Serine-threonine kinase11 (STK11) is a tumor suppressor gene which plays a key role in regulating cell growth and apoptosis. It is widely known as a multitasking kinase and engaged in cell polarity, cell cycle arrest, chromatin remodeling, energy metabolism, and Wnt signaling. The substitutions of single amino acids in highly conserved regions of the STK11 protein are associated with Peutz–Jeghers syndrome (PJS), which is an autosomal dominant inherited disorder. The abnormal function of the STK11 protein is still not well understood. In this study, we classified disease susceptible single nucleotide polymorphisms (SNPs) in STK11 by using different computational algorithms. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking and molecular dynamics analysis. Our results show that W239R and W308C variants are likely to be highly deleterious mutations found in the catalytic kinase domain, which may destabilize structure and disrupt the activation of the STK11 protein as well as reduce its catalytic efficiency. The W239R mutant is likely to have a greater impact on destabilizing the protein structure compared to the W308C mutant. In conclusion, these mutants can help to further realize the large pool of disease susceptibilities linked with catalytic kinase domain activation of STK11 and assist to develop an effective drug for associated diseases. About 0.5 million SNPs are present in the coding region of human genome2. Substitution of amino acids in conserved regions of the protein may exert an effect on protein structure, stability, and function. High-risk nonsynonymous SNPs (nsSNPs) are responsible for altering protein function, which cause various diseases in humans5. Indeed, many studies have reported that ≥50% of the variations linked with hereditary genetic disorders are due to nsSNPs8. In recent years, nsSNPs in cancer-causing genes have received considerable attention. In various studies, multiple nsSNPs have been identified which influence the probability of infections, and the extension of inflammatory disorders and autoimmune diseases11. Immunity-related genes are highly polymorphic and many nsSNPs have remained uncharacterized in these genes.Single nucleotide polymorphisms (SNPs) are found in every 200–300 base pairs of the human genome and serve as genetic markers12. The STK11 gene acts as a tumor suppressor gene having significant influence in controlling cell growth as well as apoptosis.Human STK11 (serine/threonine kinase 11) protein, also known as LKB1 (liver kinase B1), is found at chromosome 19p13.3. STK11 protein is composed of 9 coding exons along with a 433 amino acid long coding sequence and one non-coding exon. The catalytic kinase domain is highly conserved in STK11 protein, which is comprised of residues in 49–309 positions15. STK11 also has an impact on polarity, metabolism, proliferation in cancer cells through phosphorylation, and activation of AMPK and its related kinases. STK11 forms a heterotrimeric complex in vivo with STRADα and MO25α. Both STRADα and MO25α have been involved in relocalization of STK11 from the nucleus to cytoplasm16. Cytoplasmic localization is critical for the growth suppressive function of STK1115.A cellular function of STK11 has been regulated through a number of protein-protein interactions. Cell cycle arrest mediated through STK11 is involved in p53-dependent apoptosis pathways as well as in VEGF and Brg1-dependent growth arrest17. The most common malignancy associated with PJS is colorectal cancer, followed by breast, small bowel, gastric, and pancreatic cancers19. The cumulative lifetime risk of developing cancers in individuals with PJS at ages 20, 30, 40, 50, 60 and 70 years are 2%, 5%, 17%, 31%, 60% and 85%, respectively17. Recently, studies have shown that about 57–88% of PJS cases concurrently occur with one or more mutations in STK11 protein, including point mutations and large genomic deletions, duplications, or insertions23.Germline mutations in the STK11 gene are responsible for Peutz–Jeghers syndrome (PJS), which is an inherited autosomal dominant disorder and is distinguished by gastrointestinal hamartomatous polyps and mucocutaneous pigmentations. The occurrence of PJS is estimated between 1 in 8300 to 1 in 280000 individualsin silico approaches have been developed particularly for screening functional SNPs and detecting the effect of deleterious nsSNPs in the candidate protein. Many tools can also predict the structural changes based on single amino acid substitution in the protein25. Based on in silico algorithms, the functional SNPs in BRAF (B-Raf proto-oncogene), BRCA1(Breast cancer type 1 susceptibility protein)26, and ATM (Ataxia-Telangiectasia Mutated) genes27 have been classified successfully from a wide range of disease susceptible SNPs based on their functional and structural consequences. Recent computational approaches are focused on cancer studies by involving either in the prediction of the most damaging SNPs from large databases or in population-based data analysis30.Over the past few years, several In this study, detailed investigations have been carried out to postulate 63 nsSNPs in the STK11 protein and to evaluate their deleterious or pathogenic effects on the protein. Employing different prediction algorithms, we classify high-risk nsSNPs and identify their structural and functional impact on the STK11 protein. Moreover, molecular dynamics (MD) and docking calculations are performed to better understand the impact of mutation on protein structure at the secondary and tertiary level.The complete workflow, tools, and databases applied to identify the damaging SNPs in human STK11 and their structural/functional consequences due to mutation, are summarized in Fig. The polymorphism information of the STK11 gene was collected from the NCBI dbSNP database, which contains a total of 2283 SNPs for STK11 protein. Out of 2283 SNPs, 1535 are in intron region, 308 are nsSNPs (missense), 257 are coding synonymous, 146 are in 5′ UTR region, and 81 are in 3′ UTR region. It can be noticed from Fig. in-silico prediction pipeline: P-Mut, SNAP2, PON-P, and Mutation Assessor. We noticed 37 nsSNPs to be pathological in P-Mut, 51 nsSNPs as affected in SNAP2, 37 nsSNPs as pathological in PON-P, and 57 nsSNPs to be predicted as disease-associated in Mutation Assessor algorithms , catalytic kinase domain (49–309), and C-terminal domain (310–433) are found in STK11. The two nsSNPs (W239R and W308C) that we have selected are located in the catalytic kinase domain  analysis. We performed MSA among the nine different species through use of the MEGA 6 program. It was revealed that conserved regions are homologous in nine species, represented as asterisk signs (“*”) in Fig. ConSurf web browser was used to measure the intensity of evolutionary conservation at each residue position in STK11 protein. By using the Bayesian method, the ConSurf server recognizes putative functional and structural amino acids and identifies their evolutionary conservation profileHomo sapiens) and chimpanzee (Pan troglodytes) are close neighbors  in the favored region, 11 residues (3.7%) in the allowed region, and only 5 residues (1.7%) in the outer region  values with significant deviation observed for the W239R mutant, as shown in Fig. From Rg (radius of gyration) analysis of native and mutant structures Fig. , it can 2), as shown in Fig. 40.Solvent accessible surface area (SASA) analysis indicated that mutant W239R has a higher average SASA value (15498.11 Å2) than mutant W308C (15121.88 Å2) and native STK11  were analyzed using principal component analysis (PCA) to understand the structural quality of proteins during MD simulation. The energy and structural information are the composition of the following variables; bond energies, bond angle energies, dihedral angle energies, planarity energies, Van der Waals energies, and electrostatic energies. The exploratory PCA analysis of these variables revealed the similarity and dissimilarity between native and mutant proteins in the scores plot Fig. . The sco10-helix was observed in mutant W239R  are present in native STK11, whereas these strands have converted to coils in mutant W239R. On the other hand, W308C mutant showed variation at positions 217–219 and 338–340, which transformed from 310-helixes to turns, and also showed variation by converting to a 310-helix from a turn at position 294–296  are disrupted and formation of 339R Fig. . Furtherin silico prediction algorithms disclose two nsSNPs, W239R and W308C, that are highly deleterious based on their compared prediction scores. Typically, conserved residues are involved to control the biological system in proteins like stability and/or folding41. Functional amino acids are located at enzymatic sites and show substantial protein-protein interaction. These residues are more highly conserved compared to other residues in the protein42. For assessing the deleterious impact of two nsSNPs (W239R and W308C) in the STK11 protein, we estimated the evolutionary conservation profile of all amino acids position in STK11 protein via ConSurf web server. This server predicted that tryptophans at the 239th and 308th positions are buried structural residues with a conservation score of 9. For evaluating ConSurf result, we executed the multiple sequence alignment (MSA) among the nine different species through the MEGA 6 program, which revealed that the conserved regions were homologous in nine species. Maximum conserved residues are found in the kinase domain, which is essential for stabilizing the secondary structure and in activating the STK11 protein. Moreover, the phylogenetic tree reveals that human and chimpanzee are close neighbors. One study reported that 99% sequence homology is found between the human and chimpanzee STK11 proteins34. This result indicated that the catalytic kinase domain is evolutionarily conserved in STK11 protein.All 44. The mutation introduces a charge which may lead to repulsion between the mutant residues and neighboring residues44. Hence, this variant altered the binding interaction with surrounding residues, thereby disturbing normal biological processes. The mutant W308C involves the substitution of tryptophan to cysteine, where tryptophan is larger in size than cysteine. Moreover, a buried structural tryptophan residue is more hydrophobic than the cysteine residue, which probably will not fit at the 308th position. As a result, this variant will cause a loss of hydrophobic interactions in the core of the protein, as predicted by ProjectHOPE. Structural mutations affect buried residues in the protein core, causing changes in amino acid size and charge, hydrogen bonds, salt bridges, and S–S bridges. These changes cause loss of thermodynamic stability as well as aberrant folding and aggregation of the proteins45.In the catalytical kinase domain, the W239R mutant leads to substitution of tryptophan (a nonpolar aromatic amino acid) by arginine (a basic amino acid). Although the mutated residue is smaller in size than the wild-type residue, it is likely to disturb the interaction between other domains that are important for the protein activity as predicted by Project HOPE. Mass and charge difference in the protein affect spatiotemporal dynamics of protein-protein interactions47. ATP is strongly bound to the binding cleft of STK11, so these mutants disrupt the favorable contacts which are essential for the functional activity of STK11. Moreover, the