Protein–Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction. Computational approaches are orders of magnitude cheaper and faster than experimental ones, leading to proliferation of multiple methods aimed to predict which residues belong to the interface of an interaction.We present BIPSPI, a new machine learning-based method for the prediction of partner-specific PPI sites. Contrary to most binding site prediction methods, the proposed approach takes into account a pair of interacting proteins rather than a single one in order to predict partner-specific binding sites. BIPSPI has been trained employing sequence-based and structural features from both protein partners of each complex compiled in the Protein–Protein Docking Benchmark version 5.0 and in an additional set independently compiled. Also, a version trained only on sequences has been developed. The performance of our approach has been assessed by a leave-one-out cross-validation over different benchmarks, outperforming state-of-the-art methods.http://bipspi.cnb.csic.es. BIPSPI code is available at https://github.com/bioinsilico/BIPSPI. Docker image is available at https://hub.docker.com/r/bioinsilico/bipspi/.BIPSPI web server is freely available at Bioinformatics online. Protein–Protein Interactions (PPI’s) are at the basis of virtually every cellular process. Therefore, elucidating the biochemical underpinnings of interactions is a fundamental step for improving our understanding of cellular mechanisms and diseases. Much research has been done on PPI’s, especially at cellular level, which has led to the availability of many interactomes  through a sliding window as features to predict binding sites on protein sequences  method using the set of features described in PAIRpred , a new machine-learning based method for the partner-specific prediction of residue–residue contacts and binding sites. BIPSPI can predict interface residues from either protein sequences or protein structures. To that end, BIPSPI employs multiple structural and/or sequence-based amino acid features that are combined through an Extreme Gradient Boosting (XGBoost)  model anBIPSPI classifier has been trained to predict interfaces from protein structures and/or sequences. This section describes the implementation of the method using structural data. A full description of the sequence-based classifier is available in Different sets of protein complexes were used to train and evaluate the performance of BIPSPI predicting protein interfaces. The first one was the Protein–Protein Docking Benchmark version 5  following a similar approach as the one used to compile the different Protein–Protein Docking Benchmark versions. This dataset was built selecting PDB dimers of at least 35 amino acids long, with resolutions better than 3 Å and for which >90% of their residues were structurally determined. Similar to DBv5 and DBv3, non-redundancy between protein complexes was established at SCOP family level in such a way that only dimers with one SCOP domain per partner were considered and none of the dimers shared the same combination of SCOP families . This random sample, which also makes training faster, is drawn at protein complex level, in such a way that all complexes contribute to the dataset with the same relation of positive and negative cases. Finally, it is important to notice that no sampling is done for evaluation and, hence, it is performed on whole proteins data.Residue pairs are codified as a vector of numerical features. In this work, a protein A is defined as a collection of residues Each single residue is encoded by a set of sequence-based and structural features. Sequence-based features include amino acid type, codified as a vector of 22 binary elements (one-hot encoded), sequence profiles computed with PSI-BLAST . StructuResidue environments are also described and included into the vector of features in such a way that for each feature its environment feature is calculated. Several residue environment definitions have been employed in different works, two of the most common ones are: (i) sequential environment through sliding window . In thesSequential environment is obtained by a sliding window approach of length 11 amino acids in which all sequence-based features described above are concatenated for all residues of the window. On the other hand, structural environment features are computed from all sequence-based and structural features of each residue employing a structural neighbourhood definition based on Voronoi Diagrams . Specifically, each of the classifiers of the method was trained with the sampled pairs of all protein complexes except for the ones that belong to the left-out complex. This evaluation procedure is the same that was used in PAIRpred and PPiPP and, when trained over DBv3, allows for a straight and fair comparison with those methods. In addition, several CAPRI targets interfaces were predicted as an independent benchmark. Residue–residue contact predictions (RRCP) were evaluated with the AUC values of ROC curves averaged over protein complexes . In addition to XGBoost algorithm, which has not been widely explored in bioinformatics, we have also analyzed Random Forest  as classA interacting with PB) with the scores of residues involved in the interaction of the same partner but with other proteins (e.g. protein PA interacting with PC or with PD). For this analysis, equivalent proteins (sequence identity >90%) that interact with different partners were identified from our datasets DBv5 and DImS. As a result, 46 different proteins, involving 123 interactions, were found in DBv5 and 17 proteins, involving 43 interactions, in DImS . This approach increased the performance when compared with the maximum score value proposed in PAIRpred. Finally, we have also found that averaging the predicted binding site scores through a sliding window (see Section 2.5) increased the final performance.We have compared our approach with four other methods  that also use a machine-learning based approach and have been designed to predict partner-specific binding sites. In order to make comparisons with PAIRpred and PPiPP easier, we have used the same evaluation protocol consisting in a leave-one-out cross-validation over DBv3 complexes. Comparison with GCNN was carried out as described in the original publication  are in complex with a PCSK9-binding adnectin protein. PCSK9 plays an important role in the regulation of low-density lipoprotein (LDL) serum levels thanks to its LDL receptor degrading activity and it has been demonstrated that self-association of PCKS9, that occurs at the catalytic region, increase that activity , the ‘Comunidad Autónoma de Madrid’ through Grant: B2017/BMD-3817. Horizon 2020 through Grant CORBEL , ELIXIR-EXCELERATE  and West-Life . J. Segura is recipient of a ‘Juan de la Cierva’ fellowship and R. Sanchez-Garcia is recipient of a FPU fellowship. The authors acknowledge the support and the use of resources of Instruct, a Landmark ESFRI project.Conflict of Interest: none declared.Supplementary MaterialClick here for additional data file.


Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge.In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods.To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks. Additionally, next-generation sequencing technologies also generate large amounts of DNA/RNA sequences which are then translated into protein peptides in the form of stacked amino acid residues. These protein sequences consist of fundamental molecules which perform biological functions for various species %, 4.27%,S.cerevisiae training dataset as the overall training dataset and assess the prediction performance on the rest seven species using various metrics. In the experiment, the S.cerevisiae training dataset includes 36,006 negative and positive samples. The prediction performance of the rest seven species is presented in Table D. melanogaster, E. coli, C. elegans, H. sapiens, H. pylori, M. musculus, R. norvegicus are 96.76%,97.70%,98.44%,98.50%,98.84%,98.69%,99.77% respectively. Consequently, although only using single species training dataset, our multi-modal deep representation learning framework is still outperforms other methods using various evaluation metrics. Furthermore, we also examines D. melanogaster species and R. norvegicus species, which have not been explored by other methods yet and also achieves promising prediction accuracy.We not only tested our model within the same species, but also used the Cyptoplasm, Endoplasimic reticulum, Golgi apparatus, Lysosome, Mitochondrion, Nucleus. The five cross validation ROC curve is plotted in Fig. To compare other methods comprehensively and extending our previous work, we usedhttps://www.uniprot.org/. We use all the proteins in the DIP dataset and acquire the families they belong to from the database. Amongst the protein families in the dataset, we only choose those families with more than 15 samples. This results in the top frequent 99 protein families to verify our results. We present the training accuracy and validation accuracy in Fig. In addition to interaction prediction, we performed downstream multi-family protein classification tasks as well using the same features from the unsupervised learning phase. In our experiments, family annotations are obtained from the UniProt database As can be seen in Figs. S.cerevisiae dataset in DIP, our accuracy achieved 99.79% while the performance of the other four methods was 95.28%,95.15%,99.40% and 74.56% respectively. As for the Recall score for S.cerevisiae species, our recall scores achieved 98.13% while the values of the other four methods are 97.07%,93.92%,94.14% and 37.25% respectively. For the HPRD dataset, we can see from the results that the mean ROC score achieves 0.9942, which is consistent with other species in the DIP dataset.In this paper, we compare our multi-modal deep learning framework with representative traditional machine learning methods and state of the art methods. These state of the art methods include deep learning methods. Then, we verify our method across all eight species provided by DIP_FULL and HPRD datasets. For instance, for the Additionally, we predict protein families based on the deep protein representation features with our models on the DIP dataset. Our method achieves up to 33.9% improvements in terms of Micro-F1 score and achieves up to 74.4% improvements in terms of Macro-F1 score over the best performance among Random Forest, SVC and GaussianNB classifiers. Different from other protein family classification methods–33 whichIn summary, our multi-modal deep representation learning framework harvest features that are highly predictive of protein function. It captures both sequential protein raw information with the topological structure to improve the PPI prediction accuracy and multi-class classification accuracy given the complex, non-linear interaction networks PPI network. We apply our methods on both DIP and HPRD datasets. After applying the CBOW model based on generated metapaths, our model is able to take into account the graph topological information into account. We use various mainstream metrics to assess the performance over the new released DIP_20170205 FULL dataset including eight species and HPRD datasets. Through extensive comparisons with both traditional machine learning methods and state of the art deep learning methods, we prove that our method outperforms most of them over the same datasets.


