There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region.A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules.−1), but the overall average contribution of a single water to the protein-protein interaction energy is unfavorable . Analysis of the waters without favorable interactions with either protein suggests that this is a conserved phenomenon: 42% of these waters have SASA ≤ 10 Å2 and are thus largely buried, and 69% of these are within predominantly hydrophobic environments or “hydrophobic bubbles”. Such water molecules may have an important biological purpose in mediating protein-protein interactions.The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be independent of the crystallographic resolution. Interactions with residue backbones are consistent for all classes and account for 21.5% of all interactions. Interactions with polar residues are significantly more common for the first group and interactions with non-polar residues dominate the last group. Waters interacting with both proteins stabilize on average the proteins' interaction (−0.46 kcal mol Our analyses employ the HINT model for biomolecular interaction, which is based on experimental logP for 1-octanol/water partitioning. HINT simultaneously accounts for enthalpic, entropic and solvation contributions to biological association Together, HINT and Rank comprise what we have termed “Relevance”, a global metric for describing the conservation of water between unliganded and ligand-bound states The highest-level view is that there are three distinct roles for waters at these interfaces: bridging, i.e., having significant interactions with both proteins; non-bridging, i.e., having significant interactions with only one of the two proteins; or simply trapped without significant interactions with either protein. More detailed analyses may reveal additional details such as whether these classifications are dependent on the resolution of the underlying X-ray crystallographic experiment, e.g., are trapped waters more or less likely to be detected at high-resolution? We can also evaluate whether residue types have differences in interaction preferences for waters in these three categories, e.g., what residue types are most often involved in interactions with bridging waters? Water is unique in its ability to simultaneously provide two hydrogen-bond acceptor sites and two donor sites. Thus, it can effectively bridge in every way possible: donor-to-donor, donor-to-acceptor and acceptor-to-acceptor.2O-Arg and Glu-H2O-Arg interactions but Asp-H2O-Asp or Glu-H2O-Glu interactions are relatively infrequent, even compared to Asp-H2O-Glu. Also of note is that certain unfavorable interaction motifs are conserved. The results from this work have implications for the design of compounds that can break protein-protein interactions.In this report we describe a detailed analysis of protein-protein interfaces in 179 high-resolution (better than 2.30 Å) X-ray crystal structures of protein-protein complexes extracted from the RCSB Protein Data Bank The protein-protein complexes data set was obtained from the RSCB Protein Data Bank 2+, SO42–, etc., may affect the orientation of nearby water molecules, but should not appreciably impact whether and how those waters interact with the protein pair. While larger cofactors and ligands leave inclusion volumes at the interface when removed, only waters meeting the distance restraint are included in the data set, so the deletion of larger cofactors unlikely causes significant changes to water orientations or roles.The downloaded coordinate files were prepared by first removing ligands or cofactors other than water. About one-third of the interfaces examined in this study had cofactors or ligands at or near the interface. Smaller ionic cofactors, e.g., Mg−1 Å−1 while the non-hydrogen atoms were treated as an aggregate. Water molecules that were within 4.0 Å from atoms on both of the interacting proteins were retained with each protein-protein complex. Together, the water data set is comprised of 4741 unique water molecules, which is 5.4% of all waters in these complexes (ranging from 0.5% to 17.9%).Then, using Sybyl 8.1 TOTAL) is a double sum over all atom-atom pairs of the product (bij) of the hydrophobic atom constants  and atom solvent accessible surface areas (Si) for the interacting atoms, mediated by a function of the distance between the atoms:ij is a simple exponential function, e-rij is an adaptation of the Lennard-Jones function ij is a logic function assuming +1 or −1 values, depending on the polar (Lewis acid or base) nature of interacting atoms. HINT parameters and controls were as in previous studies i terms for backbone amide nitrogens by adding 30 Å2 was used in this study to improve the relative energetics of inter- and intramolecular hydrogen bonds involving these nitrogens. Water molecules are partitioned as a “solvent set” with analogous HINT parameters. Previous work −1 of free energy.Each model contains two proteins and an array of solvents, and was analyzed with HINT −1) with more than one residue on a protein, that water's count was fractionally distributed to interacting residues based on the absolute values of the relative HINT scores for those residues that interact with it, i.e., ic are the interaction HINT scores by residue type (i) interacting with water n. Similarly, the fractions of interactions with interfacial water molecules arising from backbone and sidechain atoms were calculated by weighted counts with Aic representing the interaction HINT scores by i, separated into c  =  sidechain or c  =  backbone subsets. Heat maps for frequency and interaction scores and map clustering were calculated and drawn with R Each crystallographically observed water's orientation was optimized by an exhaustive protocol n is the distance between the water's oxygen and the target's heavy atom n (n is the number of interaction hydrogen bond donor/acceptor (doneptor) targets up to a maximum of 4). This is scaled relative to 2.8 Å, the presumed ideal hydrogen bond length. θTd is the optimum tetrahedral angle (109.5°) and θnm is the angle between targets n and m . The algorithm thus allows a maximum number of 4 doneptor targets (≤ 2 donors and ≤ 2 acceptors). To properly weight the geometrical quality of hydrogen bonds, targets that have an angle less than 60° with respect to other  targets are rejected Rank represents the weighted number of potential hydrogen bonds for each water molecule with respect to a pseudo-receptor of atoms from the target molecule(s) surrounding the water. Rank is calculated as:R is the percent probability for water conservation based on Rank and PH the probability based on HINT score. WR and WH are the weights for these probabilities, respectively. The values for PR, PH, WR and WH are as shown in Relevance is a synthesis of HINT score and Rank 2. All such grid points are summed to obtain the atom's SASA.  was developed for this work based on the concept of Lee and Richards ASA. See .Protein-protein complexes are under intense scrutiny as possible targets for new therapies, particularly in cancer We the role(s) that water molecules can play are independent of the stage: water will interact favorably with up to two hydrogen bond donors and up to two hydrogen bond acceptors, and will generally avoid interaction with hydrophobic functional groups, regardless of whether these groups are in small organic molecules or in proteins.As described above, water Relevance et al.We applied the Relevance algorithm to the set of water molecules at protein-protein interfaces to understand their roles in these complexes. The water set for each complex was comprised of all water molecules that were within 4.0 Å of atoms in both proteins. This set, from 179 proteins, was comprised of 4741 unique water molecules, with between 1 and 69 waters (average 27) at the protein-protein interfaces. The orientation of each water molecule was exhaustively optimized et al. reported that 30% of waters at protein-protein interfaces are bridging, and while their definition of interaction is loose – the water must only be within 3.5 Å of a polar  protein atom to be counted as bridging More interesting are the evaluations of Relevance with respect to the partner proteins of the complexes. Applying this original definition of Relevance (≥ 0.5 for each protein) identifies only 43 waters (<1%) as bridging. Rodier both proteins, 53% (2514) have Relevance ≥ 0.25 with one member of the protein pair and 26% (1209) are not Relevant with respect to either (see vide infra). While the choice of 0.25 as a threshold to determine the Relevance/non-Relevance of a water molecule with respect to a single protein in a protein-protein complex is somewhat arbitrary  of the interface waters have Relevance ≥ 0.25 with respect to ther see . This surary see , values We used a data set comprised of protein X-ray crystal structures with resolutions better than 2.30 Å to construct a representative set of high-quality water molecules. The number of water molecules located and placed by crystallographers during refinement has been shown to be dependent on the resolution of the reflection data et al.