deviation observed in the bound ATP molecule can lead to a reduction in catalytic efficiency of STK11.To further evaluate our hypothesis as to whether W239R and W308C mutants have a deleterious effect on STK11 protein, we performed molecular docking and MD simulation analysis. From docking analysis of STK11 with ATP, it is well revealed that both mutants perturbed the binding pocket quite significantly. The most prominent change was noticed in W239R where a significant loss of H-bond interactions within the binding pocket residues can be observed when compared to that in the native protein. In the STK11-ATP complex, ATP binds to a cleft between N- and C-lobes of the protein kinases through formation of H-bonds with the glycine-rich loop (G58-K62), β3 strand (K78), the hinge regions (E130-C132), αD-helix (E130-M136), the catalytic loop (H174-N181), and the activation loop (S193-A198)46. The disruption in the activation loop regions (D194-E223) may cause inhibition in activation of the STK11-MO25α complex. On the other hand, the W308C mutant showed a stable structure like the native type, but higher residual fluctuation can be observed in the residues (I322-S334) of the C-terminal flanking tail. The W308C mutant lost five polar contacts with H154 and H313 and one hydrophobic contact with L282 , P217-F219 (from β9΄-αEF loop), P221-I224 (from αEF-helix), G276-C278 (from αG-αH loop), and S307-F309 (from αI-helix), as shown in Fig. 10-helix in mutant W239R structure, as shown in Fig. 46. Furthermore, H174-D176 motif, β7, and β8 sheets are essential for forming the catalytic region. The activation loop segment is formed by D194-G196 motif, β9, β9′, and β9′′ and ends at P221-E223 motif, in which H174 from H174-D176 motif and D199 from D194-G196 motif are coordinated to Mg2+ along with PO4− ions of ATP46. Consequently, the W239R mutant changed the conformation of the C-lobe in the kinase domain  is converted to a β-strand in the mutant structure. The proportion of αC-helix (94–104), αF-helix (234–250), and αI-helix (300–310) are slightly reduced in the W308C mutant structure when compared to those in the native structure  of STK11 interact with MO25α46. Boudeau et al. reported that mutations located at positions L67, F157, K175-L182, W239-G242, and R297-W308 residues of STK11 abolished binding to STRADα and MO25α16. Scott et al. were first to identify the W308C mutant in a 42-year old patient with PJS31.In the W308C mutant structure, the most prominent alteration was observed from the amino acid residue positions of P294-A295 (from αH-αI loop) and V338-Y340  acquiring a 3ion Figs  and 10b.et al.50.From PCA analysis, mutant W239R showed a significant difference in flexibility than native and W308C mutant. This deviation might be due to disruption of secondary structure elements, which in turn affect the protein folding, thereby decreasing the stability of protein. Therefore, we suggest that the W239R mutant might have a great impact on protein function. This hypothesis is in good concordance with the results obtained by Rungsung This study reports two nsSNPs (W239R and W308C) that were found to be deleterious and have a mutational impact on structure and function of the STK11 protein. These mutations may lead to disruption of the original conformation of the native protein. The mutant W239R structure displays a significant difference in binding with the STRADα-MO25α complex, which could lead to disruption in activation and reduction in catalytic efficiency of the STK11 protein. Our molecular dynamics approach presented a change of deviation in important regions of the mutant structures when compared to the native protein structure. These deviations can interrupt the confirmation of the secondary structure, and thereby, the stability of protein may be disrupted. We also noticed that the ATP binding capability of the mutant proteins was less than that of the native protein. Although the W308C mutant has been previously associated with Peutz-Jeghers Syndrome (PJS) according to the literature, no one has predicted the W239R mutant to be linked with any diseases. Therefore, it is likely that predisposition of the unreported nsSNP can induce disease by altering protein activation or efficiency. The findings of this study will help in future genome association studies to distinguish deleterious SNPs associated with different individual patients with PJS. Hence, comprehensive clinical-trial-based studies are required on a large population to characterize this data on SNPs and also experimental mutational studies are required for the validation of the findings.51, SNPs information from NCBI dbSNP52, and the protein sequence was retrieved from UniProt database (UniProtKB ID Q15831)53.The SNP data for the human STK11 gene was collected from various web-based data sources such as: OMIM (Online Mendelian Inheritance in Man)54. Another algorithm, PolyPhen2, utilizes the protein sequence and substitution of amino acids in protein sequence to predict the structural and functional effect on the protein. If amino acid are changed or a mutation is found in protein sequence, it classifies SNPs as possibly damaging (probabilistic score >0.15), probably damaging (probabilistic score >0.85), and benign (remaining). Furthermore, PolyPhen2 calculates the position-specific independent count (PSIC) score for each variant in protein. The difference of PSIC score between variants indicates that the functional influence of mutants on protein function directly55. The Protein Variation Effect Analyzer (PROVEAN) algorithm was used to predict the damaging effect of nsSNPS in the STK11 protein sequences. This tool utilizes delta alignment scores on the basis of the variant version and reference of the protein sequence regarding the homologous sequences. An equal score or below the threshold of −2.5 indicates deleterious nsSNP alignment56. The change in stability caused by mutation was predicted by I-Mutant 3.0. It is a support vector machine (SVM) based tool server. I-Mutant 3.0 prediction is classified into three categories, such as neutral mutation , large decrease , and large increase . I-Mutant predicts the Gibbs free energy change (ΔG) dependent on the difference of mutated protein and native type protein. PMUT allows the fast and accurate prediction (~80% success rate in humans) of the pathological character of single amino acid point mutations based on the use of neural networks. Inputing a FASTA sequence in the PMut server provided results which showed the difference among neutral variations and disease-related protein sequence. A prediction score more than 0.5 indicates nsSNPs having a pathological impact on protein function57. SNAP2 is a neural network based classifier. It was used to predict the functional impact of single amino acid substitutions in the STK11 protein. This server accepts a FASTA sequence and provides a prediction score  that reflects the likelihood of a specific mutation to alter the native protein function58. Another algorithm, PON-P2, predicts the pathogenicity (harmfulness) of amino acid substitutions. It is a machine learning-based tool and utilizes amino acid properties, GO annotations, evolutionarily conserved sequence and functional annotations. PON-P2 distributes variants into 3 groups: pathogenic, neutral, or unknown classes59. The Mutation Assessor algorithm predicts the functional impact of amino acid substitutions. The functional impact is evaluated based on evolutionary conservation of the affected amino acid in protein homologs60.Functional consequences of nsSNPs in the coding region were predicted by using SIFT, PolyPhen 2, I-Mutant 3.0, PROVEAN, P-Mut, SNAP2, PON-P, and Mutation Assessor algorithms. SIFT uses the homology sequence of the proteins and alignment of natural nsSNPs with orthologous and paralogous protein sequences to predict nsSNPs as deleterious. A SIFT score less than 0.05 indicates deleterious impact of nsSNPs on protein functionhttp://www.ebi.ac.uk/interpro/) tool was used for identification of different conserved domains in the STK11 protein and also mapping of nsSNPs positions in different domains61. Protein sequence in FASTA format or protein ID was inserted as a query to predict domains and motifs.The InterPro , Pan troglodytes (H2QET9_PANTR), Gallus gallus (STK11_CHICK), Rattus norvegicus (STK11_RAT), Mus musculus (STK11_MOUSE), Xenopus tropicalis (F6PM85_XENTR), Bos taurus (E1BCU9_BOVIN), and Ovis aries (W5PNM2_SHEEP) were retrieved from the UniProtKB and were subjected to evolutionary conservation. Then, multiple sequence alignment (MSA) was executed by the ClustalW tool and the phylogenetic tree was created using 1,000 bootstraps in the MEGA6 package65.To execute phylogenetic analysis for human STK11 protein sequence (UniProtKB ID STK11_HUMAN) and eight different species protein sequences such as 66. UTRScan was used to predict high-risk nsSNPs in UTRs67. Its input format requires submission of a protein′s FASTA format. Output was in the form of signal name and its position in the transcript.The 5′ and 3′UTRs have significant roles in the post-transcriptional regulation of gene expression, message stability, translational efficiency, and subcellular localization38. Only the protein kinase domain of the proteins was modeled which is comprised of residues 47–342. Structural validation was carried out by the RAMPAGE server68 and the 3D structures were visualized by Discovery Studio 4.169.The mutant SNPs can notably alter the stability of proteins. Consequently, 3D structures of native and mutant proteins were constructed to investigate the structural stability and deviations difference within native and mutant proteins. We generated the 3D structure of native and mutant proteins using the SWISS-MODEL serverhttp://www.cmbi.ru.nl/hope/home) was used for predicting the structural impact of amino acids substitutions on the STK11 protein. To evaluate the structural features of mutations on the native protein, Project HOPE utilized the tertiary structure of the proteins that are available in the Distributed Annotation System (DAS) servers and Uniprot database. If necessary, the Project HOPE web server can build homology models separately. This server also provides significant information about the structural changes between mutant and native residues70.Project HOPE web server  and was optimized in the ground state by DFT  using the B3LYP 6–31 G(d) basis set. Docking analysis of native and mutant proteins with ATP was performed by AutoDock Vina74. In AutoDock Vina, the parameters were set at default and the grid box was set such that it covered the full structure of the proteins. Torsional rotation of all rotatable bonds was allowed for ligands and molecular docking was performed. After that, the binding complex of target proteins and ligand were obtained by PyMoL75 and visualized in Discovery Studio 4.169.Native and mutant modeled STK11 structures were docked against the STRADα-MO25α complex. To obtain the STRADα-MO25α complex all the chains and hetero atoms in the PDB file of PDB ID: 2WTK were removed except chain A and B which corresponds to MO25α and STRADα proteins respectively. This complex was loaded as a receptor molecule in the PatchDock server76 to reveal changes at the atomic level in different time scales for native and mutant STK11-ATP complexes. Before starting the simulation, the structures of native and mutant proteins were cleaned and also the H-bond