Association of Epstein-Barr virus (EBV) encoded latent gene products with host ribosomal proteins (RPs) has not been fully explored, despite their involvement in the aetiology of several human cancers. To gain an insight into their plausible interactions, we employed a computational approach that encompasses structural alignment, gene ontology analysis, pathway analysis, and molecular docking.In this study, the alignment analysis based on structural similarity allows the prediction of 48 potential interactions between 27 human RPs and the EBV proteins EBNA1, LMP1, LMP2A, and LMP2B. Gene ontology analysis of the putative protein-protein interactions (PPIs) reveals their probable involvement in RNA binding, ribosome biogenesis, metabolic and biosynthetic processes, and gene regulation. Pathway analysis shows their possible participation in viral infection strategies , as well as oncogenesis . Finally, our molecular docking assay predicts the functional interactions of EBNA1 with four RPs individually: EBNA1-eS10, EBNA1-eS25, EBNA1-uL10 and EBNA1-uL11.These interactions have never been revealed previously via either experimental or in silico approach. We envisage that the calculated interactions between the ribosomal and EBV proteins herein would provide a hypothetical model for future experimental studies on the functional relationship between ribosomal proteins and EBV infection.The online version of this article (10.1186/s12860-019-0219-y) contains supplementary material, which is available to authorized users. Epstein-Barr virus (EBV), a type of herpesvirus that is common in human, has been known to be associated with cancers such as Hodgkin’s lymphoma, Burkitt’s lymphoma, gastric cancer, and nasopharyngeal carcinoma . At the As such, computational approaches for predicting host-virus protein interactions can provide viable hypothetical model for identifying potential protein-protein interaction scenarios to benefit future experimental design on the study of EBV-RP interactions. A valid in silico method for this purpose is the structural similarity-based strategy from the sequence-to-structure-to-function paradigm . This apThe best 3-D structural models of EBV proteins generated using I-TASSER Fig.  were selFurther analysis using data from HPRD and IntAct, demonstrated these hEBV to be associated with nearly five thousand human proteins. From these, we narrow down the list to those that are RPs Table . These 3An extensive search through the IntAct database led to the retrieval of 143 experimentally determined PPIs between EBV and Human proteins. Of these, 14 were also present in the current study as shown in Table Annotations based on both Molecular Function (MF) and Biological Process (BP) categories, derived from GO term enrichment analysis via DAVID, provided limited functional scenarios of the predicted RPs-EBV proteins interaction Fig. . The GO The statistically significant enriched pathways in the predicted PPIs between EBV proteins and targeted RPs is summarised in Table Based on the theoretical assumption that direct interaction between proteins requires their presence in the same cellular compartment, our interaction dataset was analysed to discern protein subcellular co-localization. Protein pairs which did not contain shared GO cellular component (CC) terms were considered false positive and excluded. Our result of a refined PPI between EBV and ribosomal proteins is illustrated in Fig. Docking assays were conducted for EBNA1 and its inferred ribosomal protein interactors eS10, eS25, uL10 and uL11. Prior to this, for the construction of 3D models for the four RPs, template proteins were selected by subjecting the amino acid sequences of the ribosomal proteins to NCBI PSI-BLAST  against Methanococcus jannaschii (PDB ID: 5COL) at 2.25 Å resolution with 32% identity and E-value of 7e-55. Prediction analysis was also done using RaptorX and I-TASSER. After structure refinement, the best model was selected on the basis of quality assessment for geometrical and stereochemical parameters  at 3.0 Å resolution with 54% identity and E-value 2e-54 was chosen. For the eS25 model, Chain 8 of the crystal structure of eukaryotic 40S ribosomal subunit in complex with initiation factor-1 (PDB ID: 2XZM) at 3.93 Å resolution with 32% identity and E-value of 5e-15 was selected. The uL10 model was predicted by choosing the structural template Chain M of the yeast 80S ribosome (PDB ID: 3O5H) at 4.0 Å resolution with 54% identity and E-value of 1e-124. Lastly, the uL11 model was generated using the template structure of ribosomal protein L11 from Out of the docked conformations generated by ClusPro, potential EBNA1-eS10 complex was selected from the balanced category on the basis of higher cluster size and lowest energy, which were consequentially found to be 93 and − 1160.5 kcal/mol, respectively. The top ten solutions predicted and refined through PatchDock/FireDock reveals desolvation energy of − 1.80 kcal/mol and global free energy of − 0.31 kcal/mol for the best docked conformation. Thus, the energy profiles obtained from both servers indicated high interaction probability for EBNA1 and eS10. Interface of the individual EBNA1-eS10 complex were further analysed to identify residues in the interactions, which were within 3.5 Å of each other  has a desolvation and global free energy of 0.54 kcal/mol and 8.55 kcal/mol respectively. Despite a fair energy profile, the ClusPro binding mode demonstrated high interaction probability for EBNA1 and eS25. Interface analysis of the individual EBNA1-eS25 complex was conducted to identify residues in the interactions, which were within 3.5 Å of each other  and lowest energy  amongst the ClusPro-generated complexes. The best docked conformation from the top ten solutions predicted and refined via PatchDock/FireDock has a desolvation and global free energy of − 2.55 and − 0.18 kcal/mol respectively. Thus, the energy profiles obtained from both servers indicated high probability for EBNA1-uL10 interaction. In addition, interface analysis identified interacting residues within 3.5 Å of each other  and lowest energy  amongst other complexes. At the same time, the best among the top ten conformations predicted using PatchDock/FireDock has a desolvation and global free energy of − 0.11 kcal/mol and − 1.55 kcal/mol respectively. These energy profiles indicate the likelihood of the interaction between EBNA1 and uL11. The ClusPro model is selected based on the maximum number of contact residues and visual examination of interface region  means that the host cell must be induced to be translationally competent at all times. This situation requires the persistent synthesis of viral and cellular proteins to ensure the viability of the virus and host . StudiesThe roles of RPs in tumourigenesis are widely known and partly explained , 3. HoweRPeS10 is evident in the congenital disorder of Diamond-Blackfan anaemia [The physiological significance of our predicted EBNA1-eS10 and EBNA1-uL11 complexes may be difficult to speculate based on literature. Mutation of  anaemia , and cau anaemia . In the  anaemia . For allConversely, the postulated EBNA1-eS25 complex is more functionally relevant. EBNA1 interacts with the cellular ubiquitin specific protease (USP7/HAUSP) to destabilise p53 by competitively inhibiting USP7-p53 interaction –33. eS25Based on structural similarity-based prediction protocol, we have provided in silico evidence of 48 de novo biologically relevant protein-protein interactions among 27 ribosomal proteins and four EBV proteins. We have further postulated that the resultant complexes derived from these interactions may be associated with the functions of viral infection and oncogenesis. From focus molecular docking analysis, we derived four statistically feasible docked complexes between the EBV protein EBNA1 and each of its predicted RP interactors . Only two of these complexes (EBNA1-eS25 and EBNA1-uL10) are suspected to have functional significance in EBV-mediated oncogenesis.The multi-step protocol which was implemented for the computational elucidation of interactions between EBV proteins and human ribosomal proteins has been delineated in a schematic overview in Fig. Experimentally verified full length crystal structures of EBNA1, LMP1, LMP2A and LMP2B are not available in Protein Data Bank (PDB), and hence were modeled using the I-TASSER platform , 38. TheThe computer-generated tertiary structures of EBV proteins were refined to reduce side chain steric clashes and bond length errors using the ModRefiner programme . Model qTo identify potential human/host proteins that interact with EBV proteins, the cellular protein partners of hEBV proteins were extracted from IntAct and HPRD databases. These databases are open resources containing literature-curated molecular interactions established through in vitro and/or in vivo methods , 42. ThePredicted interactions were evaluated by comparative studies between the computational resultant dataset and a benchmark experimental dataset, in order to determine the potential reliability of the results. However, large-scale experimental data are not available for EBV-ribosomal protein interactions. To circumvent this limitation, a benchmark dataset was created by retrieving all experimental interactions between EBV and human proteins from the IntAct database, and comparing them to the EBV - human protein-protein interactions predicted in the current study.p-values were computed with Bonferroni correction for multiple hypothesis testing and –log10 transformed for graphical representation of data.Gene Ontology (GO) term enrichment analysis  of the pPathway enrichment analysis was carried out using the KEGG Orthology Based Annotation System (KOBAS 3.0) , 55 and Direct interaction among two proteins requires that they share the same cellular compartment. Hence, our interaction dataset was further analysed for protein subcellular co-localization. The co-localization information for the predicted interacting proteins was assimilated based on their shared GO terms in the Cellular Compartment (CC) category. GO annotations for individual EBV proteins and human ribosomal proteins were obtained via the QuickGO tool . This weMolecular docking assay for specific cases was performed to assess the reliability of our predictions, wherein the molecular and mechanistic details of interactions between EBV protein (EBNA1) and ribosomal proteins  were evaluated. EBNA1 was chosen because it is the only viral protein expressed in all EBV-associated tumours . Prior tDocking analysis of EBNA1 against each of the four ribosomal proteins was performed using two well-recognized docking tools, namely ClusPro , 67, andThe optimal EBNA1 - ribosomal protein complex from each docking assay was evaluated for best score values, and subsequently examined visually via SWISS-PdbViewer v4.1.0 . For eacAdditional file 1:Table S1. Probable interface residues of EBNA1 and eS10 explored through the dual docking protocols. The interacting residues of EBNA1 and eS10 binding sites are indicated. Table S2. Predicted interfacial residues involved in hydrophobic and ionic interactions within the EBNA1-eS10 complex. (DOCX 18 kb)Additional file 2:Table S3. Probable interface residues of EBNA1 and eS25 explored through the dual docking protocols. The interacting residues of EBNA1 and eS25 binding sites are indicated. Table S4. Predicted interfacial residues involved in hydrophobic and ionic interactions within the EBNA1-eS25 complex. (DOCX 18 kb)Additional file 3:Table S5. Predicted interfacial residues of EBNA1 and uL10 explored through the dual docking protocols. The interacting residues of EBNA1 and uL10 binding sites are indicated. Table S6. Predicted interfacial residues involved in hydrophobic and ionic interactions within the EBNA1-uL10 complex (DOCX 18 kb)Additional file 4:Table S7. Probable interfacial residues of EBNA1 and uL11 explored through the dual docking protocols. The interacting residues of EBNA1 and uL11 binding sites are indicated. Table S8. Predicted interfacial residues involved in hydrophobic and ionic interactions within the EBNA1-uL11 complex. (DOCX 17 kb)


Unlike the already well-documented human serum proteome, there are still limitations regarding analyzing and interpreting the various physiological changes and disease states of the serum proteomes found in duck. Serum proteome in duck under the condition of mycotoxin contamination in feed has not yet been examined. This study aimed to introduce the characterization of the circulating proteomes in duck serum during exposure to aflatoxin B1 (AFB1).Duck serum samples were collected from four experimental groups, gel-based mass spectrometry was then applied, and finally, 445 proteins were identified in pulled serum sample.Gallus gallus] was up-regulated in the group with the highest AFB1 contamination.Among these 445 proteins, 377 were present in at least one group from all. There were 35 proteins which were expressed when the duck was exposed to AFB1. The protein library that allows the identification of a large number of different proteins in duck serum will be enhanced by the addition of these peptide spectral data. It is noteworthy that chromodomain-helicase-DNA-binding protein 7 (CHD7) [CHD7 protein might be somehow relative to aflatoxicosis in the duck that causes poor performance and economic loss. Moreover, other proteins present in duck serum were also added in the protein library. Mycotoxin is commonly found in poultry feed. Incidence of mycotoxicosis could potentially cause several diseases associated with poor performance, impair immune or organ function and results in severe economic loss in the poultry industry. Poultry feeds in Thailand that is located in tropical zone are commonly contaminated with low level of aflatoxin. Aflatoxin B1 (AFB1) is known as a hepatotoxic and carcinogenic agent in human and several animal species.Other reports have mentioned the ways in which poultry performance and serum profiles are affected by aflatoxin . Among pEimeria spp. or avian pathogenic Escherichia coli exposure and hen physiology during growth were reported .Duck serum samples were collected from four experimental groups, following gel-based MS approached, amount of 445 proteins were identified in pulled serum sample . Of thosAnasplatyrhynchos], protein Wnt-9b [G. gallus], AT-rich interactive domain-containing protein 4A [G. gallus], stAR-related lipid transfer protein 8-like [Meleagris gallopavo], CHD7 [G. gallus], zinc finger matrin-type protein 1-like [A. platyrhynchos], OTU domain-containing protein 7B-like [M. gallopavo], BTB/POZ domain-containing protein 3, partial [A. platyrhynchos], and C-C motif chemokine 4-like [M. gallopavo].Venn diagram shows the number of differentials expressed proteins found between groups of duck after day 5  and day G. gallus], putative interleukin 17 receptor E-like, partial [A. platyrhynchos], CHD7 [G. gallus], and sodium bicarbonate cotransporter 3-like [M. gallopavo].As shown in The preliminary results of five ontology classifications including molecular function, biological process, cellular components, protein class, and pathway class were classified. Functional protein classifications by molecular function were composed of catalytic activity (44%), binding (30%), receptor activity (17%), and structural molecule activity (9%). The categorization of biological function of protein expression provided 12 different component ratios which were a majority group of cellular process (30.60%), followed by metabolic process (26.50%), multicellular organismal process (10.20%), cellular component organization or biogenesis (8.20%), developmental process (6.10%), response to stimulus (6.10%), biological regulation (6.10), immune system process (4.10), and localization (2.00%). Categorization of the most abundant proteins by cellular components composed of membrane (20%), followed by organelle (27%), cell part (26%), macromolecular complex (13%), cell junction (7%), and extracellular region (7%), respectively.In case of distribution of the proteins by protein class, a majority protein function was nucleic acid binding (17%) while other minor classes were receptor (13%), hydrolase (7%), cell junction protein (7%), enzyme modulator (7%), lyase (7%), cytoskeletal protein (7%), transferase (7%), transcription factor (7%), transfer/carrier protein (3%), ligase (3%), calcium-binding protein (3%), oxidoreductase (3%), isomerase (3%), transporter (3%), and signaling molecule (3%). There were five categories which equally abundant proteins shown under pathway class; Ras Pathway (20%), integrin signaling pathway (20%), CCKR signaling map (20%), TCA cycle (20%), and vasopressin synthesis (20%).http://stitch.embl.de/). The summary of the biological interaction of the proteins that were identified involved the construction of a protein-protein functional network with the use of the online software Stitch4.0 ; ADNP homeobox protein two isoform X1 [observed . The ADNobserved .G. gallus], was found in the AFB1 contaminated group at the level of 250 µg/kg feed (Gr4). Interestingly, two proteins were downregulated in the group orally supplemented with AFB1 at the dose of 25 µg/kg BW (Gr1); ADNP homeobox protein 2 isoform X1 [A. platyrhynchos], and mitochondrial ornithine transporter 1 [G. gallus].As illustrated in the Venn diagram , a singlG. gallus], has been identified in this study and has been found upregulated in only Group 4 (the highest AFB1 concentration). A class of ATP-dependent chromatin remodeling enzyme that functions in the invoking changes in the interaction between DNA and nucleosomes is represented by the CHD. It is a factor in cellular processes, for example, replication, transcription, recombination, repair, and development [It is interesting to note that one protein, CHD7 [elopment .The CHD7 is an ATP-dependent chromatin-remodeling link that regulates polymerase II transcription as well as the specific step of polymerase I transcription termination. In addition, it may regulate negatively DNA replication (UNIPORT). Mutation of the CHD7 gene has been reported to be linked to “CHARGE” syndrome in human embryo which “R” refers to retardation of growth and development when other abbreviation letters are ignorable .One group of heme-thiolate monooxygenases is comprised of cytochromes P450. CYP3A4  is involved in aflatoxin metabolism as observed in the networks of the protein-protein interactions . In addiTherefore, CHD7 might be somehow relative to aflatoxicosis in the duck that results in poor performance and economic loss. In this study, the low resolution of the 1-D analysis may cause the failure to detect these proteins. Thus, increased enhancement of the resolution of 2-D analysis will be necessary for future studies. Furthermore, because the proteome is dependent on context, defining the precise state of the animals that the plasma is extracted from is critical.In the future, further independent validation of these biomarkers should be confirmed using greater number of samples and more techniques including Western blot to obtain the greatest diagnostic power for the assessment of aflatoxicosis in duck from the healthy duck.In summary, this is the first study to achieve the establishment of a database of serum proteins of duck following exposure to AFB1. The information that is necessary in case of the additional exploration of the levels and patterns of duck serum proteins using different forms of administration and with various concentrations of the toxin is provided by this database. Based on the changes in the protein levels that occur during development, it is suggested that there is the possibility that they have an effect on the stage of exposure to aflatoxins. At present, evaluation of these proteins as candidates for uses in future investigations of markers for aflatoxicosis in duck is being conducted.NT designed the experimental study and wrote the manuscript. JR supported to prepare pre-fractionation protein by SDS-PAGE and samples for MS analysis. SR was responsible for proteomics part and assisted for data analysis. All authors contributed to the submitted manuscript.


