Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations.In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners.An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces. It is now accepted as evident that protein-protein interactions (PPIs) are of fundamental importance in the vast majority of molecular events that occur in living organisms. Proteins can interact to form stable macromolecular assemblies that are able to perform many complex biological functions. They can also form transient interactions that collectively constitute dynamic networks of interactions that regulate how organisms operate. Protein-protein interactions are also of crucial importance to bacteria and viruses, which interfere with the host PPI network during infection ,2. LogicShape complementarity, surface hydrophobicity and charge complementarity have all been recognized as key factors of recognition in early studies ,5. More A number of methods for predicting PPIs have been developed, targeting two distinct aspects of the problem: protein-protein binding site prediction and protein docking. In the former case, the challenge is to identify the surface residues involved in the formation of protein-protein complexes; see -10 for rThe fact that false complexes obtain good scores during cross-docking studies raises two important and orthogonal questions: Are scoring functions so poor that they cannot discriminate interacting from non-interacting proteins , or The fact that biological interactions in the cell are tightly orchestrated by localization and co-regulation mechanisms indeed suggests that significant nonspecific interactions may be common. It has been proposed that co-localization is necessary to control specific interactions, given the size of cells and the lifetime of individual proteins . So far,In this paper, we investigate what can be termed the "twilight zone" of protein-protein interactions, by using computational docking, and building on an interesting outcome of earlier cross-docking experiments: "false" complexes seem to favor interfaces containing residues belonging to "true" interaction sites ,18. Thistarget data set, encompasses 198 proteins. Only unbound forms were used in all docking experiments. The structures were downloaded from http://zlab.umassmed.edu/benchmark/benchmark4.tgz.We have extracted protein structures from the docking benchmark data set assembled by Hwang et al., version 4.0 . This seThese 198 proteins were docked against arbitrary partners taken from Nh3D, a data set of representative structures of each domain at the Topology level of the CATH structural classification database . Startincompact probe data set. We further checked for known interactions between these probes and the target proteins using the IntAct database .where IND, computed for each exposed residue as follows. Each residue was taken as the seed of a local surface patch, including all atoms of neighboring exposed residues within a 10 Å radius. To avoid discontinuities of these local surface patches , residues were filtered using hierarchical clustering with a single linkage procedure; the resulting tree was truncated using an empirical cutoff of 4.2 and secondary clusters were removed . Each residue was also associated with a patch score, which is the mean number of normalized docking hits occurring in the patch around this residue.The link between docking hits and surface shape was investigated using: (i) a local planarity analysis; (ii) the relative closeness to the geometrical center of the protein. The local planarity was measured with a planarity index, Pes plane . A low PGEOCEN) of an exposed residue is then the ratio between its distance to the GEOCEN, and the mean distance of all exposed residues to the GEOCEN. A value below 1 consequently indicates that a residue is closer than average to the GEOCEN.To determine the closeness of an exposed residue to the geometric center (GEOCEN) of a protein, we built on the definition of Nicola and Vakser , by firsAll statistical tests were performed using R . DockingThe correlation between the number of docking hits and the patch planarity was measured using Pearson’s product moment correlation coefficient. Since the distributions we observe are not all Gaussian, we computed empirical p-values with 1000 replicates.To take into account multiple testing, p-values were systematically corrected using the Benjamini-Hochberg method (BH) . Unlike N distribution. The Chi-squared test is a test of independence between variables. It determines whether or not the variables are independent, but, when independence is rejected, it does not quantify the association between the variables. To get a quantitative measure of the association, we considered the Cramer coefficient, Cramer’s V, defined by:The amino-acid composition bias in regions with many docking hits was assessed using the Chi-squared test. Significant contributions to Chi-squared were detected by looking at the Pearson residuals: N is the total count of the table and r and c are the table dimensions. Cramer’s  V is, by