There is a large amount of PPI data available, generated from high throughput experimental data. The enormous size of the data persuaded us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems, and so, the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make a weighted PPI network and to reduce the noise of PPI. Gene expression data are also used to make dynamic subnetworks from PPI. A memetic algorithm is used to bicluster gene expression data and to create a dynamic subnetwork for each bicluster. Experimental results show that DPCT can detect protein complexes with better correctness than state-of-the-art detection algorithms. The source code and datasets of DPCT used can be found at  Protein complexes are modules made up of some proteins, which become a group at a specific time and situation, to become a functional part of a biological process . Moreover, considering each bicluster, all of its proteins that are active in some time point, have a better chance to participate in forming a protein complex.BiCAMWI  which uses a memetic metaheuristic algorithm for biclustering gene expression data, which can detect more accurate biclusters and is time efficient rather than a genetic algorithm. Using TAP data along with GO gives us a precise weighted PPI network. A post-processing step in DPCT analyses and aggregates also detects protein complexes from each dynamic PPI subnetwork and removes highly similar or redundant protein complexes.In this study, we present a novel In section Materials and Methods, we introduce datasets and benchmarks used in the evaluation of DPCT and define all phases of DPCT separately. In section Experiments and Results, we evaluate the proposed method and compare it with state-of-the-art methods. Section Analytical Discussion assesses the effect of each novelty in DPCT and we conclude the paper in section Conclusion.We use DIP and BioGrid PPI networks to measure the effectiveness of the proposed method. The DIP ; where WLCMS and WMALDI are the normalized values of TAP datasets which indicate the purification score between P1 and P2. If the interaction exists in just one of the TAP sources, WTAP is set to the available score, and for the interaction that does not exist in both of TAP datasets, WTAP is set to 0.5 and it does not affect the overall weight of the interaction.Many state-of-the-art protein complex detection methods use gene ontology (GO) for PPI network noise reduction, i.e., by assigning a weight to each interaction of the PPI network. In DPCT, as shown in 1 and P2 in the PPI network based on BP, CC, and MF which we name; WBP, WCC, and WMF. GossTo can also run a post-processing step including a local search through the graph to increase the accuracy of weighting. Based on Equation (2), WGO is equal to the average of these three weights. After these steps, WTAP and WGO are merged to make the final weight for each interaction among proteins of the PPI network. To make WTAP a coefficient for noise reduction, α is defined by Equation (3).The main source of making a weighted PPI network in DPCT is GO. GO is a dataset with three graphs namely; Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The GossTo .According to Equation (3), α − (σ[i] × ε)|], the value will be 1 and otherwise 0. Note that ε is a penalty factor for the normalization process.To normalize GE, considering GE[m][n] where m is the size of protein array P = {pDPCT uses a memetic algorithm to bicluster the NGE dataset. The memetic algorithm in its early definition was a modified genetic algorithm with the capability of local refinement by a local-search operator to find a solution for the traveling salesman problem  are removed from the population. If the end condition is satisfied , the loop breaks at line 14. In line 16 the population set is sorted based on fitness values. Afterward, the best solutions are returned as our final GE biclusters in line 17. Below, the encoding of chromosomes, and the operators of our memetic algorithm are described.Each bicluster is an induced matrix of GE (gene expression) so it can be described like B[I][J] such that I ⊆ P and J ⊆ T. For the memetic algorithm, each chromosome maps the structure of a bicluster and is a vector of size m+n genes, where m and n show the number of proteins and time points, respectively. Each gene of the chromosome has a binary value, where 1 and 0 means presence and absence of the corresponding protein or time point in the chromosome. As you can see in * |J| expression values and we expect most of them to be active. To calculate Fb, we find the proportion of active to inactive expressions. In the best case, if all selected proteins are active in all selected time points, Fb becomes equal to 1 and in the worst case, if there is no active protein among all selected proteins and time points, Fb will be 0.The goodness of a bicluster depends on the number of its active proteins. We used a memetic algorithm to search for a group of proteins and time points i.e., a bicluster such that most selected proteins are active in the selected time points. Equation (8) is used to measure the quality of each detected bicluster. As mentioned above, a bicluster is a two-dimensional matrix, like B[I][J] such that I is a subset of P and J is a submatrix of T. For each selected protein, we have |J| time points, so we have |I| In the proposed memetic algorithm, a binary tournament is used to select two best individuals for crossover. Two points crossover is used, and crossover is only applied to the protein part of the chromosome, so the time point part will not be affected. Having two selected individuals, two crossover points on the protein part of each individual are selected randomly and the part of parent chromosomes between selected points will be swapped with the probability of 0.9; as a result, we have two new individuals.A mutation operator with a different probability is used for the protein part and time point part. For the protein part, each cell of the chromosome will be flipped with the probability of 0.001 and in the time point part, each cell will be flipped with the probability of 0.1. As described above, crossover did not apply to the time point part so we increased the probability of mutation for the time point part to allow the memetic algorithm to walk through different situations and to find better solutions.Local search of the memetic algorithm is applied to any new individual added to the population. In local search, each bit of the chromosome is selected and flipped with the probability of 0.8. If the fitness of the derived chromosome is not increased, the selected bit is reversed to its previous value, otherwise, the new value is saved for the bit in the chromosome. Then the procedure will continue for the next bit.i, we make a correspondent dynamic subnetwork of PPI, DTWPPI#i that contains only active proteins which are members of bicluster Bi and their interactions.After normalizing gene expression data and finding biclusters using a memetic algorithm, the third step is to extract dynamic subnetworks from PPI correspondent to the detected gene biclusters. For each bicluster BIn section Making Dynamic PPI Subnetworks, the static PPI network is divided into some DTWPPI (Dynamic TAP-Aware Weighted PPI) that has the most active proteins at one or more than one time points. These DTWPPIs may overlap due to the nature of biclustering. We apply the CAMWI  and is considered a protein complex. All proteins of the CommonPart are removed from all protein complexes in the corresponding category. In the next phase, in lines 19–23 of Algorithm 2, each protein in each category is checked and if it participates in at least half of the protein complexes in the corresponding category, it is appended to the CommonPart of the category and the protein is removed from all protein complexes in the corresponding category. In this stage, the CommonPart is added to the final result.1, v2, …, vm} proteins and E = {e1, e2, …, en} edges among proteins; WCC(v) for each protein can be determined by Equation (10). Where W(e) demonstrates the weight of edge e, and L is the set of all edges of v. WCC(C) for each protein complex C is computed by Equation (11). At the end of the 4th phase, all categories must be empty, and the final set contains the detected protein complexes by DPCT.Remaining proteins in each category will enter the last phase of analysis and aggregation algorithm which is defined in lines 25–31. In this phase, each remaining protein will be added to a copy of CommonPart and if the new complex has a greater weighted clustering coefficient (WCC)  can be determined by Equation (9) and if J≥th then ci is considered as a true detected complex. In this study, like other state-of-the-art articles, we set th to 0.25. The precision measure represents how much of the detected complexes are matched correctly with the benchmark and the recall measure represents how much of the real complexes are detected using the DPCT method. The F-1 measure is the harmonic mean of precision and recall and can be used to assess the overall performance of detection methods. Precision, recall, and F-1 measures are defined in Equations (12–14) , the efficacy of TAP on GO is controlled by γ. γ can be set from [0 . 