ß- Cß cutoffs) at protein-protein interfaces that are generally similar except that Asp and Glu appear more than twice as frequently and Cys and Phe appear less than half as frequently in our water-mediated observations. Likewise, our results are in qualitative agreement with the report of Teyra and Pisabarro for “dual” and “wet” interactions between residues at protein-protein interfaces vide infra).Given the three general categories of interface waters we have described, the preferences these water molecules show for the types of amino acid residues within the interfaces were examined. First, for all interface waters, the preferences are tabulated by interaction counts . As expe−1); thus, the average interaction of a water with only one of its neighboring proteins would be half of that value, i.e., essentially negligible. −1), Glu , or Arg , vs. the cost of interacting with others, e.g., Pro , Ala , Met , Leu , Ile  or Val . The biggest surprise here is that Lys, while responsible for 4.6% of interactions with interface waters has, on average, a minimal contribution to the water score. This is partly because Lys, if NZ is protonated as expected, is only a hydrogen bond donor and is unable to accept from water, but also, the long hydrophobic polymethylene sidechain of Lys may be interacting unfavorably with some water molecules compared with the other “basic” residue Arg that has multiple polar atoms and can act as an acceptor through its sidechain π system. Also, Lys with its flexible sidechain is more likely to be disordered and its atomic coordinates are thus less certain. Furthermore, Jones and Thornton While optimizing and scoring, each water molecule in the present report was treated as a small ligand in a site defined by neighboring residues. The average HINT score for the waters in the entire data set is -17 , 9  and 236 , respectively , while on average the interaction with sidechain atoms is unfavorable . Obviously, this can be explained by the ability, although usually shielded by the sidechain, of the backbone to be both a hydrogen bond donor (via NH) and acceptor (via O). Teyra and Pisabarro −1) to 57  and 67  for waters Relevant to zero, one and both proteins, respectively. At the same time, the average sidechain interaction scores respond dramatically, increasing from −333  to −48  and 169 .However, the total score only tells part of the story and obscures the operational details on how the waters actually interact with the proteins. −1, excludes a small number of waters (<1.5%) from having any recorded interaction with one (or in rare cases both) of the proteins. As shown above, in 2O-Glu scores higher than Asp-H2O-Asp or Glu-H2O-Glu – or partner extensively with the hydrogen bond donor or amphiprotic residues , but not significantly with His or Trp. The most unfavorable pairings involve the most hydrophobic and aliphatic residues Ala, Ile, Leu, Pro and Val. The intermediate effect of Phe may be due to its aromatic ring being a potential hydrogen bond acceptor. The scores for waters with Relevance to neither protein  and 541 score units .Frequencies weighted as described in Cluster analysis of the matrices behind the heat maps of This analysis of 4741 water molecules at 179 protein-protein interfaces has revealed new information about the various roles that water can play at interfaces. Our analysis was anchored by the HINT free energy forcefield and the Relevance metric. The former characterizes the types and qualities of interactions between the interface waters and proteins, while the latter is a simple parameter that was previously shown to identify water molecules conserved/non-conserved in ligand binding sites Previous studies of water in the interface between interacting proteins have generally relied solely on interatomic distances in non-protonated crystallographic models to mark interactions between waters and proteins. This approach, however, often poorly represents the complex and subtle energetics and geometric preferences of hydrogen bonding. Thus, we performed this study with all atoms after exhaustive optimization of all water orientations de novo which water molecules are or will be energetically important. Only 59 (33%) of the protein-protein complexes have an overall favorable water contribution considering all interface waters, but 145 (81%) have a favorable contribution from waters Relevant to one/both proteins and nearly all, 173 (97%), have a favorable contribution from waters that are Relevant to both (the other 6 protein pairs have no waters of this class). The average scores are: -2072 , -84  and 1297  for the water sets at these interfaces Relevant to 0, 1 and 2 proteins, respectively. While each water at each protein-protein interface should be evaluated for its own specific environment and role, the overall analysis shows that the total water contribution can be quite important: ranging up to 5845  per protein pair for the water sets Relevant to both proteins and presumably “bridging”. Also, the Relevance-based classification scheme we have proposed certainly has merit for facilely selecting waters that should be considered in modeling protein-protein complexes.This is an important question since most protein-protein docking utilities ignore the actual  presence of water at putative interfaces. Unfortunately, it is difficult to determine et al. 2) that appeared to be bridged by waters at the TEM1/BLIP (1jtg) interface; only six of the eight pairs are truly bridged by water (residue-residue distance > 3.8 Å), yielding an average ΔΔGKA−1, i.e., essentially having an energetically neutral effect on interface stability much as shown above  for an average interface water in our analysis. Only four waters support these six pairs because two of the waters interact with more than one residue on one of the partner proteins (one highly Relevant to both proteins and the other Relevant to only BLIP), and it is thus impossible to isolate the specific energetic contribution from experimental double mutant data for these two waters. Of the remaining two waters, our analysis shows that one (HOH72) is Relevant to only TEM1 and the other (HOH111) is not Relevant to either protein, supporting the view that the former is strongly associated with TEM1's Glu104 and weakly associated with BLIP's Ser146, while the latter is only weakly associated with Gln99 and repulsive with respect to Ser128. Even here, interpretation is not straightforward: mutating these residues to Ala may or may not excise the putative bridging waters, just change their environment. In fact, there may even be space for more than one water in some of the double mutant complexes.The energetic role of bridging water molecules at interfaces is clear and well understood, although difficult to experimentally quantify Another, more subtle, role is that bridging waters also serve as nano-scale pH buffers see . By simpet al.2 to define an internal or buried water, Rodier et al. estimated that 71% of interface waters in complexes are in these rings et al. for all waters in the data set – 64% of the waters have SASA > 5 Å2. However, this definition is somewhat arbitrary: 50% have SASA > 10 Å2 and only 42% have SASA > 15 Å2. Note that, because all water molecules in the interface set are used in defining the non-available volume, our algorithm calculates low accessibility for water molecules clustered together in isolated non-accessible pockets. Also, the presence of void spaces, including those from (deleted) ligands or cofactors, near waters in these pockets would overestimate their solvent accessibility. The average SASAs for Relevance 0, 1 and 2 waters are 16.7, 14.2 and 13.9 Å2, respectively.The majority of water molecules in this study appeared to be largely associated with one or the other of the interacting proteins. Rodier 2 and SASA ≤ 10 Å2, somewhat higher fractions of buried waters with Relevance 1 than with Relevance 2. As stated above, the ring waters may be considered part of the bulk water network that just happens to reach into an interface neck, which would stabilize waters that have little or no viable interaction with protein  the interface are trapped in hydrophobic environments or what we term “hydrophobic bubbles”. This is 7.4% of all waters in the data set.et al. described the role of some waters found at the 1.77 Å structure of the colicin E9 endonuclease-immunity protein 2 interface as “aggravating” the binding between the two proteins 2 surface contact area) of waters were found in the two groups 2 and this is independent of Relevance class. It appears that protein-protein interfaces, independent of the longevity of their association, commonly include water molecules that do not have favorable interactions with either protein, although the possibility that some or maybe even many of these waters are incorrectly assigned electron density or other crystallographic artifacts cannot be completely discounted It would appear that these hydrophobic bubbles represent a conserved motif. One intriguing possibility is that a certain amount of instability is required in protein-protein interfaces to ensure that the associations are dynamic. Meenan et al. recently reported 2O-Glu interactions, in waters Relevant to one or both proteins, is notably higher than Asp-H2O-Asp or Glu-H2O-Glu interactions. This suggests that water molecules may act as spacers to effectively lengthen Asp sidechains to mimic Glu sidechains. We observed a similar role for Asp+H2O in protein/DNA interactions The principle of correlated mutations is that interface contacts co-evolve to maintain or enhance biologically important associations Water Relevance may be used as a metric to predict the locations of water molecules computationally. We previously described de novo predict their structure. One additional consideration is adjusting the ionization states of residues involved in dry and wet interactions. For this, we are currently adapting our Computational Titration procedure This work represents one small piece of our overarching goal of estimating the energetics of protein-protein associations and being able to Figure S1Algorithm for calculating solvent accessible surface area (SASA). (A) grid is constructed around water (blue) of interest including atoms from two proteins (green and red); (B) grid boxes fully or partially occupied by atoms  are set as unavailable; (C) a set of spheres (yellow dashed lines) centered (black circles) at a distance rVdW + rsolvent from the water of interest are constructed (rVdW  =  rsolvent  =  1.4 Å in this case); (D) if entire volume, i.e., all grid boxes, of one of these spheres is available, then the surface area represented by the center of that sphere (black cube) is solvent accessible. All such areas are summed to obtain the SASA for the water molecule of interest.(TIFF)Click here for additional data file.Table S1Protein complexes examined in study with interface parameters and water roles.(PDF)Click here for additional data file.Table S2Water Rank, HINT score, Relevance and solvent accessible surface area for full data set.(PDF)Click here for additional data file.Table S3Water Rank, HINT score, Relevance and solvent accessible surface area for low-resolution data set.(PDF)Click here for additional data file.Table S4Water Rank, HINT score, Relevance and solvent accessible surface area for homo-dimer data set.(PDF)Click here for additional data file.