The dyserythropoietic anemia disease is a genetic disorder of erythropoiesis characterized by morphological abnormalities oferythroblasts. This is caused by human gene C15orf41 mutation. The uncharacterized C15orf41 protein is involved in the formation ofa functional complex structure. The uncharacterized C15orf41 protein is thermostable, unstable and acidic. This is associated with TPD domain (135 to 265 residue position) and three PTM sites such as K50 (Acetylation), T114 (Phosphorylation) andK176 (Ubiquitination). C15orf41 is paralogous to isoform-1 (gi|194018542|) and open reading frame isoform-CRA_c (gi|119612744|)of Homo sapiens located at chromosome 15. It interacts with the human ATP (Adenosine Triphosphate) binding domain 4 (ATPBD4)having similarity score 0.725 as per protein-protein interaction (PPI) network analysis. This data provides valuable insights towardsthe functional characterization of human gene C15orf41. The human uncharacterized gene C15orf41 is located atchromosome 15 encodes a protein with two predicted helix-turnhelixdomains. Mutations of this gene are found in the family ofcongenital dyserythropoietic anemia type-I [The structural and functional characteristics of proteins play thesignificant role in drug design and discovery. Investigations ofthese proteins characteristics experimentally in the wet lab aremuch laborious, time consuming and costly. Thecomputational/statistical tools of bioinformatics reduce this costand time significantly to characterize the uncharacterizedproteins. These tools are widely used for homology modeling ofsequence profiles and predicting the three-dimensional 3D)structure of the targeted protein. The homology modeling isutilized when the experimentally obtained structure isunavailable. It can provide a useful 3D model for the protein ofinterest that is related to at least one known protein structure. It isalso used to predict the 3D structure of one or more proteins ofknown structure for a given protein sequence based on theprimarily sequence alignment. The inclusive municipal sequencesare increasing in some databases like SwissProt  and NCBIDstructuThe target/query sequence (human uncharacterized proteinC15orf41) was collected in FASTA format using the accessionnumber Q9Y2V0 from the UniProt protein database(http://www.uniprot.org/uniprot/Q9Y2V0) .ReferenHomology modelling are used to identify the structure of thequery protein sequence based on one or more known proteinstructures and on the production of an alignment that mapsresidues in the query sequence to residues in the templatesequence. The quality of the homology model is associated withthe quality of the sequence alignment approach and templatestructure. Homology model produces high-quality structural models when the target and template are closely related.Homology search of the target/query protein sequence with thereference/template sequences are performed using the onlinebioinformatics tool "BLASTp " of NCBI database .Various physiochemical properties of the query protein like thenumber of amino acids, molecular weight, hypotheticalisoelectric point (pI), amino acid composition (%), number ofpositively (Arg + Lys) and negatively charged (Asp + Glu)residues, extinction coefficient, instability index, aliphatic indexand Grand Average of Hydropathicity (GRAVY) are investigatedusing the online ExPASy's ProtParam tool(http://expasy.org/tools/protparam.html).A protein consists of one or more domains for functionalactivities in the cellular processes. Some domains show theirfunctions regularly and some are active during the evolutiononly. The post translational modification (PTM) is themodification of amino acid covalent based on the proteinsequence and it is important issues for regulating of biologicaland physiological functions in the cell . The queThe computational/statistical offline bioinformatics tool is usedThe secondary structure of the query protein sequence ispredicted using the online bioinformatics tool SABLE(http://sable.cchmc.org/) . Then itProtein-protein interaction (PPI) networks analysis of a queryprotein with the template/reference proteins is important formore accurate prediction of its function. The PPI network isperformed using online STRING (http://string-db.org/)bioinformatics tool .The detail workflow of this study is shown in Structural and functional characteristics of human proteinC15orf41 (query protein) was analyzed using the several onlineand offline bioinformatics tools. At first, we performed thehomology modeling of query protein with thereference/template protein sequences using the BLASTp toolfrom NCBI database. Out of which, the best homology(template/reference) protein sequences were selected based onthe different criteria such as maximum score (580), querycoverage (100%), identity (99%) and e-value (0.0), and selectedprotein sequences werre used for the further analyses. Thecomputation of amino acid composition of query proteinsequence using ExPASY's ProtParam tool detected very highpercentages of isoleucine (7.5%) and leucine (11.4%) as comparedto other amino acids  of this In this study, suggests that the physiochemical properties of thequery protein seem to be thermostable, unstable and acidic. Wefound that the query protein has three PTM sites such as K50(Acetylation), T114 (Phosphorylation) and K176 (Ubiquitination).The functional similarity is the isoform-1 and isoform-CRA_c ofHomo sapiens, Gorilla (C15orf41) and Chimpanzee(C15H15orf41) with the query protein based on the phylogeneticanalysis. The study protein showed the similar functionalbehavior compare with the known proteins functionality ofGorilla and Chimpanzee. The human ATPBD4 is the highinteracting score protein with the query protein. Thiscomputational study would be helpful for theresearchers/scientists/biologists to characterize the otheruncharacteristic proteins.The authors declare that they have no competing interests.