network was optimized77. Then, a cubic cell was formed by extending 8 Å on each side of the protein and a periodic boundary condition was maintained. The AMBER14 force field was applied for simulations78. The system was implemented by adding water molecules and NaCl salt at 0.9% concentration to replicate the physiological ion concentration. For short-range Coulomb and van der Waals interaction, the cut-off radius was set to 8 Å. The long-range electrostatic interactions were measured by the PME  method79. MD simulation of each system was run for 150 ns at 310 K with a time step interval of 2.5 fs. The trajectory files were evaluated to get RMSD, RMSF, Rg, SASA, H-bond, and secondary structure analysis.MD simulations were performed using the YASARA dynamics program81 Any potential energy variabilities due to hidden data structures such as groups of samples, local fluctuations in data densities, and unique samples can be visualized by this technique. PCA decomposes the multivariate response arranged in an X matrix into a product of two new matrices as indicated in the following equation:The PCA method projects multivariate energy factors into low-dimensional space, which highlights the variabilities present in the collected MD trajectory data although they do not specifically aim to identify them.Tk is the matrix of scores which represents how sample relate to each other, k is the number of factors included in the model and E is the matrix of residuals, which contains the information not retained by the model. Mutant proteins may have different energy profile compared to the native protein which should be discernable by this data decomposition mechanism. All calculations were performed with MATLAB 2011a  equipped with the PLS Toolbox v. 6.2.1 . In this analysis, the last 50 ns of MD simulation trajectory data of different potential energy were used. Before applying PCA, autoscale function was used to preprocess the data. First 4 PCs explained >80% of the energy variation.where Supporting information


Transsynaptic connections enabling cell–cell adhesion and cellular communication are a vital part of synapse formation, maintenance and function. A recently discovered interaction between the Adhesion GPCRs Latrophilins and the type II single transmembrane proteins Teneurins at mammalian synapses is vital for synapse formation and dendrite branching. While the understanding of the effects and the molecular interplay of this Latrophilin-Teneurin partnership is not entirely understood, its significance is highlighted by behavioral and neurological phenotypes in various animal models. As both groups of molecules, Latrophilins and Teneurins, are generally highly conserved, have overlapping expression and often similar functions across phyla, it can be speculated that this interaction, which has been proven essential in mammalian systems, also occurs in invertebrates to control shaping of synapses. Knowledge of the generality of this interaction is especially of interest due to its possible involvement in neuropathologies. Further, several invertebrates serve as model organisms for addressing various neurobiological research questions. So far, an interaction of Latrophilins and Teneurins has not been observed in invertebrates, but our knowledge on both groups of molecules is by far not complete. In this review, we give an overview on existing experimental evidence arguing for as well as against a potential Latrophilin-Teneurin interaction beyond mammals. By combining these insights with evolutionary aspects on each of the interaction partners we provide and discuss a comprehensive picture on the functions of both molecules in invertebrates and the likeliness of an evolutionary conservation of their interaction. The formation of synapses is one of the key steps in warranting the development of a functioning neuronal network. This highly complex process is not fully understood, but it involves various interactions of molecules with adhesive and transmembrane signaling properties. A pair of proteins which has recently taken the stage to be essential for synaptic organization in many vertebrates are Latrophilins and Teneurins. Both have already been separately recognized as synaptic cell surface proteins several decades ago.Ten3 display neurological defects, in particular deficits in visually mediated behavior (Teneurins are large type II one-transmembrane domain proteins with a cytoplasmic N-terminus and an extracellularly located C-terminus containing tyrosine-aspartate (YD) repeats and numerous epidermal growth factor (EGF) domains . They habehavior . Similarbehavior .Drosophila melanogaster as a model (section “Latrophilins and Teneurins in D. melanogaster–No Evidence for Interaction”).The molecular details underlying Teneurin function involve the formation of homotypic or heterotypic dimers depending on the synapse type [summarized in The functions of Latrophilins have by far not been as well characterized as the ones of Teneurins. Latrophilins belong to the class of Adhesion G protein-coupled receptors . The three mammalian homologs (LPHN1–3/ADGRL1-3) comprise an intracellular C-terminus, a seven transmembrane region (7TM) and an extracellular N-terminus containing a rhamnose-binding lectin (RBL), an olfactomedin (OLF), a hormone binding (HRM) and a GPCR autoproteolysis-inducing (GAIN) domain, which harbors the GPCR proteolytic site (GPS) . LatrophTeneurins and Latrophilins are both found enriched in neuronal growth cones . RecentlAlthough the molecular details of the interaction between Latrophilins and Teneurins have not been clarified yet, the regions within both molecules taking part in the intermolecular interaction have been roughly identified  using biAs both, Latrophilins and Teneurins, can act as ligand, it is conceivable that each of them functions as receptor transducing signals into their host cell. It has not been determined beyond doubt to date which of them is the ligand and which the signal-receiving molecule or if both of them signal. However, some studies show that Teneurins are cleaved at several distinct sites rendering liberated fragments , which aDrosophila melanogaster (D. melanogaster–No Evidence for Interaction” and “Latrophilins and Teneurins in C. elegans Development Do Not Function as Ligand-Receptor Pair”). The receptors also have functions beyond synapse formation. It has been shown in mice that TEN4 is required for mesoderm induction and gastrulation . In contrast, for all that is known to date, the functional conservation of Latrophilins throughout phyla has not been shown beyond doubt (sections “Latrophilins and Teneurins in D. melanogaster–No Evidence for Interaction” and “Latrophilins and Teneurins in C. elegans Development Do Not Function as Ligand-Receptor Pair”).Due to the obvious relevance of the Latrophilin-Teneurin interaction in mammals the question of the generality of this partnership and thus, its conservation, arises. This question is especially of interest as invertebrate models are often used for elucidation of neurobiological aspects and understanding of association with pathologies. The described interaction between Latrophilins and Teneurins is so far limited to vertebrates, it has not been shown in invertebrate systems to date. However, Teneurins and Latrophilins are both highly conserved groups of molecules. First discovered in the fruit fly nogaster , Teneurinogaster . Similarrulation . ConsistD. melanogaster and C. elegans exists suggesting that an interaction of the two is conceivable. However, there is also some information arguing against this assumption which will be discussed below.Due to the overall similar conservation of the two molecules it has been postulated that their interaction and its physiological impact are also evolutionarily old and conserved . AlthougD. melanogaster as pair-rule genes tenascin-like molecule accessory (Ten-a)   and tena (Ten-a) , which w (Ten-a) . The str (Ten-a)  and funcs system  and seves system . Furthers system . Both fus system . It is cto touch . This Adproteins . The detproteins , but it C. elegans, Teneurins and Latrophilins appear to have very similar functions and at first sight, it can be speculated that they form a classical interaction as described in mammals. However, a closer look prohibits this conclusion as of yet. In contrast to vertebrates or Drosophila, the nematode only has one Teneurin gene, ten-1. However, several transcript variants exist. The two most prominent ones are generated by two different transcription start sites: one variant with a longer (280 amino acids) intracellular domain and one with a short (36 amino acids) N-terminus , in cells of the gut, the somatic gonad, distal tip cell, and in few muscle cells (short TEN-1 variant)  and ten-1(ok641) exhibit morphogenesis defects  . Interestip cell , suggest defects . Howevernd ten-1 . In line elegans . HoweverTen1, DrosophilaTen-a and Ten-m and the C. elegansten-1 revealed the presence of both, conserved intron locations and exon sequences  and choanoflagellates  there is already a number of Adhesion GPCR-encoding genes indicating their stable integration into animal genomes. Due to high sequence distances it is hard to assign them to Latrophilins or to another of the eight distinct groups of vertebrate Adhesion GPCRs  and EMR (ADGRE) groups (Branchiostoma belcheri) there are obviously no orthologs or paralogs of the ADGRE group, which contains EMR1-4 (ADGRE1-4) and CD97 (ADGRE5)  and Gi/o .In the genomes of the roundworm on GPCRs  revealed) groups . In tuni(ADGRE5) . Howeverparalogs . Most inR groups . PhylogeR groups . It has R groups  differ iCiona intestinalis) (Branchiostoma belcheri), and Chondrichthyes . In Hemichordata, Echinodermata, Mollusca, Nematoda, Arthropoda, Tardigrada, and Brachiopoda only the GAIN, RBL, and HRM domain (sometimes degenerated or absent) are mostly present  , in lanc present , but non present , indicatAnalyses on Latrophilin-Teneurin interactions provide strong evidence that the main site of interaction is the RBL domain with contribution of a short sequence C-terminal of the domain . AlthougDrosophila and a general evolutionary conservation makes it tempting to conclude that this transsynaptic interaction is old and also meaningful in invertebrate species. However, a closer look at phylogenetic evidence and existing data sheds light on a different picture.Synapse formation is a highly complex and tightly regulated process and although several aspects have been already well understood, many details are still obscure. Latrophilins and Teneurins are both transmembrane proteins which have been described to have implications in synaptogenesis and synapse function. While for Teneurins this has been shown in vertebrate and invertebrate systems, a lot of information is still lacking for Latrophilins. However, a transsynaptic interaction of the two is essential for adhesion and synapse formation in mammals. The question of whether this interaction represents a common principle in the generation of synapses throughout phyla remains unanswered, mainly due to lacking experimental evidence, but is highly intriguing. Similarities in expression, seemingly functional redundancy in Drosophila, are not one-to-one homologs of mammalian Latrophilins, but also bear resemblance to other Adhesion GPCRs. This argument is further supported by experimental data highlighting distinct signaling cascades activated by