These results show that our approach can combine complementary strengths of most widely used BLAST-based function prediction methods, rarely used in function prediction but more sensitive profile-profile comparison-based homology detection methods, and non-homology-based domain co-occurrence networks, to effectively extend the power of function prediction from high homology, to low homology, to no homology (ab initio cases).Predicting protein function from sequence is useful for biochemical experiment design, mutagenesis analysis, protein engineering, protein design, biological pathway analysis, drug design, disease diagnosis, and genome annotation as a vast number of protein sequences with unknown function are routinely being generated by DNA, RNA and protein sequencing in the genomic era. However, despite significant progresses in the last several years, the accuracy of protein function prediction still needs to be improved in order to be used effectively in practice, particularly when little or no homology exists between a target protein and proteins with annotated function. Here, we developed a method that integrated profile-sequence alignment, profile-profile alignment, and Domain Co-Occurrence Networks (DCN) to predict protein function at different levels of complexity, ranging from obvious homology, to remote homology, to no homology. We tested the method blindingly in the 2011 Critical Assessment of Function Annotation (CAFA). Our experiments demonstrated that our three-level prediction method effectively increased the recall of function prediction while maintaining a reasonable precision. Particularly, our method can predict function terms defined by the Gene Ontology more accurately than three standard baseline methods in most situations, handle multi-domain proteins naturally, and make  In the genome era, high-throughput genome, transcriptome, and proteome sequencing is generating an enormous amount of omics data such as gene and protein sequences. Since experimental characterization of these proteins can only be carried out at a selectively small scale, large-scale and high-throughput computational prediction methods are needed to annotate the structures and functions of most of these proteins in order for the biomedical research to effectively utilize this vast resource to study genotype - phenotype relationships. To fill the gap, a variety of computational methods have been developed to predict protein function from protein sequence from different perspectives.The most commonly used approach to function prediction is based on sequence homology. It uses a sequence comparison/alignment tool to search a target protein sequence against protein sequences with known function annotations in a protein database, and if some homologous hits are found, their function annotations may be transferred to the target protein as predictions. GOtcha , OntoBlaPhylogenetic relationships between proteins have been proven to be helpful for inferring protein functions -9. ParalApart from homologous relationships mentioned above, network-based methods exploit other relationships stored in protein networks. Assuming that neighboring proteins interacting in a protein-protein interaction (PPI) network may have similar protein function, some early network-based methods use the functions of the direct (radius-one) neighbors of a target protein in a PPI to infer its function. More advances in this direction include the consideration of statistically enriched functions within neighbors ,16, the In an effort to directly link a protein with its function, machine learning methods, such as Support Vector Machines (SVM) and Artificial Neural Networks, have been developed to predict protein function from scratch. Machine learning methods usually generate features from protein sequence, secondary structure, hydrophobicity, subcellular location, and solvent accessibility, and then use these features as inputs to train a classifier to assign proteins to a number of predefined function categories. ProtFun  aims to http://biofunctionprediction.org/) . One caveat is that multiple GO terms having the same confidence score received the same average rank. For example, if the confidence scores of top three GO term A, B, C are 0.9, 0.8, 0.8, respectively, the rank of them will be 1, 2.5 (i.e. (2+3)/2), 2.5, respectively. The top n groups (GO terms in each group have the same confidence scores) of GO terms were used for evaluation, which makes the actual number of GO terms possibly higher than n. The precision and recall of a protein i were calculated as:We evaluated these methods using precision and recall of GO terms on top n predicted GO terms and the actual GO terms  of protein i were propagated to the root of the Gene Ontology Directed Acyclic Graph (DAG). All the GO term nodes present in the paths of predicted GO terms toward the root were considered predicted GO terms; and all the GO term nodes existing in the paths of the actual GO terms toward the root were considered as true GO terms. The overlapping GO terms/nodes between predicted ones and true ones were considered correctly predicted nodes. For each n in  at step size 0.01, we calculated a series of precision-recall pairs for each of six predictors.We also calculated precisions and recalls according to a sliding threshold scheme, in which only the predictions with confidence scores higher than a threshold value Figure ab initio situation may be low.In order to further analyze the amount of contributions made by profile-sequence alignment (PSI-BLAST), profile-profile alignment (HHSearch), and domain co-occurrence networks (DCN), we plotted a precision-recall curve of predictor 1 in Figure g1 and g2, we obtained their paths r1 and r2 to the root of the GO DAG, and calculated the similarity score between g1 and g2 as:In addition to precision and recall measures based on the exact match of GO terms, we evaluated these predictors in terms of semantic similarity between true GO terms and predicted GO terms. For two GO terms θ(r1) and θ(r2) denotes the number of GO terms of paths r1 and r2, respectively. The numerator is the number of common GO terms shared by paths r1 and r2. For a target protein, every predicted GO term was compared with each of the true GO terms to calculate similarity scores; and the highest score was considered as the similarity score between a specific predicted GO term and the actual GO terms. Averaging the similarities over all predicted GO terms of a target generated the similarity score for the target. The average similarity scores of all the target protein were used as the prediction similarity score of a predictor. We computed the similarity scores of all predictors for top 1st, 2nd, ..., 20th ranked, predicted GO terms respectively. It is worth noting that, because the GO terms that have the same confidence scores get the same rank, the set of top 1st .. nth ranked GO terms may actually contain more than n GO terms. The average similarity scores of these predictors were plotted in Figure st .. nth ranked GO terms selected for evaluation than the former. In addition to the average similarity score for each target, we also calculated the best similarity score of a target - the highest similarity score among all GO term predictions for the target and averaged the best similarity scores over all the targets for each predictor. Figure where Mus musculus (house mouse). Figure http://spdbv.vital-it.ch/). In order to make function prediction, the DCN method executed PSI-BLAST to search the target protein against our pre-built protein sequence database containing the proteome of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, 15 plants, and 398 bacteria species (detailed organism names can be found at . The second level applied a profile-profile alignment tool HHSearch .where H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, 15 plant species, and 398 single-chromosome prokaryotic organisms  to search against both Swiss-Prot and the Gene Ontology database, and additionally applied the DCN-based "aggregated neighbor-counting" method  to make found at ). The orfound at  using a For the domains of the target protein detected by HHSearch at level two, the predictor gathered the GO terms of their radius-one neighboring domains in this DCN as predictions, whose confidence scores was proportional to their occurrence frequencies. If no GO terms could be found in radius-one neighboring domains, it extended to the search to radius-two neighboring domains and made predictions according to the same procedure. The confidence of a predicted GO term was calculated asf is the occurrence-frequency of the GO term - the number of the neighboring domains that have the GO term function divided by the total number of occurrences of all predicted GO terms. Thus, the confidence score of DCN-based predictions is in range  of HHSearch was used as in Predictor-1. We assigned weights 4, 2, and 1 to a GO term generated by PSI-BLAST, HHSearch, and DCN-based "aggregated neighbor-counting" method, respectively. The weighted frequency of each GO term was calculated and normalized. The normalized score was used as the confidence score of an individual GO term. For proteins without any predictions generated using these three methods, an additional PSI-BLAST search of the protein against Gene Ontology and Swiss-Prot with the default e-value threshold (i.e. 10) was executed in order to gather more hits if possible.Only a PSI-BLAST search against Swiss-Prot with the default threshold (i.e. 10) was performed in predictor 3. All of the PSI-BLAST hits were included to make prediction. The occurrence frequency of a GO term among all hits was used as its confidence score.The authors declare that they have no competing interests.JC conceived and designed the method and the system. ZW implemented the method, built the system, carried out the CAFA experiments. RC, ZW, JC evaluated and analyzed data. ZW, RC, JC wrote the manuscript. All the authors approved the manuscript.


Genomics and proteomics experiments produce a large amount of data that are awaiting functional elucidation. An important step in analyzing such data is to identify functional units, which consist of proteins that play coherent roles to carry out the function. Importantly, functional coherence is not identical with functional similarity. For example, proteins in the same pathway may not share the same Gene Ontology (GO) terms, but they work in a coordinated fashion so that the aimed function can be performed. Thus, simply applying existing functional similarity measures might not be the best solution to identify functional units in omics data.i.e. proteins in the same pathways, co-localized proteins, and protein complexes, with statistically significant score values showing a better accuracy than existing functional similarity scores. The scores are also capable of detecting protein pairs that interact with each other. It is further shown that the functional coherence scores can accurately assign proteins to their respective pathways.We have designed two scores for quantifying the functional coherence by considering association of GO terms observed in two biological contexts, co-occurrences in protein annotations and co-mentions in literature in the PubMed database. The counted co-occurrences of GO terms were normalized in a similar fashion as the statistical amino acid contact potential is computed in the protein structure prediction field. We demonstrate that the developed scores can identify functionally coherent protein sets, We have developed two scores which quantify the functional coherence of sets of proteins. The scores reflect the actual associations of GO terms observed either in protein annotations or in literature. It has been shown that they have the ability to accurately distinguish biologically relevant groups of proteins from random ones as well as a good discriminative power for detecting interacting pairs of proteins. The scores were further successfully applied for assigning proteins to pathways. Elucidating the role of proteins is a central problem in molecular biology. Computational methods play indispensable roles in various aspects of the functional elucidation of proteins, including database searches ,2, captue.g. limited coverage in genome annotations and the need for homologous proteins [Realizing weaknesses of conventional homology search methods, proteins -20, variproteins -27, thosproteins , and metproteins ,28-35.Besides function prediction, computational methods are also required for the interpretation of large-scale experimental data in the biological context . Omics det al. have developed a score named funsim, which combines the similarity of GO terms in BP and MF domains based on IC of LCA [To enable the above mentioned computational function analyses, it is necessary to establish a measure that quantifies functional associations