construction, always between 0 (independence) and 1 (full dependence) and has a clear proportional interpretation: it is the fraction of the maximal departure from independence that one would observe in case of full dependence [Where pendence .The difference between AUC values (see “Prediction assessment”) was assessed using the non-parametric DeLong’s test  implemenBiological interfaces were defined using the bound structures of the docking data set version 4.0  with a 5FP denotes the number of false positives , TN denotes the number of true negatives (residues outside the interface and rejected by the prediction), TP denotes the number of true positives (interface residues predicted as such) and FN denotes the number of false negatives (interface residues missed by the prediction). The computation of FPR and TPR for various thresholds enables a receiver operating characteristic (ROC) curve to be drawn. A unique measure of performance is then given by the resulting AUC value (area under the curve) that is equal to 0.5 for a random prediction and 1.0 for a perfect prediction. The prediction was carried out using exposed residues (RSA > 5%). AUC values were computed using the pROC package [The performance in prediction was assessed using the False Positive Rate (FPR) and True Positive Rate (TPR) defined by  package .VORFFIP is a recent and sophisticated method for predicting protein interaction surfaces which uses different forms of information including structural features, energy terms, evolutionary conservation and crystallographic B-factors, with a Voronoi-based environment description, combined in a two-step random forest classifier . The autJET is a method based on evolutionary trace . It usesPiQSi is a curated database of protein quaternary structures . StructuWe first study the distribution of docking hits on the surface of the 198 target proteins after docking with the 314 compact probes. For each exposed residue, the number of times it belongs to an interface is recorded. Global results are given in Figure Extending the global analysis to individual proteins, we compute the distributions of observed and expected number of hits separately for each protein, and compare them with the Cramer test. The target proteins are binned in terms of their length range, and the number of significant p-values in each bin is shown in Figure The data shown on Figure In this section, we explore the features of favored regions in terms of protein shape, using measures of local planarity and closeness to the geometrical center of the target proteins.IND planarity index described in the Methods section. The PIND of each residue is then compared with the corresponding patch score, which is the mean number of docking hits per residue in the local patch generated around the residue. To allow comparison among proteins of different size, the number of hits per exposed residue has to be normalized for each protein as explained in the Methods section. The global correlation between planarity index and patch score is very weak, with a Pearson correlation coefficient rho equal to −0.026 , implying that there is no link between local planarity and accumulated docking hits .The local planarity around each exposed residue of the 198 target proteins is measured with the PGEOCEN index described in the Methods section quantifies the proximity of an exposed residue to the geometrical center of a protein compared to the average distance of exposed residues to the geometrical center. The comparison of RGEOCEN with the normalized number of docking hits per exposed residue reveals a global trend: the correlation is significantly negative, with a rho coefficient equal to −0.25, see Figure GEOCEN for each protein: negative rho corresponds to proteins with a high dispersion of the relative distances, that is, conformationally anisotropic proteins  is achieved using the first 10 models and only the 25 shortest probes. This suggests that arbitrary docking could be used in practical applications, since predictive power can be obtained with a very limited number of docking computations.We now consider the performance of this approach compared with other existing methods  and either alone, or combined with other data, provides significant information for predicting biologically-relevant protein interfaces.Although the predictive power of arbitrary docking itself is significant, some proteins seem very difficult to treat. Further examination of the difficult cases led to interesting cases of proteins that probably have multiple interaction interfaces. The apparent failure of arbitrary docking can indeed result from detecting interfaces that exist in alternate complexed forms of a protein, distinct from those described in the docking benchmark data set. Figure To extend this type of analysis to all the proteins of the target data set, we developed the procedure outlined in Figure The data presented in Figure We conclude this study by showing the insight arbitrary docking can provide with a test case: the protein PEBP (Phosphatidylethanolamine binding protein). This