Dephosphorylation Database (DEPOD) is a manually curated resource that harbors human phosphatases, their protein and non-protein substrates, dephosphorylation sites and the associated signaling pathways. We report here an update to DEPOD by integrating and/or linking to annotations from 69 other open access databases including disease associations, phosphorylating kinases, protein interactions, and also genome browsers. We also provide tools to visualize protein interactions, protein structures, phosphorylation networks, evolutionary conservation of proteins, dephosphorylation sites, and short linear motifs within various proteins. The updated version of DEPOD contains 254 human phosphatases, 336 protein and 83 non-protein substrates, and 1215 manually curated phosphatase-substrate relationships. In addition, we have improved the data access as all the data in DEPOD can now be easily downloaded in a user-friendly format. With multiple significant improvements, DEPOD continues serving as a key resource for research on phosphatase-kinase networks.The human Database URL: www.depod.org DEPhOsphorylation Database (DEPOD) was the first resource that curated information from literature about the phosphatase genes in humans along with their protein and non-protein substrates, the dephosphorylation sites, the protein interactions involving them and the signaling pathways regulated by them  or prov by them . AdditioWe have now updated and also improved DEPOD significantly on the following three fronts: A. compilation, by adding new phosphatases and substrates; B. annotations, by integrating annotations from 69 other open access databases, and including two new modules on disease associations and protein kinases that are experimentally known to phosphorylate a given protein in DEPOD; and C. data access and visualization, by better organizing and interlinking the information and incorporating new visualization tools to facilitate data navigation.DEPOD is now a unique resource that gives information about the bidirectional regulation of a phosphorylation event. New hypotheses may now be formulated based on this information and also based on the evolutionary conservation of proteins, the dephosphorylation sites and short linear motifs (SLiMs) within them. Disease associations in DEPOD may be of great value to increasing efforts to target phosphatases therapeutically . Overallet al. (Phosphatase genes in humans were fetched from Ensembl GRCh38p12) using a keyword-based search. Candidates were filtered using 186 phosphatase-related gene ontology (GO) terms  to filte2 using aet al.  was usedUnlike in DEPODv1.1, we have now renamed the superfamilies and families so that the nomenclature sufficiently reflects the substrate specificities of phosphatases and relates to the historic classes. For example, ‘family 1’ in DEPODv1.1 is now called ‘Class I Cys-based PTPs’ in accordance with previous literature , 11. Phyin vitro and in vivo (in human cell lines) assays by single or multiple research groups. A complete list of PubMed IDs processed during this compilation along with the corresponding decisions and the underlying rationale can be found in Fourteen different protein interaction databases were queried via PSICQUIC service  using thhttps://omim.org/) and ClinVar   have been provided to explore genomic features of an entity. se (CTD)  has alsoEach protein entry in DEPOD has now been linked with 69 other publicly accessible databases by creating a mapping between DEPOD entry names and other identifiers used in the corresponding databases. Some of these identifiers can be accessed using the links provided in DEPOD entries including the ‘Query our ID-mapping table’ link while others are provided as an excel sheet in the ‘Download’ section.et al. (For protein tyrosine phosphatases, DEPOD now provides their signature catalytic motifs incorporated from Alonso et al. .DEPOD provides protein interacting partners of phosphatases and their protein substrates under the ‘Interacting Proteins’ tab along with annotations about the detection methods, interaction types, interaction scores and links to original literature evidences supporting the interactions from BioGRID , IntAct We have added a new module on protein kinases that are experimentally demonstrated to phosphorylate various protein entries in DEPOD by integrating information from PhosphoSitePlus , PhosphoDEPOD provides information about the evolutionary conservation of entire proteins, the SLiMs and also the phospho and dephosphorylation sites within proteins. This information enables users to formulate hypotheses about the functional importance of a particular motif or site depending on whether or not it is conserved.Non-protein substrates of phosphatases is a unique feature of DEPOD. Substantially improving this area, non-protein substrates have now been annotated with their molecular formulae, SMILES and InChI notations and InChI keys. They have also been linked to KEGG , ChEBI , ChEMBL DEPOD can be queried, accessed and navigated in a number of different ways. All protein entries in DEPOD can be queried using UniProt accessions, gene and protein names, NCBI gene identifiers and GenBank identifiers. Non-protein substrates may be queried using ChEBI or KEGG identifiers, compound names, molecular formulae and SMILES notations. Other query mechanisms such as quicksearch utility, KEGG or REACTOME pathway-based queries, dephosphorylation site-based queries and sequence-based queries have been retained from DEPODv1.1..mol and.sdf formats.Data in DEPOD can now be easily accessed via various links provided with each entry. Users can download FASTA formatted sequences of protein substrates of phosphatases under the ‘Protein substrates’ sub-tab under the ‘Substrates’ tab in each phosphatase entry. The ‘Interacting Proteins’ tab provides a link to download excel sheets containing the protein interacting partners of protein entries in DEPOD from the three protein interaction databases. A non-redundant list created by merging all protein interacting partners together can also be downloaded under the ‘All’ sub-tab. The ‘Basic Information’ tab for non-protein entries provides links to download structures inThe four major ways to navigate DEPOD have been retained from v1.1. Human phosphatases can be browsed using a link on the homepage. CATH  fold-basUnder the ‘Basic Information’ tab, users can select and load PDB identifiers of their choice and interactively visualize the corresponding structure using Web3DMol . A link We now explain the use of some of the new and unique features of the database with TP53 as example, which users can search using the gene symbol or protein name under the ‘Search Substrates’ tab. Lack of phosphorylation at Thr55 of TP53 leads to its nuclear localization and eventually DNA damage-induced cell death . Manualldepod@bioss.uni-freiburg.de. More information on how to use and navigate DEPOD along with a brief description of different fields in DEPOD entries can be found under the ‘User Manual’ and ‘About DEPOD’ links in the left-hand navigation panel.All data in DEPOD remains freely accessible and usable for academic and non-profitable usage and is provided under the ‘Download’ section on the homepage. Licensing information for non-academic and for-profit users should be inquired directly under DEPOD has been used in many studies to formulate hypotheses or as a key resource for phosphatase substrates and networks (e.g. , 41, 42.DEPOD_supplementary_data_r1_baz133Click here for additional data file.DEPOD_Supplementary_Table_S1_baz133Click here for additional data file.DEPOD_Supplementary_Table_S2_R1_baz133Click here for additional data file.DEPOD_Supplementary_Table_S3_baz133Click here for additional data file.


Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that take into account the particular biological and biophysical properties of MSF complexes. Here, we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current protein structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode. Intrinsically disordered proteins (IDPs) are crucial elements of the molecular machinery indispensable for complex life ,2. IDPs Depending on the partner protein and the specifics of the interaction, IDPs can bind through several mechanisms. Several IDPs recognize and bind to ordered protein domains, usually through a linear sequence motif . While sA major advancement in the field of IDP interactions in recent years was the development of specialized interaction databases for various mechanisms including coupled folding and binding ,24, fuzzWe now have knowledge of over 140,000 protein structures deposited in the Protein Data Bank (PDB) , a majorHere we present the first classification method designed to identify biologically relevant types of protein complexes formed via mutual synergistic folding. Our work aims to answer specific questions about the types of MSF complexes based on the currently known more than 200 examples. Are there intrinsic classes of MSF complexes or are all known examples basically unique in terms of sequence and structure? If meaningful groups are definable in an objective way, what are the characteristics of each group in terms of sequence composition and adopted structure? In addition, how is the formation of MSF complexes regulated? Are mechanisms known to be important for other molecular interactions relevant to these complexes as well? If so, are there differences between various MSF groups regarding these regulatory mechanisms and other biologically relevant properties, such as binding strength and subcellular localization?Complexes formed by mutual synergistic folding were taken from the MFIB  database , and eacIn contrast, members of clusters 3 and 4 are typically enriched in Gly and Pro and contain a higher-than-average fraction of aromatic residues. Again, polar/charged residue balance is a distinguishing feature, with clusters 3 and 4 showing preferences for polar and charged residues, respectively. Also, similarly to clusters 1 and 2, there is a notable difference in heterogeneity values between clusters 3 and 4: members of clusters 3 and 4 are typically composed of proteins with very similar and different residue compositions, respectively.The structural properties of the studied complexes were quantified using various features describing secondary structure compositions, various molecular surfaces, and incorporating hydrophobicity measures and atomic contacts see . In