Meiotic chromosomes endure rapid prophase movements that ease the formation of interhomologue recombination intermediates that drive synapsis, crossing over, and segregation process. To generate these fast moves, the meiotic telomere complex (MTC) enables telomere-inner nuclear membrane attachment during meiotic prophase I and transfers cytoskeletal signals via another complex: the LINC complex. Furthermore, disruption or mutations of any of the MTC genes  alters telomere association with the nuclear envelope leading to impairment of homologous pairing and synapsis, a meiotic arrest, and consequently to male infertility. To decipher the effect of TERB1, TERB2, and MAJIN missense mutations on protein structure, stability, and function, different bioinformatic tools were used in this study including VEP, Mutabind2, Haddock, Prodigy, Ligplot, ConSurf, DUET and MusiteDeep. In total, thirty mutations were predicted to be deleterious using VEP web server: seventeen for TERB1, eleven for TERB2, and two for MAJIN. All these single nucleotide polymorphisms were further analyzed and only 11 SNPs  were found to be the most damaging by at least six software tools and exert deleterious effect on the TERB1, TERB2, and MAJIN protein structures and likely functions. They revealed high conservation, less stability, and having a role in posttranslational modifications. This in silico approach provides information to gain further insights about variants that might affect stability, change binding affinity, and edit protein-protein interactions to facilitate their identification and functional characterization associated with male infertility. Telomeres have critical meiosis-specific functions in the reduction of chromosome numbers by assuring the integrity of the genome during meiosis. One such function is the attachment of telomeres to trans-nuclear envelope protein complexes that join telomeres to motor proteins in the cytoplasm. The active movement of telomeres and chromosomes during the first meiotic prophase is enabled by these trans-nuclear envelope linkages between telomeres and cytoplasmic motor proteins. Movements of chromosomes/telomeres facilitate the meiotic recombination process and ensure high fidelity pairing of homologous chromosomes which is a prerequisite for their correct segregation during the first meiotic division .Meiosis is a gametogenesis-specific cell division that entails unique chromosomal regulations such as pairing, synapsis, and homologous chromosome recombination. These processes are ensured by the dynamic chromosomal rearrangements that happen during meiotic prophase I which is prolonged and more complicated than mitosis. Indeed, it can last many days and account for up to 90% of the total time spent in meiosis . During The recently discovered tripartite complex comprising telomere repeats-binding bouquet formation proteins 1 and 2 (TERB1 and TERB2) and membrane-anchored junction protein (MAJIN) has been demonstrated to be involved in these processes by tethering telomere ends to the nuclear envelope (NE), transmitting cytoskeletal forces via the LINC complex to drive these rapid movements, and assembling of meiotic mammalian telomeres on the NE during prophase I .In mammals, the meiotic telomere complex (MTC) reuses and integrates the functions of two complexes, Shelterin and LINC, which otherwise play distinct significant roles outside of meiosis . The MTCMammalian telomeres contain a specific protein complex that accomplishes two functions: protecting the natural chromosome end from all factors of the DNA damage response within the cell and negative regulation of telomerase by segregation of its telomeric DNA substrate. Both functions of this complex refer to the name shelterin . The sheBecause of their TRF homology domains, the shelterin complex's subunits TRF1 and TRF2 perform a fundamental role by recognizing telomeric DNA, and by recruiting the other shelterin constituents onto telomeric DNA , 9, wherVia a perinuclear interaction, SUN1 (Sad1 and UNC84 domain containing 1) and KASH5 (Klarsicht/ANC-1/Syne/homology 5) proteins assemble into the mammalian meiosis specific LINC complex (linker of nucleoskeleton and cytoskeleton), providing the binding sites for telomeres on the inner surface of the nuclear envelope (NE) , 12 and Another protein complex consisting of TERB1 (telomere repeat binding bouquet formation protein 1), TERB2 (telomere repeat binding bouquet formation protein 2), and MAJIN (membrane anchored junction protein) has been demonstrated to build up a second actual linkage for telomere connection to the nuclear envelope by assuring meiotic telomere attachments in mammals , 22.The meiosis-specific telomere regulator TERB1 is a molecular scaffold that simultaneously interacts with SUN1 and meiotic cohesin subunit SA3 through its N-terminal ARM (armadillo) repeat domain and C-terminal Myb domain, respectively, providing telomere attachment to the inner nuclear membrane (INM) and driving the chromosome movement essential for homologous pairing and recombination , 22.Through a region surrounding its TERB2-binding site, TERB1 interacts directly with shelterin constituent TRF1, including a peptide interaction that resembles TIN2 binding to the TRF1 dimeric cleft , 23. ConTherefore, an interaction of the meiotic telomere complex with TRF1 seems to be transient in the cell. MAJIN–TERB2–TERB1 assembles on the nuclear envelope and endures TRF1-dependent recruitment of telomere ends during leptotene and zygotene , 22; CDKThus, in mice, knocking out the Terb1 gene disturbs the whole interaction network and affects homolog pairing, synapsis, and recombination, resulting in early spermatogenesis and oogenesis . HoweverMAJIN is a putative transmembrane (TM) protein, located at the inner surface of the NE; it serves as an anchoring component for the inner nuclear membrane through a transmembrane helix at its C-terminus . MAJIN hLately, computational analysis is designed to more accurately predict the impacts of mutations on proteins interactions. By using modern bioinformatics tools as a systematic in silico approach, our study is aimed at identifying the deleterious SNPs in the TERB1, TERB2, and MAJIN genes and predicting their significant pathogenic impact on the functions, stability, and structures of TERB1-TERB2 complex and TERB2-MAJIN complex.In this study, we carried out a systematic in silico approach using modern bioinformatics tools to predict deleterious SNPs in the TERB1, TERB2, and MAJIN genes and identify their significant pathogenic impact on the functions and structures of the TERB1, TERB2, and MAJIN proteins .https://www.ensembl.org): TERB1 (ENST00000433154.6), TERB2 (ENST00000340827.4), and MAJIN (ENST00000301896.6).The variant data related to the meiotic telomere complex components (TERB1-TERB2-MAJIN) was retrieved from Ensembl  was utilized. This tool provides an indication of the effect of the amino acid change using protein biophysical properties . These dThe Sorting Intolerant from Tolerant (SIFT) algorithm is predicting whether an amino acid substitution affects a protein function. It takes into account the position of the mutation as well as the type of amino acid change. SIFT assesses the probability that an amino acid at a particular location will be tolerated based on the most frequently tolerated amino acid. The substitution is predicted to be deleterious, if the normalized value is smaller than a cutoff. Scores range from 0 to 1. The smaller the score, the more likely the SNP has damaging effect –28.Polymorphism phenotyping (PolyPhen2) is a tool that predicts the possible impact of amino acid substitutions on the human protein structure and function using structural and comparative evolutionary considerations. PolyPhen2 results are available for human proteins. It classifies the substitution as probably damaging (score = 1 : 0) and possibly damaging or benign (score = 0 : 0) .Other pathogenicity predictor scores such as MutationTaster, MetaSVM, and MetaLR are available for human data via VEP plugins:MutationTaster is used for a quick assessment of the disease-causing potential of DNA sequence changes. Evolutionary conservation, splice-site changes, protein feature loss, and changes that could affect the amount of mRNA are all examined using MutationTaster, which predicts an alteration as one of the four possible types: “A” (disease causing automatic), “D” (disease-causing), “N” (polymorphism), or “P” (polymorphism automatic) , 31;MetaSVM (SVM (Support Vector Machine)) is a linear model that can be used to solve classification and regression problems. The theory behind SVM is simple: it finds and creates a line (hyperplane) that separates the data into different classes. Prediction using SVM approach in Ensembl database reveals “T” as tolerated or “D” as damaging or deleterious. The score cutoff between “D” and “T” is 0, and higher scores are more deleterious ;In MetaLR, to predict the deleteriousness of missense variants, logistic regression (LR) is used to combine nine independent variant deleteriousness scores with allele frequency information. Variants are categorized as “tolerated” or “damaging”; a score between 0 and 1 is also provided where variants with higher scores are more likely to be deleterious .https://www.rcsb.org/): The PDB ID for TERB1-TERB2 complex is 6J07 and for TERB2-MAJIN complex is 6GNY  by providing the following results: The ΔΔGbind  which is the predicted change in binding affinity induced by a mutation; in deleterious (yes or no), MutaBind2 server classifies a mutation as deleterious if ΔΔG ≥ 1.5 or ≤−1.5 kcal mol−1, ΔΔEvdw is the change of van der Waals interaction energy upon a mutation, ΔΔGsolv is the change of polar solvation energy upon mutation, and ΔΔGfold is the change of stability of protein complex upon mutation [In order to estimate the impacts of single mutations on protein-protein interactions (PPI), we performed computational analysis on the human crystal structure of TERB1-TERB2 and TERB2-MAJIN complexes using MutaBind2 . This tomutation .The chains of each complex (A and B of the 6J07 complex and C and D of the 6GNY complex) were separated using the