiased estimate of model performance, PepNN was re-trained on the training datasets used in different studies prior to the evaluation of their test sets. Unsurprisingly, we found that PepNN-Struct consistently outperforms PepNN-Seq , but performing worse in terms of Matthews correlation coefficient (MCC).To evaluate the performance of PepNN-Struct in the absence of co-crystallized protein structures, we additionally benchmarked its performance using AlphaFold models of receptors in TS092ed Table . In facted Table . To asseed Table , the dif29. More recently, AlphaFold-Multimer was developed to model protein-protein complexes, and outperforms the initial AlphaFold implementation on this task30. AlphaFold-Multimer was additionally shown to outperform conventional docking approaches on the peptide-protein complex prediction31. Given that AlphaFold-based methods represent the current state-of-the-art in peptide-protein complex predictions, we compared binding site predictions generated by PepNN-Struct to binding sites derived from AlphaFold, (using the previously described gap method32) and AlphaFold-Multimer predictions. Consistent with what was shown in previous studies, AlphaFold and, to a greater extent, AlphaFold-Mulimer achieve remarkable accuracy in peptide-protein complex modelling in a large number of cases, achieving a higher MCC than PepNN overall , there was only a small overall decrease in the ROC AUC when a poly-glycine peptide was given  domain of the Deleted-in-liver cancer 1 (DLC1) protein , the test dataset used to evaluate the PepBind approach12 (TS639), and the test dataset used to evaluate SPRINT-Str26 (TS125). In these datasets, the input protein was derived from the co-crystallized protein-peptide complex structure. In the case of TS092, an additional benchmark was included with AlphaFold models of the proteins in the complexes.In addition to TS092, the models were also tested on benchmark datasets compiled in other studies. This includes the test dataset used to evaluate the Interpep approach19, with the exception that additional node features are added to encode the side chain conformation at each residue. In this representation, a local coordinate system is defined at each residue based on the relative position of the Cα to the other backbone atoms19. The edges between residues encode information about the distance between the resides, the relative direction from one Cα to another, a quaternion representation of the rotation matrix between the local coordinate systems, and an embedding of the relative positions of the residues in the protein sequence19. The nodes include a one-hot representation of the amino acid identity and the torsional backbone angles19.In the case of PepNN-Struct, input protein structures are encoded using a previously described graph representation19. A one-hot encoding is used to represent protein and peptide sequence information. The pre-trained contextualized language model, ProtBert23, is used to embed the protein sequence in PepNN-Seq, and this encoding is concatenated to the node features in PepNN-Struct.To encode information about the side-chain conformation, the centroid of the heavy side chain atoms at each residue is calculated. The direction of the atom centroid from the Cα is represented using a unit vector based on the defined local coordinate system. The distance is encoded using a radial basis function, similar to the encoding used for inter-residue distances in the aforementioned graph representation18, as well the Structured Transformer, developed for the design of proteins with a designated input structure19. Like these models, the PepNN architecture consists of repeating attention and feed forward layers 32. Predictions on TS092 were done with templates with dates before 2018/04/30. Predictions on the other datasets, and predictions of receptor structures, were done without templates.To generate protein-peptide complexes with AlphaFold, the sequences were concatenated and 200 was added to the residue index of the peptide residues, as done in ColabFold48. Multiple testing correction for identifying highly scoring protein domains was done using the statsmodels python package49. The DeLong test for identifying differences in ROC curves was done using the pROC R package50.To compare the distribution of scores and metrics across proteins in a dataset, Wilcoxon signed-rank and rank-sum tests were done using the SciPy python library51) were used as input structures for the ClusPro webserver40. The top ten poses, ranked by population, were used for downstream analysis.The structure of the SARS-CoV-2 ORF7a encoded accessory protein (PDB ID 6W37) and mouse BST-2/Tetherin Ectodomain (PDB ID 3NI0HEK293T cells were maintained in DMEM (ATCC) supplemented with 10% FBS and 1% pen/strep/glutamine, and the appropriate selection antibiotics when required. HA antibodies were obtained from Santa Cruz (7392) and Flag antibodies were purchased from Sigma (A8592).Transfected cells were scraped from six-well dishes and lysed with lysis buffer ) for 30 min at 4 °C. The insoluble pellet was removed following a 10,000 rpm spin for 5 min at 4 °C. Lysates were analyzed by SDS-PAGE/western blot using 4–20% Mini-PROTEAN Tris-glycine gels (Bio-Rad) transferred to PVDF membranes and blocked in 5% milk containing PBS-Tween-20 (0.1%) for 1 h. PVDF membranes were then incubated with specified primary antibodies followed by incubation with horseradish peroxidase-conjugated secondary antibodies (Santa Cruz Biotechnology) and detected using enhanced chemiluminescence . HA antibodies were used at a dilution of 1:2000 and Flag antibodies were used at a dilution of 1:1000.HEK293T cells were cotransfected with Flag-tagged protein and HA-tagged protein. Cells were lysed 48 h after transfections with radioimmune precipitation assay buffer ) for 30 min at 4 °C and coimmunoprecipitated with Flag beads (Clontech). The resulting immunocomplexes were analyzed by Western blot using the appropriate antibodies. Protein samples were separated using 4–20% Mini-PROTEAN Tris-glycine gels (Bio-Rad) transferred to PVDF membranes and blocked in 5% milk containing PBS-Tween-20 (0.1%) for 1 h. PVDF membranes were then incubated with specified primary antibodies followed by incubation with horseradish peroxidase-conjugated secondary antibodies (Santa Cruz Biotechnology) and detected using enhanced chemiluminescence .Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary


FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as  Developmental Delay (DD) is a neurodevelopmental psychiatric disorder that affects individuals’ learning, cognitive, and intellectual abilities from an early age . SeveralProteins interact with other proteins to form complexes to perform functions and complete molecular processes in cells . ProteinSince the PPI interface residue shows a higher evolutionarily conservation ,28,29,30To investigate the contribution of DNMs missense on the protein-protein interaction interface to developmental delay, we collected DD and sibling controls’ DNM missenses from the PsyMuKB database  Supplem. Proteinhttp://psymukb.net (accessed on 7 July 2022)) [We downloaded the de novo mutations data from the PsyMuKB database (y 2022))  for devehttps://doc-openbio.readthedocs.io/projects/annovar/en/latest/ (accessed on 7 July 2022)) to measure the functional impact of DNM missenses by both SIFT and Polyphen-2 scores [http://genetics.bwh.harvard.edu/pph2/ (accessed on 7 July 2022)) is a tool that predicts the possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations [We used ANNOVAR (2 scores  SIFT pre2 scores . The SIFerations . The Polhttp://interactomeinsider.yulab.org/ (accessed on 7 July 2022)) [http://www.uniprot.org/uploadlists/ (accessed on 7 July 2022)) [Protein-protein interaction interface data were downloaded from the interactome INSIDER database (y 2022))  to convep was calculated by adding the total sequence length of each region in all proteins and dividing it by the length of all proteins combined. The number of observed mutations in each region was called S, and N is the total number of dnMis missense mutations. An exact binomial test was computed from p, S, and N. CIs are based on the 95% CI for an exact binomial and then transformed to the risk ratio (enrichment).A total number of 473 in DD and 125 in control proteins with PPI interface DNM missenses containing at least one interaction interface and one known domain was included for calculating DNM missense distribution. The protein sequences were divided into ‘interaction interface’, ’domain’, and ‘other’. Interaction interfaces were determined by the interactome INSIDER database . Domain ig and respective interfaces, we assumed that the observed mutation number for a given interface follows a binomial distribution, binomial , T is the total mutation number observed in one gene, and gip is the estimated mutation rate for the region of interest. Length(ig) is the sequence length of the protein product of gene i. pgig = p-Value—the probability of observing > k mutations around this interface out of T total mutations observed in this gene—using the following equation:A pair of protein-protein interactions harboring a statistically significant excess number of PPI interface DNM missenses in one or the other of the two protein-binding partners would be defined as a PsychiPPI. For each gene p-Values of two proteins. All p-Values were adjusted for multiple testing using the FDR correction. A PPI will be recognized as PsychiPPI only if q ≤ 0.05.The significance of each PPI was defined as the http://interactomeinsider.yulab.org/ (accessed on 7 July 2022)) [We downloaded the comprehensive human binary protein-protein interactome data from the interactome INSIDER database  on the basis of the number of protein-truncating variants. The pLI score ranges from 0–1 for the most tolerant to the most intolerant genes, respectively. A gene with a pLI score over 0.9 will be classified as intolerant to Lof mutation. We obtained the pLI score for each candidate gene from ExAC database .Human cells have two copies of most genes. If a mutation alters one copy, the other is usually sufficient to maintain gene function. For haploinsufficient genes, both copies must be functioning for the organism to have a