As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein–protein, protein–nucleic acid and a subset of protein–chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo ( High-throughput sequencing (HTS) has led to the systematic identification of thousands of protein variants  from whiThe current flood of variant and modification data is concurrent with a growing set of protein 3D structures and interactions. Virtually all protein domains now have at least one representative structure, and the number of interactions for which structures are known or modelled grows continuously . IntenseTo study even a single residue change in terms of potential functional impacts can require simultaneous consideration of dozens of 3D structures and interactions, and almost invariably requires consideration of homologous proteins; even if a structure of a particular protein is available, functional insights might come only from one with lower sequence similarity that is nevertheless bound to a relevant ligand. To study sets of thousands of protein changes accordingly requires the integration of a vast set of information. Ideally, one would model each change in every available biomolecular interaction context, but to do so would be time-consuming and unfruitful owing to the small likelihood that any one of the thousands of models required would alone be functionally informative. Moreover, comparative modelling at low sequence identities is difficult and prone to inaccuracies, and often simple inspections of an alignment with a template 3D structure are as effective as models.Several efforts have been made to bridge interactomes and structures e.g. ,10–13) t–13 t10–1There are also many methods to predict deleterious mutations, which typically consider protein sequence conservation and/or properties from individual protein structures (or homologues) to estimate deleteriousness e.g. –20). New. New20))To help predict and understand the impact of mutations or modifications on protein function, we present here a new online resource Mechismo  that identifies residue changes (mutations or modifications), from uploaded sets of thousands, likely to have functional consequences by affecting interactions with proteins, small molecules or nucleic acids. To predict the functional impact of a residue change, the method makes use of all possible homologous structures and all available protein–protein interactions and provides an easy interface for non-experts to access a vast and complicated underlying data set.We extracted sequence fragments from 3D structures  as eitheWe defined interactions between proteins and small molecules or DNA/RNA by identifying side-chains within 5Å of atoms excluding solvents commonly used in crystallization and experimental modifications . We alsoE-value threshold of 0.0001 and storing the best match for each position in the UniProt sequence. Best matches (by sequence identity) of pairs of UniProt sequences from the same species to representative 3D interactions were selected as models for the interaction. For all sequences we also defined protein domains by significant HMMscan matches to Pfam domains  with a sequence to structure similarity of ≥90%, and a negative set as those residues not in the positives and predicted to interact with molecules using templates structures with ≤20% identity. The negative set includes some positives as these can still occur at low similarities, thus making confidence measures conservative. We then computed the proportion of sites recovered using model proteome sequences sharing sequence identities between 20–90% with structures, computing true-positive and false-positive rates (TPR and FPR) for each type of interaction  and sequence identity. Note that these values are for whether a changed amino acid is in contact with another molecule; the accuracy of predicting directions (i.e. enhancing or diminishing) is described in the next section. The entire data set is available from We computed interface pair-potentials  for resifexp) as the product of interface residue frequencies (protein–protein interactions), or as the frequency of the residue in proteins binding to DNA/RNA or particular chemical classes (i.e. a molar-fraction random model (fobs) to be in contact with a particular residue, chemical or DNA/RNA, and computed:IPPaa is the value for a particular pair .For each type of interaction  we defined the expected frequency (om model ). For eaIPPaa values for protein–protein, protein–DNA/RNA and interactions with Zn, Mg, Fe, Mn, Cu ions. Updates will include similar parameters for repetitive chemical moieties, such as phosphate, sulphate and carboxylate. Note that IPPaa values shown in the figures are multiplied by 10 and capped at 9 for clarity .For the majority of chemical classes, there was insignificant enrichment or depletion of residues to make the measures above useful (i.e. log odds <1), so these were excluded. This is likely due to the failure of this generic approach to capture the diversity in molecules with varied properties (e.g. adenosine triphosphate (ATP) has both hydrophobic and negatively charged moieties). Nevertheless, we obtained satisfactory IEchange is the score associated with mutations or modifications, with contacts involving the mutated/modified residue being presumed to be the same as the original residue. Note that we also presume the same contacts when the template structure differs from the original sequence. High positive values indicate modifications predicted to favour an interaction , whereas negative values would be predicted to disfavour it .We defined the benchmark set by extracting all human mutations from Uniprot (MUTAGEN and VARIANT) marked features and their associated text. From these 135 275 mutations we then looked for descriptions containing ‘bind*’ or ‘interact*’ leaving 6283, which we inspected manually, labelling them whether they disabled/enabled (the positives for receiver operator characteristic (ROC) analysis) or had no effect (negatives) on the interaction with particular proteins, complexes, protein classes, chemicals, chemical classes, DNA or RNA. For sites targeting common complexes or classes , we allowed the possibility that the mutation could affect interactions with any of the proteins in these human complexes or classes. This expansion plus the fact that many sites described effects on multiple individual proteins , led to a final set of 12 527 mutation/interactions pairs.IEchange values plus 1 for each protein, chemical or DNA/RNA site found. This addition allows for incalculable IEchange values in chemicals and gives non-zero values to all sites at any functional interfaces. We also defined a total protein impact score as the sum of these values for all sites in a protein from a particular data set.We defined a total positional impact (Mechismo) score as the sum of the highest absolute We compared mechismo  to full protein modelling by first selecting a random sample of 4146 non-redundant interfaces ; for chemicals we defined redundancy according to the chemical classes in Supplementary Table S1 . For each of these we found all other interfaces (i.e. templates) involving homologous proteins or similar chemicals/DNA/RNA and grouped these according to sequence identity  to the selected interface. We then selected one interface from each bin and used the alignment from the database to construct models using the Automodel feature in MODELLER . We thenWe created shuffled data sets by both moving particular modifications to random positions in the same protein (sites only) or by moving them to a random position in a randomly selected protein from the same data set (proteins and sites). In doing so we did not permit the original site to be chosen, which for rare amino acids in shorter proteins could be problematic for the sites-only data set (e.g. if a protein of 30 amino acids has only a single Arginine that is mutated). For certain contexts (e.g. PTMs) it is worthwhile to consider sets with a similar distribution of surface/buried residues. We constructed this set from the entire data set, but taking the best 3D template for any proteome segment (any significant BLAST match as above) and computing NACCESS  accessibWe used ConDel  to extraSaccharomyces cerevisiae, GC-content and restriction sites. The sequences encoding the proteins were flanked by attb-Gateway sites (Invitrogen) for further cloning. All constructs were shuttled into the Donor vector pDONR221 by Gateway BP-reaction and subsequently by LR-reaction into the Y2H bait and prey vectors pDEST32 and pDEST22, respectively, for the Yeast two-Hybrid experiments. All constructs were sequence verified.For the interactions with mutations discussed for RhoA (see Results and Discussion), we obtained 11 clones as sequence optimized synthetic DNA from commercial suppliers (LifeTechnologies/IDT), in terms of codon optimization for expression in We performed two-hybrid assays following an altered ‘Testing specific Two-Hybird interaction’ protocol of the ProQuest™ Two-Hybrid System Handbook (Invitrogen). Briefly, we co-transformed all interaction pairs (Interactor/RhoA) into yeast strain MaV203 . Colonies from each transformation were grown on 15-cm plates of synthetic complete media lacking leucine and tryptophan (Sc-Leu-Trp). After 2–3 days we picked three individual colonies of each transformation and suspended them in 100 μl autoclaved saline in a 96-well plate. To achieve a uniform cell density, we transferred 10 μl of the suspension to 150 μl SC-Leu-Trp medium in another 96-well plate. These cultures were grown overnight at 30°C in an incubator without shaking to reach saturation. We re-suspended the cultures by vigorous shaking followed by replication with a 96-needle replicator onto rectangular SC-Leu-Trp-His readout agar plates containing different concentrations  of the inhibitor 3-aminotriazol. We interpreted phenotypes 2–5 days after plating.S. cerevisae to 55.2% in E. coli.The core database currently combines 86k 3D structures  cross-referenced to 846k protein–protein interactions and 60k proteins in eight model organisms Figure  with 59kUploaded proteins and changes are mapped to the database and 3D interfaces filtered by thresholds for sequence/structure similarity and for interactions to be used. Users can modify the stringency of data considered in terms of the lowest sequence similarity and the nature of protein–protein interactions . In all the examples discussed below, we used the least stringent setting: considering protein–protein interactions with any published evidence of a physical association between proteins, and any sequence identity to statistically significantly matched structures (‘low stringency’ on the website). Increasing stringency can be useful when considering proteins with substantial structural and/or interaction partners .After job completion, the user is presented with overall totals for proteins/positions, a network view of interactions and changes, and summary tables for proteins