Latrophilin homologs of different species: While mammalian LPHN1 can signal via Gs or Gi proteins (Drosophila dCirl activates Gi proteins and C. elegans LAT-1 Gs proteins. A functional evaluation of these different cascades will shed light on the impact of these cascades.Our phylogenetic analyses indicate that, although basal metazoans already contain Adhesion GPCR, clearly Latrophilin-7TM-related sequences only appeared at the level of Eumetazoa and thus, later in evolution than Teneurins. The connected N-termini contain RBL- and HRM-like domains but not constantly. Further, the OLF domain only appears in the N-termini of Latrophilins in early chordate evolution. Although the conserved cysteine bonds and a few other conserved positions allow for assignments as RBL and HRM domains, most of the remaining sequence is highly variable in these domains. This indicates that the RBL and HRM domains in Latrophilins may have specific functions in the different species and/or underwent co-evolution with interaction partners rather than mediating evolutionarily conserved protein-protein interactions. This analysis yields some evidence that a conserved interaction of Latrophilins and Teneurins in invertebrates might not be likely. It cannot be excluded that additional, not yet identified interaction sites in Latrophilins exist, which represent highly conserved sequences. Further, the role of other proteins or molecules aiding or promoting the interaction cannot be evaluated. For instance, dystroglycans have been discussed to be part of a larger complex . Howeverproteins , DrosophWe cannot exclude an interaction between Latrophilins and Teneurins in invertebrates, however, the mode of interaction might be realized differently from their mammalian counterparts. While both groups of proteins have essential functions in invertebrates and the ones of Teneurins in particular are highly conserved roles across phyla, they might not realize this role via the help of Latrophilins. Invertebrates have less complex regulatory circuits and hence, different requirements for synapse formation and function. Thus, it would not be surprising that they utilize different mechanisms to establish and maintain synapses and their function.Future analyses need to focus on gaining a better understanding of the physiological functions mediated by both, Latrophilins and Teneurins, in mammals and invertebrates. These will help understand similarities as well as differences in the function of each receptor in different contexts and aid the understanding of the molecular mechanisms underlying synaptogenesis and neuronal wiring in vertebrates and invertebrates. It will be highly interesting to gain information on the existence and composition of potential synaptic complexes involving Latrophilins and/or Teneurins. Further, identifying interaction interfaces of mammalian Latrophilins with Teneurins can be highly informative for the prediction and characterization of a potential interaction in other species.TS and SP researched and wrote 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.


Recent developments in sequencing and growth of bioinformatics resources provide us with vast depositories of protein network and single nucleotide polymorphism data. It allows us to re-examine, on a larger and more comprehensive scale, the relationship between protein–protein interactions and protein variability and evolutionary rates. This relationship has remained far from unambiguously resolved for quite a long time, reflecting shifting analysis approaches in the literature, and growing data availability. In this study, we utilized several public genomic databases to investigate this relationship in human, mouse, pig, chicken, and zebrafish. We observed strong non-linear relationship patterns (tending towards convex decreasing function shapes) between protein variability and the density of corresponding protein–protein interactions across all five species. To investigate further, we carried out stochastic simulations, modeling the interplay between protein connectivity and variability. Our results indicate that a simple negative linear correlation model, often suggested (or tacitly assumed) in the literature, as either a null or an alternative hypothesis, is not a good fit with the observed data. After considering different  simulation models, we found that a convex decreasing protein variability–connectivity function  led to a much better fit with the real data. We conclude that simple correlation models might be inadequate for describing protein variability–connectivity interplay in vertebrates; they often tend towards false negatives .The online version of this article (10.1007/s00239-019-09899-z) contains supplementary material, which is available to authorized users. Protein–protein interactions (PPIs) play an important role in realizing specific functions in biological systems. One way to represent PPIs is via protein networks. In a protein network, a link between two proteins is a visualization of a dependency relationship. The latter can have many biological meanings, ranging from direct or indirect physical/chemical interactions to co-expression to ontological proximity  this relationship has been a notable research goal over the last 10–15 years [see   and protein connectivity—although it has been argued that the “hub” protein assignment might be a somewhat artificial notion  are counted in various species datasets available as public resources, and then, after invoking basic transformations , linear correlation coefficients  between protein evolutionary rate and connectivity are derived. However, such simple relationships might be a poor fit in this particular situation. In general, it is possible, even likely, to mistake even a very pronounced pattern for the absence of correlation in biological systems if an overly simplistic model is used for the correlation analysis Dietrich ; we beliAfter analyzing SNP data from five different species  using different public genomic datasets, we confirm that there is a tendency towards negative dependency between protein connectivity and protein variability at the interspecific (ortholog) variability level (but not at the intraspecific variability level). However, this relationship is clearly non-linear; corresponding distributions exhibit a distinct shape largely invariant across the different species and databases. Using simulations, we show that incorporation of the non-linear  variability–connectivity functions in the simulated evolutionary process results in the variability–connectivity relationship patterns that approximate the observed, real, protein variability–connectivity data sufficiently well; along the way, we propose a simple but mathematically rigorous way to stochastically model the interplay of protein connectivity and protein evolutionary rates.Our principal analyses were centered around the protein connectivity data for human, mouse, pig, chicken, and zebrafish assembled in the STRING database  data throughout the study. Both present biological interest; in addition, the former is more congruent with our simulation framework (as detailed below), while the latter is directly comparable with the majority of the results in the literature. We used total SNP counts, dN, dS, and dN/dS ratio to evaluate protein variability. The former are, again, more congruent with our simulation framework, while dN and dN/dS ratio arguably do the best job of quantifying selection pressures in sufficiently divergent sequences . We did not utilize intraspecific dN/dS data, because it is unclear whether dN/dS is at all meaningful in the context of segregating polymorphisms  which could be reliably and easily cross-linked with the protein evolutionary rate data, ending with the aforementioned three databases and five species, with the bulk of the results generated from the STRING human and mouse data. We were especially interested in the vertebrates because most of the previous research has been carried out in plants, yeast, and To cross-link connectivity and variability data, three public resources were used: UniProt  for all nodes.We tried the above algorithm with 1000–10,000 nodes. On a late model workstation, one 1000-node run takes ~ 2 s, whereas one 10,000-node run takes more than 30 min. We noticed that the protein connectivity distribution shape was essentially the same for any number of nodes, the only difference being in scaling along the protein connectivity axis. For example, to put it in biological context, for 1000 nodes, simulated protein connectivity scale was similar to that of the actually observed human Reactome “direct complex” subclass data. Because we were more interested in the distribution shape and patterns rather than the absolute values, we largely limited ourselves to 1000-node simulations throughout the study, which made the simulations computationally feasible without involving exotic computing resources. . We modeled the total SNP count dynamic over generations  using binomial distribution  we create a new list of random values following the binomial distribution. The list grows by one in each loop (addition of a new node). The binomial distribution-generated number is the number of SNPs in a new generation, for each node. Finally, we take inventory of all the SNPs across the generations, and return SNP vectors for all the nodes.For the second and third modeling scenarios, we assume that protein connectivity and protein variability are not independent, and generate protein variability as a function of its connectivity. We will call it a variability–connectivity (V–C) function.  For the purposes of this study, we will limit ourselves to the negative (decreasing) functions. Under these conditions, we start by taking the number of nodes and the V–C function type as inputs in the iterative process. In each loop (generation), we use the method described in the preceding paragraph to generate a protein connectivity matrix. Using the updated matrix, and summing column-wise, we obtain a new protein connectivity vector after adding one new node. We then apply V–C function to this updated protein connectivity vector to estimate the probability of having one SNP count for the protein in this current generation. Then we use random sampling binomial distribution function to generate a vector of new SNPs for all the nodes in this generation; subsequently, we sum up SNPs counts for each generation, ascertain the maximum SNP count value, and multiply it by 100 to achieve uniform mRNA length. Finally, we divide the SNP counts by the mRNA length to calculate the protein variability. This procedure returns two vectors, one—containing protein connectivities, and another—protein variabilities. The crucial question, and the cornerstone of the present study, is the choice of the V–C function(s). This study was partly motivated by a simple observation that the actual, observed, protein variability–connectivity patterns did not look particularly linear . In fact, in our analyses  function might be a much better fit with the real data, and that this observation might help in resolving the variability–connectivity correlation (or absence thereof) conundrum. Of course, for each observed variability–connectivity dataset it is possible to over-parameterize the V–C functions to the extent that the fit will be asymptotically perfect , but such overfitting does not make much predictive modeling  sense. Eventually, with all these considerations in mind, we picked the negative linear V–C function for our second modeling scenario, and the exponential decay function for our third modeling scenario, with the probability of having a new SNP being, respectively, eitherc is the protein connectivity.  