UCSF CHIMERA program . Mutant The High Ambiguity Driven protein-protein Docking (HADDOCK) web server was employed to perform protein docking. The HADDOCK2.4 uses chemical shift perturbation data resulting from NMR titration experiments, mutagenesis data, and bioinformatics predictions . In the G) have greater binding affinity and correlates with mutations that stabilize the protein structures [Subsequently, the protein binding energy prediction (PRODIGY) web server was used to predict the binding affinity of the protein-protein complexes from their 3D structure , 40. ComLigplot software uses the 3D coordinates of a protein and its bound protein/ligand to automatically produce schematic diagrams. These diagrams show the pattern of interactions between the two molecules and are very useful for comparing different structures since they provide clear and helpful information of the intermolecular interactions and their strengths, including hydrogen bonds, hydrophobic interactions, and atom accessibilities .G). It further defines whether these changes increase or decrease the stability of the protein. Positive ΔΔG value denotes that protein stability increased, and negative ΔΔG means that protein stability decreased [The stability of the protein was checked using the Protein Stability Change Upon Mutation (DUET), which is an online server for an integrated computational approach to study missense mutations in proteins. It combines two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by consolidating the results of the separate methods in an optimized predictor using Support Vector Machines (SVM) . DUET precreased .Site Directed Mutator (SDM) is an algorithm of statistical potential energy function that calculates a stability score based on environment-specific amino acid substitution frequencies within homologous protein families, which is comparable to the free energy difference between wild-type and mutant proteins .Mutation Cutoff Scanning Matrix (mCSM) relies on graph-based signatures used as a novel approach to the study of missense mutations and the prediction of their effects. These signatures encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models .To predict the evolutionary conservation of the amino acids in a protein sequence, we used a ConSurf bioinformatic tool that provides evolutionary profiles of each of the amino acids in the protein, based on phylogenetic relations between homologous sequences to reveal regions that are important for structure and/or function . The tooYASARA View is an open-source program for the molecular graphics, modeling, visualization, and analysis of the three-dimensional protein's structures. The structural deviations between the native and mutated models were analyzed using the YASARA View program, by measuring the root mean square deviation (RMSD) which is the average distance between the atoms of the superimposed proteins . Its valA posttranslational modification is crucial for cell signaling and affects the function of the protein. TERB1, TERB2, and MAJIN proteins' posttranslational modifications were predicted using MusiteDeep, an online service that includes a deep learning framework for predicting protein posttranslational modification (PTM) sites .The SNP dataset was collected from Ensembl database and had almost the same classification in the three studied proteins: about 96% involved germinal cells, while 3% concerned somatic cells. Different consequence types of these SNPs were detected: intron variant with the higher percentage , followed by missense variant , and other variants—3 prime UTR, 5 prime UTR, synonymous, splice region, and frameshift. In this study, seventy missense mutations that could have been mapped on the protein structure were included: 18 variants for TERB1, 31 variants for TERB2, and 21 variants for MAJIN .Thirty missense SNPs were predicted to be deleterious using SIFT (where tolerance index score was ranged from 0 to 0.02). The SNPs predicted by SIFT were validated by PolyPhen2, MutationTaster, MetaSVM, and MetaLR. In particular, PolyPhen2 results showed that eight AA substitutions  were predicted to be possibly damaging, while twenty-two SNPs were probably damaging: P649A, K632E, Y631S, Y631H, Y631N, H621Q, H621L, C618R, R605G, and S604G in TERB1 gene; W8R, G25R, G25V, F64S, G84S, G84D, P90S, G187W, D191N, and G198R in TERB2 gene; and R53H and R53C in MAJIN gene. The MutationTaster tool classified all these AA substitutions as deleterious .G ≥ 1.5: seventeen variants for TERB1, eleven variants for TERB2, and two variants for MAJIN  which correlates with lower binding affinity. These regions are represented by seven mutant complexes of TERB1-TERB2 complex  with a ΔΔG ≈ −10 kcal/mol and four complexes of the TERB2-MAJIN complex  with a ΔΔG ≈ −11.5 kcal/mol , Leu (2), Arg (8), Lys, and His) of TERB1 and amino acids , Lys (2), Thr, Asp (5), Gln, Ala (2), Asn (2), Glu (2), and Ile) of TERB2. In this complex, chain A (TERB2) revealed 12 hydrophobic interactions , Leu, Phe, Asn, His, Ile, and Glu) while chain B (TERB1) showed 14 hydrophobic interactions , Asn, Ala (2), Gly, Phe (2), and Tyr) (Ligplot result of the wild-type complex and Tyr) .R620C-W8R complex showed that hydrogen bonds formed by Gly, Val, Ser (3), Arg (8), Leu, His, Leu, Ile, Thr, Asp, Asn, and Gln amino acids were deleted, while new bonds with other residues (Asp (5), Asn, Thr (4), Gln, Arg, and His) were added. Further hydrophobic interactions have been added underlying the following Gly, Gln, Val, and Ser amino acids, whereas Ile, Cys, and Asn amino acids were eliminated.Mutated complex represented some differences in the hydrogen bonds and hydrophobic interactions: some contacts were added and others were removed. S604G-W8R; on the other hand, eight amino acids  were removed. Four hydrophobic contacts  were added, and eight were deleted .Nine amino acids  were added to hydrogen bonds of the mutated complex S604G-G25V complex showed that hydrogen bonds constituted by Gly, Asp, Asn, Arg, Tyr, Gln, Thr, and His residues were added and Leu, Val, Asp, Glu, Thr, Asn, Arg, Gln, His, Lys, Ser, and Ile were removed. Some hydrophobic interactions involving Gln, Lys, Ser, Arg, Val, and Pro have been added, and Arg, Cys, Asn, Val, Lys, Glu, Pro, and His were eliminated.C618R-F64S revealed that hydrogen bonds formed by Asp, Ile, Asn, Thr, Gly, Gln, Arg, and His amino acids were added, while those formed by Gly, Val, Ser, Arg, Lys, His, Leu, Ile, Ala, Thr, Glu, Asp, and Gln were canceled. For hydrophobic contacts, Lys, Gln, Pro, Val, and Arg amino acids were added and Arg, Cys, Asp, His, Pro, Asn, and Gly amino acids were deleted.The mutated complex C618R-G84D. Nine hydrophobic interactions  were added, while four  were removed.Eight amino acids  were added, and nine  were removed in the hydrogen bonds of the mutant complex Y631N-G84D revealed that hydrogen bonds formed by Asp, Tyr, Asn, Arg, Cys, Ile, His, and Phe amino acids were added and hydrogen bonds formed by Leu, Val, Asp, Glu, Thr, Gly, Ser, Lys, Arg, and Ile amino acids were removed. For hydrophobic contacts, six amino acids  were added while nine amino acids  were canceled  were added, and five  were removed.TERB2-MAJIN undergoes 20 hydrogen bonds formed between amino acids , Arg (6), Phe, His (3), Asn, Ala (2), and Gly) of MAJIN chain and amino acids (Leu (3), Asp (3), Phe (3), His (2), Tyr (3), Gly (3), Lys, and Ile (2)) of TERB2 chain. MAJIN protein carries 19 hydrophobic contacts (Leu (5), Ser (3), Phe (3), Ile, Ala, Thr, Val, Asn, Gln, Asp, and Gly) while 13 hydrophobic interactions (Gly (2), Ala, Val, Ile, Glu, Tyr, Met, Lys (2), Leu (2), and Ser) were detected in TERB2 protein.The second wild complex WT TERB2-R53H showed that six amino acids  were added to hydrogen bond interactions, while three  were removed. The hydrophobic interactions listed two amino acids (Ile and Asp) as deleted and only one amino acid (Pro) as added.The mutated complex MAJIN-L189P, and five were removed . Hydrophobic contacts added three new residues  to the list, while four residues Leu, Ile, Val, and Gly were taken off . Some hydrophobic interactions implying Gly, His, and Asn have been added, and Asp, Val, and Ile have been eliminated , and six were canceled . In the hydrogen bonds, six amino acids  were added, while one (Gln) was removed.Five amino acids  were added to hydrogen bonding of the mutated complex aken off . The mutiminated . About tDUET, ConSurf, and MusiteDeep web servers were used to identify, respectively, the mutation effects on protein stability and the conserved domains in proteins and predict directly the posttranslational modification (PTM) site from the raw protein sequence. The mutated structures were also analyzed by YASARA View, with regard to evaluate conformational variations by calculating the RMSD.The results of mCSM, SDM, and DUET algorithms showed that 23 amino acids substitutions were recorded as destabilizing by the three algorithms: P649A, K632E, Y631S, Y631H, Y631N, I624T, H621Q, C618R, F607V, R605Q, R605G, C592Y, and C592R of TERB1; W8R, F64S, G84S, G84D, P90S, G25R, G187W, L189P, and G198R of TERB2; and R53C of MAJIN . FurtherCompared to those in nonconserved regions, amino acids located in conserved regions were predicted to be highly damaging. ConSurf predicts amino acids to play structural or functional roles based on conservation and solvent accessibility. Residues are predicted as functional when they are highly conserved and exposed and as structural when they are highly conserved and buried.Phylogenetic conservation analysis of TERB2 protein showed that G25, P90, G187, D191, and G198 are highly conserved residues and predicted to be exposed and functional. On the other hand, W8, F64, G84, and L189 are highly conserved residues and predicted to be buried and structural. Finally, R53 is highly conserved and predicted to be exposed and a functional residue in MAJIN protein .Analysis of TERB1 protein from the ConSurf server revealed that P649 and S604 are highly conserved residues and predicted to be exposed and functional, while I624, H621, C618, F607, and C592 are highly conserved residues and predicted to be buried and