between proteins. Controlled vocabularies of annotation terms, such as the Gene Ontology (GO) , provideC of LCA . In the functional similarity may be suitable for certain purposes, e.g. for evaluating the accuracy of function prediction or for investigating functional similarity between a particular protein to others (e.g. homologous proteins). However, the situation can be different in omics data analyses, where many genes rather than a pair need to be handled to identify the set of gene products that are working in functionally coherent fashion. Functional coherence is exhibited in biologically relevant protein sets, for example, in the same biological pathways, subcellular localizations, the same protein complexes, proteins involved in the same stage of development, and disease. Importantly, proteins in a functionally coherent set may not necessarily have the same or similar GO terms in all the three GO domains, but their GO terms should be coherent with respect to each other so that the aimed function can be performed in a coordinated fashion. As an illustration, consider proteins in the same KEGG pathway. These proteins have different MF annotations because they carry out different enzymatic reactions. Moreover, interestingly, in general they also do not necessarily share a common BP annotation. For example, the pyruvate metabolism pathway (KEGG pathway ID: 00620) has 33 proteins, which are annotated with 48 unique BP domain terms. Among them there are only 8 proteins that are annotated with pyruvate metabolic process (GO:0006090) and each of the rest of the 47 GO BP terms are assigned to fewer number of proteins. The data for all the 101 KEGG pathways of yeast has been made available as Additional File The pairwise et al. treated BP annotations of proteins as a vector of GO terms and computed pairwise protein similarity using the cosine distance [et al. have extended the concept of pairwise common ancestors of GO terms to the set of most specific common ancestors of the annotation sets of two proteins [et al. [There are only a handful of previous works done for assessing the functional coherence. A type of related works consider GO terms that are enriched in a protein group ,35,53,54distance . They coproteins ,58. They [et al.  performe [et al.  to reprefunctional coherence and not necessarily the similarity. Recently the GO database has newly introduced the relationships between Molecular Function (MF) and Biological Process (BP) domains to represent biological knowledge about the pathways and roles of genes [In this work, we propose two association scores for GO terms, which are aimed to evaluate the functional coherence of sets of proteins. The proposed scores quantify the associations of GO terms as the frequency of co-occurrence in two different biological contexts, in the GOA  protein of genes . Comparei.e. proteins in the same KEGG pathways, cellular locations, and protein complexes better than the above mentioned existing methods. In addition, we also show that these functional coherence score can accurately assign proteins to the KEGG pathways where the proteins belong. The current approach can be easily applied to other biological data sources to mine the associations and other ontologies as well, since it is not assuming any underlined structure in the ontology.We demonstrate that the developed association scores can identify functionally coherent protein sets, We have developed two function association scores, the CAS and the PAS. The CAS quantifies the frequency of GO terms that co-occur in the gene annotations, while the PAS takes into account co-occurrence of GO terms in the PubMed abstracts. The Gene Ontology database used in this study contains 17,316 Biological Process (BP), 2,534 Cellular Component (CC), and 9,428 Molecular Function (MF) domain terms, which result in a total of 29,278 terms. Among over 857,201,284 possible GO term pairs, 5,610,201 pairs (0.654%) obtained a non-zero value for the CAS while 3,320,265 pairs (0.387%) had a non-zero PAS.A characteristic of the CAS and the PAS is that they capture the cross-domain associations between the GO terms. Out of 5,610,201 non-zero CAS, 1,996,485 (35.6%) are for cross-domain term pairs. As for PAS, which has in total of 3,320,265 GO terms pairs with non-zero scores, 1,194,900 (36.0%) are cross-domain terms. Distributions of GO term associations within the same domain Figure  and for establishment of mitotic sister chromatid cohesion and GO:0030892 mitotic cohesin complex where we observe higher CAS (19.3644) corresponding to higher PAS (4.2390). But for some cases, such as GO:0000404 loop DNA binding and GO:0032139 dinucleotide insertion or deletion binding, we obtained a higher CAS of 116.1866 and a lower PAS of 3.6299 × 10-6. Also for some cases like GO:0019219 regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process and GO:0034404 nucleobase, nucleoside and nucleotide biosynthetic process we find a lower CAS (0.00679) corresponding to a higher PAS (10.3277). Thus with the use of two scores that are based on different data sources we are able to capture much diverse relationships between terms.Figure GO:0051095 regulation of helicase activity and GO:0043570 maintenance of DNA repeat elements, GO:0000920 cytokinetic cell separation and GO:0034407 cell wall 1,3-beta-D-glucan metabolic process in the BP domain by CAS , Q01158 )), which emits florescent light. The second annotations are for chloroplastic ATP synthase gamma chain 1, (UniProt AC: Q01908). This protein is a component of the CF0 complex, which is embedded in thylakoid membrane and is important in regulating ATPase activity [part-of relationship from Molecular Function (MF) term to the Biological Process (BP) term in which proteins plays the role [part-of relationships in the latest version of GO database (2011-06). Thus the CAS and the PAS can be used to computationally obtain missing process-function links, for example, GO:0019064 viral envelope fusion with host membrane and GO:0046812 host cell surface binding, that can help increase the completeness of the relationships between the GO vocabulary terms.In Table activity . The nexactivity . The fouactivity . The fifactivity ,68. The the role . The latGO:0001735 prenylcysteine oxidase activity is frequently mentioned in literature discussing a protein that plays a role in GO:0030328 prenylcysteine catabolic process.The latter ten examples are cases where the PAS is higher than the CAS. Since the PAS ranges at lower values than the CAS Figure , the subIn Table QuickGO , which iTo summarize, the CAS and the PAS have moderate correlation with an existing score, funsim. The CAS and the PAS capture associations within the same domain as well relationship between cross-domain GO terms unlike funsim, which only defines the similarity between pair of GO terms from the same domain. Notably, CAS and PAS capture many biologically relevant cross-domain GO term associations , which are aimed to capture the biologically related protein sets. Significance of the coherence scores is decided based on how well they are able to separate the biologically relevant protein sets from the randomly generated protein sets. This experiment is to show the proof of principle for the proposed functional coherence scores. We have used three datasets of functionally coherent protein sets in yeast, namely, KEGG pathways , proteinet al. [et al. [GO:0001948 glycoprotein binding and GO:0030492 hemoglobin binding is their deepest shared GO ancestor term GO:0005515 protein binding. See Methods for derivation of the Chagoyen and the Pandey scores. For all the five scores, the coherence of a set of proteins is defined as the average of the scores for all the pairs of proteins.In addition to the CAS and the PAS coherence scores developed here, we have also used three existing functional similarity scores, the modified funsim score , a scoreet al.  , Pentose and glucuronate interconversions , Ethylbenzene degradation , and alpha-Linolenic acid metabolism .In Figure Similar trends were observed for the protein complex sets Figure  and the i.e. more significant) value than the PAS p-values for many cases in the KEGG pathway sets  used to describe the same biological process. Also proteins in the same group in the GOcc dataset have the same CC term by design. Here we reevaluate the CAS and the PAS coherence score for the KEGG pathway dataset and the GOcc dataset by excluding the apparently related GO domain. Note that the other three scores compared in Figure Figure The drop of the scores Figure  certainlNext, we test the proposed functional coherence scores on the protein-protein interaction (PPI) networks of yeast and human. We examine if the scores are able to detect the interacting proteins (true positives) as opposed to the non-interacting protein pairs (true negatives). The yeast PPI network contains 72,053 interacting protein pairs while 33,099 interactions are included in the human PPI data (see Methods). The same number of non-interacting protein pairs as the interacting protein pairs are extracted from the proteins included in the PPI networks. The p-value for pairs of proteins is computed for the CAS (Eqn. 3), the PAS (Eqn. 4), the funsim (Eqn. 11), the Chagoyen (Eqn. 17), and the Pandey (Eqn. 21) scores, and they are sorted in ascending order of the p-value. Then we computed the Receiver Operator Characteristic (ROC) curves for each scores on the yeast and the human PPI datasets.The results on the yeast PPI dataset Figure  show thaBP (Eqn. 15) Chagoyen_coherence (Eqn. 19), and the Pandey_coherence (Eqn. 24), the CAS and the PAS were also computed without the BP annotations, CAS(BP-) and PAS(BP-). This is to remove the potentially apparent information of pathways encoded in the BP terms (i.e. proteins in the same KEGG pathway share the same BP terms in many cases). As for the funsim score, we have also used only BP annotations, which is referred as GOscore_coherenceBP, because the funsim score did not perform well in the previous experiments in Figures Finally, we used the functional coherence scores to predict the most likely KEGG pathway in which the protein plays a role. For a query protein the coherence score is computed against each KEGG pathway and then the pathways are sorted and ranked based on the coherence score. We examined if the correct pathway is scored at the top ranks. For this experiment, the KEGG pathway dataset which contains 101 pathways was used and cumulative percentages of proteins that are assigned correctly to their pathway were computed. Eight scores were compared. In addition to the CAS_coherence (Eqn. 7), PAS_coherence (Eqn. 8), funsim_coherence (Eqn. 14), GOscore_coherence-) assigned 63.22% and 80.60% proteins at the first rank and within the top ten ranks, respectively, whereas the PAS(BP-) has similar accuracy with 60.56% (at the first rank) and 79.12% (within top ten ranks). The Chagoyen score made the correct pathway assignment for 58.4% of proteins at the first rank and 89.73% within the top ten ranks. The accuracy using the Chagoyen score within the tenth ranks is very close to those by the CAS and PAS and better than CAS(BP-) and PAS(BP-). However, note again that the Chagoyen score consists solely of BP terms and it is not defined without the BP domain. The funsim score and the Pandey score performed significantly worse than the CAS and the PAS in this experiment, too.As shown in Figure is a relation) where one term is a more specific representation of the other, there are some new relationships which connect MF-BP terms . By using the CAS and the PAS we can automatically find the strength of associations between terms from any two domains of GO like MF-BP or BP-CC or CC-MF, and these associations are not restricted to the relationships provided by the GO hierarchy. About 36% of the CAS and the PAS associations are for cross-domain GO term pairs, and their scores are comparable to the same domain terms , considers the frequency that pairs of GO terms have been annotated to the same proteins. On the other hand, the PubMed Association Score (PAS) quantifies the number of occurrences that GO term pairs appear in literature abstracts as compared to the random chance. While most common form of the relationship defined by the GO is between the terms of the same domain  and one which considers co-mentions of terms in the literature (PAS). The CAS and the PAS are shown to have the ability to accurately separate biologically relevant groups of proteins, http://archive.geneontology.org/ database version 2009-08. The Gene Ontology Annotation (GOA) database [http://www.ebi.ac.uk/GOA/archive.html. Inferred Electronic Annotations (IEA) were excluded to increase the reliability of functional data. There are 46,686 protein - GO term association pairs for Saccharomyces cerevisiae (yeast) and 90,823 associations for Homo sapiens (human).The hierarchical structure of Gene Ontology (GO) and GO term definitions are obtained from the Gene Ontology Consortium ,86http:/database  version database  identifiGO:0003700 and GO:0051169, we first obtain their respective term definitions as 'transcription factor activity' and 'nuclear transport' from the GO database and remove words 'and, or, not' from their definitions. The remaining words in the definition are used to construct URL, e.g. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=xml&rettype=full&term=transcription+factor+activity, which yields an xml that is then parsed to obtain the count of PubMed abstracts associated with the given term. For retrieving the counts of abstracts with two GO terms we appended the terms in the query URL and obtain the count. The ESearch query interface uses the MeSH indexing to incorporate the synonyms and the term variations. This provides us with a convenient way to retrieve the information that has been represented using different terms for the same concepts. The January 2010 version of the PubMed database was used.We used the NCBI's Entrez ESearch utility for obtaining the count of PubMed abstracts related to the particular GO terms. For example, for computing the PubMed association between terms http://kiharalab.org/functionSim/.A coherent set of proteins are those which take part in the same biological context in a cell. For example, they can be a set of proteins playing roles in the same pathway, proteins involved in a disease or those responsible in a certain stage of development. Here we have prepared three types of coherent sets of yeast proteins: proteins in the same KEGG pathways, proteins included in the same protein complexes, and those which have the same subcellular localization. Along with these, two datasets of interacting protein pairs from yeast and human were prepared Details are described below. All the datasets are available at We downloaded yeast pathways from the ftp site of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database . This daet al. [http://wodaklab.org/cyc2008/downloads. The catalogue provides protein complexes with Saccharomyces Genome Database (SGD) [For the yeast protein complex dataset, we have used a latest catalogue, YHTP2008 of 400 protein complexes compiled from genome-wide high throughput studies by Pu et al. http://wose (SGD)  identifiWe have constructed sets of yeast proteins with the same cellular component (CC) GO terms. Yeast proteins with non IEA GO annotations in the CC domain are selected from the GOA database. Then, for each such yeast proteins, CC terms are enriched by using the parental annotation transfers based on the true path in the GO hierarchy. Thus all ancestors of a GO term are incorporated as annotations for a protein. A total of 560 protein sets were obtained with sizes ranging from 2 to 4814. Very large protein sets contain proteins with a too general CC term. Therefore 481 sets with a size up to 100 were selected for analysis Figure .Saccharomyces cerevisiae (budding yeast) and Homo sapiens (human) interaction data available at the BioGRID database [We have used database   as the actual interaction in each of the organisms.i and j, is computed as follows:The Co-occurrence Association Score (CAS) quantifies the frequency that two GO terms co-occur in annotation of a single gene relative to random chance. The CAS for two GO terms, C(i) is the number of sequences in the database which have GO term i. Similarly, C is the number of sequences in the database which have a pair of GO terms, i and j. Thus, the numerator quantifies the fraction of sequences with annotations i and j relative to the total number of GO term pairs annotating the same proteins. The denominator is the expected number of times the two GO terms, i and j, co-occur in single proteins. This formulation is essentially similar to the method to compute a knowledge-based statistical amino acid contact potential [Here otential ,62.For the GO terms annotating sequences in the GOA database, those with the evidence code of Inferred electronic annotations (IEA) are discarded. Along with the original annotations, parental GO terms to the original GO term annotations following the true path rule are also considered in computing the CAS. This procedure adds information of the GO hierarchy in the scoring scheme in an implicit fashion. GO pairs which do not co-occur in a gene are assigned with zero for their CAS.i and j, are input in the Entrez ESearch web query interface to obtain the number of PubMed abstracts that have a given pair of terms. Along with a pair of terms, we obtain the number of PubMed abstracts which contain each individual term. Using the same equation (Eqn. 1) used for computing the CAS, the PubMed Association Score (PAS) for two GO terms, i and j, are defined asThis score is based on the number of times a given pair of GO terms co-occurs in abstracts in the PubMed database at the National Center for Biotechnology Information (NCBI). Text definition of GO terms is obtained from the GO database. The text definition of two GO terms, Pub is the number of PubMed abstracts which have two GO terms i and j, and Pub(i) is the number of abstracts which have a GO term i. Because PubMed includes nearly 19 million references, it is computationally challenging to obtain the exact total number of abstracts for all the co-occurring pairs in the database, k, lPubΣ. Thus, for the second term, which can be considered as a scaling factor for PAS, the corresponding value computed for the CAS in Eqn. 1 is used.where x Pand y Pwith x Aand y Anumber of annotations, respectively, the score is defined as follows:A protein is usually annotated with multiple GO terms. Using the CAS and the PAS, we evaluate how well two sets of annotations from two proteins are associated. For two proteins, xi Prefers to the th iannotation of protein xP. Thus, each annotation for x Pis compared with all GO terms from yP, and the one which gives the maximum score is chosen. Then the best matching score for each xi Pis averaged by x1/A. The same procedure is performed for yP, and a larger value is taken as the CAS or the PAS association between the two proteins, x Pand yP. This matrix based comparison is proposed by Schlicker et al. [r et al. .S of n proteins, termed as a coherence score of a set, using either CAS_prot or PAS_prot score. Note that these scores are commutative, i.e. CAS_prot  = CAS_prot and PAS_prot = PAS_prot.Now we compute the functional homogeneity of a set P to a set of proteins, S, is defined asSpecifically, the coherence of a protein et al. [et al. [We compare the CAS and the PAS coherence score with three existing related scores, the semantic similarity score , a scoreet al.  and anot [et al. ,58. The c1 and c2, is quantified using the information content of the common ancestors of the two terms:The Semantic similarity measure was proposed to obtain the functional similarity between a pair of proteins . The simp(c) is the fraction of proteins in the GOA database that are annotated with the GO term c, which is common ancestor of terms c1 and c2. Similarity between annotations of two proteins x Pand yP, is defined in the same way as Eqns. 3 & 4:xi, Pyj) is the semantic similarity score for two GO terms xi Pand yj, Ax Pand y Aare the number of terms in the two sets. A comprehensive score, funsim, combines the scores for the three domains [where the GO domain is either BP, MF, or CC. Note that GOscore is only computed for sets of GO terms in the same domain, since the semantic similarity score (Eqn. 9) uses the GO hierarchy structure. sim is normalized by the total number of protein-GO term associations in the reference database (Eqn. 16).Chagoyen otations . A protes denotes a sequence in the database and t sdenotes a GO term in the sequence s. The functional similarity of two proteins, i Pand jP, is defined as the dot product between vectors of w(t) for all GO terms (Eqn. 17). Parental terms for original GO annotations are also considered to incorporate the GO hierarchy.Here P and a set S are defined as the average of the score:The functional coherence of a set of proteins and the coherence between a protein i cand jc, is defined asThis functional similarity score for a protein pair uses a set of common ancestors of annotations of two proteins instead of aggregating pairwise similarity between annotations of both proteins ,58. OnlyAncestorci is the set of ancestors of the terms i cin the GO hierarchy. c Gis the set of proteins associated with the term c and r Gis the total set of proteins in the database. The functional similarity between a pair of proteins, i Pand jP, with annotation sets i Sand jS, respectively, is given byGpi, pj) Λ, the non redundant set of common ancestors between i Sand jS. It is defined asl ≤ ck cindicates k cis ancestor of lc.where S and that of between a protein P and set S are referred as Pandey_coherence. It is defined in the same way as aforementioned scores:The coherence score of a set of proteins et al. [S given by coherence(S), then number of proteins P in set S which satisfy the criteria sim ≥ coherence(S) are obtained as s. Similarly for each protein P in the reference set R (whole genome), we find r proteins that satisfy the same criteria sim  ≥ coherence(S). R is the entire set of proteins in an organism. Now we can compute the p-value of coherence score of set S using hyper-geometric distribution for the number of proteins satisfying the given criteria. P-value is given by Eqn. 25 as the probability of observing s or more proteins satisfying the criteria, given that r proteins from reference set R satisfy the criteria.We use the method proposed by Chagoyen et al.  to compuMC participated in design, implemented the algorithms for analysis, and comparison of different techniques, and drafted the paper. DK conceived of the study, participated in its design, and finalized the manuscript. SP implemented the Chagoyen score. All authors read and approved the final manuscript.Analysis of GO Biological Process (BP) annotations of proteins in the KEGG yeast pathways. For 101 KEGG pathways in yeast, the Biological Process (BP) GO annotations assigned to proteins in each pathway are counted. The pathway name, the number of proteins in the pathway, and the number of unique GO BP annotations have been provided in this file.Click here for fileSupplementary Figures S1-S4. Coherence score distribution for different datasets.Click here for file


The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking. Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery. Congenital Disorders of Glycosylation (CDG), Alzheimer’s, and Parkinson’sCompartmentalization of cellular processes is one of the main characteristics of eukaryal cells and allows for a spatial separation of different processes within the cell Genome-scale network reconstruction is a comprehensive compilations of the molecular components and their mechanistic interactions involved in one or multiple cellular processes Here, we developed a genome-scale network reconstruction approach to enable quantitative analysis of this complex machinery and capture its protein-specific function. The genome-scale model for the secretory machinery of yeast was built using a bottom-up approach. We then used the model as scaffold to compare the secretion system of yeast and human. By using protein abundance data for yeast, we further utilized the model to estimate the metabolic demands associated to the processing of clients by the secretory machinery. Finally the specific activities of each molecular component of the machinery were calculated.In our aim to integrate all available mechanistic knowledge into a scaffold for the study of the protein secretory machinery we used a bottom-up systems biology approach, which is based on collecting, assembling and integrating all relevant information and data by a combination of a comprehensive literature survey and searches in different databases .SCR1). These 163 components represent the core components of the protein secretory machinery that are directly involved in the translocation, folding, post-translational modifications and transport of the proteins as well as biosynthesis pathways leading to the precursors required for glycosylation and glycosylphosphatidylinositol (GPI) attachment (The resulting reconstructed network includes 162 proteins and one RNA component (tachment .+-ATPase (Pma1p) To reduce the complexity, we divided the machinery into 16 subsystems (S1–S16) based on the function that each subsystem performs . In ordedolichol and GPI-biosynthesis pathways, which provide the precursors for the glycosylation and the formation of GPI-anchored proteins, include the biosynthetic reactions.  