choice below in the "very low variability proteins. Figure After fitting the straight lines for the linear model fit visualization, and computing the Spearman rank correlation coefficients, we observe insignificant negative correlation in the STRING Fig. a data, sSupplementary Fig. 2  depicts dN/dS ratio versus protein connectivity relationship observed in human/chimpanzee orthologs . Here, we see broadly the same distribution shapes as in Fig. Figure Supplementary Fig. 9  depicts dN/dS, dN, and dS versus protein connectivity relationships observed in mouse/rat orthologs (STRING database).  The dN/dS and dN patterns remain similar to the ones observed in human/chimpanzee orthologs . The dS pattern, however, is somewhat different. There are very few proteins with low dS, reflecting comparatively high divergence between mouse and rat. It is reasonable to assume that the patterns of dS–connectivity relationships would, in general, become less pronounced with increasingly more divergent sequences, especially if the synonymous sites are not under significant selection pressure. Just as with the human/chimpanzee ortholog data , we observe the general trend towards negative correlation , which is more pronounced compared to the mouse intraspecific variability data Fig. a.All of the above results  while aiming to keep the resulting counts and values in the realistic range. Corresponding simulations are shown in the Supplementary Material 5 (negative linear) and Supplementary Material 6 . The former (negative linear) results in the largely alike distributions, of which the distribution shown in Fig. k ~ = 0) to the “compression” into very few unique data points (when k > 4)—the distribution shown in Fig. k = 1) is representative of the distributions with mid-k values, and at a first glance is not dissimilar to the ones observed in the real data . Therefore, to illustrate on the human STRING data example, in Fig. The goal of this study was not necessarily to find the V–C function that would lead to the perfect fit to the observed data, but rather to show that (i) no V–C function at all might not lead to a good fit , averaged over 100 simulations for each parameter combination . Even the lowest ED value  was significantly higher than the typical  ED value for the exponential decay model data  to quantify the distributional similarities/dissimilarities between the real and the simulated data. Both ED and EMD generalize easily to the multidimensional data and are more appropriate than the “standard” Kolmogorov–Smirnov distance for the distributions that are known to be possibly significantly dissimilar and/or have relatively heavy tails/numerous outliers; both distances are scale sensitive, range from zero to infinity, and are proportional to the empirical distribution sample sizes , large-scale genomic vertebrate datasets suggest that there is a tendency towards weak but often significant negative correlation between the protein connectivity and interspecific variability ; while the association was found to be moderate, connectivity still possessed predictive value (independently of orthology) with respect to conserved synteny  data. Correlation  was used to assess the “strength” of all components; they were subsequently integrated (and compared) in the principal component analysis framework. Eventually, PPI/centrality-related components were found to be at least as important as the gene expression-related ones in contributing (independently) to dN and dN/dS. The effect was most pronounced in H. sapiens. Interestingly, while the protein interaction/centrality results in Alvarez-Ponce et al.  would have arguably strengthened Alvarez-Ponce, Feyertag, and Chakraborty’s main conclusion . We should note here that our analyses did not include expression levels and patterns (or any components other than protein connectivity)—therefore, at this time, we would rather not speculate on the correlation (or independence) of PPI-related factors and expression-related factors contributing to the rates of protein evolution—leaving it to the future, multi-component, analyses and simulations.The results of our study dovetail, in a complementary way, with the recent work by Alvarez-Ponce et al. , the goae et al.  , to be considered for the individual follow-up studies, are generated. How do we prioritize/rank them for the future research? In other words, can we come up with the multidimensional measure of how “interesting,” or “important,” a specific candidate gene (protein) is? PPI data  should probably factor into it. So should expression patterns, selection pressures, etc. Recently, Zhang, Xiao, and Hu developed such an integrated metric   STRING data. (B) Reactome “Direct Complex” data. (C) Reactome “Indirect Complex” data. (D) Reactome “Reaction” data. (E) Reactome “Neighboring Reaction” data. (F) APID dataSupplementary material 1 (TIFF 5320 kb). Supplementary Fig. 1. Density plots of human intraspecific protein variability vs. protein connectivity. (A) STRING data. (B) Reactome “Direct Complex” data. (C) Reactome “Indirect Complex” data. (D) Reactome “Reaction” data. (E) Reactome “Neighboring Reaction” data. (F) APID dataSupplementary material 2 (TIFF 6218 kb). Supplementary Fig. 2 Density plots (left panes) and 3D surface plots (right panes) of human / chimpanzee ortholog protein dN/dS ratio vs. protein connectivity, shown in the log-log scale. (A) STRING data. (B) Reactome “Direct Complex” data. (C) Reactome “Indirect Complex” data. (D) Reactome “Reaction” data. (E) Reactome “Neighboring Reaction” data. (F) APID dataSupplementary material 3 (TIFF 4327 kb). Supplementary Fig. 3 Density plots of human / chimpanzee ortholog protein dN/dS ratio vs. protein connectivity. (A) STRING data. (B) Reactome “Direct Complex” data. (C) Reactome “Indirect Complex” data. (D) Reactome “Reaction” data. (E) Reactome “Neighboring Reaction” data. (F) APID dataSupplementary material 4 (TIFF 6293 kb). Supplementary Fig. 4 Density plots (left panes) and 3D surface plots (right panes) of human / chimpanzee ortholog protein dN values vs. protein connectivity, shown in the log-log scale. (A) STRING data. (B) Reactome “Direct Complex” data. (C) Reactome “Indirect Complex” data. (D) Reactome “Reaction” data. (E) Reactome “Neighboring Reaction” data. (F) APID dataSupplementary material 5 (TIFF 4476 kb). Supplementary Fig. 5 Density plots of human / chimpanzee ortholog protein dN values vs. protein connectivity. (A) STRING data. (B) Reactome “Direct Complex” data. (C) Reactome “Indirect Complex” data. (D) Reactome “Reaction” data. (E) Reactome “Neighboring Reaction” data. (F) APID dataSupplementary material 6 (TIFF 6120 kb). Supplementary Fig. 6 Density plots (left panes) and 3D surface plots (right panes) of human / chimpanzee ortholog protein dS values vs. protein connectivity, shown in the log-log scale. (A) STRING data. (B) Reactome “Direct Complex” data. (C) Reactome “Indirect Complex” data. (D) Reactome “Reaction” data. (E) Reactome “Neighboring Reaction” data. (F) APID dataSupplementary material 7 (TIFF 4187 kb). Supplementary Fig. 7 Density plots of human / chimpanzee ortholog protein dS values vs. protein connectivity (STRING data), shown in the log-log scaleSupplementary material 8 (TIFF 3464 kb). Supplementary Fig. 8 Density plots of mouse, pig, chicken and zebrafish intraspecific protein variability vs. protein connectivity (STRING data). (A) dN/dS ratio vs. protein connectivity. (B) dN vs. protein connectivity. (C) dS vs. protein connectivitySupplementary material 9 (TIFF 3762 kb). Supplementary Fig. 9 Density plots (left panes) and 3D surface plots (right panes) of mouse / rat ortholog protein variability vs. protein connectivity (STRING data), shown in the log-log scale. (A) dN/dS ratio vs. protein connectivity. (B) dN vs. protein connectivity. (C) dS vs. protein connectivitySupplementary material 10 (TIFF 3498 kb). Supplementary Fig. 10 Density plots of mouse / rat ortholog protein variability vs. protein connectivity, shown in the log-log scale. (A) protein variability and connectivity are modeled independently (see “first modeling scenario” in Methods). (B) protein variability and connectivity are linked via negative linear function (second modeling scenario). (C) protein variability and connectivity are linked via exponential decay function (third modeling scenario)Supplementary material 11 (TIFF 492 kb). Supplementary Fig. 11 Density plots of simulated protein variability via negative linear function (green dots). (C) intraspecific variability data (red dots); protein variability and connectivity are linked via exponential decay function (green dots). (D) human / chimpanzee ortholog protein dN values (red dots); protein variability and connectivity are linked via exponential decay function (green dots). (E) human / chimpanzee ortholog protein dS values (red dots); protein variability and connectivity are linked via exponential decay function (green dots)Supplementary material 12 (TIFF 2052 kb). Supplementary Fig. 12 Scatter plots of observed human data from STRING database (red dots) superimposed on the simulation results (green dots), shown in the log-log scale. (A) intraspecific variability data (red dots); protein variability and connectivity are modeled independently (green dots). (B) intraspecific variability data (red dots); protein variability and protein connectivity are linked Supplementary material 13 (PDF 100 kb). Supplementary Material 1. Python code for the analyses / simulations in this studySupplementary material 14 (PDF 36 kb). Supplementary Material 2. Annotation for the data filesSupplementary material 15 (RAR 7284 kb). Supplementary Material 3. Datasets part 1 Supplementary material 16 (RAR 7856 kb). Supplementary Material 4. Datasets part 2 vs. protein connectivity, with protein variability and connectivity linked via negative linear function (second modeling scenario). Intercept and slope parameter values are shown next to each simulation plotSupplementary material 17 (PDF 1795 kb). Supplementary Material 5. Simulation results (negative linear V-C function). Density plots (left panes) and 3D surface plots (right panes) of simulated protein variability vs. protein connectivity, with protein variability and connectivity linked via exponential decay function (third modeling scenario). Exponential decay constant parameter value is shown next to each simulation plotSupplementary material 18 (PDF 1341 kb). Supplementary Material 6. Simulation results . Density plots (left panes) and 3D surface plots (right panes) of simulated protein variability vs. protein connectivity distribution  and observed human data from STRING database, shown across a wide range of negative linear function parameters (a and b), averaged over 100 simulations for each parameter combination. Simulations were carried out for 15903 nodes, to match exactly with the human STRING data. Darker orange color indicates higher ED values , less saturated yellow color --- lower ED valuesSupplementary material 19 (PDF 86 kb). Supplementary Material 7. ED measure  between the simulated protein variability Below is the link to the electronic supplementary material.