structural. K632, R620, S608, and R605 are exposed residues: R620 is variable, while K632 and S608 are conserved. Y631 is a conserved and buried residue .In our study, 29 mutations out of 30 showed higher RMSD values which indicate greater variation between wild and mutant protein structures. Therefore, four variants C592R, G25V, G198R, and R53H reflected maximal structural dissimilarity with TERB1-TERB2 complex and TERB2-MAJIN complex  and were therefore used for superimposing . One resMusiteDeep is applied to predict the effect of SNPs on posttranslational modification (PTM) process of the human TERB1, TERB2, and MAJIN proteins. MusiteDeep identified sites for O-linked glycosylation: R605 in TERB1 protein and W8, G84, G187, L189, D191, and G198 in TERB2 protein sequence . S-PalmiA cumulative score (CS) was calculated for the 30 damaging variants based on seven in silico tools used for predicting the effect of SNPs  to further understand and justify how the most deleterious mutations were identified in this study .All of the listed 30 SNPs were predicted as deleterious or damaging by SIFT (SIFT < 0.05), PolyPhen2 (possibly and probably damaging SNPs), and Mutabind2 (ΔGG ≥ 1.5) ; and onlThe results of several recently published papers have contributed to our understanding of the meiotic telomere complex assembly in mammals and its possible interaction with Shelterin and LINC complexes, thus providing a significant advance in the identification of the importance of meiotic telomeres complex which has been gradually elucidated and clarified by various authors. In 2014, Shibuya et al. showed that TERB1 = CCDC79 structure contains two armadillo repeats at the N terminus, a coiled coil motif in the middle region and, remarkably, a Myb domain at the C terminus, a structure which resembles that of TRF1. At this point, many roles were dedicated to TERB1 . It is iIn addition to TERB1, two downstream meiotic proteins, TERB2 and MAJIN, are recruited to the meiotic telomeres by TERB1 in mammalian meiosis . ShibuyaIn 2019, Wang et al. concluded that the intact MTC complex is required for gametogenesis and fertility, and targeted disruption of the complex induces disordered synapsis and impairs meiotic double-strand break (DSB) repair and abolishes telomere-NE linkage, together leading to a complete meiotic arrest in prophase I in both sexes. Hence, both the MTC complex and SUN1 (the LINC complex) contribute to the stable telomere-NE association and are important for efficient progression of prophase I .Last year, Salas-Huetos et al. strongly confirmed that disruption of any of the three genes coding for the meiotic telomere complex (MTC) can cause NOA in men. It has been demonstrated previously that disruption of Terb1 in the mouse results in meiotic arrest and impairment of homologous pairing and synapsis, ultimately resulting in infertility in both males and females . LikewisTaken together, they showed that the disruption or mutations of any of the MTC genes impairs telomere association with the inner nuclear membrane, which is requisite for chromosomal synapsis and chromosome movement during meiosis. Failure of these events triggers meiotic checkpoints leading to a meiotic arrest and consequently to male infertility/NOA . The maiBioinformatic analysis provides powerful insights into understanding and predicting the effects of mutations on protein structures, including how mutations alter protein stability, binding energy, binding affinity, evolutionary conservation and interactions with other proteins and their relation to susceptibility of genetic diseases. In the current study, various algorithms, namely, VEP tools , Mutabind2, Haddock, Prodigy, Ligplot, DUET, ConSurf, and MusiteDeep, were used to identify the most deleterious SNPs of the TERB1, TERB2, and MAJIN genes. Accordingly, eleven SNPs out of thirty  scored the highest cumulative scores (CS = 6) predicted by different bioinformatic tools and were located in conserved regions with a score conservation of 9, which may affect the structure and function of proteins .Protein-protein docking aims to anticipate molecular complex's structure given the individual solved structures of its constituents, which can be determined experimentally or predicted. In the present study, docking was performed by HADDOCK web server that displays results as cluster's structures, where the first top cluster produced is deemed reliable and should be downloaded in order to be loaded on the PRODIGY web server to calculate binding energy. Binding affinity refers to the strength or interaction energy of the binding between the protein and its partner. According to the results of heatmap, the lighter colored areas represent the complexes with higher binding energy and less binding affinity: R620C-W8R, S604G-W8R, S604G-G25V, C618R-F64S, C618R-G84D, Y631N-G84D, and WT TERB1-G25R of TERB1-TERB2 complex and WT TERB2-R53C, WT MAJIN-L189P, WT MAJIN-D191N, and L198P-R53C) of TERB2-MAJIN complex . GeneralMissense mutations can affect protein-protein interactions via various molecular mechanisms, including changing folding free energy of interacting partners, modifying protein stability, or disrupting noncovalent interactions essential for complex formation and function. Binding affinity is also influenced by noncovalent interactions between biomolecules, such as the loss or gain of hydrogen bonding and/or hydrophobic contacts in the mutated complex compared to the wild-type complex. After visualization by Ligplot, we concluded that the eleven previous complexes with the lower binding affinity showed differences in protein-protein interactions where some contacts were added and others were deleted, which explains the diminution of binding affinity within these complexes .G) predicted by the 3 algorithms which indicate that they destabilized the protein's stability, except for the S604G that was destabilizing only by two algorithms  as they were agreed by six software. Combinations of multiple in silico tools provided information required to predict the effects of these mutations on the functional and structural deviations of TERB1, TERB2, and MAJIN proteins. This work has highlighted the need to take into account the type of protein-protein interactions when characterizing the variants within the complexes. Hence, our result added to previous knowledge and supported earlier findings that may be helpful for further understanding the role of meiotic telomere complex (MTC) in male infertility. Nevertheless, bioinformatics tools cannot replace conclusive experiments, and their results should be verified by supplementary data.


Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks. Essential proteins play an indispensable role in the survival of organisms, and the criticality of proteins is mainly determined by their biological functions. Studies have shown that essential proteins have abundant functions such as translation, transcription, and replication . The preIn recent years, researchers have proposed many computational methods to identify essential proteins relying on different ideas and technologies. Researchers have proposed many classic algorithms for predicting essential proteins based on PPI network topological characteristics, such as degree centrality (DC) , informaIn order to make up for the limitations of incomplete protein interaction networks, many research groups have combined PPI networks with other biological information in recent years to improve the accuracy of essential protein identification. In this article, we utilize non-negative matrix tri-factorization (NMTF) to deal with the challenges introduced above and propose a novel method named NTMEP for identifying essential proteins. NTMEP focuses on the following three important aspects. First, it is well known that the multiple kinds of biological data about proteins can be integrated to construct a weighted PPI network with similar functions. As a result, the more different types of data are used, the more artificial noise is produced inevitably. Considering this problem, NTMEP constructs the weighted PPI by using original protein–protein interaction information merely. Second, the NMTF algorithm is extensively used for many applications in pattern recognition, text mining, DNA gene expressions, and so on. This is also extended to community detection and the recommendation system. Hence, to mine more potential protein–protein associations, the NMTF algorithm is introduced in our progress. It takes the internal possibility of associations between proteins into account, which contributes to generation of a more reliable prediction model that excludes the noisy candidates. Third, distinct from previous approaches, we employ homologous and subcellular localization information in the course of ranking proteins, which can improve the accuracy of predicting essential proteins effectively.Our purpose is to develop a novel method which can improve the accuracy of predicting essential proteins. We firstly constructed a weighted PPI network to represent the complex relationships between proteins. Moreover, a novel prediction method based on NMTF was proposed specifically for the network to find the potential associations between proteins. Finally, the PageRank algorithm was performed to identify the essential protein candidates by integrating subcellular localization and homologous information.G be the PPI network that contains node set V =  (n is the number of proteins) and edge set E =  where  is the interaction between protein pi and pj with weighted value wm which was set to 1 in original protein–protein interaction information.Let pi and pj share at least one common neighbor, we assume that pi and pj are interacting. This kind of connection between proteins is called the co-neighbor (CoN) relationships and calculated as follows:In this subsection, a weighted PPI network was constructed in which the association value of two proteins would be calculated based on their topological characteristics. In analyzing the topological characteristics of PPI networks, researchers have found that the PPI networks are one kind of small-world and scale-free network. Therefore, the