protein, also called RKIP (Raf Kinase Inhibitor Protein), is a known inhibitor of several protein kinases, including those of the Raf/MEK/ERK pathways and the Aurora B pathway. PEBP also inhibits the GRK2 kinase, when activated by protein kinase C. These functionalities are the result of direct physical interactions, although no structural details of the complexes exist . Nevertheless, the structures of PEBP and some of its partners are available in the PDB. We thus subjected PEBP to arbitrary docking to highlight putative interaction sites, and also docked PEBP with its known partners using the Clus-Pro web server . We consThe results of this study are illustrated in Figure in silico. In vitro, the multiple solvent crystal structures (MSCS) method involves solving the X-ray structure of a protein in different organic solvents. The solvent molecules effectively probe the protein surface and tend to form clusters at protein binding sites [In silico, the FTMAP algorithm has been developed to perform a fast Fourier surface mapping using the rigid-body docking of 16 small molecules with a given target protein [Lastly, it is interesting to contrast this study with other related research on protein binding sites. Here, we addressed a specific question: do computational docking experiments applied to random protein partners lead to specific bound conformations? We found that, generally, such conformations are not random and the interactions tend to favor specific sites on the protein surfaces. A similar behavior is observed for interactions between proteins and small molecules, both in vitro and ng sites . In sili protein .Another notable feature of protein-protein interfaces is the uneven contribution of interface residues to the binding free energy. Generally, only a few residues, termed hot spots, make major contribution. Hot spot residues are enriched in tryptophan, tyrosine and arginine, but depleted in leucine, threonine and valine. They are preferentially located towards the center of the protein interface and appear in clusters -64. TherWe have shown that docking target proteins against an arbitrary set of proteins, leads to a non-random localization of interaction interfaces. These interfaces are neither systematically planar nor curved, but do tend to be closer than average to the center the protein. These predicted interfaces have been shown to contain information on the location of functional, biological interfaces, including alternative interfaces with multiple partners. An appropriate choice of random partners and of docking models analyzed makes arbitrary docking a practical tool for interface detection. The results can be used alone, or can complement data coming from other sources.The authors declare that they have no competing interests.JM participated in the design of the study, performed the statistical analysis and drafted the paper. RL participated in the design of the study and helped to draft the manuscript. Both authors read and approved the final manuscript.IND computation, Additional file IND and accumulated docking hits, Additional file GEOCEN, the relative distance to the geometric protein centers, Additional file This file contains: Additional file 1 figure  Click here for file


Among the many factors determining protein evolutionary rate, protein-protein interaction degree (PPID) has been intensively investigated in recent years, but its precise effect on protein evolutionary rate is still heavily debated.We first confirmed that the correlation between protein evolutionary rate and PPID varies considerably across different protein interaction datasets. Specifically, because of the maximal inconsistency between yeast two-hybrid and other datasets, we reasoned that the difference in experimental methods contributes to our inability to clearly define how PPID affects protein evolutionary rate. To address this, we integrated protein interaction and gene co-expression data to derive a co-expressed protein-protein interaction degree (ePPID) measure, which reflects the number of partners with which a protein can permanently interact. Thus, irrespective of the experimental method employed, we found that (1) ePPID is a better predictor of protein evolutionary rate than PPID, (2) ePPID is a more robust predictor of protein evolutionary rate than PPID, and (3) the contribution of ePPID to protein evolutionary rate is statistically independent of expression level. Analysis of hub proteins in the Structural Interaction Network further supported ePPID as a better predictor of protein evolutionary rate than the number of distinct binding interfaces and clarified the slower evolution of co-expressed multi-interface hub proteins over that of other hub proteins.Our study firmly established ePPID as a robust predictor of protein evolutionary rate, irrespective of experimental method, and underscored the importance of permanent interactions in shaping the evolutionary outcome. Among the many factors determining protein evolutionary rate -5, proteThe relationship between protein evolutionary rate