Although a considerable number of proteins operate as multiprotein complexes and not on their own, organism-wide studies so far are only able to quantify individual proteins or protein-coding genes in a condition-specific manner for a sizeable number of samples, but not their assemblies. Consequently, there exist large amounts of transcriptomic data and an increasing amount of data on proteome abundance, but quantitative knowledge on complexomes is missing. This deficiency impedes the applicability of the powerful tool of differential analysis in the realm of macromolecular complexes. Here, we present a pipeline for differential analysis of protein complexes based on predicted or manually assigned complexes and inferred complex abundances, which can be easily applied on a whole-genome scale.We observed for simulated data that results obtained by our complex abundance estimation algorithm were in better agreement with the ground truth and physicochemically more reasonable compared to previous efforts that used linear programming while running in a fraction of the time. The practical usability of the method was assessed in the context of transcription factor complexes in human monocyte and lymphoblastoid samples. We demonstrated that our new method is robust against false-positive detection and reports deregulated complexomes that can only be partially explained by differential analysis of individual protein-coding genes. Furthermore we showed that deregulated complexes identified by the tool potentially harbor significant yet unused information content.https://sourceforge.net/projects/complexchange/.CompleXChange allows to analyze deregulation of the protein complexome on a whole-genome scale by integrating a plethora of input data that is already available. A platform-independent Java binary, a user guide with example data and the source code are freely available at The online version of this article (10.1186/s12859-019-2852-z) contains supplementary material, which is available to authorized users. Cellular function is a team effort because proteins rarely perform their biochemical tasks all alone. Instead, proteins frequently collide with other gene products in the crowded environment of the cell, they may selectively bind to other proteins driven by physical interactions, they may dynamically assemble into complexes in a well-coordinated manner and accomplish their tasks cooperatively , 2. SuchWhereas the experimental detection of protein complexes is generally speaking a mature field, it is still time-consuming and subject to high false-discovery rates. Quantitative profiling of the complete complexome in a condition-specific way is currently not feasible in a high-throughput fashion –9. More Nowadays a plethora of data on gene expression and an increasing amount of data on proteome abundances enable to also approach the dynamics of the condition-specific complexome by computational methods. Guided by static compilations of protein interactions, the correlation of gene expression or protein abundance between putative interaction partners was used as a proxy to study their collective behavior , 14, 15.Whereas databases of experimentally detected protein complexes continue to serve the community well, they are inherently incomplete - especially when it comes to dynamic combinatorial complexes - and thus can only partially explain all the relevant interplay , 21. ProHere, we present the differential analysis software CompleXChange as a terminal step of a pipeline consisting of PPIXpress-contextualized and DACO-derived protein complexes, or arbitrary alternative input protein complexomes. The tool quantifies protein complexes, includes several statistical testing procedures, is open-source and can easily scale up to 10,000s of interdependent complexes on a standard computer.https://sourceforge.net/projects/complexchange/. The general workflow is outlined in Fig. CompleXChange facilitates differential analyses of the protein complexome. It is intended to be used with input data on two groups of samples for which protein complexes and protein abundances are predicted by the tools JDACO    are preferentially distributed to complexes they limit: To counteract optimization confinement  and accelerate convergence, a logistic saturation function that is decreasing rapidly with each iteration sets a distribution prefactor i∈P||Li|>0}:pi,res=(1−λ)pi,res. Finally, the complete residual capacities of all proteins are distributed equally as i∈P:pi,res=0. From here, the algorithm proceeds with Step 1 in a new iteration.with thus remaining capacitites ∀{After annotating each complex detected by JDACO with an abundance value per sample, we statistically evaluate the numerical difference of the abundance of individual complexes between groups see Fig. C. To limFurthermore, CompleXChange includes an optional analysis that determines seed proteins that occur more often than expected by chance in up- or down-regulated complexes. If this option is selected, a ranked list of protein complexes is constructed by assigning a score to each evaluated complex. This score is set as the negative logarithm of their raw p-value and the sign of their direction of deregulation. In doing so, the task resembles the established approach Gene Set Enrichment Analysis (GSEA) but is applied to proteins in scored protein complexes in an analogous way. The implementation is done according to the original GSEA paper . By defaThe results and their discussion will be divided into three major parts. First, we introduce the datasets that were used in our evaluation, then we assess the performance of CompleXChange. Finally, we analyze the results of an application of CompleXChange to derive the differential transcription factor complexome of classical and non-classical monocytes.Raw RNA-seq data for 17 samples of classical monocytes (CMs) and for 17 samples of non-classical human monocytes (NCMs)   complexes were predicted for each sample with JDACO 1.0  by emploTo illustrate how CompleXChange can also be used in alternative workflows, we downloaded the manually curated human protein complexome of 2916 complexes in CORUM (3.0)  and the We first evaluated the performance of the algorithm that approximates protein complex abundances implemented in CompleXChange. For this, we compared the ComplexChange results to an approach where the problem was formulated as a linear program , 19. SimUsing both the CompleXChange algorithm and an existing approach based on linear programming (LP) we computed abundance values of predicted protein complexes for all 92 samples on monocytes and lymphoblastoids. The LP approach was implemented according to the equations in  using thp<10−16, two-sided Wilcoxon signed-rank test paired by sample). Notably, the LP formulation resulted in many zero solutions. On average, 85%±1% of all complexes in a sample were assigned an abundance of zero  consequently sets the maximum abundance of all complexes it is part of. For the construction of this synthetic dataset, the total abundance pi,tot of each limiting protein is randomly drawn from sample data. To ensure that such a sampling does not suffer from biological bias, we assessed if protein abundances correlate with the number of complexes a protein participates in. In the data on (N)CMs and LCLs this was clearly not the case (average correlation −0.005±0.003). The arbitrary association of proteins with abundance values should therefore be unproblematic.As pointed out before, there exists so far no adequate experimental reference data to test the complex abundance estimation against. In lieu of this, we generated input data of known ground truth by randomized construction on the basis of realistic complex compositions and expression values from our prepared samples. The construction reverses the simple idea that a protein which is exhaustively incorporated into complexes and has no unbound portion . These in turn specify the pi,tot of the artificial input data. Model parameter I, the distribution of limiting protein abundances among their associated complexes, was realized using three independent modules: equal distribution with modeled noise (abbreviated as eqd-[noise parameter]), sampled from an empirical distribution (ed) from ComplexChange approximation results and an assumption-free random distribution (rndd). Model parameter II is the unbound ratio parameter that models the extent of residual capacities of non-limiting proteins. The detailed construction schemes as well as our estimates on reasonable noise parameter ranges are documented in Suppl. Additional file Distributing a respective share of all limiting proteins to the complexes in which they take part can then be modeled in various ways (model parameter I). All  see Eq. -2 and onTo judge the relative performance of the CompleXChange approximation algorithm we also applied the LP approach and two randomized modifications of the CompleXChange algorithm to the artificial reference data. In the first randomized variant of the algorithm, input abundance values of proteins were shuffled before applying the abundance estimation method (abbreviated rnd (in)), i.e. input protein abundances did not match the abundance of the proteins associated in the ground truth. In the second variant, the complex abundance results derived from the correct input data were shuffled (abbreviated rnd (out)). We tested 12 combinations of parameters over all 92 samples for 20 iterations each for all methods , 5049, 5Finally, we applied CompleXChange to detect deregulated TF complexes (TFCs) between classical and non-classical monocytes whereby complexomes were predicted for each sample with PPIXpress and JDACO. This cellular transition was chosen because the expected differences should be comparably small and the number of samples in the dataset appeared sufficient. We used non-parametric testing, FDR 0.05 and default settings otherwise and enabled the option to assess if seed proteins (here: transcription factors) are enriched in up- or down-regulated complexes. CompleXChange reported 978 deregulated TFCs and 35 enriched TFs therein. Figure Differentially expressed