1] and for γ = 1; the GO score will be multiplied by a factor between 0.5 and 1.5 based on the TAP score. We can set γ based on the availability and the missing rate of TAP data and the reliability of GO data.To find a good value for γ, we run DPCT with different values of γ. vice versa. Another parameter that is used in the second phase of DPCT is ε, our threshold to discretize gene expression data. So for the same gene expression data, ε is supposed not to change for different PPI networks. Considering Equation (7), higher values of ε lead to having more inactive genes and it increases the sparseness of the gene expression matrix and The last parameter that is used in the second phase of DPCT is, which declares the number of derived biclusters and, consequently, the number of dynamic PPI subnetworks. We applied DPCT on BioGrid and DIP datasets as input PPI networks and used CYC2008 and MIPS as benchmarks to confirm the correctness of detected protein complexes. We ran DPCT on a computer with 2.5 GHz Intel CoreI7 CPU and 4 GB of RAM and it took about 12 s on average. We also ran BiCAMWI and CAMWI on the same machine and they required 25 and 3.5 s to finish, respectively. Based on reports, COACH takes <40 s on a 3.5 GHz CPU and 3 GB of RAM to generate results, and TINCD takes between 785 and 4,200 s to conclude its calculation on a system with a 2 × 2.1 GHz CPU and 12 GB RAM.Due to the high level of noise in the PPI network, DPCT used GO and TAP data to assign a weight to each interaction of the PPI network. To assess the effect of using TAP in the accuracy of detected protein complexes, we ran DPCT without considering TAP data and we compared its results with normal the DPCT method which uses both GO and TAP data. In the TAP-OFF situation, a weighted PPI network was created with GO data only. In DPCT, a memetic algorithm is proposed to bicluster gene expression data and to make dynamic PPI subnetworks. Some state-of-the-art methods for protein complex detection, use metaheuristic algorithms to bicluster gene expression data. BiCAMWI is a method that defines and uses GA-DCT, a novel genetic algorithm for clustering gene expression data. To investigate the effect of our proposed memetic algorithm, we used GA-DCT instead of our proposed memetic algorithm. The last phase of the DPCT method is analysis and aggregation, a post-processing phase. After detecting protein complexes from each dynamic PPI subnetwork, due to the nature of biclustering, there may be many redundant and highly similar protein complexes in the result set. In this post-processing phase, all highly similar protein complexes are aggregated and also, we can control and decrease the negative effect of the parameter which controls the number of dynamic PPI subnetworks. By increasing the value of, the total number of dynamic subnetworks will grow up; therefore, the total number of redundant or highly similar detected protein complexes are increased. In this situation, the analysis and aggregation phase remove or merge redundant protein complexes.In order to assess the effect of the post-processing phase, we ran DPCT without its last phase. In the DIP dataset with the CYC2008 benchmark, when DPCT uses the post-processing phase, we see a rise in precision and F-1 measures by 1.4 and 0.9% respectively. Moreover, for the MIPS benchmark, DPCT with the analysis and aggregation phase, gain the rise of precision, recall, and F-1 Measures by 2.6, 2.6, and 2.7% respectively. As the results show, the last phase of DPCT, not only removes redundant detected protein complexes, so decreases the size of the result set, but also in most cases, it leverages the quality of results and removes incorrectly detected protein complexes.Protein complexes play an important role in the cellular system. Many researchers therefore work on developing new methods to detect protein complexes. There are many methods that use different approaches like clustering, seed generation, and core-attachment to detect protein complexes. In recent years, many methods first make a dynamic PPI network and then detect protein complexes from the network.In this paper, we present DPCT, a novel method to detect protein complexes. DPCT uses TAP data as minor data, and as the complement of GO to make a more accurate weighted PPI network. An extended memetic algorithm is used to bicluster gene expression data and to consequently make a dynamic PPI subnetwork. After applying the third phase of DPCT on each dynamic PPI subnetwork, detected protein complexes are aggregated so that redundant detected protein complexes are removed from the final result set. A comparison between DPCT and other state-of-the-art methods shows that DPCT can detect protein complexes with better values of precision, recall, and F-1 measures.For future works, we suggest using a learner to fuse the weights that come from TAP and GO to obtain a better weighted PPI network. Gene expression is experimental data that may have a high noise rate so any effort to decrease the noise rate of gene expression data can increase the quality of the detection method. Using other methods to make dynamic PPI subnetworks that are more consistent with biological concepts, such as other evolutionary algorithms, can be helpful in increasing the accuracy of protein complex detection.https://github.com/alisn72/DPCT.The datasets used in this study can be found at AS was responsible for development phase of the main algorithm and also draft the article. SJ also revised the drafted article and approved the content to publish the paper. All authors were responsible for designing the algorithm.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.


Plasmodium genus, of which P. falciparum is the most prevalent. Several previous studies predicted human-plasmodium protein-protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host-pathogen interaction networks. To predict potential putative HP-PPIs, we use an integrative computational approach based on the combination of multiple OMICS-based methods including human red blood cells (RBC) and Plasmodium falciparum 3D7 strain expressed proteins, domain-domain based PPI, similarity of gene ontology terms, structure similarity method homology identification, and machine learning prediction. Our results reported a set of 716 protein interactions involving 302 human proteins and 130 Plasmodium proteins. This work provides a list of potential human-Plasmodium interacting proteins. These findings will contribute to better understand the mechanisms underlying the molecular determinism of malaria disease and potentially to identify candidate pharmacological targets.Host-pathogen molecular cross-talks are critical in determining the pathophysiology of a specific infection. Most of these cross-talks are mediated via protein-protein interactions between the host and the pathogen (HP-PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life-threatening disease caused by an obligate intracellular parasite belonging to the  P. falciparum strain being the most prevalent. The infection is transmitted by an infected mosquito that bites a human, leading to the multiplication of the parasites in the host's liver before infecting and destroying red blood cells. In 2018, WHO estimated 228 million cases of malaria occurring worldwide  with around 405000 deaths from malaria globally , of which 93% of them are recorded in Africa. Thus, identifying the human and Plasmodium proteins involved in the infection can provide insights into the underlying molecular mechanisms of pathogenicity and potentially identify new putative pharmacological targets. Experimental methods that have been used to predict the host-pathogen protein interactions include the yeast two-hybrid (Y2H) system, affinity purification (AP) [Plasmodium falciparum 3D7 interactions combined to machine learning techniques in order to better understand the mechanisms underlying the malaria disease.Infectious diseases represent a major public health challenge that results from molecular cross-talks between pathogens and their hosts. These cross-talks are mostly mediated by protein-protein interactions occurring between host and pathogen (HP-PPI). PPI correspond to physical interactions between proteins that represent the key elements of the infection mechanism and play crucial roles in the evolution of the infections, as they may turn the balance in favor of the spread of the pathogen or its clearance. Malaria is an infectious disease caused by one of the five types of the protozoan parasite Plasmodium with ion (AP)  and theiion (AP) . Moreoveion (AP) –19. DepeIn order to decrease false-positive predictions, we have performed an integrative computational approach by integrating five distinct OMICS based approaches and different datasets.Plasmodium falciparum 3D7 proteins for merozoite, ring, trophozoite, and schizont stages were extracted from PlasmoDB and from other studies published earlier [We collected lists of gene names related to mass-spectroscopic proteome analyses of human red blood cells (RBC) from two distinct resources. We have integrated 1,578 human proteins previously reported as expressed in RBC . We also earlier , 23. To  earlier . Moreove earlier . Finally earlier . In totahttp://instruct.yulab.org/ [http://ipfam.org [It is well established that protein domains are the key mediators of any protein-protein interaction. Exploiting domains as building blocks for PPI prediction have been widely used , 28. Sevlab.org/  and iPfapfam.org  providinPlasmodium falciparum 3D7 gene ontologies and annotation information were retrieved (On February 2020) from the Gene Ontology project website http://current.geneontology.org/annotations/index.html.Protein partners from PPIs may participate in related and/or similar biological processes . The genx, y) in source species, identify the homolog x′ in the host and the homolog y′ in the pathogen and then conclude that  pair also forms a PPI [http://www.phi-base.org/index.jsp) [http://www.reactome.org) [https://thebiogrid.org) [http://www.ebi.ac.uk/intact) [P. falciparum 3D7 collected from PlasmoDB (https://plasmodb.org/common/downloads/) against human protein sequences. Blastp Best reciprocal hit (BRH) approach with Evalue ≤ 10−5 was used for homology investigation [The rationale behind the homology-based method is that conserved interactions between a pair of proteins are expected to have interacting homologs in other species. The conserved interaction is called “Interolog.” Considering a template PPI pair (ms a PPI . Known hdex.jsp) , proteinome.org) , Biogridrid.org) , and Int/intact) . Homologtigation .http://bhapp.c2b2.columbia.edu/PrePPI) [http://www.compbio.dundee.ac.uk/SNAPPI/downloads.jsp) [http://scop2.mrc-lmb.cam.ac.uk/), [https://3did.irbbarcelona.org/) [Multiple studies used a structure similarity-based method and use template PPIs to detect similar interacting pairs within host and