and PPID is mostly studied through hub proteins, i.e., proteins with a large number of interaction partners, from many different aspects -23. For Therefore, in this paper, we first re-investigated the relationship between protein evolutionary rate and protein-protein interaction degree (PPID) and confirmed that the correlation between protein evolutionary rate and PPID varies considerably across different protein interaction datasets. We then integrated protein interaction and gene co-expression data to derive a co-expressed protein-protein interaction degree (ePPID) measure, which reflects the number of partners with which a protein can permanently interact. Our results demonstrated that ePPID is a more robust predictor of protein evolutionary rate than PPID. It was further found that the contribution of ePPID to protein evolutionary rate is statistically independent of expression level. Finally, we established that ePPID could predict protein evolutionary rate better than the number of distinct binding interfaces for hub proteins in the Structural Interaction Network and clarified the slower evolution of co-expressed multi-interface hub proteins over that of other hub proteins.Researchers have found very different correlations between PPID and protein evolutionary rate ,8-17. ToWe next studied if the protein abundance effect may account for the above significant difference between the Y2 H and other datasets. We computed Spearman rank correlations between PPID and protein abundance  and founProteins with higher PPID are assumed to have a greater proportion of residues involved in interactions and thus evolve more slowly than proteins with lower PPID ,9. This Several studies have addressed the difference in selective constraints between permanent and transient interactions on protein evolution ,20,22,39Therefore, we proposed a co-expressed protein-protein interaction degree (ePPID), defined as the maximal number of co-expressed interaction partners of a given protein in all gene expression datasets we used . Despite this effect, permanent interactions do place higher selective constrains on protein evolution than transient interactions do, further illustrating why our ePPID measure could better predict protein evolutionary rate.However, the variance of protein evolutionary rate explained by ePPID is still the lowest in the "Y2H-union" dataset, which may be explained in three ways. First, ePPID cannot filter out all transient protein interactions, partly because of noise in the gene expression datasets we used. Second, Y2 H datasets may contain co-expressed protein pairs which are localized to different cellular compartments and seldom interact in nature. Third, ePPID may be underestimated based on incompleteness of Y2 H datasets ,32, whicA number of genomic variables, such as expression level ,25,50-52For this purpose, we collected two expression-related variables, mRNA abundance and protein abundance; two function-related variables, gene dispensability and gene pleiotropy, which were measured by the associated number of GO biological process terms of each gene; and two network-related variables, ePPID and betweenness (see Methods). We then carried out a principal component regression ,56 of prResults show that the first principal component explains much more variance of protein evolutionary rate than the other components in all the six datasets. Thus, in the following, we focus on the first principal component to study the percentage contribution of ePPID. In the "Combined-AP/MS", "LC-multiple", "Updated-HC", "DIP-CORE" and "DIP-FULL" datasets, the contribution of ePPID to the first principal component is more than that of all other variables. In the "Y2H-union" dataset, the ePPID contribution is more than betweenness, but less than the other four variables. Consistently, the independent contribution of ePPID to the total variance of protein evolutionary rate dN explained by all the six principal components in most datasets is comparable to that of the expression-related variables of mRNA abundance and protein abundance  in the "SIN" dataset  and studIt is reported that protein evolutionary rate is actually more reflective of the number of distinct binding interfaces . Yet we It is also reported that multi-interface hubs have lower evolutionary rate than singlish-interface hubs  . TherefoTo see whether our result for yeast can be obtained in other species, we obtained the relevant data for human and computed ePPID for each protein  and protein evolutionary rate.Proteins with higher PPID are assumed to have a greater proportion of residues involved in interactions and thus evolve more slowly than proteins with lower PPID ,9. This In this work, we performed extensive studies to identify how protein interactions, as measured by PPID, affect protein evolutionary rate. By carefully comparing experimental setups, we observed that Y2 H assays may have introduced a considerable amount of transient protein interactions. On this basis, we hypothesized that the difference in experimental methods contributes to our