ordeThe obtained clusters show distinguishing structural features. Members of cluster 1 incorporate the highest amount of nonhelical secondary structure elements. These complexes heavily rely on a large number of buried hydrophobic residues for stability, and most stabilizing atomic contacts are formed between residues of the same protein, relying less on intermolecular interactions, which tend to be mostly polar in nature.In contrast, members of cluster 2 adopt mainly helical structures. The stability of these complexes seems to rely more on the interactions formed between the subunits, mostly formed between side chains. The importance of interchain interactions is also reflected in the large relative interface and small relative buried surface areas.Cluster 3 and 4 complexes exhibit similar features, including a balanced ratio of various secondary structure elements and polar/hydrophobic balance of various molecular surfaces and contacts. For both clusters, interchain contacts rely mostly on side chain–side chain and backbone–backbone contacts. The main difference between the two clusters is the relative role of the interface between the participating proteins. Cluster 3 members have a larger-than-average interface, in terms of both molecular surface and number of contacts, meanwhile cluster 4 complexes have a very restricted interface size, incorporating only a few atomic contacts.Members of cluster 5 are the most similar to the average in most structural features. There are only weak distinguishing features, including a slightly increased helical content at the expense of extended structural elements, a moderate increase in the role of backbone–side chain interactions in interchain contacts, and the increased ratio of interchain contacts. However, these deviations in average parameter values are modest and—with the exception of the decreased extended structure content—none of them reaches 20% compared to the average values calculated for all complexes.Considering together the previously established sequence- and structure-based clusters, in total 20 types of complexes can be defined . The numThe complex types defined so far are based on structure and sequence features. However, if these types represent biologically meaningful classes, there should be other relevant differences between them in terms of the energetics of the interaction, binding strength, subcellular localization, or the biological regulation of the interaction. In the next chapters, we describe each complex type with biologically important characteristics and assess the potential differences between the members of each class.From a biological perspective, the strength of association between interacting protein chains and the stability of the resulting complex is of utmost importance. Unfortunately, complexes formed exclusively by IDPs via MSF generally lack targeted measurements concerning thermodynamic and stability parameters. However, low-resolution energy calculations and prediction algorithms can give an indication about the characteristic energetics properties of the uncovered complex types in general. While these methods might have fairly large errors in individual cases, they are well equipped for comparative studies between groups of complexes.In order to assess the energetic properties of complexes, we employed an energy calculation scheme using low-resolution force fields based on statistical potentials (see Data and Methods). As a reference, energetic properties were calculated for complexes formed exclusively by ordered proteins and complexes formed by an IDP binding to an ordered partner via coupled folding and binding (CFB) . The next two types with low Kds are the near-ordered complexes (Type 1) and interface-heavy oligomerization modules (Type 3). These three types together possibly cover most cases of the interactions where the complex needs to stay stable for an extended period of time, such as histone dimes (Type 5), complexes with enzymatic activity (Type 1) and several transcription factors (Type 3). Coiled-coil-like structures and oligomerization modules with small interfaces in general have a higher Kd, indicating that several transiently bound complexes belong to these types.The transient or obligate nature of interactions provides clues about their roles in biological systems. This is at least partially describable through K domains , these vomplexes , with FiWhile the energetics of various interactions can provide clues about their transient/obligatory nature, the regulatory mechanisms can give more direct evidence. For example, while most IDP enzymes (belonging to Type 1) form particularly stable oligomers, indicating an obligate interaction, for example the oligomeric state of superoxide dismutase (SOD1) is known to be controlled by post-translational modification (PTM) serving as an on/off switch ; meaningTo have a more systematic picture of the extent of regulatory mechanisms in MSF interactions, In addition, complex formation can also be regulated through the availability of the subunits participating in the interaction. This availability can depend on the alternative mRNA splicing of the corresponding genes, where certain isoforms lack the binding site . Also, eIn addition to regulatory mechanisms detailed in the previous chapter, a crucial element in the spatio-temporal control of protein function is subcellular localization . In ordeThe least amount of information is available for Type 1, near-ordered complexes. Albeit GO terms are lacking for most complexes, even the limited annotations highlight that these complexes are able to efficiently function in the extracellular space, which in general is fairly uncommon for IDPs. Coil- and zipper-type helical complexes (Type 2) are somewhat more often attached to the membrane or function in the intracellular space, or in non-nuclear environments, such as the lysosome. In contrast, oligomerization modules (Types 3 and 4) are most prevalent in the nucleus and the intracellular space, which is in line with the function of the high number of transcription factors in these groups. However, modules with a large interface (Type 3) are relatively often found in other compartments, while modules with smaller interfaces (Type 4) also function in the extracellular space. Complexes adopting a handshake-like fold are enriched in histones, which is reflected in their enrichment in the nucleus and the chromatin  or interface-light (Type 4) complexes. In both cases, the sequences can be highly variable, and the unifying features are mostly structural. Both types typically have an average-sized relative buried area with balanced hydrophobic/polar composition. However, interface-heavy complexes have a large, slightly polar interface that plays a major role in achieving the tightly bound structures. In contrast, interface-light complexes form a more helical structure and have smaller hydrophobic interfaces that play a more diminished role in achieving the stability of a less tightly bound system. This hints at interface-light complexes being more transient, also supported by the fact that these complexes have a higher number of known regulatory PTMs and are also modulated by alternative splicing. Both type 3 and type 4 complexes preferentially occur in nuclear and intracellular processes, as several of them are ribbon–helix–helix (interface-heavy) or basic helix–loop–helix (interface-light) transcription factors, able to shuttle between the nuclear and the intracellular spaces. In addition to the similarities in subcellular localization, type 4 complexes preferentially occur in the extracellular space, and type 3 complexes in other cell compartments, as well.The fifth type of complexes typically adopts a handshake-like fold, characteristic of histones and homologous proteins. While these structures are usually largely helical, the interacting proteins often contain a relatively high ratio of prolines and glycines, in addition to the enrichment of aromatic residues. While they are depleted in polar residues, both the interface and the buried surface have a fairly balanced hydrophobic/polar makeup. The complexes are relatively tightly bound, and interchain interactions play a fairly large role in stabilizing the interaction. This type of complex has the highest ratio of both PTMs and competitive interactions, providing a large amount of regulation. In addition, PTMs are highly heterogeneous, containing phosphorylations, acetylations, methylations, and ubiquitinations as well. Members of this cluster primarily serve DNA/chromosome-related functions, and hence are usually located in the nucleus.While types 1–5 represent well-defined groups with members of clear unifying similarities, the final group serves as an umbrella term for complexes that are not members of any previous structural/sequential class. In accord, these complexes cannot be described by simple characteristic features and are the most sequentially and structurally heterogeneous group. This group contains highly specialized interactions that present unique protein complexes, which are regulated through all three control mechanisms and occur in all studied subcellular localizations.The described MSF classification method bears similarity to the approach employed in CATH, as both approaches use a hierarchical classification of PDB structures. However, CATH does not consider interactions and simply relies on the secondary structure elements and their connectivity and arrangement, in contrast to the presented analysis taking into account protein chain interactions too, together with sequence composition features.Considering “Class” and “Architecture” definitions, there is very little correspondence between the CATH and the new MSF classification. If the two schemes showed a high degree of similarity, the matrix in Here, we present the first approach aiming at the classification of complex structures formed exclusively by disordered proteins via mutual synergistic folding. We developed and applied a method that can classify these complexes into various types based on sequence- and structure-based properties. The classification scheme takes into account on the one hand, the overall sequence and structure properties of the complex, and on the other hand, the interaction itself, quantifying the role of intra- and intermolecular interactions in relation to the overall contact/surface properties of the structure. As the classification protocol is based on