pathogen proteins . Such a /PrePPI) , SNAPPI-ads.jsp) , SCOP2 (ac.uk/), , and thena.org/) .Plasmodium falciparum 3D7 genes .Collected data from several databases were structured in different formats with different features. They were parsed to extract relevant information. Accession IDs in UniProt were mapped to HUGO Gene Nomenclature Committee (HGNC) symbols for human and PF IDs for Plasmodium. We also used PlasmoDB gff files to convert PF ID into aliases . Final results were presented into a tab-separated file format displaying the potential interactions between Human genes (HGNC symbol) and Human and Plasmodium proteins involved in predicted interactions using the present computational approaches were subject to gene set enrichment analysis in order to identify significantly enriched pathways. Functional analysis was performed using StringDB .Domain-domain interaction data collected (On February 2020) from the3DID database and other data from  were useK-nearest neighbors (KNN) classifier [Eight classifiers have been evaluated, namely, the assifier , the logassifier , the decassifier , the ranassifier , the adaassifier  classifiassifier , the Gauassifier , and theassifier .k-fold cross-validation is used. It is a powerful preventative measure against overfitting. K is fixed at 10, which means that the training dataset is divided into 10 equal parts and the process will run 10 times, each with a different holdout set. This allows us to keep our test set as an unseen dataset for selecting the final tuned model [Precision. It refers to the percentage of results, which are relevant. It is the ratio of correctly predicted positive observation to the total positive observationsRecall. It refers to the percentage of total relevant results correctly predicted points out of all the data pointsAccuracy. It is the number of correctly predicted labels out of all the class labelsF1-Score. It is the weighted average of Precision and Recall. It takes both false positive and false negatives into accountAUC. The area under the curve is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curveROC Curve. It plots true positive rate (sensitivity) on the y-axis and false positive rate (specificity) on the x-axisFor validation, ed model . To evalThe five performance measures are defined as follows:where TP, TN, FP, FN, TPR, and FPR represent true positive, true negative, false positive, false negative, true positive rate, and false-positive rate, respectively  host-pathogen putative interactions involving 4,609 distinct human proteins and 334 different parasite proteins.Considering the whole sets of distinct 2,430 plasmodium RBC-expressed proteins and 1,889 human RBC-expressed proteins integrated in the present analysis, the number of possible interactions across the host erythrocyte and the parasite proteins would tremendously be very large. To reduce false-positive predictions, we have combined appropriate approaches as previously detailed, thereby resulting in the prediction of probable host-parasite interactions. A detailed representation of the approach followed is shown in Among the 4,609 human proteins involved in the predicted interactions using at least three distinct approaches, only 366   were idee − 10), the ubiquitin-mediated proteolysis (hsa04120) (FDR = 4.10e − 09), the focal adhesion pathway (hsa04510) (FDR = 6.74e − 07), the regulation of actin cytoskeleton pathway (hsa04810) with an FDR = 1.04e − 06, and the bacterial invasion of epithelial cells pathway (hsa05100) (FDR = 1.93E − 06) and the spliceosome (hsa03040) (FDR = 1.73e − 06). Reactome-enriched pathways include the immune system (FDR = 2.51e − 20), the membrane trafficking pathway (FDR = 6.36e − 20), the vesicle-mediated transport (FDR = 1.13e − 19), and the adaptive immune system (FDR = 7.95e − 15). Among the most enriched protein domains found with SMART and PFAM, we report “the ADP-ribosylation factor family” (FDR = 1.06e − 28), “the RAS, ROC, DAP kinase domain” (FDR = 1.01e − 25), “the Ras family” (FDR = 7.97e − 25) and the “Gtr1/RagA G protein conserved region” (FDR = 2.53e − 15).The functional analysis of the previous network showed that the most enriched KEGG pathways include the endocytosis pathway (hsa04144)  , only 16e − 14, the metabolic pathway (pfa01100) (FDR = 0.0085), and the propanoate metabolism (pfa00640) (FDR = 0.0125). Among the most enriched Pfam domains, we report the PFEMP DBL domain (FDR = 1.35E − 22), the N-terminal segments of PfEMP1 (FDR = 2.11E − 21), the Duffy-binding domain (FDR = 7.18E − 21), and the acidic terminal segments, variant surface antigen of PfEMP1 (FDR = 1.09E − 20). Interestingly, P. falciparum erythrocytic stages and may represent good parasite therapeutic targets.The functional analysis (using an FDR cutoff of 0.05) of this network showed that the most enriched KEGG pathways include malaria pathway (pfa05144)  expressed in both ring and trophozoite stages, two proteins  expressed in both trophozoites and merozoites, 1 protein (PF11_0097) expressed in both trophozoites and schizont, 1 protein (PF11_0362) expressed in both ring and schizont, and 32 expressed in the 4 stages  and show that it is essential for cell intoxication [Plasmodium falciparum malaria parasites during erythrocyte invasion and that ABCB6 may mediate P. falciparum invasion through species protein-protein molecular interactions [P. falciparum merozoites, and the addition of recombinant ICAM-4 to parasite cultures blocks invasion of erythrocytes by newly released merozoites [P. falciparum infection. The existing overlap with previous studies consolidates the reliability and credibility of the present approach that could be applied to investigate other host-pathogen protein-protein interactions. We reported a set of proteins involved in transition from parasite stage to another including PFE1035c and PF13_0044 that are enriched in pyrimidine metabolism KEGG pathway and in PFEMP DBL domain (PF03011) (FDR = 0.00066), Duffy-binding domain (PF05424) (FDR = 0.00066), acidic terminal segments, variant surface antigen of PfEMP1 (PF15445) (FDR = 0.00066), and N-terminal segments of PfEMP1 (PF15447) (FDR = 0.00066). These proteins may present a good therapeutic target to avoid parasite transition state and stop parasite life cycle progress. A previous study reported the important role of the purine and pyrimidine pathways for P. falciparum cell growth and division. Indeed, the rapid rate of nucleic acid synthesis during the intraerythrocytic growth phase makes purine and pyrimidine metabolic pathways promising targets for novel drug development [Plasmodium falciparum hub proteins (Subsequently, we report a set of human-xication . A recenractions . Moreoverozoites . BCAM isrozoites . Given telopment . Furtherelopment . Moreoveproteins  having tPlasmodium protein-protein interactions using an integrative computational approach. We report a set of biologically relevant host-pathogen interactions that will enrich existing resources and may contribute to a better understanding of the etiology of the disease. The present approach is not restricted to a particular host or pathogen but can be applied for predicting other host-pathogen interactions unless gene expression data is available. The detailed interaction map between proteins from the pathogen and the human host would help to identify key hubs in the infection physiopathology process.Computational methods may play important roles in paving the way for experimental host-pathogen interactions verification by highlighting key potential interactions and limiting the experimental scope leading to expense reduction and rapid knowledge generation. Here, we investigated human-


Among the realm of repeat containing proteins that commonly serve as “scaffolds” promoting protein-protein interactions, there is a family of proteins containing between 2 and 20 tetratricopeptide repeats (TPRs), which are functional motifs consisting of 34 amino acids. The most distinguishing feature of TPR domains is their ability to stack continuously one upon the other, with these stacked repeats being able to affect interaction with binding partners either sequentially or in combination. It is known that many repeat-containing proteins are characterized by high levels of intrinsic disorder, and that many protein tandem repeats can be intrinsically disordered. Furthermore, it seems that TPR-containing proteins share many characteristics with hybrid proteins containing ordered domains and intrinsically disordered protein regions. However, there has not been a systematic analysis of the intrinsic disorder status of TPR proteins. To fill this gap, we analyzed 166 human TPR proteins to determine the degree to which proteins containing TPR motifs are affected by intrinsic disorder. Our analysis revealed that these proteins are characterized by different levels of intrinsic disorder and contain functional disordered regions that are utilized for protein-protein interactions and often serve as targets of various posttranslational modifications. The predominant “lock-and-key” model of protein functionality, according to which the biological function of a protein is determined by its highly ordered structure defined by its unique amino acid sequence , has obsThe emergence of informatics in general, with its ability to make information and its analysis more readily available, and bioinformatics in particular, by providing sophisticated databases and tools for protein analyses, led to a breakthrough in ID biology. Since the beginning of the 21st century, the number of publications about IDPs and IDPRs has skyrocketed, led primarily by publications from four research groups ,4,5,6. OAstonishingly, given its only very recent acceptance into the scientific world, the natural abundance of disorder in the proteome has been shown to be at approximately 60%, meaning approximately 60% of proteins in eukaryotes display at least some degree of disorder ,38,39,402 phenomenon ”. This search yielded 166 different human proteins, which were used to perform computational analysis for abundance of intrinsic disorder. Sequences of these proteins in FASTA format and some functional information was extracted from UniProt [https://www.rcsb.org/) [On 1 October 2019, all human TPR repeat-containing proteins listed in UniProt ( UniProt . UniProtsb.org/) .