topological features of the PPI network can be used to predict essential proteins. In recent years, the item of common neighbors of two proteins in the PPI network has been used in many prediction algorithms to realize the task of predicting essential proteins. They demonstrate that the more common neighbors exist between two proteins, the more deeply is the association they have with other. In this article, if proteins SNei(i) and SNei(j) present the neighborhood sets of pi and pj, respectively. As can be seen from the above equation, the value of the CoN relationships of the two-protein range is between 0 and 1.where Non-negative matrix tri-factorization as a general technology takes or compresses a data matrix into a compact latent space. It has been used to model topics in text data , to predPCoN) to mine the potential interactions of proteins. In contrast to classic non-negative matrix factorization  is a joint non-convex problem, we employ the rule of multiplicative iteration to solve the objective function on the basis of using auxiliary functions. The squared Frobenius norm can be written as | | X| | 2 = Tr(TXX); therefore, Eq. (3) equals to:where ||⋅||F, S, and G are as follows, respectively:Its partial derivative equations for factor F is as follows:It is well known that the static point can be detected using the Karush–Kuhn–Tucker (KKT) complementarity conditions. The KKT condition for factor In this connection, the conditions are assumed to be functional if the derivative is zero:G and S can be derived as follows:Similarly, the updating rules for The multiplication iteration rules are shown as follows:Y, which is closest to PCoN, can be computed. Finally, to recover the symmetry of the protein–protein interactions, we transformed the matrix Y to a symmetrical transition probability matrix From the above Eq. (9), the optimal matrix Through the description of the above algorithm, based on the information of the original PPI network, an optimized weighted PPI network can be established. Therefore, we can use an iterative method to rank protein scores. This method mainly includes two parts: the calculation of the initial score and the calculation of the ranking score, as detailed below.Saccharomyces cerevisiae PPI network as an example, pi in the PPI network G = , we mainly use the homology information to calculate the homology score (SH) of the protein. Among them, SH (pi) refers to the conservative score of pi, and the calculation formula is as follows:In this part, we will initially score each protein in the PPI network using homologous and subcellular localization information. Taking the H(pi) refers to the number of times that the protein pi has direct homologous proteins in the reference species.Among them, SL) of the protein based on the subcellular localization information. If the protein pi exists in the final subcellular localization dataset R, then the frequency of each subcellular location r can be calculated by the following formula:We know that an important feature of proteins is subcellular localization. By studying the characteristics of protein subcellular localization, researchers  found thSN represents the relationship between the protein and the subcellular location data set, SN(r) refers to the number of proteins corresponding to the subcellular location r, and n is the number of subcellular locations.where pi, the subcellular localization score SL (pi) refers to the highest score for all subcellular locations.Based on a fixed protein C(pi) represents the subcellular location corresponding to the protein pi.where SL(pi) of protein pi is expressed as follows:Finally, according to Eq. (11–13), the unique initial score pi is called SF(pi), and The ranking of protein Among them, the function of the parameter α (0 ≤ α < 1) is to adjust the weight of the two scores in the final ranking score. Based on the above analysis, the protein ranking score is a linear combination of its initial score and the neighborhood correlation score at the edge of the network. Therefore, formula (15) can be rewritten in matrix vector format as follows:In our study, the Jacobi iterative method is used to solve Eq. (16), as shown below:tth iteration.where Through the above analysis, we conclude that the overall framework of the NMTF-based model for the identification of essential protein (NTMEP) can be referred to as the following Input: A PPI network G, subcellular localization information, homologous proteins information, stopping error ε, parameters k, α, and KOutput: Top K proteins sorted by SF in descending orderPCoN of the weighted PPI network according to Eq. (1)Step 1: Calculate adjacency matrix PCoN to Step 2: Reconstruct matrix SI with SF0 = SI and t = 0Step 3: Initialize initial vector FtS according Eq. (17)Step 4: Compute SFt – SFt–1| | < ε, then FPR S = FtS and terminate the algorithm. Otherwise, let t = t+1 and repeat Step 4Step 5. If | | SF in the descending orderStep 6. Sort proteins by the value of K of sorted proteinsStep 7. Output top In the experiments, we use four data sets including protein–protein interaction set, experimentally verified essential protein set, subcellular location set, and homologous protein information set. We downloaded the relationships among proteins from the DIP database , which iIn the NTMEP, the parameter α in Eq. (16), which used to weigh up the contribution of neighbor-induced score and initial score, was set to 0, 0.1, 0.2,…, and 1. While considering only the neighbor-induced score, α was set to 1. On the other hand, α was set to 0 when considering only the initial score. The impact of the parameter α to the performance of NTMEP is presented in To comparatively study the performance of NTMEP in predicting essential proteins, we also implement 10 types of representative essential proteins prediction methods, like DC , IC Est, CC Wan, BC Li , SC Tew, NC Zha, PeC Li, CoEWC , POEM Z, and JDCk of the ranking list means the more real essential proteins are predicted successfully. Parameter k, which is set to 100, 200, 300, 400, 500, and 600, denotes the number of essential protein candidates selected. The number of real essential proteins within top k candidates is shown in k cutoffs and ranked 92, 85.5, 78.7, 73.8, 69.4, and 65.2% of positive samples in top 100, 200, 300, 400, 500, and 600, respectively. Especially, as for the top 100 of essential protein candidates, NTMEP has higher predict accuracy 46, 48, 55, 48, 51, 37, 18, 19, 11, and 12% than that obtained from DC, IC, CC, BC, SC, NC, PeC, CoEWC, POEM, and JDC, respectively. In those competitive methods, JDC had the best accuracy and ranked 80, 76.5, 74.7, 66.8, 63, and 59.2% in the top 100–600, respectively. Compared with JDC, NTMEP improved by 15% in top 100, 11.8% in top 200, 5.4% in top 300, 10.5% in top 400, 10.2% in top 500, and 10.1% in top 600.The higher number of essential proteins within the top k, is ranged from 1 to 5,093. We compute the scores of all proteins by using each algorithm and sorted it in descending order, respectively. The top k proteins are selected as a positive set, namely, essential protein candidates, and others as the negative set, namely, non-essential protein candidates. To obtain a fair and convincing comparison, the precision–recall (PR) curve is used to evaluate the prediction performance for essential proteins of our method and other state-of-the-art methods. The value of cutoffs, presented as In this subsection, we employ the jackknife curves to assess the performance of our NTMEP method and other existing methods , the various top number of ranked proteins as candidates. The jackknife curves of all the methods are displayed in In summary, these results demonstrated the powerful ability of NTMEP in identifying essential proteins. This finding is reasonable because our method adopts NMTF to find the potential interactions between proteins, which could provide additional interaction information and help to improve the prediction results by a large margin.This subsection will analyze the difference between NTMEP and other prediction methods through experimental results. Firstly, 11 protein sets were constructed by NTMEP and other 10 prediction methods , and each protein set contains the top 100 essential proteins predicted by each prediction method. The number of proteins that overlap between the NTMEP method and other methods and the number of proteins that differ are shown in In As shown in In reviewing the literature, previous studies developed many computational methods to predict essential proteins effectively. However, these methods do not take full account of the false-positive and -negative noise generated from high-throughput experimentation and the process of the weighted PPI network construction. To get the utmost out of the complex association between proteins, NMTF is introduced into our proposed method. Moreover, subcellular localization and homologous protein information are used in the final scoring stage instead of the stage of establishing the weighted network. Also, a comprehensive experiment is carried out and the results show that our new method can obtain a better performance compared with other methods. A possible explanation for these results might be that there are deep relationships between proteins which are not founded by high-throughput experimentation, and fusion of multiple data raises the cost and reduces the overall efficiency of the process. These results add to the rapidly expanding field of computational methods for predicting essential proteins. It is unfortunate that the study did not solve the problem of noise generated by multisource data fusion. This is an important issue for future research.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.ZZ and MJ obtained the protein–protein interaction data, benchmark essential protein dataset, subcellular location data, and homologous protein information. ZZ, MJ, and XQ designed the new method, NTMEP, and analyzed the results. ZZ, DW, and WZ drafted and revised the manuscript together. All authors have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.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.