hierarchical clustering, it is freely scalable. Tuning the resolution via changing the number of sequence-based or structure-based clusters, the method can be used to yield any number of types and subtypes. The presented classification is a top-level one highlighting the major types of MSF classes, and this six-way classification scheme will be used to better define MSF complex types in the MFIB  databaseWhile both sequence- and structure-based parameters are taken into account when defining the final complex types, the two sets of descriptors have different roles in the scalability of the method. In our presented approach to defining complex types, the main features are structural properties, while sequence parameters are more descriptive in the sense that they highlight the sequential features needed to be able to fold into a complex of given structural properties . HoweverIn addition to providing a scalable classification scheme, the described method and the defined complex types have biological relevance. The presented complex types have different biological properties; although only information describing the sequence and structure properties were put in, the resulting types show different properties in terms of the energetics and strength of the interactions , the reld values  interactions seem to form obligate complexes. However, there are several cases where these interactions do break up in a biological setting, most notably for histones. Histone H4 is able to form dimers with at least eight different H3 variants [For most known MSF complexes, the resulting structure is instrumental for proper function, such as the coiled-coil structure for the SNAP receptor (SNARE) complex in mediating membrane fusion , the dimvariants , and it variants . The posvariants . In addid measurements are lacking for the majority of these interactions, and interactions in structural detail have usually been only analyzed in a single PTM state. Therefore, the molecular details and biologically relevant steps of the regulation of these interactions are difficult to assess; but from a biological sense, it is probable that even several low Kd complexes can dissociate rapidly in certain cases. At least some regulatory mechanisms are currently known for about 36% of studied MSF complexes, but the real numbers are bound to be higher. This means that most probably the majority of MSF complexes are not obligate complexes, where the disordered state is physiologically irrelevant, but can exist in both the stable bound state and the disordered unbound state as well, under native conditions. Thus, MSF complexes are integral parts or direct targets of regulatory networks, although the extent of regulation varies with the interaction type considered.Exploring the precise regulatory mechanisms for MSF complexes would be highly informative. Unfortunately, experimental KEscherichia coli and the autoinhibitory GTPase-binding domain (GBD) in host WASP proteins  hijacks the native LLPS-mediated cellular processes [Apart from the studied regulatory mechanisms, additional layers of spatio-temporal regulation can play crucial roles for MSF complexes, similarly to other IDP interactions . An emerrocesses , showingThe difference between complex types in various biological and biophysical properties shows that these type-definitions reflect true biological differences. Apart from being useful for complex classification, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. This can be the basis for developing future prediction methods, where these sequence- and structure-based parameters can be used as input for the prediction of biological features of complexes. In addition, the establishment of MSF complex types has direct implications, as knowledge present for a specific complex might be transferable to other complexes of the same type. For example, certain pathological conditions arise through the aggregation of IDPs. A well-known example is transthyretin (TTR) aggregation that can lead to various amyloid diseases, such as senile systemic amyloidosis . AnotherSuch structural classification approaches can have a high impact on structure research, most importantly in the study of protein structure or evolution, in training and/or benchmarking algorithms, augmenting existing datasets with annotations, and examining the classification of a specific protein or a small set of proteins . Up to dMSF complexes were taken from the MFIB database . Two entAs a reference, two other datasets of protein complexes were used. A set of complexes formed exclusively by ordered single-domain protein interactors was taken from . These 6Similarly to the approach described in , the foli between any pair of constituent chains. The average dissimilarities for various sequence-based clusters are shown in Secondary structure assignment was performed by DSSP , using aMolecular surfaces were calculated using Naccess . SolventContacts were defined at the atomic level. Two atoms were considered to be in contact if their distance is shorter than the sum of the two atoms’ van der Waals radii plus 1 Angstrom. For exact structural feature values for all MSF entries, see Standard Pearson correlation values were calculated between all sequence and structure features . If two Both sequence and filtered structure parameters were used as input for clustering separately. First, hierarchical clustering was done using the scaled features as input, using Euclidean distance and Ward’s method . Then, kInteraction energies for residues were calculated using the statistical potentials described in . These id value prediction, and thus the number of values used in representing the average per-complex type Kds (d values are listed in Dissociation constants for MSF complexes were estimated using the method described in . In eachtype Kds  is calcuPost-translational modifications were taken from the 2 October 2017 version of PhosphoSitePlus , PhosphoSubcellular localization was represented using GeneOntology  terms fr


One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at The online version of this article (10.1186/s13321-019-0373-4) contains supplementary material, which is available to authorized users. Structure-based molecular docking is one of the key components in computer-aided drug design –3. DockiA wide variety of scoring functions has been developed to approximate energy functions. Based on the derivation ways, scoring functions are usually classified into physics-based methods, empirical scoring functions, knowledge-based potentials, and descriptor-based scoring functions . PhysicsPreviously, a knowledge-based scoring function called DLIGAND  was devercut = 15 Å, obs is the number of atomic pair  within the spherical shell of distance r observed in a given structure database, and ∆r(∆rcut) is the bin width at r(rcut). A constant value of 0.5 Å was used for ∆r at all bins and ∆rcut = ∆r. Here, we employed residue-specific atomic types for protein atoms that leads to 167 atomic types for protein atoms. This is different from DLIGAND, where both protein and ligand atoms were represented by mol2 atom types, and thus only 13 atom types were utilized for protein atoms.We have used the same approach as the DLIGAND  to derivobs for the number of observed pairs between protein atoms, which are converted to protein–ligand interactions by mapping indices for protein atoms to 11 mol2 atom types .The ability of DLIGAND2 to perform virtual screening was also evalued on the DUD-E dataset . There aThe accuracy of each scoring function was evaluated by the LogAUC and enrichment factor (EF).x% of the DUD-E dataset, where x equals to 1, 5 and 10 respectively.x% screened candidates, the number of active compounds, and the total number of compounds in the screened library, respectively.As described in DUD-E Ref.  and our For a fair comparison with the machine-learning-based scoring function (RF-Score-VS ) trainedDifferent from the united mol2 atom type used by DLIGAND, the improved version DLIGAND2 has employed residue-specific types for protein atoms, which expanded atom types from 12 types to 169 atom types. Sufficient statistics for this larger number of atom types is ensured by using 12,450 protein chains for training. Residue-specific atom types enable the discrimination of the properties  and surrounding environments of atoms. As shown in Fig. 20, AutoDock-hybrid and X-ScoreHM) were all trained directly for binding affinity prediction. The scoring function of Autodock Vina achieves a PCC of 0.56, which is lower than DLIGAND2 but higher than DLIGAND. According to the root mean square error (RMSE), DLIGAND2 (RMSE of 1.85) ranks the 10th after ChemPLP@GOLD (RMSE of 1.84), which is the best in all knowledge-based potential functions. The improvement in correlation coefficients is encouraging as DLIGAND2 was trained on protein structures only.Figure The docking power refers to whether a scoring function can correctly identify the native ligand poses from the predicted poses. Table The ranking power of a scoring function refers to its ability to correctly rank binders of a given target protein by their predicted binding affinities based on the poses from the crystal structures and optimized structures. Table The above benchmark study is based on experimentally determined, protein–ligand complex structures. We further tested DLIGAND2’s ability to predict protein–ligand binding affinities by using predicted complex structures from docking. To remove random fluctuations, we generated 10 poses for each pair of protein and ligand by each docking method, and the highest score among 10 poses by each scoring function was used to represent the predicted binding affinity, respectively. As shown in Table 20 and ID-Score because they were trained on the PBDbind refined set.When re-assessed by DLIGAND2, the PCCs of predicted binding affinity consistently improve over all eight docking methods to the levels from 0.498 to 0.537 with an average of 0.523, and the RMSE from 1.69 to 1.76 with an average of 1.71. This indicates the main bottleneck of current docking method is the scoring function, as also disclosed in the previous study ), ΔvinaR20, and X-Score) using the poses generated by AutoDock Vina.The DUD-E dataset is used to examine the ability to separate true ligands from decoys, a practically important problem in virtual screening. Here, we employed the DUD-E dataset to evaluate the screening power of scoring functions. The performance of