and changes. Additional pages describe individual proteins/changes, and still others describe structural matches, giving alignments, domain diagrams, views of changed positions in structures and references describing interactions. For simplicity and speed, we do not construct or download homology models, but present alignments beside matched structures, highlighting differences between the original sequence and the structure that might affect prediction accuracy (see below).Even if the structure of a particular protein is known, those with interacting proteins, chemicals or DNA/RNA are often only available for homologues with lower sequence identities. To use such a diversity of structures requires measures of prediction confidence as a function of sequence similarity, which we devised by defining gold-standard positives and negatives and testing the ability of sequence/structure matches with different degrees of identity to predict them .Residues at functional interfaces in structures identical or nearly identical to proteins from model organisms are considered to be positives, and those from structures with very low sequence similarity (but not in the positives) are defined as negatives. The negatives likely contain many positives that are only identified at very low sequence identities, making the benchmark conservative. All other structures (20–90% sequence identity) are then used to predict all positive and negative sites. ROC analysis gives FPRs that provide confidence measures when predicting a site for each type of interaction at any sequence identity Figure . These vA natural question is whether it would be more accurate to construct homology models of all proteins, as opposed to interrogating an alignment beside a 3D structure. We tested this by sampling 4000 3D interfaces of all three types, modelling  them on IEchange  that measures the effect of changing an amino acid at an interaction interface by computing the difference between these log odds scores for the original residue and those for the mutation or modification.Mutations and modifications can either enhance or diminish biomolecular interactions. To predict such effects, we used pair-potentials  for prot+, Asp (but not Glu) favouring Ca++ and small polar residues (Ser/Thr) favouring Mg++/Mn++  and 508 disease variants could be mapped to particular proteins, 559/164 to specific chemicals and 498/133 to DNA or RNA interactions. As expected, the data are heavily biased towards disabling rather than enabling or neutral effects on interactions: 79.0% (4051 mutagen and 634 disease variants) are disabling, 3.2% (143 and 39) enabling and 17.8% (926 and 132) are neutral. Many mutations are annotated as affecting multiple molecules, making a final set of 12 527 mutation/molecule pairs (with some redundancy owing to molecule descriptions such as ‘actin’ or ‘RNA polymerase II’), of which about 10% (1257) could be matched to a protein structure for which there were known/predicted structures related to the interaction observed (1038 mutagen and 219 variants).ROC curves Figure  show thaIEchange values for protein, chemical and nucleic acid effects , and an overall score for proteins/genes as the sum of these values for all changes in each protein. This allows the most functionally impacted sites and proteins from large data sets to be identified.To score and rank changes we defined an overall Mechismo score for individual sites as the sum of highest absolute edgetic effects . Both are also enriched in mutations at protein and nucleic acid binding sites relative to shuffled sets, but the differences are not significant.To demonstrate the use of this approach on HTS data we considered two cancer data sets. The first is a set of 641 non-synonymous mutations in 569 proteins identified in Medulloblastoma tumors by a combination of exome and whole genome sequencing . After iAs known from the original analyses of the gene sets, the two samples differ substantially in the proteins that are mutated, but Mechismo also highlights differences in how proteins common to both sets are affected. For example, both cancers have roughly the same proportion of variants in the tumor suppressor TP53 . However, whereas none of those in Medulloblastoma are predicted to have strong functional consequences Figure , in PancE. coli . Some are known to regulate enzymatic function, such as Ser-113 in isocitrate dehydrogenase, and Ser-102 in phosphoglucosamine mutase . ApplyiE. coli  predictse mutase , the latMany methods attempt to distinguish deleterious from neutral mutations using information about residue conservation and individual known/predicted structures e.g. –18), tho, tho18))P << 0.001) and this improves as a function of the Mechismo score . Mechismo by itself is thus a relatively poor overall predictor of deleteriousness Figure ) with thThere are also 435 known deleterious sites in the benchmark set that are at functional sites  but which are not predicted to be deleterious by any of the methods, mostly owing to poor conservation, including several sites in TP53 . A more detailed investigation into the possibility of combining this information with deleterious predictors will be published elsewhere.Despite many technical advances, it will take decades until structures of most interactions are available. This makes methods to extrapolate information from known structures to homologous proteins important for understanding biological mechanism. The mechanistic basis of why particular changes in proteins have the effect that they do is one of the next great challenges in biology and utterly requires a deeper integration of HTS and proteomics techniques with information related to protein 3D structures. When doing so, however, it is critical to exploit even weakly homologous structures, since this greatly increases the coverage of functional sites. This is particularly true for chemical or DNA/RNA sites as homologous proteins very often use a similar location to bind their ligands , and it Mechismo provides a rapid structural and mechanistic view of this mesmerizing volume of data, and will be useful for prioritizing modifications/variants, improving methods to predict deleterious mutations and understanding the biological or disease mechanisms of large sequencing or proteomics data sets.Supplementary Data are available at NAR Online.


Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs), from Functionally uncorrelated Contacts (FunCs), is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations. Proteins interact with and bind to other proteins forming both transient and long-term networks of specific complexes whose interfaces have highly-specific amino acid interactions Development of experimental and computational techniques to identify PPIs has shed light on the determinants of specific interactions, as well as on some general features for different types of interactions Several potential reasons for this exist. Both experimentally and computationally, it has been observed that few of the residues present in a PPI are essential for maintenance of the integrity of the interface An additional reason PPI principles may be difficult to elucidate can be found in how the experimental data used to develop computational methods like docking is organized and utilized. Most data for the patterns of amino acid characteristics at PPIs come from atomic resolution structures of protein complexes deposited at the Protein Data Bank (PDB) Several groups have classified PPIs into different operationally defined categories such as, homo- and hetero- complexes, obligate and non-obligate complexes, and transient and permanent complexes , all with differing success particularly toward the center of the interfaces. This may be related to the finding that cores of interfaces have greater sequence conservation, than interfaces rims In light of these improvements, a hybrid approach that includes the statistical analysis of (a) atomic-resolution interface geometries and (b) CAS-based energy data of protein structures pre-classified based on functional importance (FLIP/FunC) may be successful, both in improving detection of interfaces and increasing our understanding of general principles of interface formation. To test this concept, we collected a set of PPI structures available in the PDB starting from a subset of members of commonly used sets to test PPI and docking software The novelty of this approach, which we term the Energy Centrality Relationship (ECR), is that through the combination of geometric and energetic data, we are able to not only reproduce functional classifications, and describe physical chemical sources of these differences, but also have a model that is consistent with natural selection pressures on protein interfaces.After construction, our FLIPdb database referenced 160 PPIs between 233 protein chains that were contained in 94 PDB structural files. This set was categorized and divided into 100 FLIP interfaces and 60 FunC interfaces. We further sub-categorized these PPIs into 7 FLIP and 2 FunC sub-categories: 1) antibody-antigen (AbAg); 2) immunoglobin Heavy Chain/Light Chain (AbHL); 3) Enzyme-Enzyme, both transient and persistent (Enzyme); 4) having a generally persistent structure that provides mechanical stability, such as cytoskeletal or viral proteins ; 5) peptide/protein inhibitors to an enzyme (Inhibitor); 6) proteins whose function is to recognize peptides/proteins (Receptor); 7) proteins regulated by post-translational modification by another protein (Regulated); 8) PPIs in an asymmetric crystal unit NOT found to be FLIP (FunC); and 9) PPIs obtained by applying crystal symmetry transforms to FLIP structures (XFunC). This set of PPIs . The FLIPs were also found to generally have a negative slope, indicative of a central tendency, whereas the FunCs generally had near flat or small magnitude positive slopes. The small magnitude slope and poor R2 suggests little geometric central tendencies in the FunC. These trends were generally maintained throughout FLIPdb, with most FLIPs having a radially symmetrical central tendency and most FunCs demonstrating little-to-no correlation with distance from the center of the interface. One-way pairwise ANOVA at an α = 0.10 analyzing the slopes and intercepts indicated that the differences between FLIP and FunC were significant with P<0.0006 and P<0.09, respectively.We used Baker's CAS method a priori reason FLIP PPIs should demonstrate a central tendency relative to FunC PPIs. Unless an organizing principle was involved, one might expect an interface to have a random correlation between CAS ΔΔG and geometry  from the interface center (slope_ΔΔG), the extrapolated maximum ΔΔG sensitivity at the interface center (intcpt_ΔΔG), and the adherence of the ΔΔG and Δr data to a linear relationship . Three features were found that describe the net sensitivity of an interface to CAS: net sum of all ΔΔG changes (Sum_ΔΔG), mean ΔΔG for all interface residues (Avg_ΔΔG), and total number of