One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures. Protein–peptides interactions play essential roles in many cellular processes, and in recent years peptides have become attractive scaffolds for the design of new therapeutics . TherefoComputational prediction of protein–peptide complexes using molecular docking is a very challenging task . First oCABS-dock treats peptide ligands as fully flexible chains. The method allows for modeling structural changes of the receptor protein that are induced by peptide binding ,7,8. AppThe problem of scoring protein–peptide poses is one of the major challenges in the field of molecular docking . ScoringIn this paper, we describe and test the MD-based strategy for scoring protein–peptide complex models provided by the CABS-dock method. The scoring is based on protein–peptide interaction energy values estimated from a series of short MD simulations for the sets of 1000 predicted models. Extensive MD simulations for this large number of evaluated models would be extremely computationally expensive, and at least for this reason not practical. Therefore, we propose a simpler, and as we show quite effective, protocol for ranking the large number of CG CABS-dock models using AA MD. First, we use the Modeller method  for recoAdditionally, we investigate the impact of the quality of reconstruction of protein receptor structure (mainly proper packing of protein side-chains) on the calculated protein–peptide interaction energy values and the resulting scoring accuracy. For this purpose, in all analyzed molecular models of protein–peptide complexes (reconstructed to AA representation), the structures of receptor proteins were substituted by their original AA crystal structures and the scoring procedure was repeated for all systems.An appropriate set of protein–peptide structures is needed to evaluate the performance of the scoring method. In particular, the benchmark set should contain a large population of alternative structural models of different protein–peptide complexes, with peptides that exhaustively sample the conformational space within the receptor binding site and its vicinity. For this purpose, we have chosen docking results for 66 unique protein–peptide complexes from the CABS-dock simulations . The selIn this work AA and CG MD simulations are used to score a large number of molecular models of protein–peptide complexes. All models were scored according to their energy of protein–peptide interactions estimated based on short MD simulations in explicit solvent. During the MD simulation, positional restraints were imposed on C-alpha atoms. This way, the geometries of amino acid side chains were optimized and the initial positions and conformations of the protein–peptide backbones were maintained during the MD simulations. The measured changes in RMSD values for C-alpha atoms of peptide ligands were small, not exceeding 1 Å for structures resulting from the AA MD and 1.8 Å for the CG MD simulations. To account for even small changes in peptide ligand conformation the RMSD values for the bound peptides were recalculated after each MD simulation. The observed relations between the measured RMSD values and interaction energy values for the group of 66 protein–peptide complexes are shown in The same MD-based scoring procedure was used to calculate the protein–peptide interaction energy for the crystal structures taken from the PDB database and used as reference structures for all scored complexes. Obtained energy values were marked on the plots with red dots see . InteresThe scoring results obtained using three different force fields  are compared to CABS-dock benchmark data in Scoring results for the sets of 10 top-scored models indicate that the proposed MD-based scoring procedure using AA force fields of AMBER and CHARMM delivers more accurate scoring results than the default CABS-dock scoring. This is true for high-resolution complex structures with RMSD values below 2 Å A,B. AddiWhen comparing the sets of 100 top-scored models, one can see the AA MD-based scoring approach’s superiority over the CABS-dock scoring. In the considered range of RMSD values from 0 to 6 Å, the AA MD-based procedure identified a significantly larger number of models with lower RMSD values when compared to CABS-dock results D,E.In contrast to scoring results obtained using AA MD simulations, the CABS-dock scoring method shows much better performance when compared to scoring employing CG MARTINI force filed for sets of top 10 models, whereas both scoring methods showed similar accuracy for ranking sets of top 100 models C,F.An example of ranking results for the three protein–peptide complexes for which the AA MD-based scoring procedure provided more accurate results than the default CABS-dock scoring method is shown in Two plots showing RMSD values and corresponding interaction energy values calculated using AMBER and CHARMM parametrization for each scored set of models are presented in The three peptide ligands in analyzed systems have similar peptide chain lengths , but when forming a complex, they adopt different secondary structures, including a coil-like conformation, a short α-helix, and an extended conformation forming a long β-sheet with parts of protein receptor. In The CABS method uses discretized coordinates and a simplified representation of the protein chain. It allows very fast and efficient sampling of conformational space, which gives an advantage over other existing methods dedicated to protein–peptide docking. Docked ligands are fully flexible, and some structural changes are also allowed for receptor proteins that facilitate complex formation. However, this approach requires the reconstruction of full atomic details from C-alpha trace representation at the last stage of the docking procedure. This step is conducted using the Modeller method . UnfortuThe molecular models of protein–peptide complexes used in this study were taken from the CABS-dock benchmark conducted for peptide-bound systems. It means that the initial structure of the receptor protein used for docking was taken directly from the protein–peptide crystallographic structure and presented bound conformation of the receptor protein. However, all resulting complex models contained a protein receptor whose structure was slightly different from its initial bound conformation due to the above-mentioned features of the CABS model, namely receptor flexibility during simulation followed by its reconstruction to AA representation. To analyze the effect of the changes in receptor structure  on the scoring results, we repeated the scoring procedure using AA MD with one modification—the receptor protein in scored complexes was replaced by the original structure taken from the crystallographic structure of the receptor–protein complex.The ranking results indicate that the usage of receptor crystal structures significantly improves the scoring accuracy when compared to the CABS-dock driven data see . For scoThe AA MD-based scoring procedure showed a much better performance for scoring of sets of top 100 models. In the RMSD range from 1 to 4 Å, the significant majority of models presenting lower RMSD values were identified using the proposed scoring procedure. Besides, for less accurate complex structures presenting RMSD values above 4 Å, for about 2/3 of all analyzed cases, MD-based scoring was also able to deliver models with lower RMSD values.The scoring results obtained using experimental receptor structures in the modeled complexes strongly suggest the need to improve all atom models’ reconstruction techniques and/or optimization strategies. In our experience, the improvement can be conducted primarily by enhancing the accuracy of backbone reconstruction since it significantly affects the subsequent side-chain reconstruction and energy-based scoring, as demonstrated in protein structure prediction ,22CABS-dock is an efficient method for global docking of peptides to known (or approximately known) receptor–protein structures. The CABS-dock is available as the webserver ,5 or theFor the docking simulations, CABS-dock uses the CABS CG model  based onBy default, CABS-dock generates 10,000 predictions in C-alpha trace representation of protein–peptide structures. The CABS-dock scoring procedure leads to the sets of 10 or 100 top-scored models and consists of two steps. First, 1000 top-scored models are selected based on CABS CG energy values (100 models having the lowest interaction energy are selected from every 10 replicas of a simulated system). Second, a set of 1000 top-scored models is subjected to structural clustering using a k-medoids algorithm, with the number k of clusters equal to 10 or 100, thus leading to 10 or 100 top-scored models.In this work, we used the sets of models obtained from the CABS-dock docking protocol described by Blaszczyk et al. . The proThe major stages of the scoring procedure are shown in 2) imposed on C-alpha atoms of both the receptor protein and the peptide ligand. The simulation time step was 2 fs. Trajectory frames were recorded every 2 ps. The periodic boundary conditions were used. The temperature was set to 300 K and pressure was maintained at 1013 hPa. Long-range electrostatic interactions were approximated by the PME method [CHARMM and AMBER-based scoring of AA models was conducted using the following procedure: First, the geometry of the system was optimized during 2000 steps of steepest descent algorithm. During the optimization, C-alpha atoms were kept at their initial positions to prevent distortions of the analyzed structure. In the next step, 100 ps MD simulations were conducted with position restraints using force constant 1000 kJ/(mol*nmE method  with a cE method . In the 2). Due to the CG representation of the MARTINI model, a longer simulation step of 20 fs was used and the simulation for each analyzed model lasted 10 ns. A set of simulation parameters dedicated to the MARTINI force field was applied [A similar procedure for calculating the protein–peptide interaction energy described above was used for systems simulated in CG representation using the MARTINI model. Systems in atomistic coordinates were translated into CG representation and corresponding topologies were created using the martinize.py program. After optimization of the system geometry , MD simulation was conducted with position restraints imposed on BB united atoms using force constant 1000 kJ/ and for the best model in the set of top 10 models . Finally, ranking results were compared to CABS-dock scoring  based on structural clustering of the set of 1000 generated models for each protein–peptide complex (the CABS-dock data set). All simulations and data analysis were performed using the Gromacs program suit version 5.1.4 . The VMDTo investigate how the reconstruction quality of the receptor structure may impact the scoring results, we repeated the whole scoring procedure using AA force-fields for all 66 systems with one significant modification. In each scored complex model (resulting from CABS-dock docking simulation), the protein receptor structure was replaced by its crystal structure . Scoring results were compared to previously collected data.Efficient molecular docking of peptide ligands to protein receptors is a highly expected and necessary tool for a deeper understanding of many important molecular biology problems and, in particular, for the efficient design of new peptide-based drugs and therapies. Classical molecular docking tools have proven to be very efficient in studies of the interaction of small ligands with proteins and protein complexes, provided that the receptor structure is not significantly changed upon the docking and the binding site is approximately known. Peptides are specific ligands because they can adapt to a huge number of conformations and binding poses. Due to the large conformational space that needs to be explored, free docking of peptide ligands based on classical MD tools is now not possible. Only a significant simplification of the problem, based on CG modeling approaches, opens a possibility for attempting fully unrestrained docking of peptides. CABS-dock is such a tool that has proven to be quite effective in unrestrained and fully flexible docking of even quite large peptides  to protein receptors. CABS-dock docking generates huge numbers of possible structures for a relatively low computational cost, although selecting the best results is not easy at the CG modeling stage (due to the low resolution of the produced models and due to the smoothened energy surface). Therefore, a kind of multiscale ranking protocol is needed that merges CG with more precise AA modeling.In summary, we proposed and tested a multiscale modeling protocol that allows more efficient use of the CABS-dock results for a very moderate cost of short MD simulations. The proposed re-ranking of the CABS-dock results works well for high and moderate resolution models; however, it is less efficient for the generally less accurate solutions. Analysis of the re-ranking results also indicated the necessity of very accurate AA reconstruction of the CG models. Additionally, it should be pointed out that the presented modeling protocol can be easily extended by unrestrained AA MD simulations aiming at long-timescale refinement of protein–peptide complexes. This is, however, much more expensive computationally and beyond the scope of the present work.


Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (i) existing molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain and (ii) existing methods focus on limited labeled data while ignoring the value of massive unlabeled molecular data.Drug–target interaction (DTI) prediction is a foundational task for We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (i) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction and (ii) an augmented transformer encoder to better extract and capture the semantic relations among sub-structures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real-world data and show it improved DTI prediction performance compared to state-of-the-art baselines.https://github.com/kexinhuang12345/moltrans.The model scripts are available at Bioinformatics online. Drug discovery is notoriously costly and time-consuming due to the need of experimental search over large drug compound space. Drug–target protein interaction (DTI) prediction serves as the foundation for finding new drugs  and new indications of existing drugs (i.e. drug repositioning), since the therapeutic effects of drug compounds are detected by examining DTIs  . DespiteInadequate modeling of interaction mechanism. Existing works  mining that is adaptable to extract high-quality fit-sized sub-structures for both protein and drug. In addition, MolTrans includes a bio-inspired interaction module imitating the real biological DTI process. The new sub-structure fingerprints enable a tractable path for understanding which sub-structure combination has more relevance to the outcome through an explicit map in the interaction module.Leverage massive unlabeled biomedical data. MolTrans mines through millions of drugs and proteins sequences from multiple unlabeled data sources to extract high-quality sub-structures of drugs and proteins. The vast data result in a much higher quality sub-structures than using small training dataset alone. We also augment the representation using transformers  DTI Prediction). Given compound sequence n drugs and protein sequence m proteins, the DTI prediction task can be casted as to learn a function mapping Problem 1  among themselves to capture these contextual information, we further augment the embedding using a transformer encoder layers (MolTrans includes an interaction module that consists of two layers: (i) an interaction tensor to model pairwise sub-structural interaction and (ii) a CNN layer over interaction map to extract neighborhood interaction.Pairwise interaction. To model the pairwise interaction, for each sub-sequence i in protein and sub-sequence j in drug, we haveI becomes a two-dimensional interaction map. If a value in the map is high, it will be activated in the downstream layer and have a higher likelihood of DTI interaction. Through end-to-end learning, if a pair of sub-structures indeed interact, they will have high interaction score in the corresponding sub-structure pair position in the interaction map. Thus, by examining this map, we directly see which sub-structure pairs contribute to the final outcome.Neighborhood interaction. Nearby sub-structure of proteins and drugs influence each other in triggering the interactions. Hence, besides modeling the individual pairwise interaction, it is also necessary to model the interaction to the nearby regions. We achieve this through a CNN  and PR-AUC  as metrics to measure the binary classification performance. In addition, we use sensitivity and specificity metrics where the threshold is the one that has the best F1 score in the validation set.Evaluation strategies. We divided the dataset into training, validation and testing sets in a 7:1:2 ratio. For every experiment, we conduct five independent runs with different random splits of dataset. We then select the best performing model based on ROC-AUC performance from the validation set. The selected model via validation is then evaluated on the test set with the result reported below.Technologies. We use a server with 2 Intel Xeon E5-2670v2 2.5 GHz CPUs, 128 GB RAM and 2 NVIDIA Tesla P40 GPUs.We compared MolTrans with the following baselines. We focus on state-of-the-art deep learning models as they have demonstrated superior performance over shallow models.LR . The result is reported in https://www.guidetopharmacology.org/targets.jsp). We find 1908 enzymes interactions, 533 GPCRs interactions, 496 ion channels interactions and 104 nuclear receptors interactions. We find MolTrans is robust in all of the above individual protein family  and nuclear receptors. We retrieve one test set of BIOSNAP and map the target proteins to the four protein families using GtoPdb database (n family . ParticuI can provide hints on which sub-structure leads to the interaction. A high value cell in the interaction map stands for a potentially activated interaction between drug and target sub-structure that is important to the final interaction outcome. Thus, to visualize, we generate a heat map for I to see which cells have high values. We then select a threshold to mask out the majority of cells that have low values. We then examine literature to see if the remaining cells contain clues to the interaction outcome.To answer Q5, we show through examples how the interaction map We first feed drug 2-nonyl n-oxide, and the protein cytochrome b-c1 complex unit 1 into MolTrans, and we visualize the interaction map by filtering scalars that are larger than a threshold in We conduct an ablation study on the full data setting with the following setup:I output and feed into the decoder.-CNN: we remove the CNN from interaction module, and flatten the interaction map -AugEmbed: we remove the transformer in the augmented embedding module and feed the interaction module with the positional and content embedding.-Interaction: we further remove the interaction module from -AugEmbed. It degenerates to a decoder on top of the FCS fingerprint. Note that removing the interaction module alone is not a valid model design.Small: we use smaller dataset to train FCS: DrugBank for drug and BindingDB for protein. We adjust the minimal frequency to output a similar number of sub-structured as FCS-large.-FCS: we replace FCS embedding with ECFP4 fingerprint for drug and PSC descriptor for protein. The rest of the models remains the same, i.e. they are then fed into transformers, interaction module and decoder.From In this work, we introduce MolTrans, an end-to-end biological inspired deep learning-based framework that models DTI process. We test under realistic drug discovery setting and evaluate with state-of-the-art baselines. We demonstrate empirically that MolTrans has competitive performance in accurately predicting DTI under all settings with an improved explainability.Financial Support: This work was in part supported by the National Science Foundation award SCH SCH-2014438, IIS-1418511, CCF-1533768, IIS-1838042, the National Institute of Health award NIH R01 1R01NS107291-01 and R56HL138415, and IQVIA.Conflict of Interest: none declared.All data used in the study are from public resources. BIOSNAP is available at http://snap.stanford.edu/biodata/datasets/10002/10002-ChG-Miner.html; DAVIS is available at http://staff.cs.utu.fi/~aatapa/data/DrugTarget/; BindingDB is available at https://www.bindingdb.org/bind/index.jsp. Data processing scripts are also provided in MolTrans GitHub code repository.btaa880_Supplementary_DataClick here for additional data file.


The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering  algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering. This ining data .The integration of the IoT and the web (Web of Things) had led to the creation of new kinds of services and applications in different domains: remote healthcare , indoor https://www.bisresearch.com/industry-report/global-sensors-in-iot-devices-market-report-forecast.html). There will be a significant impact worldwide as the number of connected IoT devices that communicate, sense, and share information grows. These interconnected devices generate a large amount of data, which must then be collected, analyzed, and used; this demonstrates we have an enormously searching space  Devices Market, 2016–2022, ng space .Different interconnected sensors may be especially useful for monitoring and recording environmental phenomena such as temperature, sound, pollution levels, humidity, and wind . TherefoWe sought to overcome these limitations with an efficient approach to searching for and selecting the appropriate sensors for a query with minimal time consumption and a high quality. The proposed architecture consists of categorizing all current sensors in the network into three semantic categories (SCs); Society, Industry, and Environment. In parallel, for each semantic category, several semantic sensor network types (SSNTs) were created, and then the Whale-based Sensor Clustering Algorithm was applied to cluster the sensors using their contextual information. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was then used to select the appropriate sensors for the user query.The remainder of this article is organized as follows: “Related works” is devoted to a description and review of the most studies focused on sensor search and selection techniques. The “Preliminaries” section includes a definition of the problem, the WhaleCLUST Algorithm, and the TOPSIS Algorithm. “Proposal” presents the proposed model on suitable way formulation. “Experiments” discusses the main implementation results. Finally, “Conclusion” provides a concluding summary and future work.Existing methods used to address sensor search issues can be categorized into content-based methods that look for sensors that generate such data, and context-based methods that use the sensors properties.Content-based sensor searches can be conducted by processing and clustering the data generated by the different sensors, respectively. The aim is to obtain the optimal subset of devices that provide the desired data at any instance of time.A meta-heuristic algorithm for clustering and automatic clustering on big IoT data was proposed by Another meta-heuristic algorithm for clustering and automatic clustering on big IoT data was suggested by e.g., removal or connection of new sensors in the sensor network).The content-based method summarizes the processing of all collected data obtained from an enormous number of connected sensors in an IoT network; it is not practical to treat all the collected data, leading many researchers to choose context-based method approach. Context-awareness is an efficient technique for selecting data that requires additional processing and attention.Context is the characterization of an object in an environment at any place and time; this is extracted from the expression of one or more IoT resources . Below, The Global Sensor Networks (GSN)  is a datA context-aware sensor search selection and ranking model called CASSARAM , 2013c sThe Elimination-Selection (ES) model  combinedA comparison of the above-mentioned search techniques (context-based and content-based approaches) is summarized in With the same aims as content-based and context-aware methods, There are several architectures and methods that may be used to search for a service based on the content and the context of sensors. However, each study has its restrictions, as mentioned above. The mechanisms to resolve a search task are specific to a given goal. Here, we propose an architecture consisting of several gateways distributed in the network and managed by a server. We applied new methods on this architecture to consider their limitations while keeping an acceptable response time and a low network overload. Furthermore, the deletion or addition of sensors is a crucial challenge that should be considered in the context of a dynamic IoT environment.This section includes the necessary background for understanding the context of our study, including the problem definition, WhaleCLUST, and TOPSIS algorithms.etc.), gateways, and a server. These devices have a specific communication/transmission, sensing /reception ranges, and different constraints in terms of energy consumption and processing capacities. The gateways should be able to collect and process the data generated from sensors based on their constraints. Each gateway manages its sub-network and the server manages those gateways in the same manner. There are many sensors in each sub-network (gateway) that can be abstracted as services and the combination of these devices