® VLXT [® VSL2 [® VL3 [http://www.pondr.com), and the IUPred computational platform that allows identification of either short or long regions of intrinsic disorder, IUPred-L and IUPred-S [® VLXT [® VL3 [® VLS2B [® FIT [Initially, human TPR proteins were analyzed by five per-residue predictors, such as PONDR® VLXT , PONDR®  [® VSL2 , and PON2 [® VL3  availablIUPred-S . We util [® VLXT , PONDR® T [® VL3 , PONDR® [® VLS2B , PONDR® B [® FIT , IUPred2B [® FIT ,135. TheNext, the outputs of two binary predictors, the charge-hydropathy (CH) plot ,14 and t2P2 database (http://d2p2.pro/) [2P2 database uses outputs of IUPred [® VLXT [® VSL2B [2P2 displays 9 colored bars representing the location of disordered regions as predicted by these different disorder predictors. In the middle of the D2P2 plots, the blue-green-white bar shows the predicted disorder agreement between nine disorder predictors , with blue and green parts corresponding to disordered regions by consensus. Above the disorder consensus bar are two lines with colored and numbered bars that show the positions of the predicted (mostly structured) SCOP domains [Complementary disorder evaluations together with important disorder-related functional information were retrieved from the Dp2.pro/) , which ip2.pro/) . D2P2 daf IUPred , PONDR®  [® VLXT , PrDOS [ [® VLXT , PONDR® [® VSL2B ,139, PV2[® VSL2B , and ESp[® VSL2B . The vis domains ,142 usin domains . Yellow  domains , whereas domains , which ihttp://string-db.org/. STRING generates a network of protein-protein interactions based on predicted and experimentally-validated information on the interaction partners of a protein of interest [Information on the interactability of human TPR proteins was retrieved using Search Tool for the Retrieval of Interacting Genes; STRING, interest . In the interest .https://string-db.org/) for each protein using a custom value of 500 maximum first-shell interactions at medium, high, and highest confidence levels . Resulting PPI networks were further analyzed using STRING-embedded routines in order to retrieve the network-related statistics, such as: the number of nodes (proteins); the number of edges (interactions); average node degree (average number of interactions per protein); average local clustering coefficient ; expected number of edges (which is a number of interactions among the proteins in a random set of proteins of similar size); and a PPI enrichment p-value .In this study, STRING was utilized in three different modes: to generate the inter-TPR network PPI interactions, to produce the TRP set-centered PPI network, and to create PPI networks centered at individual human TPR protein. Protein interaction networks were obtained from STRING . This analysis revealed that human TPR proteins, ranging in length from 151 to 4471 residues, contain variable levels of intrinsic disorder (1.9% to 74.9%), with disordered residues assembled into functionally important IDPRs. Disorder in these proteins is used for protein-protein interactions and serves as a signal for a variety of posttranslational modifications. Results of our analysis add a new perspective for better understanding of the functionality of TPR proteins, many of which have high disorder content. They are characterized by the presence of multiple PTM sites, disorder-based protein interaction sites that can undergo binding induced folding at interaction with specific partners, and numerous isoforms generated by alternative splicing. These features  are known to serve as a basis of the proteoform concept, where proteoforms constitute a set of structurally and functionally distinct protein molecules encoded by a single gene ,147. The


This article describes the tool, and illustrates its use in seeking candidate domains for transfer. It also briefly describes already‐published proof‐of‐concept studies in which the CRISPR effectors Cas9 and Cpf1 were placed separately under the control of tamoxifen and mefipristone, by including ligand‐binding domains of the Estrogen Receptor and Progesterone Receptor in modified versions of Cas9 and Cpf1. The advantages of drug control or the rival protein‐control technology of optogenetics, for different purposes and in different situations, are also briefly discussed.Optimizing synthetic biological systems, for example novel metabolic pathways, becomes more complicated with more protein components. One method of taming the complexity and allowing more rapid optimization is engineering external control into components. Pharmacology is essentially the science of controlling proteins using (mainly) small molecules, and a great deal of information, spread between different databases, is known about structural interactions between these ligands and their target proteins. In principle, protein engineers can use an inverse pharmacological approach to include drug response in their design, by identifying ligand‐binding domains from natural proteins that are amenable to being included in a designed protein. In this context, “amenable” means that the ligand‐binding domain is in a relatively self‐contained subsequence of the parent protein, structurally independent of the rest of the molecule so that its function should be retained in another context. The SynPharm database is a tool, built on to the Guide to P One is mathematical modeling, which tends to be most useful when the system and its interactions with a host cell are reasonably well understood.A third approach is to parameterize the system itself. Instead of being constructed with fixed components, at least parts of the system are made externally controllable, so that one physical version of the system can be used to explore different volumes of parameter space. This saves building many different versions. Most methods for doing this have centered on controlling the concentration of the protein in question by controlling the transcription of the gene that encodes it, using systems such as Tetracycline‐inducible operators  structures, and amino acids that are close to one another in space are not necessarily close to one another in the primary sequence of the peptide chain. If a drug‐binding site is formed by the spatial apposition of amino acids from many different parts of the peptide, brought to that location by the structure of the rest of the protein, it would be very difficult to re‐create the drug binding site in an engineered protein with different overall shapes and properties. The suitability of a known drug‐binding site for use in novel proteins therefore depends on the extent to which it is formed from a relatively self‐contained run of amino acids, forming a structure relatively independent of the rest of the protein. This “ligand‐binding module” can itself be highly folded, as long as it is relatively self‐contained.harmacology database, which has rich links to structural databases, we decided to build a tool to make identification of promising “drugability modules” easier.Scanning drug‐protein binding structures manually to find promising examples is possible but laborious. Given that we already curate the IUPHAR/ BPS Guide to P4https://synpharm.guidetopharmacology.org/. Its home‐page presents simple statistics about the number of ligands (drugs and drug‐like molecules) and natural protein targets about which it holds binding information , search boxes, and links to tutorials and other information. It contains no new information that is not in other databases; rather, it provides new ways to interact with that information. The manner in which it was constructed and populated has been described elsewhereThe tool we have constructed, SynPharm , is open to anyone to use at There are several possible search strategies but the most useful to the focus of this article—identifying modules to confer drugability on engineered proteins—is simply to click “search sequences that interact with a ligand.” Doing so results in an ordered table Figure , listingClicking on the index number of any protein brings up a more detailed page. The page provides a simple display of the relevant amino acid sequence, with the ligand‐interacting amino acids highlighted to show how they cluster (or do not). It also provides a rotatable 3D model of the protein with its bound ligand, which can be used to estimate how independent of the rest of the protein is the structure of the ligand‐binding site. The interaction of the human phosphodiesterase 5A with tadalafil, for example, involves a run of only 53 amino acids but the binding structure lies at the interface of two alpha helices, the angles of which would be likely to depend strongly on details of the rest of the protein , shown in Figure A famous example of ligand‐interaction domains that can be ported to other, engineered proteins is provided by nuclear hormone receptors. The ligand‐binding site of ESR1 , or the progesterone receptor, into both Cas9 and Cpf1 Figure .22 Est6The utility of the SynPharm resource will depend on several things. One is the popularity of engineering ligand control into proteins at all. At around the same time that SynPharm was being developed, alternative technologies for protein control were maturing quickly. Optogenetic technologies, in particular, have made great advances both in control of protein expression and control of protein function, particularly that of channels.The second influence on the utility of the SynPharm resource is the extent to which pre‐packaged “kit” approaches to introducing ligand control replace the need for individual protein engineers to do their own research and make their own decisions. At present, as far as the author knows, no such kits exist but if one module is developed that will work in a large range of protein hosts, then it may well dominate the field as, for example, the Tet‐operator system has come to dominate transcriptional control.