residues in the interface . The remaining two features address the number of residues extremely sensitive to Ala substitution : the number of hot residues (#hot), and the ratio of hot to total (frac_hot). One-way pairwise ANOVA at an st order linear model. Thus, for all subsequent analyses, R2_ΔΔG was dropped as a feature. The resultant 7-feature PCA reproduced 88% of the remaining data variation in the first two PCs  that reproduced 80% of the normalized data variation in the first two PCs . Analysi two PCs . Subsequ two PCs , produceFunC PPI . ClusterFunC PPI . The samFunC PPI . As withFunC PPI . Over twAnalysis of the two rounds of PCA of the training set PPI data indicated that the overall accuracy (the propensity to correctly identify FLIP or FunC) was ∼67% in each PCA round. Over both rounds of PCA, the accuracy was 76% . The MatWhile analysis of the training set data very favorably predicted distinct feature set correlations between FLIPs and FunCs, it was possible that the relationship was training set dependent and demonstrated compositional bias. In order to test this, we undertook three types of cross-validation testing: validation on two test sets and random sub-sampling validation on the training set.nd round projection accuracies were larger (73%), and the overall two-round accuracy was actually slightly higher than the training set at 78% . The additional interfaces in this set were between protein chains that generally had less than 70% sequence identity to chains in the training set . No new t at 78% . Similart at 78% . This baWe next repeated the analyses on a second test set of 170 PPI derived from the dataset of Dey and colleagues see  [7]. Theα = 0.10 for 1st through 6th order polynomial fits of number of PPI vs. Accuracy show substantial Lack of Fit error and a lack of statistical significance for each. Overall, while this suggests that little compositional bias exists until the number of PPI falls substantially below 80 (50% of the training set), it also suggests that analyzing more PPI will not dramatically improve the overall accuracy.As the accuracy and MCC varied somewhat from training set to Test-18 set to Dey-170 set, we evaluated the compositional bias of our training set using random sub-sampling validation . Sub-samTaken together these training set and random sub-sampling results suggest our method is robust to protein identity and of general applicability, though likely needing additional refinement in order to boost the accuracy to levels found in other methods Through the coupling of biological functional categorization with interface geometries and energetics, the ECR methodology produces very consistent results, both between training and testing sets, as well as between functional sub-categories of PPI. FLIP PPIs can be distinguished from FunC PPIs with 76% accuracy . In addiFrom the analysis of CAS ΔΔG energetic and geometric features, several clear patterns emerge. The first of these is that FLIPs appear to have greater overall sensitivity to Ala substitution than FunCs , 4. FLIPFLIPs also appear to have larger magnitude feature correlations along PC2 than FunCs, which cluster closer to the PC2 origin. PC2 is dominated by Slope_ΔΔG, intcpt_ΔΔG, and #total , all 3 oa priori central tendency patterns  in order to maintain or enhance the specific affinity of the two protein chains . Residuepatterns . They shpatterns .pairwise central organizing tendency is thus not unlikely and may explain why these two functional sub-categories cluster with the FunCs.The ECR concept that evolutionary pressure will produce central tendency patterns with large specificity helps explain some discrepancies in our PCA/K-means cluster data as well. Both Antibody-Antigen and Inhibitor sub-categories cluster near the FunCs and XFunCs in our analysis . While aMany protein-docking methods attempt to determine PPI structures by rapidly identifying and scoring regions of complementary shape and electrostatics In this work, we have introduced the FLIPdb, a database of protein-protein interfaces categorized by biological function. We have also introduced the Energy Centrality Relationship (ECR) method for analysis of computational alanine scan energetic distributions within protein-protein interfaces. We have successfully identified energetic and geometric features of interfaces that may be used to distinguish between functionally-linked (FLIP) and functionally uncorrelated (FunC) interfaces with a 76–80% accuracy. We have identified that FLIP interfaces have a stronger specificity and central organizing tendency than FunCs. Our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, our ECR method may be of use in reducing the false positive rate of docking calculations.We collected a set of atomic-resolution structures all of which are available in the PDB in vivo; (2) whether the proteins were known to oligomerize in vitro but under conditions similar to those within living systems; and (3) if mutations, post-translational modification, chemical modification, or small-molecule binding of residues within the interface were known to alter the function of the protein. (4) Additionally, we identified PPIs whose quaternary geometries were generally indicative of biological function, such as cytoskeletal proteins, viral capsid proteins, or immunoglobin interactions between the heavy-chains as well as immunoglobin heavy-chain:light-chain interactions outside the Fv region. We noted, but did not exclusively depend upon, whether PDB/PISA had designated the interface as being present in a Biological Assembly Unit (BioUnit). We categorized interfaces passing all these tests as FLIP. In addition, as a tool to aid our categorization, we noted whether the proteins could be simplistically sub-categorized into: (1) antibody-antigen (AbAg); (2) immunoglobin Heavy Chain/Light Chain (AbHL); (3) Enzyme-Enzyme, both transient and persistent (Enzyme); (4) having a generally persistent structure that provides mechanical stability, such as cytoskeletal or viral proteins ; (5) peptide/protein inhibitors to an enzyme (Inhibitor); (6) proteins whose function is to recognize peptides/proteins (Receptor); or (7) proteins regulated by post-translational modification by another protein (Regulated). We elected to use these 7, admittedly simplistic, operationally-defined sub-categories, rather than use SCOP For each of the resultant interfaces, we performed a limited literature search focused on identifying: (1) whether the proteins were known to oligomerize As the exclusions mentioned previously tended to eliminate FunC structures, we augmented our FunC numbers in two ways. First, we increased the number of proteins with a functionally unrelated PPI in the asymmetric unit by following the inverse of the method of Dey et al.In additional to this training set of interfaces, 18 additional interfaces (Test-18) were analyzed in order to provide a test set for cross-validation. All but two of the proteins in Test-18 had less than 70% sequence identify to proteins in the training set. Identity was determined using BLAST Finally, as the training set had 38 members in common with the set of Dey and colleagues (16 weak and 22 strong in the training set), we created a second cross-validation testing set from 32 additional weak and 138 additional strong interfaces of Dey and colleagues The CAS method of Kortemme and Baker Interfacial residues were defined using the same interface definition as in the CAS method of Kortemme and Baker 2) of the linear regression towards overall feature variation, this term was dropped and only the remaining seven features were used.Principle component analysis of the variation of CAS energetic and geometric feature data for all PPI was undertaken using JMP K-means clustering The following measures were used to assess the performance of our clustering analysis:Accuracy (ACC), the propensity to correctly identify FLIP or FunC:Matthews correlation coefficient (MCC), a measure of how much a set of predictive data agrees with a two-state model:TP  =  the number of interfaces correctly predicted as FLIPs (True Positive)TN =  the number of interfaces correctly predicted as FunCs (True Negative)FP  =  the number of interfaces wrongly predicted as FLIPs FN  =  the number of interfaces wrongly predicted as FunCs These values are shown in Figure S1PCA and K-means clustering of Dey-170 set. Projection of the 7 feature values of the PPI in the Dey-170 set through the principal components developed on the training set. Grey dots show the values of the training set. Green and red ovals represent 1 standard deviation for Euclidean distances around the cluster centroid marked by “x”. Values for Dey-170 interfaces are indicated with purple symbols representing “Strong” PPI interactions and blue symbols representing “Weak” PPI interactions. (a) and (b) shows projections through PCA 1 and 2 principal components, respectively. (a) 60% of Strong PPI and 0% Weak PPI group in cluster 1 while 40% of Strong and 100% of Weak group in cluster 2, yielding 100% precision and 100% negative predictive value. (b) After removal of the 82 PPI in cluster 1, a second projection of the 88 remaining values through PCA 2 produces new clusters with 54 and 34 members, respectively. PCA 2 Cluster 1 is 78% Strong while cluster 2 is 59% Weak. .(TIF)Click here for additional data file.Table S1FLIPdb interface composition. Structures and interfaces used in the training and testing sets. The FLIPdb database contained 160 pairwise PPI between 219 protein chains that were contained in 94 PDB structural files. The Test-18 set contains 18 pairwise PPI between 19 proteins chains contained in 7 PDB files. Based on literature review, these PPIs were categorized into the FLIP or FunC interface class . The PPIs were further sub-categorized into 7 FLIP and 2 FunC sub-categories: 1) antibody-antigen (AbAg); 2) immunoglobin Heavy Chain/Light Chain (AbHL); 3) Enzyme-Enzyme, both transient and persistent (Enzyme); 4) having a generally persistent structure that provides mechanical stability, such as cytoskeletal or viral proteins ; 5) peptide/protein inhibitors to an enzyme (Inhibitor); 6) proteins whose function is to recognize peptides/proteins (Receptor); 7) proteins regulated by post-translational modification by another protein (Regulated); 8) PPIs in an asymmetric crystal unit NOT found to be FLIP (FunC); and 9) PPIs obtained by applying crystal symmetry transforms to FLIP structures (XFunC). The Dey-170 set contains 170 pairwise PPI between 301 proteins chains contained in 139 PDB files. Categories were uncurated and sub-categories of “Strong” and “Weak” were derived from (XLSX)Click here for additional data file.Table S2Summary of protein and protein interface counts in Dey-170.(DOCX)Click here for additional data file.Table S3Pseudo-Accuracy of clustering in Dey-170 Test set.(DOCX)Click here for additional data file.Table S4Random sub-sample validation of FLIPdb training set.(DOCX)Click here for additional data file.