within these services is the basis of the IoT application within IoT middleware solutions.An IoT network is composed of a set of devices   with theWe can represent the vector properties of a cluster center j named ith property of the jth cluster center, and m is the number of properties.Hence, a swarm (search agent) refers to several candidate centers for a given area. For each search agent, object i, and the vector properties of cluster center We computed the fitness function of each search agent ij is the association weight of object Xi and cluster j, and is defined by:Simulating the spiral bubble-net feeding behavior process, the search agents update their position and orient themselves towards the best search agent. The description and the mathematical model for the spiral bubble-net feeding behavior process is broken down into three main aspects: encircling prey, bubble-net attacking method, and search for prey.Humpback whales can locate the position of prey and encircle it. The algorithm considers that the existing best search agent position is the target as a prey or close to the optimum point. The other search agents will enhance their position near the best search agent. This behavior is expressed by the following equations:p (0.5 for each), where p is a random variable generated in the range . In the shrinking encircling mechanism, the value of The bubble-net behavior of the humpback whale is characterized by two main mechanisms: shrinking encircling and spiral updating position. The two mechanisms have the same probabilities The new position of the current search agent was updated as follows:p is adopted to make the decision on the Equation used for updating the position of the current search agent; it is given as follows:The value of Most meta-heuristic algorithms search for the optimum solution using a random selection. In the bubble-net method, the humpback whales randomly search for the best search agent when the values of |A| are greater than 1 or less than −1. With this consideration, the search agent moves far away from a reference whale (the best search agent chosen at first); it is expressed as follows:The WhaleCLUST algorithm  initiateThe TOPSIS algorithm is a classical MCDA method and is of great interest to researchers in the subject. The TOPSIS algorithm is based on finding the best solution, where the best option is nearest to the ideal solution and farthest to the inferior anti-ideal solutions . The MCDThe TOPSIS algorithm can be summarized as:The first step: the normalization of the analysis matrix Q to The second step: the determination of the Ideal The third step: the calculation of the distances of each alternative  in each category. The server extracts the semantic category, type, and values of the different properties from the user query. Next, the server sends them to the gateways. In each SSNT, sensors are clustered by the Whale-based Sensor Clustering Algorithm based on their context properties  before forwarding the queries to those related SSNTs. Consequently, the relevant cluster to the requested sensor properties is selected.To improve the performance of sensor search in IoT; including time-consumption and dynamicity, we propose classifying the current sensors in the network into three semantic categories and create several SSNTs , and returning the response to the user .Connection: the user connects to the system and expresses its requirements via a system interface.User requirements: the user’s system sends the request (the user ID and user requirements) to the server.Request: the server can extract the semantic category, type of the requested sensor, and users’ requirements from a request. Next, it forwards them to the gateways. Local search and selection: using the clustering process, each gateway determines the corresponding service according to the semantic category, type, and users’ requirements from a request. The adequate service to the request is returned to the server. The details of this phase are described in the “Creation of SSNTs” section.Global search and selection: the server determines the gateways that can provide the appropriate service (sensor) for each request.We adopted the SSN Ontology  to modelThe application of IoT can be categorized into three semantic categories (SCs) based on their focus : industretc.) for the three SCs  in one of the gateways . The SSNT is based on context properties  from the gateways. It chooses the appropriate sensors based on the user requirements by calculating the distance between each suggested solution (selected sensor) and the user requirements using We used a HP- EliteBook 8440p computer with Intel i5 CPU running at 2.40 GHz, under Windows 7 (64-bit) and 8 GB of RAM to evaluate our proposed method. Accordingly, the simulation software was written in Python3 using the WhaleCLUST algorithm and the TOPSIS search method.It was difficult to determine some context properties related to the sensors in public datasets therefore we used a combination of real and synthetically generated data to evaluate our proposed method on a large scale. We collected datasets from the Linked Sensor Middleware (LSM) project .In all experiments, we considered five context properties  and 10,000 sensors. We took the averages of some experimental results and the parameters used in the WhaleCLUST model are shown in We created a model for the proposed architecture and datasets to evaluate the robustness, efficiency, and parameter impact of our proposed approach. We also explained the results obtained from the different experiments. Finally, we compared AntCLUST  and PartWe evaluated the accuracy of the WhaleCLUST algorithm and compared it with the AntCLUST and ParticuleCLUST algorithms using the number of iterations and the number of sensors. The greater the number of iterations and sensors the higher accuracy of our algorithm  and 10. We used three trials with the number of search agents set to 10, 15, and 50 according to the number of iterations to evaluate the quality of clustering and the system performance when applying the proposed algorithm. The results show that the best quality of clustering was over 96% when the number of search agents was set to 50 . This deTo demonstrate the efficiency of the search and selection phases of our approach compared to the AntCLUST and the ParticuleCLUST approaches, we measured the required processing time . When thThe advanced technology in the IoT environment allows access to multiple sensors that have similar utility. Searching and selecting the relevant and suitable sensors for user requirements is challenging. We studied the accuracy, time complexity, and scalability of the search and selection tasks to satisfy the requirements based on the context properties of sensors. We categorized the sensors into three semantic categories named environment, society, and industry. Within each SC, the SSNTs are created, where the improved Whale clustering is applied to cluster the sensors of SSNT according to the context properties. The distributed architecture consists of gateways that are connected to sensors and managed by a server, where the response is performed jointly and simultaneously among the server and the gateways. The experimental results showed that our proposed solution was promising in terms of accuracy, quality clustering, scalability, and execution time when compared to other approaches.Furthermore, the current work can provide many benefits for practitioners and researchers who want to develop or integrate IoT applications to exploit the services provided by smart city, industry, agriculture, and healthcare systems. Our work is designed to support new technologies such as 5th generation and edge computing to efficiently and quickly respond to complex IoT requests.We plan to apply our model in specific domains including pollution control based on storage capacity, energy consumption, and processing power. We will compare our study with works in the same context and domain and will consider the user request processing phase and the method used to extract the user requirements in future studies.10.7717/peerj-cs.762/supp-1Supplemental Information 1Click here for additional data file.


The fundamentals of how protein–protein/RNA/DNA interactions influence the structures and functions of the workhorses from the cells have been well documented in the 20th century. A diverse set of methods exist to determine such interactions between different components, particularly, the mass spectrometry (MS) methods, with its advanced instrumentation, has become a significant approach to analyze a diverse range of biomolecules, as well as bring insights to their biomolecular processes. This review highlights the principal role of chemistry in MS-based structural proteomics approaches, with a particular focus on the chemical cross-linking of protein–protein/DNA/RNA complexes. In addition, we discuss different methods to prepare the cross-linked samples for MS analysis and tools to identify cross-linked peptides. Cross-linking mass spectrometry (CLMS) holds promise to identify interaction sites in larger and more complex biological systems. The typical CLMS workflow allows for the measurement of the proximity in three-dimensional space of amino acids, identifying proteins in direct contact with DNA or RNA, and it provides information on the folds of proteins as well as their topology in the complexes. Principal CLMS applications, its notable successes, as well as common pipelines that bridge proteomics, molecular biology, structural systems biology, and interactomics are outlined. Decades of research into the cell biology, molecular biology, biochemistry, structural biology, and biophysics have produced a detailed understanding of individual DNA/RNA/protein molecules, and their interconnected networks. A great diversity of techniques has emerged for studying their structural interactions. However, even more complex, the structures of these workhorses from the cells are themselves dynamic, converting from one dominant form to another based on the proportions of particular proteoforms present for any given biomolecules. Accordingly, beyond the protein–protein/DNA/RNA interaction landscape, there is an entire universe to explore with respect to their structure and dynamics. One such high-throughput technique has emerged as a dominant player in understanding both interaction landscapes and their resulting protein/DNA/RNA structures, namely cross-linking mass spectrometry (CLMS). This review covers different methods that are available to study protein–protein/DNA/RNA interactions, and provides vital insight into CLMS, a collection of methods that are perfectly suited to achieve a better understanding of intra- or inter- molecular interactions.Protein–protein interactions (PPIs) play a crucial role in all biological/biomolecular processes to understand the molecular mechanism of relevant protein molecules; hence, they are often termed the workhorses of cells. Over 80% of proteins do not function in isolation, but rather exist in interactions with one another to obtain stable or transitory complexes, as demanded by their observed function ,2. PPIs Mass spectrometry (MS) is one of the powerful approaches available that is useful in several areas beyond CLMS , and itFundamentally, the mass spectrometry methods simply measure a mass-to-charge ratio of an ion. Initially, analytes are ionized and then transferred into the gas phase prior to their separation according to mass-to-charge ratios in a mass analyzer. Subsequently, ions that emerge from the mass analyzer are recorded using a detector. The most common way to ionize a peptide or protein sample is electrospray ionization (ESI) . HoweverQuantitative cross-linking mass spectrometry (QCLMS) approaches investigate protein structures as well as the dynamics of their interactions ,49,50,51Isobaric Tags for Relative and Absolute Quantitation/Tandem Mass Tag (iTRAQ/TMT) are chemical labeling, which are introduced to peptides after protease digestion, which allow relative protein quantitation via MS2 or MS3 encoded data. Nowadays, TMT/iTRAQ labeling enables comparison of up to 16 samples  in a single MS analysis. Notably, the iTRAQ/TMT reporter ions used for quantitation are cleaved from labeled peptides during fragmentation by way of collision-induced dissociation allowing for the quantitation from fragment ion relative intensities. Yu et al. , implemeMoreover, techniques, such as proximity-dependent biotin labeling (or BioID technique) in living cells, help to understand the plasticity of protein networks within heterogeneous cellular populations. In combination with nanopore technology, such an approach could help tackle pending biological questions, e.g., the identification of peptidyl-prolyl isomerases (PPIases) substrates. PPIases substrates preserve their primary structure/molecular mass, as well as the cis and trans isomers of the proline peptide bonds of the substrates interconversion by PPIases being the sole change. This subtle modification triggers important changes to the substrate’s fate, such as subcellular translocation, degradation, or rewiring of their protein–protein interaction networks. PPIase enzymes act as central molecular switches, as exemplified by the peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) that has been extensively studied and showing its involvement in multiple diseases ,59,60. HE. coli) as the host system, functionally important post-translational modifications and interacting protein partner associations may be lost during this process. Thus, various biochemical approaches must be explored when beginning a new project in order to directly enable the isolation of endogenous protein complexes from cells or tissues .,79.78,79organism ,78,79. MThree main chemical approaches that involve MS analysis on the peptide level are: (i) the exchange of labile hydrogen atoms with deuterium atoms in hydrogen/deuterium exchange (H/D exchange or HDX) methods, (ii) the covalent modification  of amino acid residues  in various covalent labeling workflows, and (iii) chemical cross-linking, in which two spatially proximate amino acid side chains are covalently coupled  72]. Pa. Pa72]. 