Malignant diseases have become a threat for health care system. A panoply of biological processes is involved as the cause of these diseases. In order to unveil the mechanistic details of these diseased states, we analyzed protein families relevant to these diseases.Our present study pivots around four apparently unrelated cancer types among which two are commonly occurring viz. Prostate Cancer, Breast Cancer and two relatively less frequent viz. Acute Lymphoblastic Leukemia and Lymphoma. Eight protein families were found to have implications for these cancer types. Our results strikingly reveal that some of the proteins with implications in the cancerous cellular states were showing the structural organization disparate from the signature of the family it constitutes. The sequences were further mapped onto respective structures and compared with the entropic profile. The structures reveal that entropic scores were able to reveal the inherent structural bias of these proteins with quantitative precision, otherwise unseen from other analysis. Subsequently, the betweenness centrality scoring of each residue from the structure network models was resorted to explore the changes in dependencies on residue owing to structural disorder.These observations help to obtain the mechanistic changes resulting from the structural orchestration of protein structures. Finally, the hydropathy indexes were obtained to validate the sequence space observations using Shannon entropy and in-turn establishing the compatibility.The online version of this article (10.1186/s12859-018-2552-0) contains supplementary material, which is available to authorized users. Biological processes are the results of highly orchestrated interactions among the biological macro-molecules. of A majority of these processes occurring within the confines of cell cytosol pivot around the coordinated functions of protein molecules. Encoded by the one-dimensional code script of DNA, the biological relevance of protein molecules reside on their three-dimensional structures. These three-dimensional protein structures arise after a series of sub-structural interactions primarily dictated by sequence information of the protein molecules. In many cases, proteins do not have a stable three-dimensional structures. These proteins are broadly known as Intrinsically Disordered Proteins (IDPs) , 2. IDPsProteins are being folded to perform specific functions. Sometimes acquired orderded globular structure may be accompanied by interaction with other proteins. The folding mechanism can be driven by different changes in protein environment. Since proteins are actively involved in different biological processes, a loss of protein structure and disruption in associated interactions can lead to a series of metabolic disruptions in turn inducing a pathological state . A wide Misfolding may cause numerous neurodegenerative and malignant diseases. Reports suggest –9, IDPs Multiple Sequence Alignment (MSA) of a protein family can provide a consensus sequence of that family which might be considered as family sequence representative with the most evolutionarily conserved set of amino acids. As the consensus sequences consist of evolutionarily conserved amino acid residues, so the consensus sequence of a protein family can represent the structural trait for almost all individual members of that protein family. Hence, the complexity score of a consensus in terms of disorder and order can summarize the structural trend of most of the individual proteins from a protein family.In this article, four diverse cancer types are considered, among which two are well known and frequent malignant diseases viz., Prostate Cancer –12, BreaIn this section, we have discussed the proposed framework. Two different databases were used viz., 1) UniProt  and (2)  UniProt UniProt  is a cumPfam  is anothFour cancer types were selected and their commonly responsible protein families in human, depending on the literature survey such as Heat shock protein beta-1 , 22, BAGPi was the probability of given amino acids and N was the number of letters in a sequence. The summation run over the 20 residues that normally were present in a protein sequence. The probability Pi represent the composition of the consensus sequence. So the entropy range lied between 0 and the log2(20)=4.32. If the Shannon entropy score of consensus sequences was less than 2.9 then it signify that, particular protein family was ordered [p-value. The p-value gives the probability of observing the test results under the null hypothesis. The lower the p-value, the lower the probability of obtaining a result like the one that was observed if the null hypothesis was true. The One Sample t Test is a parametric test. It is defined as: where  ordered , on the σ is the standard deviation of list entropy scores of a family. μ is the specified population mean of list of entropy scores of a family. where, Here, S2 is the sample variance, n is the sample size which is total number of proteins from a family. Furthermore, to understand this observation we tried to find the structure of these sequences [equences . These mequences . In thisFc was the threshold of interaction strength, the default value is 4%. Here, xij was the number of side chain atom pairs of residues i and j. Xi and Xj were the normalization factor for residues types i and j [ i and j , 43.In this paper, depending on the normal mode analysis (NMA) a correlation matrix was obtained in order to perform a cross-correlation matrix. Then by means of correlation network analysis, we generated structure networks  of diffeThe role of a particular node as a connector between other nodes viz., the importance of a residue to a network in its functioning as a bridging point can be manifested by measuring the number of shortest paths passing through that particular node. Betweenness centrality characterizes the regions of a protein that show differences in coupled motions between networks. Residues having significant contribution to intrinsic dynamics of the protein show high centrality value. Also depending on the centrality scoring, Euclidean distances among the protein mutant types were calculated and the subsequent hierarchical cluster was generated. The betweeness centrality was performed to find the bottlenecks in communication networks and community detection whereas the NMA was performed to generate structure networks of different protein depending on their tertiary structure.Reports suggested that selected eight proteins Additional file 2Shannon entropy score of proteins under Endophilin-B1 family responsible for Breast cancer. (XLSX 10 kb)Additional file 3Shannon entropy score of proteins under Breast cancer type 2 susceptibility protein family responsible for Breast cancer. (XLSX 10 kb)Additional file 4Shannon entropy score of proteins under Heat shock protein beta-1 protein family responsible for Prostate cancer. (XLSX 10 kb)Additional file 5Shannon entropy score of proteins under BAG family molecular chaperone regulator 1 protein family responsible for Prostate cancer. (XLSX 16 kb)Additional file 6Shannon entropy score of proteins und er Apoptosis regulator Bcl-2 protein family responsible for acute lymphoblastic leukemia. (XLSX 15 kb)Additional file 7Shannon entropy score of proteins under Calpain-type cysteine protease DEK1 protein family responsible for acute lymphoblastic leukemia. (XLSX 15 kb)Additional file 8Shannon entropy score of proteins under Cellular tumor antigen p53 protein family responsible for lymphoma. (XLSX 9 kb)Additional file 9Shannon entropy score of proteins under RNA-binding protein 38 protein family responsible for lymphoma. (XLSX 11 kb)


The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein–protein, protein–DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies. Protein structures are the support of essential biological functions. They are highly dynamic macromolecules and adopt an ensemble of conformations during their lifetime. Multiple resolution techniques have been elaborated to access their three-dimensional structures. X-ray crystallography and Nuclear Magnetic Resonance spectroscopy (NMR) are the most common and efficient resolution methods. The obtained structures are stored and freely available for the scientific community in the Protein Data Bank (PDB) , a widelNevertheless, a major difficulty to perform a proper benchmark for a specific method using resources, such as the PDB, is to ensure an unbiased protein dataset, i.e., specific non-redundant datasets must be produced. Multiple methodologies exist to evaluate and generate such non-redundant protein datasets using underlying amino acid sequence information, e.g., PDBSelect  or PISCEWhile most of the previous methods focus only on protein sequences, proteins bound to DNA, to RNA, to small molecules, or to amino acids containing post-translational modifications (PTM)  are moreToday, a few tools exist to gather proper protein-ligand complexes datasets. The Binding Mother of All Databases (MOAD)  includesPrevious work by Wallach and Lilien in 2009 . Small eHere, we propose a first quantitative evaluation of the structural redundancy observed in PDB focusing on protein-ligand complexes. Basic statistics on overrepresented proteins and molecules are derived. A specific clustering is performed to define the accurate number of unique complexes resulting in the generation of a refined dataset for molecular interaction studies or virtual screening protocols. Finally, we discuss and illustrate some of the surprising findings.The initial database query retrieved 110,735 interacting complexes from 3decision™ software. Multiple filtering steps such as ligand size, single bond ratio, protein chain size, and number of residues in contact with ligand  were performed. This phase contributed to a reduction of our initial dataset to 92,475 protein-ligand complexes see . These pUnsurprisingly, from the multimeric complex dataset, the most represented ligand in the PDB is the heme (PDB residue code Protoporphyrin IX Containing FE (HEM)) with 9088 occurrences, accounting for a representation of 7.9%. Expected popular ligands are present among the 10 most frequent molecules in the PDB, such as nucleobase derivatives, e.g., Adenosine-5’-Triphosphate (ATP), Nicotinamide-Adenine-Dinucleotide (NAD), Flavin Mononucleotide (FMN), S-Adenosyl-L-Homocysteine (SAH) see A. Interesingular dataset. Moreover, 15.7% of our dataset  fit this description , AMP, Adenosine-5’-Diphosphate (ADP), and ATP. Heme is slightly underrepresented compared to its overall distribution in our initial dataset with 174 cases for HEM residue code and 152 instances for Heme C (HEC) residue code. Moreover, 