Since the structural database (PDB) is limited, structural pathways are largely built from a series of predicted binary protein-protein interactions. Below, to illustrate how protein-protein interactions can help illuminate roles played by cytokines, we model some cytokine interaction complexes exploiting a powerful algorithm .Cytokines are messengers between tissues and the immune system. They play essential roles in cancer initiation, promotion, metastasis, and immunotherapy. Structural pathways of cytokine signaling which contain their interactions can help understand their action in the tumor microenvironment. Here, our aim is to provide an overview of the role of cytokines in tumor development from a structural perspective. Atomic details of protein-protein interactions can help in understanding how an upstream signal is transduced; how higher-order oligomerization modes of proteins can influence their function; how mutations, inhibitors or antagonists can change cellular consequences; why the same protein can lead to distinct outcomes, and which alternative parallel pathways can take over. They also help to design drugs/inhibitors against proteins  Interactions of proteins with themselves, with other proteins, and other small and large macromolecules define their functions. Abstract diagrams of protein interactions—where nodes are proteins and edges are interactions—are useful: they provide the cellular “master plan”; that is, a broad view of cellular signaling pathways and their circuitry ,2. NonetCytokines are complex messengers that enable crosstalk between tissues and the immune system. They also underlie events in tumor initiation and progression . Cytokinetc.), Type-II cytokine receptors (IFN-GR IL-10R), TGF-β receptors, TNF receptors, immunoglobulin superfamily receptors (IL-1R), G-protein coupled receptors, and IL-17 receptors .,95.94,95IFN-γ is an anti-viral cytokine, but some viruses, such as Ectromelia virus (ECTV) evolved a mechanism to suppress IFN-γ signaling, preventing initiation of an anti-viral host response . The genCytokines play important roles in cancer initiation, progression, angiogenesis, metastasis and immunotherapy. Cytokines are major players in cancer-related inflammation. Cytokines and lymphoreticular infiltrates comprise the majority of the tumor microenvironment and mediate a dialog between tumor and normal tissues. To survive, evade cancer-specific immunity and tolerate chemotherapy, tumors change the relative cytokine concentrations in the microenvironment. Different cytokine cocktails trigger different responses: while some combinations contribute to tumor initiation or promotion, others are used to activate anti-tumor immunity in cancer immunotherapy. Here, we focused on cytokines and their roles in cancer from a structural standpoint. Exploiting the crystal or modeled structures of protein-protein interactions, we illustrated that (1) some parallel pathways are mutually exclusive, like TNFR1- and TNFR2-dependent paths in TNF-α signaling, whereas others can co-exist, such as IL10 downstream paths; and that (2) oncogenic mutations that fall on the interface of protein complexes, such as E41* nonsense mutation on IL-10RB, may abolish the interaction and block the subsequent downstream signaling. We further showed that (3) oligomerization modes of proteins can affect function; and that (4) mimicking the natural antagonists of cytokines such as 2L in TNF-α signaling and viral protein IFN-γBP in IFN-γ signaling, can be an efficient method to develop novel therapeutic inhibitors of cytokines. Collectively, all aspects of cytokine signaling including their upstream and downstream pathways and crosstalk with other pathways, their interactions with receptors, oligomerization modes, sequence variants, mutations, all have a role in malignant transformation. Atomic details of cytokine pathways and interactions can help to better understand the mechanisms of how and why cytokines induce carcinogenesis. We conclude by emphasizing that structures of interactions are essential for a complete in-depth picture of cancer.


This paper provides a simple and rapid method for a protein-clustering strategy. The basic idea implemented here is to use computational geometry methods to predict and characterize ligand-binding pockets of a given protein structure. In addition to geometrical characteristics of the protein structure, we consider some simple biochemical properties that help recognize the best candidates for pockets in a protein’s active site.Our results are shown to produce good agreement with known empirical results.The method presented in this paper is a low-cost rapid computational method that could be used to classify proteins and other biomolecules, and furthermore could be useful in reducing the cost and time of drug discovery. Essential information regarding protein function is generally dependent on the protein’s tertiary structure. This includes the enzymatic function of a protein, and also the binding of ligands, such as small molecule inhibitors . MethodsIn order to predict ligand binding , we first need to determine a 3-dimensional structure of the protein in question, which can be done using several experimental or computational methods ,12. StruComputer-aided drug design often applies protein–ligand docking methods, most commonly structure-based methods. These methods provide support to the rational design and optimization of novel drug candidates . Many stFinding a comprehensive, fast and automated method that can accurately predict ligand-binding pockets on protein surfaces is a major challenge in virtual screening biophysics. This goal leads us to introduce a new method for finding putative ligand-binding pockets on a protein surface, and for identifying the most important characteristics of these pockets: surface area, volume, and potential interacting atoms. This information could be used to cluster protein pockets into similarity classes, and could be a valuable resource leading to a significant decrease in the cost and time expended in the drug discovery process.The method we present in this paper is based on computational geometry and voxelization concepts. In this method we do not use Delaunay tessellation, the vision criterion, or fitting spheres between atoms, in contrast to some of the methods mentioned above. The CASTp method has used the Delaunay triangulation and the Voronoi concepts to find putative pockets and voids. This method triangulates the surface atoms and clusters triangles by merging small triangles to neighboring large triangles ,17. In osome biochemical properties of the protein’s atoms and residues as is explained below. Hence, the proposed method is not purely geometrical. We demonstrate that the results obtained using this method are in good agreement with empirically known results. Hence developing it further may offer even more accurate and reliable results.Comparative modeling methods use fold assignment and template selection for comparing the target protein to a set of proteins with known structures and to search for homologous proteins that have approximately similar structures. Some of these methods are BLAST ,43, PSI-We first voxelize the volume of a box defined to contain the extreme points of the protein’s atomic positions. Then, we use the convex hull concept to obtain the smallest convex polyhedron containing all of the protein’s atoms. In 3-dimensional space, a convex hull surface is formed by triangles, as shown in Figure which we should consider asmin : xmax],  in each axis.Normally, some parts of this rectangular cube are out of the convex hull, but we do not concern ourselves with them because they will be eliminated by another criterion, namely to keep only a given number of empty voxels near each protein atom in a pocket. Figure where “extreme” indicates either a minimum or a maximum value. Figure Then, we obtain the voxels, which are contained within this generated volume, and separate the voxels into those that contain protein atoms and those which do not. Next, we identify the nearest empty voxels with respect to these protein atoms. These empty voxels give us the possible positions of ligand atoms for this particular protein pocket. At this step, we have found a large number of “pocket” envelopes and all the atoms belonging to these pockets are the “protein’s surface atoms”.In some cases, the entire space (or part thereof) under a triangle is common with another space so we say that these spaces overlap with each other. The overlap is defined by the number of atoms in common between the two pockets divided by the total number of atoms in a pocket, which means the overlap is also dependent on the size of a pocket, so that the overlap between two pockets is not symmetric. Figure If all atoms contained in a set of the pocket atoms exist in the other pocket, it has an overlap of 100%. However, the second pocket may have more atoms than the first one, i.e. it has all atoms of the first pocket plus other atoms. For example, the overlap between pockets #1 and #2 might be 100% while the overlap between pockets #2 and #1 is only 50%, because the number of atoms in pocket #2 is twice as large as the number of atoms in pocket #1, and all atoms belonging to pocket #1 are also contained in pocket #2, but only a half of the atoms in pocket #2 are also in pocket #1. Accumulating all pockets with a given overlap between them as new pockets is the next step.The final step is related to biochemical and physical criteria such as hydrophobicity, hydrogen bonding, ionic and van der Waals interactions, and also the depth, surface area and volume comparisons