genes were determined using the quantified RNA-seq data  and sleuth (v0.29.0) . For thi%±24%) of each reported deregulated complex consisted of proteins whose genes were deregulated between the two cell types. In 823 complexes  at least one protein-coding gene was deregulated and in 32 cases (3.3%) all were differentially expressed. These modes of action were also relevant in the 10 most deregulated complexes as can be seen in Fig. We first studied to what extent DE genes overlapped with the 978 deregulated complexes. On average, about a third . Among those complexes only 1.5% were detected as being deregulated. When complexes were filtered with CompleXChange to be present in at least 75% of either group, of the 2,522 complexes, 1,841 had a deregulated member (73.0%). Only 44.7% of those complexes were found to be deregulated by CompleXChange. We stored the 1,841 complexes selected by DE for the analysis of information content in the next subsection.At last, we compared the results in terms of TFs that were reported as deregulated. Whereas most of the TFs that were found to be enriched in deregulated complexes were also differentially expressed, 14 of the 35 (40%) enriched TFs were not significantly deregulated on the gene-level. Significance rankings between the two approaches can not be compared, as can be seen in Additional file α) signaling , (3) permutation tests where we sampled random complex sets as well as for (4) DE complexes , and (5) DE/all TFs (using protein abundances). The results are summarized in Table For each set of complexes (or TFs) tested we performed 100 iterations of stratified 10-fold cross-validation (CV) to account for randomness in dataset partitioning and tree building , 62. TheThe complexes reported by CompleXChange when applied to the predicted complexomes showed monotonically increasing mean accuracy and decreasing variance with increasing stringency and thus decreasing set size : Platform independent.Programming language: Java.Other requirements: Java 8 or higher.License: GNU GPLv3.Any restrictions to use by non-academics: None.Additional file 1This PDF contains supplementary text, Figures S1-S11 and Tables S1-S9 that are not included in the main text.


The complexity of carbohydrate structures, together with inconsistencies in how their 3D structures are reported, has led to difficulties in characterizing the protein–carbohydrate interfaces. In order to better understand protein–carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 3D X-ray crystal structures of protein–carbohydrate complexes. In ProCarbDB, the complete carbohydrate ligands are annotated and all their interactions are displayed. Users can also select any protein residue in the proximity of the ligand to inspect its interactions with the carbohydrate ligand and with other neighbouring protein residues. Where available, additional curated information on the binding affinity of the complex and the effects of mutations on the binding have also been provided in the database. We believe that ProCarbDB will be an invaluable resource for understanding protein–carbohydrate interfaces. The ProCarbDB web server is freely available at  Carbohydrates are amongst the most versatile classes of ligands, being able to form complex, branched glycans from monosaccharide units. This generates a complex structural pattern, commonly referred to as the glycocode, which carbohydrate-binding proteins are able to decipher . These pIn the present PDB format, the distinction between the carbohydrate ligand and its saccharide units is not trivial. Hence, interactions cannot be computed without using protein structure visualization software such as PyMol  and ChimSymbol Nomenclature for Glycans)  (Carbohydrate-active enzyme) database  of protein–carbohydrate complexes into databases. Protein–carbohydrate complexes are poorly represented in databases such as Platinum  (5.4%), Glycans)  depictioemistry)  notationdatabase , and recdatabase , which hdatabase , for prodatabase , for bacdatabase , for a gHere we describe ProCarbDB, a freely accessible, user friendly database that comprises of 5242 true protein–carbohydrate complexes. For a given PDB entry, ProCarbDB correctly annotates and displays the complete carbohydrate ligand present, the ligand interactions and binding affinities (where available), and the effects of experimentally validated mutations on the binding affinity. We believe that ProCarbDB will be an invaluable resource for understanding the features of protein–carbohydrate interfaces and their recognition patterns. It will also facilitate the development of structure-based machine-learning algorithms that can be trained to predict the binding affinity between a putative carbohydrate-binding protein and its saccharide ligand.An exhaustive list of PDB ligands classified as carbohydrates was obtained using a stand-alone copy of pdb-care  and manuStructures that contain only post-translational modifications (such as N/O-linked glycosylation).Structures where no sugar ligand was in the proximity of a protein chain (at least one atom of the ligand has to be 4Å or closer to any heavy atom of a protein residue).Structures where no protein chain was longer than 30 amino acids .Structures that contained only crystallographic adjuvants (e.g. B-octylglucoside) by using a semi-automatic text-mining algorithm based on cross-reference between well-established databases such as UniProt , PDB 7)7) and ENUsing a graph-based approach, we filtered out the possible true negatives:As a result of this filtering approach, we obtained 5242 protein–carbohydrate complexes. It is important to note that several amphipathic molecules , which are usually used as, or are very similar to, detergents, are actually true biological ligands in a number of entries, such as 1UWF and 2G3N.Using the above-mentioned graph-based approach and the CONNECT records of the PDB file, we first checked the integrity of the ligands by determining the saccharide units that constitute the whole ligand. Next, we calculated distances from terminal atoms of the ligands  to all other atoms. For some entries the distance was within the range expected for a covalent bond, but not listed in the CONNECT records. This resulted from either: (i) overlapping of residues due to the presence of stereoisomers in the crystallization solution (e.g. PDB ID: 5MTU) or (ii) broken ligands (e.g. PDB ID: 5TPC). To solve the former issue, we used the occupancy register in the PDB structure dictionary, where if the total occupancy of both units is equal to 1 they are overlapping. To solve the second issue, before generating a new bond we ensured that no superposed atoms were present and that valence rules were maintained. By using these methods, we were able to identify not only pure carbohydrate ligands but also glycoconjugates, such as PDB ID:2JDH.Linear Notation for Unique description of Carbohydrate Sequences)   notationWe mapped these crystal structures with biophysical measurements using two available databases: PDBbind  and MOADThe database architecture  was writThe website is written in HTML5 using CSS, Javascript and JQuery as well as a Bootstrap (version 4) framework. JINJA2 templating language for Python was used to dynamically generate HTML templates. All 3D rendering is done using NGL .http://www.procarbdb.science/procarb/.The database website is freely available at: http://www.procarbdb.science/procarb/. The documentation can be accessed using the ‘Help’ page from the navigation tab  ‘Multiple Results page if the submitted query returned more than one result Figure , (ii) ‘GOn the ‘Multiple Results page Figure , for eacIn order to access an individual entry, a detailed description is provided in three tabs , which are described below. Direct links to the ‘Help’ page and to the 3D interactive windows are available on each tab. The website generates intuitive and consistent URLs; hence, users can also bookmark the search pages for easy access.Users can click on the PDB ID of an entry obtained as a search result Figure  and willThe ‘Ligand Information’ tab  can be aWe also provide dedicated 3D representations for all ligands available in a ProCarbDB entry. The user can inspect here the spatial arrangement of a carbohydrate ligand and glycosidic bond order without the added complexity of viewing the entire protein–carbohydrate complex.The last tab contains the ‘Mutant Information’  that has3D rendering of macromolecules is imperative for understanding their biological function. Based on our curated data, we are able to calculate and display particularities of the entire structure such as hydrophobicity, secondary structure and Pfam domains. We are also able to map the interface formed by the protein and the complete ligand Figure . FurtherKd value of 0.3 mM according to MOAD and a Kd value of 1 mM according to PDBbind. Furthermore, there are many inconsistencies with matching the correct ligand and affinity value. For example, PDB ID: 4D4U has four different affinity values, two of which are for the same ligand on MOAD. This might be in part due to the fact that the authors of the structure could not fully identify the complete ligand (LewisY tetrasaccharide) in all the binding pockets.We annotated the complexes present in ProCarbDB with experimentally determined binding data by using already established databases such as MOAD and PDBbind. We retrieved 756 affinity values from MOAD  and 626 An example where the ligand is not properly identified is 4 × 0Z; PDBbind reports a ligand formed by four monosaccharides while the actual ligand is GM1 ganglioside, which contains five monosaccharides. These small inaccuracies in publicly available repositories are due to have major downstream effects on algorithms using their datasets as training sets. For this reason, we tried to solve these inconsistencies, or at least flag them and make it visible to the user in ProCarbDB.We mapped ProCarbDB entries to their kingdom (taxonomy) and identified Bacteria (46.3%) as the most dominant followed by eukaryota (43.2%), viruses (8.8%) and archaea (1.7%) Figure . Next, wTo further investigate the redundancy of sequences present