84.7% of these small molecules have only one representation in this unique dataset.Those 16,458 complexes represent 5239 distinct protein chains, 2232 only present once in this subset. The remaining 3007 protein chains feature, on average, 4.73 distinct ligands with a distribution largely unbalanced. As an example, two distinct binding sites of the carbonic anhydrase enzyme are interacting with 259 distinct ligands. One of these binding sites is illustrated in These singular complexes shown here highlight general tendencies towards specific protein targets in biological research field. Recurrent proteins bound with distinct ligands underlines important binding residues and conserved molecular fragment used in ligand optimization. For instance, 95.1% of prothrombin proteins found in the PDB involve residue W215 and 94.1% A190 in the binding mechanism. Other less frequent residues involve E217 in 55.0% of complexes and F227 in 24.5% of complexes see .The remaining units represent 88,319 conformations, i.e., 84.2% of our dataset. Conformations with identical ligands, protein chains, and similar binding sites were grouped and compared to each other within the group. At first, an initial number of 18,478 groups (of complexes) were generated containing between 2 and 501 conformations. As each group features at least two units, distribution of conformations count per complex was analyzed see . It shown being number of conformations. Over 1,130,000 superimpositions and weighted fragment root mean square deviation (wRMSDf) computations were performed. Groups were then split between homogeneous binding modes, i.e., identical conformations, and heterogeneous binding poses, i.e., sharing some similarity.Then, a structural alignment and quantification was performed between each unit within one group on common binding residues, e.g., f value below 1.0 Å and no distance between aligned fragments was greater than 1.5 Å.Homogeneous complexes are defined as groups for which each conformation is identical to each other, i.e., conserved binding modes. Each comparison among one group results with a wRMSDMoreover, 12,840 groups were considered as homogeneous in our dataset, equivalent to 48,075 units , displayed a significant number of distinct binding residues; thus, not selected for superimposition. Specific visual inspection of those cases indicates: (i) due to protein inner flexibility, these binding residues are identified in only one conformation as the detection depends on a distance threshold  have a pocket RMSD greater than 1.0 Å. These specific cases are largely caused by flexible secondary structure, such as loop highlighted in f is greater than 1.0 Å or one distance between compared fragments above 1.5 Å. To avoid unnecessary clustering, if every comparison within one group was greater than 2.0 Å, complexes were automatically considered as distinct. Clustering was performed in situations where both high and low wRMSDf values were observed to extract the representatives poses.Finally, the remaining 5638 complexes corresponding to 40,244 conformations (38.4% of our dataset), present at least one comparison where either wRMSDf > 2.0 Å), and (iii) 5423 representative conformations originated from complexes with scattered clusters size. The latter accounts originally for 2006 complexes represented by 20,283 conformations, with a high number of complexes represented by less than five similar representatives  2548 complexes  with only one cluster, i.e., one representative, (ii) 2360 unique conformations from complexes with only distinct conformations (wRMSDntatives . f of 0.37 Å (standard-deviation 0.18).The most redundant complex in our dataset, 501 distinct structures of heme bound to brain nitric oxide synthase, was clustered into one representative binding modes, with an average wRMSDf of 0.73Å, while alignments on other representatives display significant structural deviation . This observation illustrates an overall binding site rigidity in opposition to ligand flexibility. Overall, 1211 conformations from our initial 7642 representatives can be categorized as cautionary due to high flexibility in either the ligand or protein counterpart. Of course, the positioning of some of these ligands could be attributed to low resolutions or poor fitting of the ligands in the electron density map.Moreover, 1199 conformations displayed a ligand normalized B-factor value above 2.0Åt et al. . The avef for each conformation. Following the previous described step (singular dataset and representative of the two categories of non-singular datasets), the proposed non-redundant dataset of 39,629 complexes. Hence, this supervised approach leads to a pertinent and significant 2.64-fold reduction over the initial dataset. The final list  considering multiple binding sites per protein chain with the same ligand generating specific instances (ii) differentiating conformations through structural comparison of distinct binding modes.Our final dataset includes 31,846 complexes with only one binding mode observed in the PDB and 4366, characterized by multiple representative units. Moreover, 95.0% of those recurring complexes have less than five representative conformations. Similarly to sequence-based approach, our non-redundant dataset still retains 9997 distinct protein chains. Carbonic anhydrase 2, β-secretase 1, and cyclin-dependent kinase 2 proteins are among the recurrent complexes identified with 332, 297 and 272 occurrences respectively. These values are mostly due to the diversity of ligands crystallized with these proteins of high interest.To note, 16,771 distinct ligands are identified, with heme still being one of the most recurrent ligands. However, compared to the 9088 instances of heme observed in our initial dataset, clustering has reduced heme representation by a factor 8.4 see . SimilarThroughout this study, we’ve highlighted the diversity and conservation of ligand binding poses in complex with identical protein. The number of distinct binding modes in the PDB can be reduced to 39,629 complexes, a significant decrease compared to the initial dataset. While protein-ligand datasets were generated in the past such as scPDB , they onOur analysis features both structural analysis and supervised dataset generation that can be used to various degrees. Results and non-redundant dataset, available in supporting information, can be used for multiple purposes such as highlighting conserved and mobile residues in the binding mechanism for specific protein see . Ligand The multiple levels of redundancies observed across PDB relative to protein-ligand context have also been underlined. Specific protein chains are largely overrepresented and were analyzed thoroughly in our dataset. Hence, the 501 conformations of the most recurrent complex, heme bound to brain nitric oxide synthase, are all structurally identical from a binding perspective. Consequently, the 9088 occurrences of heme complexes can be represented by 1090 unique binding modes.Furthermore, our methodology is not biased by small discrepancies in the protein chain. Missing residues or insertion of a chimeric peptide still lead to similar binding modes that are well retrieved in our study. Structures available in poor resolutions can be validated by the co-occurrence of other redundant structures with high structural similarity for instance. We can also notice the specific interest of clusters of distinct ligand conformations. Indeed, they can reflect attractive dynamics and specifics of the binding affinity. Using different computational approaches such as scoring docking functions and molecular dynamics, they would be stimulating cases to apprehend their different binding affinities.f value of 0.8 Å. Pearson correlation coefficient computed across these two metrics in our dataset resulted in a correlation factor of 0.64, indicating a moderate correlation. Using wRMSDf in our case avoid this type of bias induced by annotations. A comparison between structural similarity metric can also be discussed. RMSD computed with atoms is generally used in structural comparison studies. This approach requires matching predefined atom labels to compute deviation distance. We were surprised to found high RMSD for many entries that were clearly identical. Indeed, sometimes atom labels were inverted across different entries leading to high distance over perfectly superposed fragment. Nonetheless, fragment RMSD has its own limitations, aromatic rings plane rotation for instance aren’t well characterized. Two perpendicular aromatic rings superimposed on their center of mass will not be quantitatively different despite the potential change in interaction geometry and, consequently, nature.Threshold used to define structural similarity can also be discussed. Indeed, evaluation of docking approach often uses a RMSD (atom-based) value less than 2.0 Å to consider a ligand pose similar to the native state. However, RMSD represent an average-like measure of similarity. Therefore, a value of 2.0 Å can highlight various cases: (i) a shift of the entire molecule as illustrated in The 128,843 protein structures were downloaded from the Protein Data Bank website; they were obtained by X-ray crystallography, NMR and cryogenic electron microscopy (cryo-EM) methods. They were processed and analyzed using our knowledge-based database Discngine 3decision™. Regarding NMR ensembles, each model was processed separately as a distinct protein-ligand conformation. Structures were annotated using multiple sources, such as UniProt , ChEMBL Those annotations allowed a precise description of the specificity of one particular protein chain, macromolecules and ligands, mainly defined as heteroatoms, across multiple structure entries. No condition filter was applied regarding the ligand type, natural or designed ligand. This decision, purely subjective, was done to offer the broadest possible view of the data available to the scientific community. Similarly, the 3decision software automatically detected crystal contacts observed at the ligand level, and ligands whose positioning was suspicious were not considered for the study. For our current study, complexes were selected by narrowing to a set of specific ligands. Only molecules with at least one ring, a molecular weight between 250 Da and 850 Da, and single bond fraction below 90%, were retained in this dataset. Ligands must have had no covalent bond to be selected. To discard ambiguities, protein chains interacting with the small molecule were characterized using their UniProtKB identifier [An initial dataset was constituted by taking all the protein chains binding to at least one ligand. One protein chain per ligand was considered. If