between a given pocket and a ligand. By using biochemical conditions, we can find which atoms and which corresponding residues could potentially participate in an interaction with the ligand’s atoms. Tables A detailed description of the algorithm is given in the following:The algorithm1. Input protein atom position data, and define a box by using the extreme positions of the atoms.2. Voxelize the box by considering the voxel with 1 Å in length, width and height.3. Compute the convex hull surrounding the protein atoms and obtain the volume of the convex hull and the surface area of atoms.4. Separate empty voxels (possible ligand atom positions) from voxels filled by the protein atoms in the convex hull.5. Define the pockets by the volume generated by the vertices of each triangle on the convex hull.6. Compute the overlap between two neighboring pockets and assemble the pockets with an overlap greater than a minimum value (reconstruct new pockets).7. Find the physical properties of the pockets such as depth, surface and volume.8. Find the residues corresponding to the pocket atoms.9. Assess the biochemical conditions ,50 as in10. Compare physical and biochemical properties between ligand atoms Table  and the Supplementary steps to compare our results with known active sites11. Compute the number of correct residues predicted in each pocket of the unliganded protein and divide it by the number of residues in an “active site” of the liganded protein as reported in the PDB, i.e.Optional step. Compute the minimum distance between the ligand atoms and each residue atoms in the pocket. Then, filter residues of a pocket with the minimum distance greater than the given values, for example 3.50 Å.In Figure 12. in vitro and in silico methods. There are many computer programs that find active sites [In reality, the geometrical criteria give initial information about physical properties for the possible protein-protein or protein-ligand docking, determining shapes, sizes, etc. For docking to occur, the recognized geometrical protein pocket should be a protein’s active site. Finding active sites is very complicated for both ve sites -23 but tcf –value for each active site. The last two columns of Table We first demonstrate our method by describing an example, namely a pair of unliganded and liganded proteins, 1A6U and 1A6W. We have used only non-water atoms of 1A6U to find its pockets. These pockets are reported in Table cf of 25% correctly predicted residues as shown in Table 2 surface area. Thus, the protein pockets should have values of depth and surface area greater than these. The minimum distance between the atoms of ARG 350H in 1A6U with the atoms of the active sites in 1A6W is 2.89 Å. Table Here, we give a summary discussion regarding the properties of the unliganded protein structure 1A6U. It has 1737 atoms and its box has 43 × 49 × 41 voxels. The convex hull completely surrounded by triangles involves 148 triangles, which means the 1A6U structure can have at most 148 possible pockets. However, only 81 pockets remain with a 0.8 overlap cutoff between pockets. By using biochemical conditions, only 20 pockets remain and then by using physical conditions of depth and surface, only 16 pockets remain. These remaining pockets are listed in Table Table cf equal to 0.5). In our computation, for example, in the unliganded protein 1A6U the best pocket has a cf equal to 0.43 and to 0.55 for the first and second active site of the liganded protein 1A6W, respectively.As can be seen in Figure unliganded proteins, and then we compared these pockets with the known active sites reported in the PDB files of the corresponding liganded proteins.For illustration purposes we have taken the set of 48 and 86 “liganded and unliganded proteins”, respectively, listed in the supplementary material of Li et al.  and downcf, of residues predicted in a given pocket is computed and the histograms of maximum cf in each protein’s pockets are reported in Figures The correct fraction, cf  of more than 25% are reported. Additional file In Table cf in each protein’s pockets are reported (with a 0.8 overlap). It shows that 73.8% of the pockets predicted by our algorithm in the 130-element data set have at least half of their residues belonging to an active site in the liganded protein, i.e. cf ≥ 0.5.We have also chosen another 130 pairs of unliganded and liganded protein structures of . This information can help us verify possible ligands having a shape and size that is geometrically compatible with the pocket, and thus could be docked to the protein. We have used some biochemical properties to find the possible interacting atoms and residues in the pockets. Our method is a low cost computational method which voxelizes the protein space, and uses the convex hull concept commonly employed in computational geometry. This method could be used to classify proteins by the geometric properties of their pockets and also by their biochemical properties. An application of this method could be useful in reducing the cost and time of drug discovery.The authors declare that they have no competing interests.SMSF and JAT conceived of the study, and participated in its design and coordination and helped to draft the manuscript. Both authors read and approved the final manuscript.Table with pairs (bound and unbound) of PDB files in the 48 element set and in the 86 element set. For each pair, the RMSD (in angstroms) is given.Click here for fileList of the full pockets for each unliganded structure in the 48-element set with a cf  of more than 25%. For each pocket the cf for each active site (“AC”) is given after the label “Res. in common with N AC:”. Residues are named in the form “<resname > <resid > <chain>”. For each residue in a pocket, the minimum distance between the residue and the ligand atoms of the corresponding liganded structure is given.Click here for fileTab-delimited text file. Table with 130 pairs of unliganded (unlig) and liganded (lig) PDB files. For each pair, the cf and the time of execution (in seconds) is given.Click here for file


In this data article, an OFFGEL fractionator coupled to LTQ Orbitrap XL MS equipment and a SGD filtering were used to detect in a biofilm-forming flor yeast strain, the maximum possible number of stress proteins under the first stage of a biofilm formation conditions (BFC) and under an initial stage of fermentation used as reference, so-called non-biofilm formation condition (NBFC). Protein functional analysis – based on cellular components and biological process GO terms – was performed for these proteins through the SGD Gene Ontology Slim Mapper tool. A detailed analysis and interpretation of the data can be found in “Stress responsive proteins of a flor yeast strain during the early stages of biofilm formation” . Comparison among stress and non-stress conditions of protein frequency sorted in each term, allows to highlight relevant GO Terms.•The bioinformatics tools applied in this study allows to interpret biological information to be used for comparative proteomics studies.•The association of proteomic data with protein activity assays and genetics may lead to the genetic improvement of flor yeast strains.3Saccharomyces cerevisiae proteome frequency, p-value and the “GO Term frequency BFC/GO Term frequency NBFC” ratio highlighted most relevant cellular components and biological processes in each condition.Here, we show sub-cellular localizations , were applied in order to clarify the sub-cellular localization and biological processes of the identified proteins.The effects of two different biofilm formation conditions (BFC and NBFC) on


The Gene Ontology (GO) is widely recognised as the gold standard bioinformatics resource for summarizing functional knowledge of gene products in a consistent and computable, information-rich language. GO describes cellular and organismal processes across all species, yet until now there has been a considerable gene annotation deficit within the neurological and immunological domains, both of which are relevant to Parkinson’s disease. Here we introduce the Parkinson’s disease GO Annotation Project, funded by Parkinson’s UK and supported by the GO Consortium, which is addressing this deficit by providing GO annotation to Parkinson’s-relevant human gene products, principally through expert literature curation. We discuss the steps taken to prioritise proteins, publications and cellular processes for annotation, examples of how GO annotations capture Parkinson’s-relevant information, and the advantages that a topic-focused annotation approach offers to users. Building on the existing GO resource, this project collates a vast amount of Parkinson’s-relevant literature into a set of high-quality annotations to be utilized by the research community. Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s disease. An increased understanding of Parkinson’s disease has resulted from the identification and characterization of genes that cause or influence the risk of developing this condition  of the gene product. GO terms are then associated with a gene product to create an ‘annotation’. GO annotations are linked to an underlying reference, and include an evidence code indicating the assay, algorithm or decision supporting the annotation. A combination of manual and computational approaches are taken to create these annotations, and include summarising published research, inference by homology to related annotated gene products, and mapping of annotations from other biological resources such as Reactome, KEGG, and InterPro . We consider a protein to be comprehensively annotated within a process when a curator is confident that the annotations cover all aspect of the protein’s function within that process. An overview of the full role of a protein within a process is typically gained from reading recent reviews. In practice, we have a limited time to annotate each protein, consequently we usually stop annotating a well-researched protein when annotation of additional papers leads to the repeated association of the same GO term to the protein. Proteins described in only a small number of papers will be annotated based on all available experimental evidence. Some proteins will be comprehensively annotated across multiple processes as we are taking a protein-based approach alongside our topic-based approach, as described below. One such fully-annotated protein is human PARK7 , which (as of 6th August 2015) has 226 annotations in total, 135 of which were created by our project  currently includes 48 proteins encoded either by familial Parkinson’s disease genes or by candidate genes identified in GWA studies. An extended priority list (http://www.ebi.ac.uk/QuickGO/GProteinSet?id=ParkinsonsUK-UCL) also contains proteins that interact with the high priority set, as identified by IntAct , response to cellular stresses  stress and nitrosative stress), Wnt signaling, lysosomal pathways, dopamine metabolism, dopamine transport, autophagy and synaptic transmission. We are annotating all key players in these processes to capture multiple aspects of Parkinson’s disease and allow examination of the cellular networks that underlie the condition. We define a key player as a protein that has been shown to be critical to a process or regulation of a process, information we acquire primarily through literature curation but also from attending meetings in the field and communicating with researchers. In addition, we are prioritising curation of Parkinson’s UK-funded research articles. Furthermore, our priority lists remain dynamic and are expanded to reflect new knowledge and scientific advancements.Inferred from Sequence or structural Similarity) evidence code.Thus far, we have manually curated nearly 300 publications, and generated over 4000 manual annotations, including over 2700 annotations to human proteins (as of 6th August 2015). Our annotation focuses on human proteins, but given the enormous knowledge contribution from model organism data (Magen and Chesselet happens_during cellular response to oxidative stress (GO:0034599)”. By referencing the cell-type and anatomy ontologies  is a phosphorylation substrate of Eif2ak3  during the Unfolded Protein Response (UPR) , ‘ERAD pathway’ (GO:0036503), ‘chaperone-mediated autophagy’ GO:0061684), ‘L-dopa biosynthetic process’ (GO:1903185) and ‘tyrosine 3-monooxygenase activator activity’ (GO:0036470), thereby enabling more descriptive annotations to be created. Using the UPR as an example; the endoplasmic reticulum UPR is one pathway by which cells deal with unfolded and misfolded proteins in the ER to restore the cell to its resting state. ER stress has extensive links to Parkinson’s disease Subscribe to our quarterly newsletter.ii)Extend our gene priority lists by supplying us with details of key proteins.iii)Suggest gene annotations that are currently missing, or literature that requires curation.To make the greatest impact in the area of Parkinson’s disease, it is important that we consult experts in the field to ensure our GO annotations and terms are accurately capturing current knowledge. Thus we have established a scientific advisory panel for consultation and we interact with researchers and authors during curation, as described above. When curating a topic, we also refer to Reactome  can be subscribed to stay informed about all GO projects.Furthermore, our project progress can be followed on our Twitter account (@UCLgene) and the go-friends email list (http://www.ebi.ac.uk/QuickGO/GAnnotation?tax=9606&source=Alzheimers_University_of_Toronto) with some notable overlap in prioritized proteins (e.g. Tau). There are a growing number of focused GO annotation projects, such as those covering the cardiovascular and transcription domains  reveals that mutations in MAPT encoding the microtubule-associated protein Tau are linked to a number of neurological disorders including Frontotemporal dementia (FTD), Progressive supranuclear palsy 1 (PSNP1), and of course Alzheimer’s disease.Stress-insult is a common theme across human health, with the antioxidant PARK7 (DJ-1) playing a central role in protecting against oxidative stress in cancer, cardiovascular disease and neurodegeneration   .All of the annotations associated with our prioritised Parkinson’s-relevant proteins are freely available for viewing or download in QuickGO (


Identifying effective biomarkers to battle complex diseases is an important but challenging task in biomedical research today. Molecular data of complex diseases is increasingly abundant due to the rapid advance of high throughput technologies. However, a great gap remains in identifying the massive molecular data to phenotypic changes, in particular, at a network level, i.e., a novel method for identifying network biomarkers is in pressing need to accurately classify and diagnose diseases from molecular data and shed light on the mechanisms of disease pathogenesis. Rather than seeking differential genes at an individual-molecule level, here we propose a novel method for identifying network biomarkers based on protein-protein interaction affinity (PPIA), which identify the differential interactions at a network level. Specifically, we firstly define PPIAs by estimating the concentrations of protein complexes based on the law of mass action upon gene expression data. Then we select a small and non-redundant group of protein-protein interactions and single proteins according to the PPIAs, that maximizes the discerning ability of cases from controls. This method is mathematically formulated as a linear programming, which can be efficiently solved and guarantees a globally optimal solution. Extensive results on experimental data in breast cancer demonstrate the effectiveness and efficiency of the proposed method for identifying network biomarkers, which not only can accurately distinguish the phenotypes but also provides significant biological insights at a network or pathway level. In addition, our method provides a new way to integrate static protein-protein interaction information with dynamical gene expression data. The rapid advance of high-throughput technologies opens a new way for biomarker identification, which is an important but challenging task in biomedical research. By exploring mRNA and protein expression profiling, many sophisticated methods have been developed to identify biomarkers for classifying and diagnosing diseases and their severities. However, most conventional biomarker discovery methods mainly rely on expression measurements of individual molecules with less focus on their associations or interactions. A typical method is to select differentially expressed genes from normal and disease samples with gene expression profiling data as candidate biomarkers, and then identifies the gene biomarkers by constructing an expression-based classifier using machine learning or pattern recognition techniques. In such a way, a number of biomarkers have been successfully identified, but the methods failed to detect effective biomarkers with a high accuracy across many datasets. Major reasons for such diagnostic failure lie in 1) the heterogeneous nature of complex diseases whose expressions may vary considerably from patient to patient  the hete. RecentlGenerally, it is difficult to measure the activities of protein-protein interactions in a cell. One way is to approximate their activities by using the available gene expression data. For example, co-expression of a pair of proteins is a simple method to assess how active a protein-protein interaction is in certain conditions. The Pearson correlation coefficient (PCC) as well as other related quantitative criteria are widely used to measure gene co-expression network ,5. By ex2, whose computation may suffer from the serious problem of the "curse of dimensionality". Thus, dimension reduction and feature selection methods are in pressing need. Feature selection is a key issue in statistics and machine learning. Traditional statistical methods, such as Akaike information criterion (AIC) and Bayesian information criterion (BIC), penalize the number of parameters in the model, but they need to screen all the subsets of the parameters, which is infeasible in terms of computational complexity in high-dimensional data. LASSO  and [B], each raised to a particular power. The chemical force is assumed to be directly proportional to the product of the active masses of the reactants, thus the affinity equation can be rewritten as:The proportionality constant was called an affinity constant, α.1 interacts with protein P2 to form a protein complex [P1P2] and perform a biological function. In our study, we need to assess the abundance of protein complex [P1P2] formed by the protein-protein interaction between protein P1 and protein P2. By the law of mass action demonstrated above, we haveAs a result we can approximate the activities of protein complexes according to the elementary reactions and the gene members of the protein complex. For example, protein P1 and x2 represent the mRNA expression levels of the corresponding proteins P1 and P2. After the translation process, the concentrations [P1] and [P2] of individual proteins P1 and P2 before various modifications and reactions, are proportional to their mRNA expression levels, i.e., [P1] ≈ x1, [P2] ≈ x2. In our computation, we assumed that the affinity constants are the same for all the protein-protein interactions and set it to one. Therefore, we can use the following equation to estimate protein-protein interaction affinity for each interaction for any pair of proteins P1 and P2:Let xXm×n, in which the expression of n genes is measured for m samples and ij xdenotes the expression level of gene or protein j in sample i. For a single protein-protein interaction, we can approximate the protein-protein interaction affinity by the law of mass action. As a result, a protein-protein interaction affinity matrix can be derived as Am×q, in which the affinity of q protein-protein interactions is calculated for m samples. ij a= iuxiv xdenotes the affinity level of protein-protein interaction j (between proteins u and v) in sample i. We set i, i w= 1,..., q, denoting the weight for each protein-protein interaction to be selected as an edge biomarker. Similarly, i, i w= q + 1,...,q + n are weights for each gene to be selected as node biomarker. These two part of biomarkers are combined to form the network biomarker set. Supposing in total that there are c classes of samples, the formulation of our method can be described as follows:Given a gene expression dataset Subject toi (between proteins u and v) in class k for all samples. k Iis the set of samples belonging to class k with total number km. Similarly i in class k for all samples. ij ξare slack variables to tolerate the data errors. Equation (1) is the objective function for the optimization problem, which consists of three terms. -4 as a threshold. The weight larger than the threshold for each protein-protein interaction is selected as a network biomarker. The second term Where The gene expression datasets for evaluation include NCBI GEO database with accession number GSE10797 , GSE79041, p2, ..., pn }, the redundancy score is defined as follows,We compared our method with node-based ellipsoidFN  and t-tepi, pj) denotes the Pearson correlation coefficients of proteins pi and pj.Where cor and 56 disease ones  in the breast cancer dataset GSE10797. Genes with missing values and with low information were filtered out from the raw data. Here low information means the entropy of genes expression distribution is smaller than 1.5. After these procedures, we got a gene set with 2,325 genes. Then we mapped the probes to human PPI network and 5,458 gene pairs were obtained. Based on our PPIA + ellipsoidFN optimization model and Random Forest classifier (we use the MATLAB toolbox with version 0.02 and license GPLv2), 3 genes and 6 interactions totally including 14 genes (Supplementary Data Set 1) were identified with leave-one-out cross-validation classification accuracy 96.97% (64/66), while DEG + ellipsoidFN method got 22 genes (Supplementary Data Set 2) with classification accuracy 93.94% (62/66). The overlap of these two sets of biomarkers contains 4 genes. Figure Also it is important to note that when we reduce the total number of genes from 2,325  to 935  by entropy distribution, our PPIA + ellipsoidFN method gained a predicting accuracy 93.94% compared to that of DEG + ellipsoidFN 84.85%. This result shows when fewer genes and PPIs are used for initial selection, network biomarkers are robust and superior to traditional single molecules. One reason is that strictly selected genes reduce noise of the data set. Generally, in a molecular network, genes work as a system and interact with each other to achieve certain functions.In order to compare the performance of t-test and PPIA + ellipsoidFN in a fair way, we selected top 50 network biomarkers including both PPIs and proteins and compared their redundancy scores. PPIA + ellipsoidFN got a redundancy score 0.2144 versus 0.2715 of t-test, and the number of the intersection of these two biomarker sets is 13. These results show that our PPIA + ellipsoidFN method can identify a set of heterogeneous network biomarkers which are considerably different from the widely used t-test. Comparisons on t-test and PPIA + ellipsoidFN are demonstrated in Table In order to strengthen the advantage of our method, we take another data set GSE7904 for further evaluation. This breast cancer data contains totally 54,675 probes and 62 samples, including 19 normal samples and 43 disease ones. After processing the data with the method above, 516 genes including 682 PPIs left. The leave-one-out cross-validation classification accuracy is 98.39% (61/62) for both PPIA + ellipsoidFN and DEG + ellipsoidFN methods. Also 2 genes and 9 interactions containing 19 genes (Supplementary Dataset 3) were selected as network biomarkers with the redundancy score 0.2968, while 18 genes (Supplementary Dataset 4) were identified by DEG + ellipsoidFN method with the redundancy score 0.3698. Both biomarker sets share 4 genes in common. This computational experiment shows that the two methods achieve the same classifying accuracy but network biomarkers reduce the redundancy score of the biomarker set. The comparison results are presented in Table Dataset GSE18229 contains 337 samples and 22,575 probes in total. Filtering the samples without subtype label and normal ones, we got 310 tumor samples including 73 basal-like, 37 claudin, 39 Her2, 99 luminal A, and 62 luminal B. The procedures of pre-preprocessing data are similar to those above in two-class case. Experimental results show that the predicting accuracy is 80% for DEG + ellipsoidFN and 79.03% for PPIA + ellipsoidFN method. Similarly the redundancy score is 0.1523 out of 170 genes (in Supplementary Dataset 6) for DEG + ellipsoidFN, and 0.1652 out of 90 genes and 38 interactions which contains 135 genes (in Supplementary Dataset 5) for PPIA + ellipsoidFN method. The overlap of these two sets of biomarkers contains 94 genes. This result illustrates that the predicting accuracy of PPIA + ellipsoidFN is slightly lower than that of DEG + ellipsoidFN. The result of the redundancy score could arise from the fact that interacting proteins are inclined to be co-expressed, which is different from gene-node biomarkers. These comparisons could also be checked in Table We also introduce dataset GSE10797 for multiple-class case. PPIA + ellipsoidFN method identifies 12 genes and 23 PPIs including 55 genes (Supplementary Dataset 7) as network biomarker with the redundancy score 0.2150 and the classifying accuracy is 83.33% (55/66). DEG + ellipsoidFN method achieves the classifying accuracy 78.79% (52/66) and identifies 161 genes (Supplementary Dataset 8) as node marker with the redundancy score 0.1663. The intersection of the two biomarker sets contains 21 genes.For data set GSE18229 PPIA + ellipsoidFN method exploiting 50 top PPIs and proteins share 15 proteins in common with F-test, and the redundancy scores are 0.3031 for F-test and 0.1693 for PPIA + ellipsoidFN. For data set GSE10797 with four-class situation, PPIA + ellipsoidFN method shares 11 PPIs and proteins in commons with F-test, and the redundancy scores are 0.2209 for PPIA + ellipsoidFN method versus 0.3280 for F-test. The results are shown in Table We obtained 9 PPIAs to optimally classify the normal and breast cancer samples with PPIA + ellipsoidFN on data set GSE10797 for two-class situation. These genes and pairs are PSMD11, ELK4, EVL, SPEN-DLX5, CREBBP-KPNA2, BGN-COL1A2, SMARCD3-JUN, IRS2-PIK3CD, and SOX10-JUN. In total, 14 genes are involved in those interactions. Evidence from a literature search implies that some of the node and interaction biomarkers above are involved in breast cancer. Lower EVL expression correlates with high invasiveness and poor patient outcome in human breast cancer , and the2 genes and 9 protein-protein interactions including 19 genes were identified as network biomarkers with our PPIA + ellipsoidFN method on GSE7904 data set. We studied their relationships with breast cancer by applying the DAVID functional annotation tool. This set of genes enriches phosphoprotein with p-value 1.2E-5. From a literature search, there is evidence that some phosphoproteins have the potential to qualify as phosphopeptide plasma biomarker candidates for the more aggressive basal and also the luminal-type breast cancers . Tumors Moreover, for dataset GSE10797 in a four-class case, we also found some evidence with the help of KEGG pathway search, we found that several pathways are statistically significant, e.g., hsadd05200 Pathways in cancer, hsa04012 ErbB signaling pathway and hsadd04510 Focal adhesion pathway. ErbB-2 is a target for cancer-initiating cells in breast and other cancers . Amplifiaij = xiu xiv There is another way to measure the co-expression pattern of the edges, which is defined by the absolute value of the difference between the two proteins, i.e., ij a= |iu x- ivx|. We compared the two methods by their classification accuracy . For two-class case, the differential form gives out the predicting accuracy 93.94% (62/66), while the product-form gains 96.97% (64/66) on dataset GSE10797. And for dataset GSE7904 the two methods both achieve the same accuracy of 98.39%. For multi-class case, the differential form achieves 76.45% versus 79.03% for the product-form on data set GSE18229. And on data set GSE10797 for four-class case, the differential form gives 75.76%, while the product-form gains 83.33%. These results show that our PPIA estimation derived from the law of mass action performs better than the differential form from classification view.In this paper we make an estimation of protein-protein interaction affinity derived from the law of mass action, i.e., In this study we propose a linear programming based optimization model to find the optimal