in ProCarbDB, we used the CD-Hit  softwareMonomers were divided, based on PDB Ligand IDs, into three classes: saccharide , glycoconjugate  and non-polymers . While saccharides contain only sugar rings, monosaccharides or oligosaccharides, glycoconjugate monomers contain at least one non-saccharide moiety, for example ‘UPG’ (uridine-5′-diphospate-glucose) includes uridine.d-galactose, with 818 entries  and glycoconjugate ligands . We observed that most protein–ligand complexes in ProCarbDB comprised only saccharide moieties (3911/5242), while the rest contain glycoconjugates (1426/5242). There is an overlap of 85 entries that are in both ligand classes due to entries having multiple ligands present in the PDB. In order to ensure that ligand data are also diverse, we counted the number of ProCarbDB entries for each monomer Figure . Most mo entries , followeCurrently ProCarbDB hosts more than 5200 true protein–carbohydrate complexes related to over 2416 PubMed Articles  and displaying the crystal complex in an interactive way will facilitate advancement of the field.To our knowledge, ProCarbDB is the first repository that is able to retrieve complete ligands via simple queries. We generate and display, in a user-friendly way, not only the interactions between the ligand and its environment, but also the non-allosteric interactions that might be responsible for the binding. The user is able to access 3D interactive windows in a standardized fashion, based on PDB architecture, in order to compare results.Furthermore, we also attributed functional information, in the form of biophysical measurements. To date, we have linked 18.4% (967) of ProCarbDB entries with at least one experimentally measured binding affinity. We identified and corrected, to the best of our capability, several under-documented issues with currently available databases such as incorrect affinity values and ligands wrongly identified as biologically active. To provide a complete panel of information, we mapped each entry to UniProt, Pfam and NCBI databases. Current efforts are directed towards gathering further mutagenesis information using manual curation, which could not be directly obtained from the external databases.We believe that ProCarbDB will have a significant impact on the field. Firstly, experimental scientists studying protein–carbohydrate complexes will be able to query ProCarbDB to check whether the protein: (i) has been previously characterized biophysically; (ii) has identified homologs or (iii) has known ligands, in which case they can inspect in depth the protein–carbohydrate interfaces. Secondly, computational scientists will have a comprehensive and refined set of coordinates defining the structures of protein–carbohydrate interfaces as well as a benchmark dataset to train machine-learning algorithms.ProCarbDB will be an invaluable resource for the understanding and modification of carbohydrate-binding sites and will facilitate the development of new computational tools to analyse these interactions and develop prediction algorithms.gkz860_Supplemental_FileClick here for additional data file.


Bacillus thuringiensis insecticidal proteins (Bt ICPs) are reliable and valuable options for pest management in crops. Protein engineering of Bt ICPs is a competitive alternative for resistance management in insects. The primary focus of the study was to reiterate the translational utility of a protein-engineered chimeric Cry toxin, Cry1AcF, for its broad spectrum insecticidal efficacy using molecular modeling and docking studies. In-depth bioinformatic analysis was undertaken for structure prediction of the Cry toxin as the ligand and aminopeptidase1 receptors (APN1) from Helicoverpa armigera (HaAPN1) and Spodoptera litura (SlAPN1) as receptors, followed by interaction studies using protein-protein docking tools. The study revealed feasible interactions between the toxin and the two receptors through H-bonding and hydrophobic interactions. Further, molecular dynamics simulations substantiated the stability of the interactions, proving the broad spectrum efficacy of Cry1AcF in controlling H. armigera and S. litura. These findings justify the utility of protein-engineered toxins in pest management. Bacillus thuringiensis (Bt) insecticidal proteins (ICPs). The global adoption of genetically engineered (GE) crop technology continues at unassuming rates since its introduction. There has been a considerable increase in the acreage of transgenic crops since 1996, making it the fastest adopted crop technology in the recent past [Battling insects for a better tomorrow is the need of the hour. Transgenic technology has emerged as a promising option to help farmers mitigate insect pressure on crops, the most competent option being the use of cry genes are unceasingly being identified by scientists worldwide for plausible introgression into crop plants through transgenesis. An array of genes as well as crops have been exploited to demonstrate the sustained use of the technology [cry1Ac and cry1F, demonstrated synergism and improved efficacy towards H. armigera [H. armigera and S. litura [A plethora of chnology ,4,5,6,7.chnology . To circchnology ,11,12,13armigera . This ch. litura ,17,18,19Bioinformatic tools are being effectively used in the area of biotechnology for in depth understanding of various molecular interactions. These tools have made structural biology much advanced and the work sophisticated, precise, and simple with high accuracy and worldwide acceptability. Elucidation of protein structures, protein-protein docking, and other tools to study interactions between the receptors in the insects and the Cry proteins have been widely demonstrated ,21,22,23cry1AcF gene against two pests, and more emphatically to demonstrate the ability of the chimeric toxin to bind and interact with APN1s from both H. armigera and S. litura by exploiting in silico modeling and molecular interactions. This understanding would aid in the development of a stable platform to explain the broad spectrum efficacy of chimeric toxins developed through protein engineering and their use in crop improvement programs.It is a known fact that the cry1 series of proteins recognize the aminopeptidase receptors 1 (APN1) in the insect gut ,25. IndiB. thuringiensis toxins [cry1AcF, belonged to cry1Ac whereas domain III was from cry1F. A NCBI-CDD (Conserved Domain Database) search demonstrated Cry1AcF to be a 623 amino acid (aa) protein consisting of three functional domains: N-terminal domain I (48–251 aa), domain II (259–461 aa), and C-terminal domain III (463–608 aa)  a. Similacry1Ac Bacillus thuringiensis delta endotoxin belonging to Kurstaki sub species), which had 85% identity with the target protein  is generally carried out by protein threading, which is also known as fold recognition. In the present study, considering the low homology of R server a. The geR server . The typinal end . Ramachainal end b,c. The d models . ProSA ad models .H. armigera and S. litura, respectively, was performed using PatchDock, a protein-protein docking software. The APN1 receptor proteins were considered as B chain, and Cry1AcF protein in both the cases as A chain. Cry proteins comprises three domains: a seven-helix-bundle domain (DI) involved in membrane insertion and pore formation; a three-antiparallel-β-sheet domain (DII) considered as the most probable candidate for receptor binding; and a β-sandwich domain (DIII) considered as a multifunctional domain involved in structural integrity, membrane penetration, ion channel function, and a major determinant of receptor binding [Protein-protein interaction analysis or protein-protein docking is one of the important aspects of structural biology that aids in the prediction of the structure of a protein-protein complex based on the structures of the individual proteins. To understand this, computational strategies have evolved with time, taking into consideration the growing knowledge on protein structure and interaction . The doc binding ,9. Our s binding  showed tHaAPN1-Cry1AcF and SlAPN1-Cry1AcF interactions, respectively. In both the complexes, hydrogen bonding is seen between the amino acids in domain II of Cry1AcF and the receptors  were found to be interacting with Cry1AcF in the SlAPN1 complex. This clearly indicated distinct interaction patterns between Cry1AcF protein and its two receptors, demonstrating efficacy of the protein-engineered toxin against both the pests through definite molecular interactions. The interactions seen in the present finding corroborated with the studies which revealed that APNs interact with Cry toxins through domain II  [HaAPN1-Cry1AcF complex, amino acids in the region, 399–494 were seen to be interacting whereas in the SlAPN1-Cry1AcF complex, amino acids from 208–269 were involved in the interaction. Asn 494 and Arg 269 were seen to play a prominent role in the complex formation of Cry1AcF with HaAPN1 and SlAPN1, respectively  analysis with respect to hydrogen bond interactions  ,25,26. Sectively . CorroboHaAPN1  of Cα atoms in a molecule with respect to a reference structure was used to study the time points when conformation changes occur and stabilize. Analysis of the fluctuation of Cα coordinates in the modeled Cry1AcF and APN1 receptors as well as the complexes demonstrated stability under simulated conditions a–c. The HaAPN1 b, the eq . The APN1 receptors from H. armigera (Genebank ID-EU568875.1) and S. litura (Genebank ID-AF320764.1) were chosen for interaction analysis with the Cry protein. The InterProScan [The chimeric rProScan  tool wasrProScan .H. armigera and S. litura was performed using modeller package ITASSER [The suitable template for homology modeling was investigated by the DELTABLAST (Domain Enhanced Lookup Time Accelerated BLAST)  search t ITASSER  using th ITASSER . Further ITASSER . The hom+ [The docking of Cry1AcF-APN1 from both insects was performed using the PatchDock protein-protein docking software  and visu+ .MD simulations were performed to optimize the models developed with both the native proteins as well as the complexes. The simulation was performed with the GROMOS96 43A1  force fi