DLIGAND and DLIGAND2 is compared to those of three top ranked scoring functions in the CASF-2013 benchmark , respectively. By comparison, AutoDock Vina performs the best in the ion channel, and is far superior to DLIGAND. The scoring function ΔvinaRF20 performs the best in miscellaneous, nuclear receptors and other enzymes. DLIGAND2 doesn’t perform well in targets of kinesin-like protein 1  and poly (ADP-ribose) polymerase-1 , likely because their binding ligand contains halogen and phosphate elements that don’t appear in training protein chains. Currently, DLIGAND2 simply treats phosphate elements equivalent to the sulfate atom type. This issue may be solved in future study by including additional ligand atoms from protein–ligand complex structures.To further compare the performance of each scoring function for different protein categories, 102 targets of DUD-E dataset are separated into eight categories and evaluated by the average EF1% are 28.89, 9.12, 18.32, 9.33 and 9.33 for DLIGAND2, Autodock Vina, ΔvinaRF20, DLIGAND and X-Score, respectively. The AUC of ID-Score is 0.553, close to 0.5 by the random selection.Among the best examples of DLIGAND2 performance, we plotted the receiver operating characteristic (ROC) for the case of PTN1 protein (protein-tyrosine phosphatase 1B). As shown in Fig. https://github.com/oddt/oddt) scoring function that was trained on the DUD-E, we have compiled a new dataset from the DEKOIS 2.0 benchmark, with all targets sorted according to their sequence identity to the DUD-E targets according to the blastpgp. Figure 1%) as a function of the number of targets sorted according to sequence identity. The average EF1% for RF-Score-vs increases as the sequence identity increases, suggesting the performance of RF-Score-VS v2 is strongly depending on similarity to its training set. AutoDock Vina also has some dependence on similarity to DUD-E targets. By comparison, DLIGAND and DLIGAND2 have the least dependence except when the number of targets is low (< 10) likely due to natural fluctuations. DLIGAND2 has the highest performance when homologous targets are excluded for sequence identity less then 30% with an average EF1% at 5.72, compared to 2.34 by AutoDock Vina, 2.73 by RF-Score-VS and 3.25 by DLIGAND.To compare with the latest RF-Score-VS v2  used in the original DLIGAND.DLIGAND2 consistently improves over DLIGAND in binding affinity prediction using either native or docking-predicted complex structures. The improvement in Pearson correlation coefficient is 8.7% for the CASF-2013 dataset by using native complex structures and 15% for the PDBbind dataset by using predicted complex structures. In addition, DLIGAND2 has significantly higher enrichment than DLIGAND in discriminating true ligands from decoys using the DUD-E dataset according to re-ranking of docked structures. These results suggest the usefulness of expanding protein atomic types in generating the DLIGAND 2 statistical potential.DLIGAND2 is the best knowledge-based energy score but not as accurate as a few empirical (X-Score) or machine-learning based (RF-Score-v2 and ID-Score) scores trained by CASF-2013 or PDBbind. The X-Score and ID-Score methods outperform Autodock vina in the CASF-2013 and PDBbind, but they all have lower performance in decoy discrimination, a practically more important problem. We have also shown that the performance of RF-score-vs strongly depends on the sequence identity of the target protein to the dataset for training the method. Though RF-score-vs was reported to perform well in the DUD-E that includes many homologous proteins to its training set, it doesn’t perform well on protein targets that are not homologous to its training set. By comparison, DLIGAND2 was derived from only protein monomer structures, ensuring a balanced performance for all targets. Considering the simplicity and fast computation, DLIGAND2 will be useful for re-scoring after docking, or being included as a term for other scoring functions.Additional file 1: Table S1. Showed 13 mol2 atom types mapped to 167 residue-specific atom types for protein atoms.Additional file 2: Table S2. It contained the list of the 4044 complexes collected from PDBind refined data set.Additional file 3: Table S3. The EF1% values of four scoring functions on DEKOIS 2.0 data set are shown.Additional file 4: Table S4. The logAUC and EF  values of six scoring functions on the DUD-E dataset are listed.


In recent years, targeting the complex network of protein–protein interactions (PPIs) has been identified as a promising drug-discovery approach to develop new therapeutic strategies. 14-3-3 is a family of eukaryotic conserved regulatory proteins which are of high interest as potential targets for pharmacological intervention in human diseases, such as cancer and neurodegenerative and metabolic disorders. This viewpoint is built on the “hub” nature of the 14-3-3 proteins, binding to several hundred identified partners, consequently implicating them in a multitude of different cellular mechanisms. In this review, we provide an overview of the structural and biological features of 14-3-3 and the modulation of 14-3-3 PPIs for discovering small molecular inhibitors and stabilizers of 14-3-3 PPIs. It was discovered in 1967 by Moore and Perez in brain tissue ,3. All iThe 14-3-3 proteins exist mainly as dimers with a monomeric molecular mass of approximately 30,000 Da. Each monomer consists of a bundle of nine alpha helices organized in an antiparallel fashion. Its functional form is represented by the homo- or the heterodimer showing the typical W-like shape, which is formed by monomers of different isoforms ,8. Salt The 14-3-3 proteins have the ability to bind to many functional signaling protein (ligands), thereby mediating their physiological effect. In this context, the association/dissociation of these protein complexes becomes the key dynamic process for the regulation of different protein mechanisms. Depending on its interaction with specific protein partners, 14-3-3 participates in several regulatory processes, such as cell cycle control, survival signaling, cell adhesion and neuronal plasticity . For thiProtein–protein interactions (PPIs) are fundamental for a broad range of biological processes; the alteration in PPI events is among the main causes of diseases, such as cancer, diabetes, and others ,24. MostProtein–protein interactions (PPIs) are involved in all diseases and, nowadays, they are considered a significant topic in drug discovery. The modulation of these PPIs with small molecules represent a key strategy for designing novel bioactive compounds to enlarge the druggable genome  with a 90-fold enhancement of the affinity between the two proteins. Structural details of the 14-3-3/PMA2/FC-A complex have been clarified via X-ray crystallography [The natural product fusicoccin-A (FC-A) is a metabolite produced by the fungus eraction . FC-A walography . The terlography . Anotherlography . Even iflography . An examlography .Cladosporium sp.501-7W. The crystal structure of CN-A in complex with 14-3-3 and the N-terminal binding motif of C-Raf, published in 2013 [Cotylenin A (CN-A) is a natural product closely related to FC-A that is produced by the fungus  in 2013   C is a coactivity . In vitractivity .The 5-8-5 fused ring scaffold of fusicoccin and cotylenin is highly complex. In search for selectivity, structure-based design has instructed the semi-synthesis of potent analogues. For example, the semi-synthetic derivative FC-THF has been shown to infer a 20-fold stabilization to the complex between 14-3-3 and the potassium channel TASK-3. The derivative bearing an additional furan ring was designed as a “mode III” specific stabilizer  Figure A. AnotheWith the aim to identify novel and chemically diverse stabilizers of 14-3-3 PPIs, in 2010, a high-throughput screening led to the identification of pyrrolidone 1. The crystal structure of the small molecule in complex with 14-3-3 and PMA2 showed how the trisubstituted pyrrolinone occupies a site that substantially overlaps with the binding pocket of FC-A  Figure A. StartiRecently, AMP has been reported to stabilize the complex of 14-3-3 with the carbohydrate-response element-binding protein (ChREBP) . DiffereMolecular tweezers are supramolecular ligands, characterized by the alternating conjunction of benzene and norbornadiene entities. The semicircular shape of the molecule creates a cavity able to selectively accommodate long and thin side chains like lysines or arginines. In 2013, Bier et al. showed how a supramolecular ligand can modulate a 14-3-3 PPI: the lysine-specific molecular tweezer binds to a 14-3-3 adapter protein and inhibits its interaction with partner proteins, such as C-Raf and Exoenzyme S (ExoS) . More reThe first reported 14-3-3 inhibitor was the R18 peptide A, identiModulation of 14-3-3 PPIs is a promising field in drug discovery. Understanding the role of 14-3-3 PPIs could give more insights into the cellular behavior of 14-3-3, which is essential for the development of new therapeutic strategies, such as 14-3-3 modulators. The examples reported in this review suggest how it may be possible to target certain types of disease and build up the first step towards a pharmacological intervention in human cells with aberrant cell proliferation and differentiation. In addition, all the studies reported present the stabilization of 14-3-3 PPIs as an excellent alternative to inhibitor-based standard methods, for instance, for the treatment of cancer formation and cell migration in tumors linked to malfunctioning of signal transduction processes. The stabilization of 14-3-3 PPIs is suggested to have the potential to be more beneficial compared to inhibition. In fact, the molecules identified stabilize the interactions by making contacts with both 14-3-3 and the PPI partner, therefore acting as “molecular glue”. Because of this binding mode, the stabilizers might provide a better opportunity for selectivity through the implementation of rational design approaches. Also, since the stabilizers bind to their targets in a non-competitive manner, their affinity does not need to be in the nanomolar range to trigger a strong effect in human cells. Finally, an important emerging area of investigation is the use of fragment-based drug discovery approaches. The fragments identified might be used as chemical probes to explore the surfaces of the protein partners and study the “hotspots” responsible for the binding . Further