one ligand was bound to multiple protein chains, i.e., multimeric complex, then each couple chain-ligand was split and considered as a distinct unit.Two complexes were grouped and compared to each other when both protein chains and ligands were considered as identical. In our case, two protein chains were identical when their UniProtKB was equal; ligands were considered as similar using their canonicalized SMILES fingerprint .Nonetheless, having two identical proteins and ligands doesn’t necessarily represent the same binding sites, e.g., one hypothetical case involving two identical ligands bound to the two extremities of the same protein chain. Thus, molecular contacts were calculated between protein chain and ligand atoms to highlight interacting residues for every unit. The classical sum of van der Waals radii + 1.0 Å between ligand and protein atoms was used as distance threshold. To be sure, we are comparing the same binding pockets; at least 3 common interacting residues across two different units were required to make a comparison.Thus, two similar complexes were superimposed through a structural alignment performed on common binding residues Cα. To perform such alignment, a minimum of 3 identical binding residues in the two different pockets was required. The superposition optimization followed the Kabsch algorithm  and was f) similarity metric was then computed given by the formula:A critical issue here is the heterogeneous atom labeling that can be a source of error in root mean square deviation (RMSD) calculation due to inverted atom labeling, particularly in ligands. To quantify structural similarity between two units, a custom root-mean square deviation (RMSD) was calculated on both ligands. Molecular fragments were generated from ligands with their corresponding center of mass calculated. Aromatic rings and functional groups were preserved while long aliphatic chains were split into multiple fragments. The Weighted fragment Root Mean Square Deviation  the superimposition quality, and (ii) the structural diversity in binding pockets, i.e., grasp the potential mobility of some residues in the pocket.f value below a threshold of 1.0 Å and (ii) no distance greater than 1.5 Å between two fragments’ center of masses. As large number of conformations can sometimes be compared between each other for highly recurrent complexes, hierarchical clustering (with complete-linkage as agglomeration method) was performed using wRMSDf measure as a distance metric using R 3.4.4 [Using these calculated metrics, rules were defined to characterize two units as similar: (i) a wRMSD R 3.4.4 . Protein-ligand conformations were finally clustered in three categories. Units that do not share similar ligand, similar chain receptor or similar binding residues, i.e., binding sites, with other complexes, were defined as singular complexes. Cases where every conformation of one complex was identified as identical, i.e., limited structural deviation, were labeled as homogeneous complexes. Groups of conformations resulted from clustering were qualified as heterogeneous.B-factors were retrieved from PDB structure and normalized as done in . In our Throughout this study, the structural diversity and redundancy of protein-ligand binding modes was assessed in the PDB for the first time. This survey of 104,777 monomeric complexes highlights the widely acknowledged redundancy in the protein-ligand context. Clustering and filtering processes have led to the description of 39,629 specific binding modes of unique protein-ligand complexes, a 2.64-fold reduction relative to the original dataset. While this type of study has been performed in the past on a smaller dataset, 2911 complexes by Drwal et al. , it is tf metric can be revised by quantifying the plane rotation shifting using basic atom distance-based matching method. Finally, a layered quantification of redundancy using protein classification such as species information or PFAM classification would allow to group complexes with highly similar binding site such as kinase for instance. We also would like to explore the possibility in future developments to look specifically at allosteric compounds implicated in the investigation of catalytic, binding, or regulatory exosite [Although the methodology offers great and innovative results compared to the approaches currently used, further improvements can be explored. For instance, RMSD of residue side chain can be computed to correlate with the observed molecular interaction. Ligand wRMSD exosite .


Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces.To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets.We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes. At the molecular level, protein receptors constantly interact with small-molecule ligands, such as metabolites or drugs. A variety of protein functions can be attributed to or regulated by these interactions . UnderstIdentifying conserved interactions between proteins and ligands that are reused across a protein family is a key factor for understanding molecular recognition processes and can contribute to rational drug design, target identification, lead discovery and ligand prediction. Interface forming residues (IFR) are residues in the molecular interface region between proteins. In accordance with Tuncbag et al. , proteinSeveral methods have been proposed to identify three-dimensional binding motifs. Here, we briefly review some recent works that are representative examples of the diverse techniques that have already been proposed.Previous solutions for detecting structural binding motifs for a set of diverse proteins and a common ligand involved protein superimposition based on the ligand and subsequent clustering of the conserved residues or atoms interacting with this ligand. The methods developed by Kuttner et al.  and NebeGonçalves-Almeida et al.  developeIn a similar manner, Pires et al.  used graThe motifs computed by the methods designed by Gonçalves-Almeida et al.  and PireDesaphy et al.  encoded Nakadai et al. , in turnRecently, a method has been proposed to extract three-dimensional binding motifs that capture information on the ligand and its surrounding residues in protein-ligand complexes. Since molecular function is frequently carried out by a limited number of residues that are reused in functionally conserved proteins during evolution, LibME  searchesIn this paper, we propose visGReMLIN , a user-friendly web server implementation of our GReMLIN method , which uTo detect common substructures, which are here called patterns or motifs, in protein-ligand interfaces, we devised a graph mining-based strategy that models the interface as bipartite graphs in which nodes represent atoms from the protein or ligand and edges represent non-covalent interactions between atoms. Nodes are labeled according to their physicochemical properties, and edges are labeled according to the type of interatomic interactions and distance criteria. Next, we perform a clustering analysis on these graphs, which is followed by frequent subgraph mining on each cluster to detect motifs.In addition, we propose a visual interactive platform to explore protein-ligand interactions and their motifs. The input module automatically searches Protein Data Bank (PDB)  for simivisGReMLIN is a large-scale, alignment-free strategy and its results do not depend on molecular orientation. Furthermore, our method does not specifically require a dataset of different proteins with the same/similar ligands or a dataset with the same/similar proteins and different ligands; it can be used for both types of datasets. In other words, visGReMLIN can be used with datasets containing promiscuous proteins or ligands.We implemented a whole new version of our strategy from scratch, improving unsupervised learning, frequent subgraph mining and the feature vectors that represent protein-ligand interactions. In addition, the visualization tool was reimplemented and new features added, for instance: (i) visGReMLIN takes as input any protein-ligand complex from PDB; (ii) specific ligands in which users are interested can be selected to compute the motifs; (iii) protein-ligand complexes that are not yet deposited in PDB can be uploaded; (iv) each user can store projects executed in his/her account, facilitating the reproducibility of the experiment; and (v) a user can share a project, allowing multiple users to view, explore and collaborate on the same project.In this section, we explain the visGReMLIN workflow. In addition, we describe the experimental design and datasets used to test and evaluate our strategy.Before starting to use visGReMLIN, the user needs to register and subsequently log in. This login is useful for organizing projects in the same place without the need to bookmark different submissions. Additionally, the user receives results via email once a process finishes.Creating a project, GReMLIN strategy and Data analytics visualization. Next, we refer to Fig. Figure Supply a PDB id and chain and let our tool automatically search PDB for similar complexes by selecting an alignment method and an identity percentage;Manually enter a dataset of previously selected complexes (type or copy and paste);Upload user’s own complexes in PDB format (structures that are not deposited in PDB).When starting a new project in visGReMLIN, there are 3 options for the user to provide a dataset of protein-ligand complexes  to be analyzed Fig.a:SupplyInput module is shown in Figure 1 of the Supplementary Material. In addition to selecting the input dataset, users can choose the cutoff they want to use for the interaction computation (see Section Graph dataset generation), the clustering algorithm and the evaluation metric (see Section Unsupervised learning).The visGReMLIN From now on we use the term GReMLIN to refer to our large-scale, graph-based strategy to detect motifs at the protein-ligand interface. GReMLIN is composed of three main blocks, which we describe in detail next.Creating a project, which is composed of a set of protein-ligand complexes  imaging and neutron diffraction. Although Woińska et al.  recentlyi,j), if the Euclidean distance between them is less than or equal to a cutoff distance. Thus, we perform contact computations between protein and ligand atoms and based on the physicochemical types of atoms (6 types) and the distance between them, we define the type of interaction established in a similar manner to Silveira et al. [http://www.chemaxon.com) at pH 7. It is important to point out that the user is allowed to select his/her own distance criteria, as shown in Figure 1 of the Additional file To compute protein-ligand interactions (PLI), we use a cutoff-dependent