set of protein-protein interaction affinities which are measured by the product of the gene expression. The physical meaning of PPIA is clear based on the law of mass action. Then, the product of the protein pairs in terms of concentration is the approximation for the activity of protein-protein interaction in the cell, which is a similar measure as the correlation based measure. In this paper, we also compared the performance of product approximation with the measure of EdgeMarker  (definedIf we don't use the ellipsoidFN method to reduce the dimension of the data, the classification accuracy will be much lower. Now we only apply the Random Forest classifier to make classification and prediction. For two-class case on data set GSE10797, random Forest could only achieve 84.89% accuracy, and on data set GSE18229 the accuracy is 63.17%. Moreover, the program needs much longer running time. This comparison could demonstrate the efficiency of our dimension reduction model ellipsoidFN.The complexity of cancer pathogenesis makes it difficult to identify a set of effective and stable biomarkers for the diagnosis of complex diseases. In this study, we incorporate PPI with gene expression data to battle cancer heterogeneity. Because PPIs bring the topological information of a network, our method has clear advantages over the node based feature selection method. Generally, our method tends to select pathways or subnetworks, which have clear and enriched biological meanings. Our main contribution in this work is to integrate PPI with expression data in a novel way by defining PPIA, which has clearer biochemical meaning. Moreover, ellipsoidFN, an efficient dimension reduction method to select a representative subset of PPIA, is utilized to perform the feature selection from high dimensional data. With the increasing biological interpretability, the redundancy score of biomarkers identified by our method increases in some cases and the predicting accuracy is comparable to DEG + ellipsoidFN. Those results also demonstrate the tradeoff between the biological interpretation and machine learning accuracy.A network is a set of edges or interactions. Those interactions are context-specific and dynamic in nature. Under different conditions, different interactions are selectively activated or deactivated. Importantly, network rewiring is a general principle of biological systems regarding disease progression or therapeutic response in epigenomic era. In this paper, we propose a new method to combine PPIA and ellipsoidFN to detect network biomarkers from pathway perspective rather than single node based identification methods. We stress "which interactions are differentially connected" instead of "which genes are differentially expressed".As we mentioned, this edge-focused study provides a general data integration framework for static network information and dynamic expression data. We note that there are different ways to consider edge information. Liu et al.  integratThe authors declare that they have no competing interests.Supplementary Dataset 1.txtClick here for fileSupplementary Dataset 2.txtClick here for fileSupplementary Dataset 3.txtClick here for fileSupplementary Dataset 4.txtClick here for fileSupplementary Dataset 5.txtClick here for fileSupplementary Dataset 6.txtClick here for fileSupplementary Dataset 7.txtClick here for fileSupplementary Dataset 8.txtClick here for fileSource code for the new methodClick here for file


The transient assembly of multiprotein complexes mediates many aspects of cell regulation and signalling in living organisms. Modulation of the formation of these complexes through targeting protein–protein interfaces can offer greater selectivity than the inhibition of protein kinases, proteases or other post-translational regulatory enzymes using substrate, co-factor or transition state mimetics. However, capitalising on protein–protein interaction interfaces as drug targets has been hindered by the nature of interfaces that tend to offer binding sites lacking the well-defined large cavities of classical drug targets. In this review we posit that interfaces formed by concerted folding and binding (disorder-to-order transitions on binding) of one partner and other examples of interfaces where a protein partner is bound through a continuous epitope from a surface-exposed helix, flexible loop or chain extension may be more tractable for the development of “orthosteric”, competitive chemical modulators; these interfaces tend to offer small-volume but deep pockets and/or larger grooves that may be bound tightly by small chemical entities. We discuss examples of such protein–protein interaction interfaces for which successful chemical modulators are being developed. Multiprotein assemblies often arise from initial weak binary interactions followed by cooperative, higher-order complex formation, giving high selectivity while at the same time being transient as required for termination of regulatory signals .Multiprotein regulatory systems are assembled mainly through protein–protein interactions (PPIs). Whereas enzyme superfamilies that mediate many signalling events may number hundreds of homologues in the human genome – more than 500 protein kinases and over 600 putative E3 ubiquitin (Ub) ligases  – multipUsing criteria derived from retrospective analyses of successful drugs, protein–protein interaction sites have historically been described as undruggable . Indeed,In this review we highlight the importance of relatively small pockets that can lead to very selective binding at PPI interfaces . We show2http://3did.irbbarcelona.org/), Domain Annotated Protein–protein Interaction Database (DAPID (http://gemdock.life.nctu.edu.tw/dapid) and PICCOLO (http://www-cryst.bioc.cam.ac.uk/piccolo), have documented structural aspects of PPIs and shown that each of these models is quite common; for reviews of structures, lists of databases and tools for studying protein–protein interactions see , flat and relatively featureless interfacial surfaces , as a per-residue measure of depth of occupation or formation of a binding site. Our preliminary data indicate that while protein-peptide interactions make better overall use of interface surface pockets on their protein partners compared to other classes of interaction , from binary and higher-order complexes. We have distinguished between interactions of proteins including enzymes with peptides on the one hand, and homologous and heterologous globular interaction interfaces on the other. We compared segmentation (binding epitope continuity), solvent accessibility, secondary structure, interatomic interactions and binding depth , systemaeraction a, interaeraction b, even i4If flexible peptides exploit well-defined pockets, is this also true of flexible regions in globular regions when they mediate protein–protein interactions?Preliminary analysis of the secondary structures utilised by deeply bound residues indicate that, while solvent inaccessible residues bound deep in pockets are very often found in helices, there are many examples of loop, bend and turn residues that are deeply bound .One example of the involvement of loop residues at interfaces is in camelid and nurse shark heavy chain-only (VHH) antibodies that are approximately 10 times smaller than conventional immunoglobulin G's, and lack light chains . The eluIt is clear that similar features may also occur in other systems where flexible loops mediate protein–protein interactions. An obvious example is in the self-association of RAD51 through loops containing the conserved FxxA repeats in nuclear protein filaments, for example defined by Shin et al.  in archaInteractions involving the termini of a protein have also been exploited in the development of peptide PPI inhibitors, for example the angiotensin II receptor antagonists mimicking Angiotensinogen . FurtherInteractions with the Met sema domain by the C-terminal serine-protease-like domain of HGF/SF are also likely to be the result of a combination of order–order and disorder-order binding. For example, this lower-affinity binding site of HGF/SF contains a number of residues linked to reduced Met signalling. This interaction is similar to the substrate processing region of serine proteases, with a core triad of homologous catalytic residues, a ‘hot-spot’, and interactions with the corresponding c220 activation domain loop . From th5Several chemical methodologies designed to modulate PPI interfaces, in particular interfaces with deep pockets and grooves, including alpha-helical mimetics  and stapAn analysis of different PPI revealed that for many targets it would be necessary to expand the available chemical diversity space in order to identify small-molecule PPI inhibitors . One of One example of a successful fragment-driven campaign against a PPI interface is the RAD51:BRCA2 interaction (introduced above). Hyvönen and coworkers have engineered a monomeric form of RAD51 by humanising a thermostable archaeal orthologue, RadA, for use in fragment screening . The iniIn the longer term small-molecule inhibitors of PPI may become second generation, less costly alternatives to the use of antibodies to directly compete for the binding sites. One application might be to use small-molecule inhibitors to target the binding sites described above of the Met interactions with HGF/SF, so providing less expensive agents than the antibodies designed to target the HGF/SF  and the As experimental data on first-generation PPI inhibitors are becoming increasingly available, several databases now record information on small-molecule inhibitors of protein–protein interactions. TIMBAL  integrat