6 intracellularly) and diversity in their binding affinities. Additionally, these include the variety of amino acid residues that can be targeted by cross-linkers and the decreased solubility of cross-linked protein complexes that, in turn, causes problems during digestion and the ionization of large cross-linked products ..149].The changing patterns of RNA–protein interactions is critical for cellular functions, which are then regenerated based on the subcellular localization and environment stimuli . CharactCross-linking methods can be used to identify protein–RNA interactions in vivo. To do so, the RNA is purified under denaturing conditions to remove non-covalent interactions and only cross-linked components are subsequently extracted for identification. The formaldehyde, a small and bifunctional cross-linker, can easily permeate the cells and cross-links macromolecules within 2 Å, involving the protein–RNA complexes, by creating a reversible covalent linkage . MethodsThe strength of the RNA–protein interaction should be mainly considered while choosing the cross-linking approach. RNA–protein dissociation constants vary widely, and such experimentally measured constants can be in the range of high nanomolar to picomolar concentrations. Generally, the weaker RNA–protein interactions are less likely to be captured by UV cross-linking as contrary to formaldehyde cross-linking. In addition, the UV cross-linking efficiency varies according to amino acid chemistry, whereas, in the formaldehyde cross-linking, nucleophilic lysine residues are strongly preferred and cross-linked b 149]. . 149]. HIn the proteomic analysis, most of the RNA-centric methods follow quantitative mass spectrometry workflows to detect proteins that are bound to RNA. These MS approaches can be either label-free or incorporate the use of chemical labels for detection or simply quantitation. Labeling methods can use stable isotope labels or chemical tagging of proteins in samples (can be termed as controls) and, thus, the enrichment scores can be obtained by the ratio of labeled peptides and true binding partners can be identified . LabelinThe majority of studies identify RNAs that are bound to a protein are performed by purifying the protein of interest. The most common way to achieve this is by making use of the long-known fact that the protein will chemically cross-link to the nucleic acid in vivo when hit by UV light at ~254 nm . This UVLately, the formaldehyde has been used as a cross-linking reagent for CLIP method on a double-stranded RNA-binding protein, which are, in most cases, thought to UV cross-link poorly . Methyle13C labeled form of biphenyldiglyoxal . Based on a general model of the physics (or biophysics), the MD simulation predicts the conformational dynamics or time course movements (at femtosecond resolution) of every atom from a biomolecule  that is used to assist understanding of important biomolecular processes ,177,178.Currently, significant progress is being made by pairing the cross-linking mass spectrometry with MDS in order to explore a wide range of biological questions concerning protein motions, interactions, and their assemblies. For example, Brodie et al. presented an integrative structural biology approach, in which short-distance cross-linking constraints are incorporated into rapid discrete molecular dynamics (DMD) simulations . They prRapidly growing technologies to map out interactions between protein–protein /RNA/DNA are critically important since the biological function of these molecules is extremely influenced by their structures, complex formation, locations and regulatory networks. Herein, we reviewed a suite of methods that are valuable for detecting such interactions between biomolecules of the cell. Special emphasis was on the combination of cross-linking with native mass spectrometry technique to yield mutual benefits while characterizing protein–protein, protein–RNA, or protein–DNA interactions, though, with a slightly modified approach. The CLMS strategies allow for capturing and identifying not just stable, but also transient, dynamic, and weakly bonding molecules and, thus, it emerged as the most striking example of multidisciplinary success among hybrid or integrative structural biology methods. In addition, CLMS provides inter-residue distances that can be integrated into the molecular modeling and MD simulation techniques, in order to achieve physiologically realistic quaternary (PPI) structures. Different means to combine the knowledge of chemistry with MS to analyze essential biological systems of interest were scrutinized. Several different cross-linkers are accessible with varied chemistries for the CLMS techniques and, therefore, one should consider the protein sequence of interest to determine what sort of combination of cross-linker and cleavage enzyme yield positive results. To start with one of the most commonly used cross-linker types, like amine reactive cross-linking  and carboxyl-amine cross-linking (COOH-NH2: EDC or sulfo-NHS zero spacer length), is advisable.Furthermore, we propose (or speculate) that the CLMS approach, though, not previously carried out, could be merged with novel interactomics techniques (proximity-dependent labeling by BioID or TurboID). Merging these two methods may bring spatial resolution to CLMS at a sub organelle level, because the BioID radius is estimated ~10 nm, and since CLMS mainly focuses on the proteins in the neighborhood of a given bait. These CLMS and proximal interactomics methods can be iteratively performed in living cells, i.e., the generated samples are the proximal species of a protein of interest labeled with biotin first and then cross-linked with interactors. Subsequently, perform streptavidin-biotin capture, followed by the digestion and cross-linked peptides identification of proximal interactors.Besides the outstanding practice of CLMS in cross-linked peptide identification, it is worth noting that the failure to detect cross-links or only a few cross-links is related to several factors. For example, the lengths of the cross-linked peptides are too short or long, defective fragmentation of the cross-linked peptide, the cross-linked peptide is below the MS detection threshold, or the cross-linker is too short to link the appropriate residues. A few additional factors are: the sequence of interest is not fitting well with the cross-linker chemistry concerning reactive or cleavable residues, imperfect reaction conditions, the whole amount is too low, and extraction difficulties of the cross-linked peptides from a gel . Therefore, the functional groups in a linker have to be tested and characterized thoroughly, as well as demonstrated precisely within the CLMS framework. Additionally, we addressed different protocols that are useful for digestion in CLMS methodologies, such as ‘in-gel’, ‘out-gel’, ‘in solution’, and FASP, with their successful usage. Alongside the CLMS application for identifying the protein–protein interactions, this review covers or describes the cross-linking methods for studying protein–RNA as well as protein–DNA interactions, which apparently arises as fertile ground for future CLMS utilization.


To understand the mechanisms of these processes, it is necessary to solve the 3D structures of the protein complexes. When protein complex structures have not yet been determined by experiment, protein-protein docking tools can be used to computationally model the structures of these complexes. Here, we present a webserver which provides access to LZerD and Multi-LZerD protein docking tools. The protocol provided by the server have performed consistently among the top in the CAPRI blind evaluation. LZerD docks pairs of structures, while Multi-LZerD can dock three or more structures simultaneously. LZerD uses a soft protein surface representation with 3D Zernike descriptors and explores the binding pose space using geometric hashing. Multi-LZerD performs multi-chain docking by combining pairwise solutions by LZerD. Both methods output full-atom docked models of the input proteins. Users can also input distance constraints between interacting or non-interacting residues as well as residues that locate at the interface or far from the interface. The webserver is equipped with a user-friendly panel that visualizes the distribution and structures of binding poses of top scoring models. The LZerD webserver is available at   Protein complexes are central to many processes in a living cell. To understand the physical mechanisms of these processes, determining the 3D structures of their associated protein complexes is a critical step. When protein complex structures have not yet been determined by experiment, it is possible to use computational tools to construct models of these complexes . A proteThere are several protein-protein docking methods and their derivatives are publicly available, such as ZDOCK , HADDOCKIn addition to this capability, The LZerD suite has been consistently ranked highly in the server category in CAPRI , the blihttps://lzerd.kiharalab.org. Currently, LZerD and Multi-LZerD interfaces are made available. The LZerD  algorithm handles pairwise docking of protein structures  as a first step. Neither registration nor an email address is required to submit jobs; however, there are productivity benefits to registration. Jobs not associated with any account may not be retained longer than two weeks, while jobs from registered users are retained for at least three months. Furthermore, registered users can view a table of retained jobs on their account, allowing for easier management of their results. Regardless of whether a user is registered, users can submit jobs for the different docking methods using their respective submission pages . Once the user has uploaded their two structures, here referred to as the receptor and the ligand, based on the order they were submitted in, they can configure any desired custom settings. Figure To assemble two protein structures, users should use LZerD . Via the web interface, up to six subunits can be submitted for a docking job. After using the Add button to set the appropriate number of subunits, users can upload their structures just like for LZerD in the previous section  there were seven targets which were modelled by using LZerD . There whttp://kiharalab.org/proteindocking/ (The LZerD webserver currently makes the highly ranked pairwise and multiple protein-protein docking functionality of LZerD and Multi-LZerD conveniently available to end users, along with the ability to integrate biological information in the form of geometric constraints. The full LZerD suite will further include functionality e.g. for assembling complexes from disordered proteins  and preddocking/ . Future