approach coupled with a distance criterion. According to previous works , 24, a ca et al. ; Santanaa et al.  and Fassa et al. . Table 1a et al. . Table 1G=, nodes can be divided into two disjoint sets, P and L, such that every edge in E connects a vertex in P to one in L [P) or ligand (L) and edges represent interactions (E) among them. Nodes and edges are labeled according to the physicochemical types of atoms and interactions. Figure Graph dataset generation gives these bipartite graphs as output.In a bipartite graph one in L . We modeThis block Figure c takes aTo summarize our dataset of graphs, we modeled the dataset of PLI graphs as a matrix that contains information from node and edge properties. For each graph, we generate a feature vector in which each position represents the presence of specific properties on a certain graph. Each vector position represents a pair of node properties separated by the edge distance (number of edges between the pair of nodes). To calculate this feature vector, we perform a breadth first search on each graph to obtain each non-cyclic path between all pairs of nodes. These paths are represented by their end nodes with their respective node properties and the distance between them. Figure To perform dimensionality and noise reduction in the matrix that represents the PLI graphs, we used singular value decomposition (SVD) , which iWe provide 3 options of clustering algorithms to the users, each of which is based on a different paradigm. K-medoids  is centrMotif prediction block  denotes the number of graphs in D that have g as a subgraph. The purpose of FSM is finding any subgraph g whose support(g)≥minSup (a minimum support threshold).The algorithm selected for FSM was gSpan , which isupport varying from 0.1 to 1.0 with a step of 0.1 (the amount by which the support varies) for each cluster. As support increases, we obtain subgraphs that are in a high fraction of the graph input dataset. Nonetheless, the number of total subgraphs tends to decrease. Additionally, as the support increases, the resulting subgraphs tend to become smaller, which is expected as it is difficult to find bigger patterns in the whole graph input dataset.By default, visGReMLIN runs FSM with Subgraphs resulting from FSM algorithms can be structurally repetitive, as a frequent subgraph can present other frequent subgraphs within it . In accoMotif prediction has as an output the PLI motifs and their mappings to the graph input dataset.The output of the FSM algorithm reveals frequent subgraphs and the graphs in the input dataset that they appear in. However, FSM does not provide a direct mapping from each node/edge of frequent subgraphs to the corresponding node/edge in the graphs from the input dataset. This mapping is interesting because it allows users to exactly identify the patterns in the dataset analyzed. To overcome this limitation of FSM, we map the maximal subgraphs to the input graph dataset using the subgraph isomorphism algorithm VF2 . The bloData analytics and visualization block  is a link to the structure on the PDB website. This module also has a text search functionality that displays only the lines that contain the entered characters in the table.In this module, we present a table that summarizes the unsupervised learning results, as shown in the Additional file Graph patterns table. As shown in the Additional file Grouping columns, we see a table in which the first column displays the Motif size (in number of nodes) segmented by the index of the group, and the second column shows the occurrences of the respective motif segmented by support value. By occurrences, we mean how many motifs were found with a specific size and considering a specific support. By clicking on the column label or on the group or support labels (colored lines inside the table), data can be sorted. A set of filters are offered in the panel Options to explore this table. One can Filter by group, Filter by minimum motif size, or Filter by minimum occurrences. By selecting Simple table, we see a heatmap table in which color is a pre-attentive attribute that encodes the frequency of motifs, as provided in the Additional file A summary of the PLI motifs is provided in the Motif view module, as presented in Figure Users can visually explore and make sense of PLI motifs through analytical interaction and navigation in the Options: provides a variety of filters to interact with the motifs. The common usage is selecting a support value and exploring motifs using the filters below:Free view or shows the PLI graphs in boxes containing PDB id, graph index, ligand name and group index by selecting Pattern viewFree view.View: displays PLI graphs without segmenting them by choosing Color nodes by: nodes can be colored according to the atom type or molecule to which they belong (blue for protein and red for ligands).Filter by atom type: atoms of the selected type are highlighted .Filter by interaction type: interactions of the selected type are highlighted .Filter by group: displays only graphs of the selected groups from unsupervised learning.Filter by vertex number: shows the graphs that contain the selected number of nodes.Motif graphs, this option removes the selection.Remove pattern selection: in case a motif has been selected in the panel Search for a residue, ligand, or atom: highlights nodes from the graphs that contain the residue/ligand/atom in the text search.Show node labels: enables or disables the display of labels for the nodes.Graphs legend: this panel presents the legends. Each atom type is associated with a specific color. The same holds for each interaction type.Input PLI graphs: PLI graphs depicting protein-ligand interfaces for a set of complexes are shown in this panel in accordance with filters from panel Options and with the motif chosen in Motif graphs. visGReMLIN shows, for each graph, PDB id and chain, graph index, ligand name and group index. By hovering the mouse over the graph, we see some details on demand, depending on the part of the graph:Protein nodes: residue name and number, atom name, chain, atom type. An example is provided in Fig.Ligand nodes: ligand name and number, atom name, chain, atom type as shown in Fig. Edges: information about connected atoms, interaction type and distance between these atoms in angstroms (Å). Fig.Motif graphs: in this panel, users can navigate through motifs, which are the frequent subgraphs from a dataset of protein-ligand complexes represented as graphs. By clicking on a motif, only PLI graphs that contain that motif are displayed on the panel Input PLI graphs. Additionally, the motif is highlighted in the context of the PLI graph. Figure Filter by group are shown.Interaction viewer) by clicking on the eye icon. Additionally, a general 2D visualization of ligands  and evaluation metric (silhouette coefficient). The support value selected was 0.6, which means that each motif was detected in at least 60% of the PLI input graphs of each group. To show the generality and real-world applicability of our strategy, we used 2 datasets of protein-ligand complexes with different aims.The PDB ids of the datasets used in both cases are provided in the Additional file Using the CDK dataset as an input, we created a project named CDK (available for access on the visGReMLIN website), which resulted in 276 PLI input graphs divided into 14 groups, as shown in the Additional file In Table Next, we present one interesting motif detected by visually inspecting the visGReMLIN results. Figure We created a project called ATP (available for access on visGReMLIN website) in which we use data from  as an invisGReMLIN found 2 motifs; one motif contains three acceptor/negative nodes connected by 2 repulsive interactions and appears in 17 input graphs. The ATP atom from this motif that interacts most often with the protein is O1G , followed by O2B  and O1A . Figure The other motif for ATP consists of two nodes, one donor/positive and the other acceptor/negative, which are connected by a salt bridge and were found in 19 input graphs. In this motif, the ATP atom that interacts most often with the protein is O1G , followed by O1B  and O2G, O2B and O1A . Fig. Of the 9 residues that He et al.  computatIn this paper, we present visGReMLIN, a user-friendly web-server that brings together a computational strategy to detect motifs at the protein-ligand interface and a visual interactive platform to explore and interpret such patterns. By motifs, we mean frequent subgraphs detected at the interfaces between proteins and ligands. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.To illustrate the ability of our strategy to support users in the detection and understanding of motifs, we conducted 2 use cases. In the first one, we used a dataset of 73 identical CDKs in a complex with a varied set of ligands. We compared our motifs to experimental results to show that visGReMLIN is able to find relevant atoms/residues that have been experimentally determined. In the second use case, we used a dataset of 50 complexes that involve the ATP ligand in a complex with different proteins. We performed a qualitative analysis to illustrate the visGReMLIN motifs for ATP, and we also compared our motifs to those found in a computational study. visGReMLIN found approximately 10% of these patterns. We believe that the visGReMLIN motifs differ because while the previous study aimed to find a unique motif composed of residues that summarize the binding for a specific dataset regardless of its size, visGReMLIN focuses on finding frequent structural arrangements of atoms (from the protein and from the ligand) at the interface between proteins and ligands.In addition, these use cases show that visGReMLIN can be used with a dataset of the same/similar proteins in complex with different ligands and with a dataset of the same/similar ligands in a complex with different proteins, which means that the tool can be used in scenarios involving protein promiscuity and ligand promiscuity. We believe that this functionality is an important result of our work, as many available methods are limited to one of these scenarios.As future work, we would like to investigate whether the motifs detected by visGReMLIN can be used to predict protein-ligand interactions. Considering that our strategy is able to characterize the interface motifs between a dataset of proteins and ligands, we are interested in using these motifs to help us to choose potential ligands for a specific protein. Additionally, we plan to systematically measure user insights and impressions about motif detection and the proposed visualization to help us improve visGReMLIN.Additional file 1 Additional figures and tables.