normal life. A gene with a haploinsufficiency score over 0.9 will be classified as a haploinsufficient gene. We acquired the haploinsufficiency score for each candidate gene from the ClinGen database .https://metascape.org/gp/index.html#/main/step1 (accessed on 7 July 2022)) [p-Values were calculated by a hypergeometric test.We used Metascape (y 2022))  for enriy 2022))  and DisGy 2022)) . The p-Vhttps://hbatlas.org/ (accessed on 7 July 2022)) [The Spatio-temporal expression data of respective genes were downloaded from Human Brain Transcriptome database (y 2022)) . The expp-Value = 2.03 × 10−70 by two-tailed exact binomial test). In sibling control, DNM missenses showed a relatively lower enrichment at the PPI interface    a. Though’s test) b. Since p-Value = 0.0013 by a two-tailed Fisher’s test)   c. The Po × 10−4) d. Moreov × 10−4) . Our SIF × 10−4) c,d showeq-Value ≤ 0.05) after FDR adjustment , and PPI interface mutated proteins from sibling control   and sibling control   or sibling control   = 1.26,  × 10−6) a. We fur × 10−5) b. At las × 10−5) c. The reIn conclusion, by analyzing the topological network properties of selected protein groups, we demonstrated that DD candidate proteins tend to locate at the human interactome network hub, which significantly impacts the protein interactome more than all the other interface mutated proteins. The network analysis can assist in interpreting how PPI interface DNM missenses could affect protein complexes and functional modules.p-Value = 4.25 × 10−4 by a two-tailed U-test). In addition, we found that in the DD group, both kind of genes residing on or off PPI interface mutations had a significantly higher probability of being haploinsufficient , at the same time, DD patients’ dnMis genes showed a higher pLI score [It is known that heterozygous deleterious DNM missenses could only affect one copy of the genes, whereas a single copy of the wild-type gene is insufficient to carry out the normal function for haploinsufficient genes . In DD pfficient . MeanwhiLI score  in both LI score ,77.p-Value < 0.001), Constraint gene set , FMRP target gene set   b, we alsgene set a.P) = −23), neurodevelopmental disorders  = −17), Mental retardation  = −16, and other diseases with similar symptoms  = −6.21), MAPK cascade  = −4.38), and skeletal system development  = −5.24) [FRFR3 is present in developmental maturation, developmental growth, regulation of growth, regulation of MAPK cascade, and response to growth factors, which are highly disease-related biological process terms [ALOX5 is a crucial part of cellular components such as the nuclear matrix, ficolin-1-rich granule, and nuclear membrane  ,79 . We found the same level of enrichment in ID DNM missense on the PPI interface . HoweverFGFR3 and ALOX5 and their mutations are possible diseases related. Moreover, previous studies have reported that mutated postmitotic principal neurons FGFR3 could result in cortical dysplasia and axonal tract abnormality during brain development in mice [FGFR3 signaling may be essential for maintaining the balance of neurogenesis and neuronal differentiation during cortical development. Several FGFR3 de novo mutations were identified in thanatophoric dysplasia (TD) patients. Some patients with these mutations have an intellectual disability and severe skeletal deformities, which correspond to the symptom of DD [ALOX5 is related to memory deficits and synaptic dysfunction in a mouse model of Alzheimer’s disease [There are a few limitations in our current research. For simplicity, our null model for calculating PsychiPPI does not consider the DNA sequence, which can influence the computation of the frequency of nonsynonymous mutations. It is possible that taking into account the nucleotide frequency may improve the predictions, but we expect that the improvement is low. The null model does not consider the known fact that the selective effect of mutations depends on the protein region where it occurs. For instance, mutations in the protein core are more detrimental than mutations in the surface, where the protein-protein interface tends to be, and have higher chances of being eliminated by natural selection at the developmental stage. This might explain our result that mutations at the protein-protein interface happen more frequently than expected under the null model both in the cases and in control, and it is a possible direction to improve the null model that we leave for future work. Previous studies have shown that disease-causing missense mutations are enriched in the protein interface compared to other protein surface regions . However in mice . Moreoveom of DD . Additio disease . DespiteThis study showed a significant association between PPI interface DNMs and developmental delay, combined with our PsychiPPI-based disease-related genes searching framework. With the development of whole exome sequencing, researchers worldwide could identify thousands of DNM missenses in a large-scale study within a relevantly short time. We may contribute to discovering disease mechanisms and early diagnosis of developmental delay and other potential neuropsychiatric diseases.