O-linked glycosylation sites, (4) the number of disulfide bonds to be formed, (5) the presence or absence of anchoring with GPI (glycosylphosphatidylinositol), (6) the number of transmembrane spanning domains, and (7) the transport signal motif for the final localization (Each secretory protein may contain in its sequence information for seven possible features: (1) the presence or absence of a signal peptide that indicates if the protein will be imported into the ER, (2) the number of lization . Once thlization . The detO-GlycBaseO-linked glycosylation sites, or dbPTM, which integrates information about different post-translational modifications Protein Specific Information Matrix (PSIM). Each row in the yeast PSIM (5882×7) represents a specific protein and each column represents one of the seven selected features. Therefore, each matrix cell contains information for a specific feature for a specific protein , which generates a protein specific reaction list for each protein ; Text S1After assigning each protein to one of the predicted secretory classes, it was found that the ER-Golgi secretory machinery potentially can process 1190 proteins. The PSIM of these proteins was used as input to the algorithms and the protein-specific reaction list for each of the proteins was generated .Secretory classes can be divided into two main categories: The classes that have N-terminal signal peptide and the classes with signal sequence in their transmembrane domain, which are mostly plasma and endomembrane proteins. This classification is important as the proteins in each category differ in translocation mechanism, especially in the way they are targeted to the translocon complex It is noticeable that the SP+ secretory classes are more diverse but less populated than the SP- classes. Many of the 162 core components of the yeast secretory machinery are themselves processed by the secretory machinery, 68 of the core components belong to 13 different SP+ secretory classes and 65 belong to 5 SP- secretory classes. The remaining 30 components are cytoplasmic proteins mainly involved in vesicular transport processes , adding complexity to the secretion process One of the potential applications of the model is to be used as a scaffold for improving our understanding of the protein secretory machinery in other eukaryotic organisms such as humans. In order to illustrate this, we used the same approach to generate a PSIM for the human proteome .  [32].Also, it is interesting that the fraction of the SP+ and SP- classes that are using different PTMs features are similar in yeast and human .The SP- secretory classes with transmembrane proteins which do not have signal peptides, they use signal sequences in their transmembrane domains to enter the ER. On the other hand, many of the plasma and endomembrane transmembrane proteins belong to SP+ classes.The extension of the approach to explore the protein secretory machinery in human cells provides a systematic platform to investigate the distribution of secretory proteins in the different classes for both organisms .Having defined the yeast and human SP+ and SP- secretory classes we performed a GO (gene onthology) enrichment analysis , in order to evaluate biological functions of the proteins in the different secretory classes. Comparing GO enrichment for yeast proteins secreted by the SP- and SP- secretory classes  we foundGO enrichment analysis for the SP- secretory classes shows that these proteins mainly are involved in transport and localization processes such as transmembrane transport (ion transport), vesicle mediated transport dealing with protein localization  etc. –13.We also performed GO enrichment analysis for the human SP+ and SP- secretory classes. The results for the SP+ secretory machinery in human cells show, in contrast to yeast, where all the proteins in this group are annotated, that there are 2,557 non-annotated proteins containing a signal peptide . Focusing on the annotated proteins, some of the GOs that indicate a statistically significant enrichment are those related to receptor binding, cytokine activity, hormone activity etc. , whereas GO terms related to signalling are the most enriched among these proteins (−1 h−1)  and metabolic precursors for glycosylation or sulfation such as GDP-man or FADH2. This allows linking the secretory machinery with the rest of the cellular metabolic processes. Using protein abundance data for yeast −1 h−1) . GTP usa−1 h−1) . We consIn addition, we calculated the metabolic costs of the dolichol and GPI biosynthesis pathways separately to give a better resolution of these two biosynthetic pathways that are connecting the secretory machinery to the metabolic network. Dol-p-man (dolichyl phosphate mannose) and UDP-GlcNAc (Uridine diphosphate-N-acetylglucosamine) are the two metabolites that connect these pathways . While wWe are aware that our model represents a simplification so it is important to note that our estimations of precursor requirements, are based on current knowledge on the yeast secretory machinery and accordingly they are uncertain for subsystems like folding or ERAD for which we do not have protein specific stoichiometry. Also in terms of glycosylation there may be uncertainties as not necessarily all glycosylation sites are being used all the time We also estimated the metabolic costs of processing the whole set of proteinspresent in some cellular compartments which are secretory machinery clients . The resMetabolic engineering of the secretory pathway is often based on altering the expression of some of the machinery components with the objective to increase secretion of a particular protein (often a heterologous) −1 h−1). Kar2p is the main chaperon in the ER −1 h−1) are two NEFs (nucleotide exchange factors) which have ATPase activity and regulate the Kar2p ATP turnover KAR2. There is some evidence in favor of the effect of the modulation of these chaperones in improving heterologous protein production KAR2 has not positive effect on the secretion level, while decreasing its expression shows negative effect For example, in the protein folding subsystem the Lhs1p is the least abundant (∼139 molecules) component with the highest SA  of the machinery components were estimated which provides information about the natural capacity of the machinery components catalytic activity.In this work, we applied, for the first time, a genome-scale modeling approach to study the complexity of the eukaryal protein secretion pathway. We used a bottom-up network reconstruction method. The model contains detailed mechanistic knowledge of the secretory machinery and can be used to integrate -In a nutshell, the reconstruction approach and the ‘PSIM’ matrix provide a framework for (i) capturing the genome-scale mechanistic details of the secretory machinery; (ii) integrating and analysing high-throughput data for evaluation of the function of different parts of the machinery and hereby increasing our knowledge of systemic properties; (iii) offering a systems biology framework for engineering industrial and therapeutic protein secretion strategies; (iv) and finally for connecting the model to other cellular processes such as metabolism.N-linked glycosylation, O-linked glycosylation, disulfide bonds, transmembrane domain, GPI-anchoring, and localization. The signal peptide is a critical feature to determine if the protein is a secretory protein or not and according to some contradiction between the UniProt and SGD signal peptide information, we used the combination of signal peptide information between UniProt KB and SGD (www.python.org). Uniprot, SGD, and KEGG databases were used throughout the reconstruction approach in iterative manner.We used UniProtKB for retrieving yeast and human proteome information for the selected PTM features including signal peptide,  and SGD . All feaS. cerevesiae secretory machinery consisted of four steps. First, based on a comprehensive literature survey (research and review papers and book chapters) on the yeast secretory pathways, the functional subsystems constituting the secretory machinery were defined. The resulting list of components was used as a starting point from which more components and corresponding publications , N-linked glycosylation site (present or absent), O-linked glycosylation site (present or absent), disulfide bound (present or absent), GPI-anchored (yes or no), transmembrane domain (present or absent) and localization   . Each senations) .p-value of 0.01) and the top 10 GO terms were selected to represent the functional role of each secretory class , the GO::TermFinder ry class .S. cervesiaeThe steady-state protein abundance data of p and The rate of each of the machinery reactions can be calculated from x  in reaction j and p.Finally, the consumption rate of the metabolites of interest in steady-state is calculated from Dolichol and GPI biosynthesis metabolic cost to produce the needed precursors for ER glycosylation and GPI transfer in steady-state  for each machinery component is defined to be the number of its catalytic cycles in j catalytic rate for production of j, and The specific activity  as a histogram function to cluster the machinery components with different ranges of their SA  with their specific feature combinations. The red spot shows the existence of a feature and gray spot indicates the absence. The first 104 class are the classes with signal peptide and the remaining 82 are without signal peptide. The class ids are depicted in the secretory class column. Features description is given at top of each feature column.(TIF)Click here for additional data file.Figure S2The correlation of the main component of the secretory machinery specific activity(SA) and protein abundance.The yy-plots for the SA(log 10)(cell−1 h−1) and corresponding protein abundance(molecules cell−1) of each of the subsystems is shown. The subsystem names are located above each plot.(TIF)Click here for additional data file.Figure S3The most metabolic demanded proteins of the secretory machinery. For each of the metabolic precursors (shown at the bottom of each plot) the top 5 proteins are plotted. For the annotation of these proteins see the (TIF)Click here for additional data file.Figure S4Average abundance of the yeast secretory machinery subsystems component.(TIF)Click here for additional data file.Table S1The components of the core protein machinery. The components of the machinery which are used as the core model components are provided in this table with the corresponded description.(XLSX)Click here for additional data file.Table S2Template reactions list for Saccharomyces cerevisiae secretory machinery model. This table provide a detailed description of the model template reaction list with the components and corresponding reference for each specific template reaction. These reactions are used as input for the algorithm.(DOCX)Click here for additional data file.Table S3Yeast and human secretory classes. This table provides the detailedinformation about the yeast and human populated secretory classes with the SGD and UniProt ID for members for each class.(XLSX)Click here for additional data file.Table S4Subsystem level metabolic demand estimation for yeast secretory proteins. The various metabolic precursors’ estimation is provided in this class based on each subsystem consumption in genome scale in cell−1 hour−1 in steady state.(XLSX)Click here for additional data file.Table S5SA of the yeast machinery component with protein abundance data. The estimated specific activity for each component which have the steady state protein abundance data.(XLSX)Click here for additional data file.Table S6Cytoscape input file for the machinery component with estimated specific activity.(XLSX)Click here for additional data file.Table S7Saccharomyces cerevisie PSIM. The yeast proteome information for post translational modification and localization information is shown in (XLSX)Click here for additional data file.Table S8Human PSIM. The human proteome information for post translational modification and localization information is shown in (XLSX)Click here for additional data file.Table S9Genome-scale protein specific reaction list for 550 yeast potential secretory proteins. This table provide the entire reaction list for the highly potential yeast secretory machinery clients in protein specific manner.(XLSX)Click here for additional data file.Table S10Yeast SP+ secretory proteins GO enrichment.(XLSX)Click here for additional data file.Table S11Yeast SP- secretory proteins GO enrichment analysis.(XLSX)Click here for additional data file.Table S12Human SP+ secretory proteins GO enrichment analysis.(XLSX)Click here for additional data file.Table S13Human SP- secretory proteins GO enrichment analysis.(XLSX)Click here for additional data file.Table S14The descendent metabolic cost for each of the secretory client’s production in steady state cell-1 hour-1.(XLSX)Click here for additional data file.Text S1The reconstruction approach. The reconstruction approach and assumption of the model is described in details in (DOCX)Click here for additional data file.


Low-affinity extracellular protein interactions are critical for cellular recognition processes, but existing methods to detect them are limited in scale, making genome-wide interaction screens technically challenging. To address this, we report here the miniaturization of the AVEXIS (avidity-based extracellular interaction screen) assay by using protein microarray technology. To achieve this, we have developed protein tags and sample preparation methods that enable the parallel purification of hundreds of recombinant proteins expressed in mammalian cells. We benchmarked the protein microarray-based assay against a set of known quantified receptor–ligand pairs and show that it is sensitive enough to detect even very weak interactions that are typical of this class of interactions. The increase in scale enables interaction screening against a dilution series of immobilized proteins on the microarray enabling the observation of saturation binding behaviors to show interaction specificity and also the estimation of interaction affinities directly from the primary screen. These methodological improvements now permit screening for novel extracellular receptor–ligand interactions on a genome-wide scale. Extracellular protein–protein interactions between secreted or membrane-tethered proteins are critical for intercellular recognition processes that initiate signaling cascades and provide cellular cohesion within multicellular organisms KD ∼ 50 μM) Despite the biochemical difficulties of identifying novel low-affinity extracellular receptor–ligand interactions, several scalable methods that take account of some or all of these biochemical challenges have been devised Escherichia coli) or synthesized in situ from arrayed plasmid expression using cell-free expression systems Protein microarray technology has become a useful tool in the discovery of novel protein–protein interactions, providing a method of capturing thousands of proteins in ordered arrays Here, we show how we have improved our recombinant protein sample preparation workflows and miniaturized the AVEXIS assay so that it can be used on a protein microarray format. Importantly, we benchmarked our protein microarray-based interaction assay against a set of well-characterized low-affinity extracellular protein interactions of known binding strengths and showed that the microarray-based method is at least as sensitive as the plate-based AVEXIS assay but uses approximately five orders of magnitude less protein. This new implementation of the AVEXIS assay will now permit us to increase the size of our interaction screens to a genome-wide scale.E. coli enzyme, BirA, and d-biotin is enzymatically monobiotinylated at a specific lysine residue (see “Bio” tag in g for 5 min and cell debris by filtering (0.2-μm filter). All samples were stored at 4 °C until use.All proteins were produced by transient transfection using an HEK293E expression system as described in Refs. 2+–NTA (nitrilotriacetic acid) resin  essentially as described previously Proteins were purified from spent tissue culture media using NiBait and prey proteins were normalized as described previously Normalized bait proteins were diluted in phosphate-buffered saline (PBS) supplemented with 0.02% Tween and 0.5% bovine serum albumin (BSA) prior to printing. Bait proteins were printed on streptavidin-coated slides that also contained an inert hydrogel coating (XanTec) using a MicroGrid II (Digilab) arrayer mounted with 200-μm solid contact pins at 22 °C and 70% relative humidity according to the manufacturer’s instructions. After printing, slides were incubated for 1 h at 70% relative humidity at 22 °C, washed in PBS for 1 h at 22 °C, spin-dried, and stored at −20 °C until use.2O and blocked with PBS containing 1% BSA and 10 mM d-biotin for 45 min. Slides were then incubated with normalized prey proteins for 1 h at 22 °C, washed three times in PBS/0.5% Tween, incubated with 1:1000 anti-Flag horseradish peroxidase (HRP) antibody (Sigma) for 1 h at 22 °C, and finally detected by TSA Alexa 555 substrate (Invitrogen) for 1 h at 22 °C. Slides were washed three times in PBS containing 0.1% Tween 20 with gentle rocking at 22 °C between each incubation step. Positive interactions were identified by scanning slides with a ScanArray Express Microarray Scanner (PerkinElmer) at a 550-nm wavelength.Prior to interaction screening, printed slides were rinsed three times with MilliQ H2+–NTA resins, thereby removing the need for the dialysis and concentration steps. Initial experiments demonstrated that adding a 6×His tag (Bio-His) directly after the biotin acceptor peptide (Bio)  A did notde (Bio) B. Therefde (Bio) A. To detde (Bio) B. The BLde (Bio) B. To furA resins D. These 2+–NTA resins arrayed in 96-well plate formats, they are not designed for sample volumes greater than approximately 500 μl. Therefore, to conveniently purify large numbers of different proteins from 50-ml volumes, we designed and custom-built a purification platform that uses compressed air to power a pneumatic piston delivering supernatants loaded in disposable 50-ml syringes through a 96-well Ni2+–NTA plate via microbore tubes at a rate of approximately 1 ml/min  In the original AVEXIS assay, interactions were detected in microtiter plates by probing bait arrays with a pentamerized, β-lactamase-tagged prey protein followed by incubation with a colorimetric soluble β-lactamase substrate, nitrocefin To determine the sensitivity of the assay to the bait and prey concentrations, the bait arrays were probed with a range of prey activities ranging from 196 to 2.42 units, where 1 unit is defined as the amount of prey protein that can turn over 1 nmol of nitrocefin substrate per minute. We observed that the ability to detect interactions was more sensitive to the immobilized bait concentration than the activity of the prey C–F. For D of approximately 2 μM, there are several reported receptor–ligand interactions that are considerably weaker than this t1/2 ∼ 0.1 s) are sufficient to drive spontaneous alignment of apposing membranes t1/2 > 2 s) to the JamA1–JamC2 interaction that can be barely detected (t1/2 < 0.4 s) (t1/2 = 1.9 s), which is known to be physiologically relevant in vivo t1/2 > 2 s). Both of these expected interactions were detected in both bait–prey orientations. The very weak JamC2–JamA1 interaction (t1/2 < 0.4 s) was detected in just one bait–prey orientation, and an interaction between JamA2 and JamC2 that had not been detected by our SPR studies was now observed, again in just one orientation (t1/2 < 0.4 s) JamB2–JamC1 and JamB2–JamC2 interactions were not detected. None of the homophilic interactions was detected, possibly due to highly avid prey–prey interactions that prevent prey–bait interactions; this prey “masking” effect was also observed in the plate-based assay Although the rat Cd200–Cd200R interaction has a relatively low affinity with a K< 0.4 s) B. All si< 0.4 s) A and proentation B. The veDefining protein interaction networks on an increasingly large scale to understand how biological processes are coordinately regulated has been a focus of recent research interest. These efforts have relied on a small number of high-throughput assays such as biochemical purification followed by mass spectrometry Miniaturizing the AVEXIS assay on a microarray compared with the microtiter plate format has several advantages. The amount of protein required per interaction test has been dramatically reduced by approximately five orders of magnitude. This now reduces the amount of sample preparation time required and is particularly important for those proteins that are expressed at low levels. It also enables an increase in the number of interaction screens that can be performed with the same protein preparation, enabling more replicates to be performed with a wider range of protein concentrations. This latter point is of particular value because by screening against a dilution series of bait proteins it was possible to determine whether binding events were saturable (and therefore specific) and to estimate a binding affinity directly from the screen. The observation that the known binding affinity of an interaction correlated with its dose sensitivity across the bait dilutions used was a surprise given the unpredictable gain in binding avidity caused by multimerization. This is likely due to the fact that we used a similar structural class of receptor proteins in the experiments described here  volumes so that very high concentrations (>10 mg/ml) of purified proteins can be achieved. This approach also retains the advantage of irreversibly capturing the monobiotinylated bait proteins on streptavidin-coated solid phases in an orientated manner that, as we showed here, may reduce the wide variance in immobilized protein activity that has been observed when proteins are captured nonspecifically by passive adsorption The arrayed receptor protein microarray slides are not limited to the study of extracellular protein interactions. We envisage that these receptor arrays will also be very useful for systematically studying interactions using whole cells or pathogens such as bacteria and viruses. In addition, these slides could be used to screen complex biological tissues such as serum samples for the identification of diagnostic disease markers.In conclusion, by developing new protein tags and streamlining the sample preparation procedures, we have used protein microarrays to miniaturize and improve the AVEXIS assay that is capable of detecting low-affinity extracellular protein interactions. By reducing the amount of protein required, the size of the screens that can be performed has increased significantly. We determined the experimental parameters necessary to detect interactions as weak as 50 μM, which are considered as a lower limit for physiological significance


The increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.Using the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.By representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment. Continuous advancements in high-throughput X-ray crystallography and genomics ,2 accounet al[ab initio fragment molecular orbital (FMO) method [One of the above methods, a post-screening analysis for visualizing protein-ligand interaction (VISCANA) which analyzes the receptor and ligand pattern of interaction on the basis of quantum theory is an approach proposed by Amari et al. VISCANA) method  to repreet al[Another method is NIPALSTREE, an approach by Bocker et al for cluset al is a newOur goal is to develop a cluster analysis for protein-ligand complexes using protein-ligand molecular interactions. We employed the empirical energy function from GEMDOCK  and the TSCC is accomplished by the joining of two clustering stages; one of protein-ligand interactions  with another of physicochemical features performed on compounds selected through the first stage of clustering. We employed our docking tool, GEMDOCK, to generate protein-ligand interactions and used the Accelrys Cerius QSAR module for obtaining physicochemical features of complexes. Based on normalized feature profiles, hierarchical and K-mean  clusteriA cluster analysis for selecting candidates from a large number of database compounds requires prior screening techniques which must employ docking and screening tools. To handle the vast results from virtual screening and use more specific information for protein-ligand binding, we utilize the empirical energy function from GEMDOCK specifically optimized for virtual screening. GEMDOCK used piecewise linear potential (PLP) that is a simple scoring function and is comparable to some scoring functions for estimating binding affinities -21. Our To demonstrate the efficiency of our method we successfully applied its combinatorial two-stage concept on five common targets by constructing two compound sets to screen against each target protein. The first compound set, a verifying dataset, was used to determine if the protein-ligand interaction descriptor is suitable for identifying compounds with similar binding modes. The second compound set, a testing dataset, was used to evaluate the database enrichment potential and the property of compounds in the same cluster by docking a diverse set of compounds spiked with known inhibitors into the same target protein as shown below.Figureet al[et al[The overview of our method is shown in gureet al. Due to gureet al. In our  al[et al. After aWe generated two sets of structure-based virtual screening results: 1) to verify if the protein-ligand interaction descriptor is suitable for identifying compounds with similar binding mode and 2) to evaluate the database enrichment potential and the property of compounds in the same cluster by docking a diverse set of compounds spiked with known inhibitors into the same target protein.α) [α) PDB ID: lgwr, 4) human dihydrofolate reductase (hDHFR) [We tested the virtual screening results against the five target proteins: 1) herpes simplex virus type 1 thymidine kinase (TK)  PDB idenα) ,30 PDB I (hDHFR) ,32 PDB I (hDHFR) ,34 PDB IThe ligand binding site was defined as a collection of amino acids using a cutoff radius of 10Å from each atom on the bound ligand, since most studies in lead discovery use a cutoff radius between 8 to 12 Å. Structure files were stored as a PDB format for GEMDOCK input and analysis.α antagonists: 11, 3) ERα agonists: 10, 4) hDHFR: 10, and 5) NA: 20. The two crystal structures of human estrogen receptors alpha have been intensively studied for their different functions (agonist 1GWR promotes coactivator binding while antagonist 3ERT blocks it) and ability to bind on the same site of the protein. The agonists play an important role in regulation of gene expression and prevention of osteoporosis while the antagonists have been used as treatment of hormone-dependent breast cancer [We constructed two compound sets for screening against each target protein. The compound sets for NA of influenza virus were derived from the Comprehensive Medical Chemistry database (CMC) and only those with molecular weights between 200 and 800 were chosen. All active compounds  were listed as the following: 1) TK: 10, 2) ERt cancer ,30.et al[http://gemdock.life.nctu.edu.tw/dock/download.php.The tested dataset contained 990 randomly selected compounds combined with known active compounds for each target protein using a method from Bissantz et al. The 990via two key functions: 1) The searching algorithm and 2) The scoring function which is based on an empirical energy function  Structure files in mol format2) Remove hydrogen atoms3) Convert to mol2 format via CORINA3.04) Calculate atom pair descriptors via AP generator (distance bins: 15)5) Store in binary coding form.A total of 825 (55 x 15) atom pair descriptors were generated for each molecular structure by removing all columns with zero values.To design a reference threshold of protein-ligand interaction, a verifying dataset was used in establishing a reference threshold of distance by determining a maximum discrimination between similar and non-similar binding modes. The equation is as follows:t is the reference threshold, intra-d<tC is the number of intra active compound pairs with the distance < threshold and interC is the number of compound pairs between active and non-active compounds.Where First, we used a protein-ligand interaction descriptor for clustering compounds with similar binding modes and applied the correlation coefficients as similarity measurements. The following formula was used:X and Y. xS is the standard deviation of X. iX is the ith value of X. n is the number of descriptors. We applied the standard UPGMA clustering method for calculating the distance between two clusters while constructing the dendrogram. The formula is defined as:where The reference threshold was calculated from the verifying dataset using equation (2) to determine the number of clusters.Second, we applied the AP descriptor for clustering compounds within each clustering stage and applied the tanimoto coefficients as similarity measurements. Formula is as follows:X and Y. |X⋂Y| is the number of ON bits common in both X and Y, and the |X⋃Y| is the number of ON bits present in either X or Y. This equation is similar to equation (4); where Choosing the crystal coordinates of TK  on a protein receptor. There are five classes of similar compounds on each target protein. We tested to see whether the mean distance between similar compounds represented by protein-ligand interactions is different than the mean distance between non-similar compounds  that had the maximum discrimination. The reference threshold of atom-pair  active compound structures.Click here for fileFigure S2. Eleven ERα (estrogen receptor) antagonist structures.Click here for fileFigure S3. Ten ERα (estrogen receptor) agonist structures.Click here for fileFigure S4. Ten hDHFR (human dihydrofolate reductase) active compound structuresClick here for fileFigure S5. Twenty NA (neuraminidase) active compound structuresClick here for fileFigure S6. Views of docked structures of known active compounds in the vicinity of the target protein TK and hierarchical clustering of protein-ligand interactions.Click here for file


The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics.Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database ( Proteins function by interacting with other biologically relevant molecules. Understanding the mechanisms of protein-protein interactions (PPI) is essential for studying life processes at the molecular level. Genome sequencing provided a vast amount of information on proteins at the sequence level. Currently, efforts focus on the function assignment of these proteins based on their three-dimensional (3D) structures and interactions. Interaction maps for specific organisms and biochemical pathways need to be complemented by the structural information. Experimental techniques are limited in their ability to produce the structures on the genome scale. Thus, computational methods are essential for this task .ab initio techniques based on shape and physicochemical complementarity. More recent approaches take advantage of statistical potentials and machine learning [Structural modeling of PPI has its origins in learning ,3. DespiRapidly increasing amount of data on PPI makes possible application of the template-based methods. Such approaches are based on the observation that monomers with similar sequences and/or structures, generally, have similar binding modes. Several groups assessed the quality of PPI modeling based on sequence alignment to complexes with known structure -9. StudiThe progress in 3D modeling of PPI is reflected in the Genome-Wide Docking Database (GWIDD) , which pGWIDD imports PPI from external sources, including the last free release of BIND  and DIP http://gwidd.bioinformatics.ku.edu) user interface . Thus, advanced sequence search options are available. Figure The database . Homology docking was performed by NEST [GWIDD unifies different external PPI data formats into a single data set, removing redundancy and retaining common data fields for all the sources. The interaction data are stored in a relational database, except for large files, such as structure coordinates, which are stored directly in the file system and are linked from the relational database. The web interface is implemented on the Linux-Apache-PostgreSQL-PHP software stack. Web user interface is built using hypertext preprocessor (PHP) and jQuery library, where PHP is for web presentation and logic as well as back-end database access; jQuery is responsible for AJAX and other JavaScript-based dynamic features. Visualization of protein structures is implemented in Jmol ( by NEST , BLAST [ by NEST , and in-GWIDD development will incorporate other structural modeling techniques, such as multi-template/threading modeling of interacting proteins, partial structural alignment , and temThe authors declare that they have no competing interests.ZZ performed calculations and implemented the web interface. PJK developed calculation pipelines, designed the web interface, analyzed data, and drafted the manuscript. IAV designed the research, analyzed data, and wrote the paper. All authors read and approved the final manuscript.