inability to clearly define how PPID affects protein evolutionary rate. This hypothesis was confirmed by introducing a new protein interaction degree measure, the co-expressed protein-protein interaction degree (ePPID). Since ePPID is a measure that integrates protein interactions with gene co-expression information, it can filter out many transient protein interactions. As a result, ePPID gives a better prediction of protein evolutionary rate than PPID in the various protein interaction datasets tested. The relationship between ePPID and protein evolutionary rate is also robustly significant in all protein interaction datasets, which was not possible when using PPID in previous studies. We also investigated the redundancy between several variables that may affect protein evolutionary rate against the contribution of ePPID and found that ePPID makes an independent contribution to protein evolutionary rate. This result suggests the novelty of ePPID as an important determinant of protein evolutionary rate. Moreover, the application on hub proteins in the Structural Interaction Network provides further support that ePPID also gives a better prediction of protein evolutionary rate than the number of distinct binding interfaces and clarified the slower evolution of co-expressed multi-interface hub proteins over that of other hub proteins.In summary, our work provides a new protein interaction degree measure by integrating protein interaction datasets with gene expression datasets. This new measure has, at least in part, resolved the longstanding debates on the role of protein interactions in affecting protein evolutionary rate. Finally, we have found that this result also holds in human. We will study if this can be observed in more species in the future.To study the effect of experimental bias, reliability and completeness, we collected nine different yeast protein interaction datasets. We used the "Y2H-union", "Combined-AP/MS" and "LC-multiple" datasets to represent typical protein interaction datasets obtained from Y2 H assays, the AP/MS method and literature curation, respectively. To account for data quality (confidence), we also used the filtered yeast interactome ("FYI"), the Structural Interaction Network ("SIN"), "DIP-CORE" and the updated high-confidence dataset ("Updated-HC") as high-confidence datasets. In addition, the "SIN" dataset was also used to study the evolutionary rate of hub proteins through a mechanistic perspective. In contrast, we used the "DIP-FULL" and "Eight-union" datasets to account for completeness. The nine datasets are listed below.1) Y2H-union: the union of three high-throughput Y2 H datasets: Uetz-screen , Ito-cor2) Combined-AP/MS: an integrated dataset  of two h3) LC-multiple: a protein interaction dataset based on the literature. Each protein interaction must have been curated from ≥2 different publications .4) FYI: the filtered yeast interactome obtained from .5) SIN: the Structural Interaction Network dataset obtained from .6) Updated-HC: the updated high-confidence dataset obtained from .7) DIP-CORE: the core dataset derived from the database of interacting proteins (DIP) ,43.8) DIP-FULL: the full dataset derived from the database of interacting proteins (DIP) .9) Eight-union: the union of the above eight datasets.The network properties of the nine datasets are summarized in Additional file Saccharomyces cerevisiae genes, were obtained from [S.cer vs S.par, S.cer vs S.mik and S.cer vs S.bay) were obtained from [Saccharomyces Genome Database [Evolutionary rate data (non-synonymous substitutions per site dN), which is based on the four-way yeast species alignments for 3,036 ned from . Specifined from  and dN wned from . Three oned from . mRNA abned from . Proteinned from . Codon aned from , was obtned from . The assned from , used asDatabase . ProteinDatabase ,47, was Database  with theDatabase .Partial correlation analysis is frequently used to determine the confounding effect of variables such as protein abundance on the relationship between PPID and protein evolutionary rate ,25,51,71Saccharomyces Genome Database [We collected ten gene expression datasets -83, eachDatabase . Genes wDatabase , and tecr is used as a similarity measure between the expression profiles of two genes. The PCC r was then converted into z-score using Fisher transformation:Pearson correlation coefficient (PCC) n is the sample size. We only considered positive correlations since they are reported to be more reflective of functional similarity than negative correlations [which approximately follows a standard normal distribution under the hypothesis of independence, where elations . Next, Pelations , and theelations ,88 and wg denote the set of interaction partners of a given protein g. By filtering out the potential transient interaction partners in PPIg, we want to integrate gene co-expression in a way that allows us to identify the partners with which protein g can permanently interact. To explain, we can calculate the z-scores of co-expression between protein g and all genes in PPIg for each gene expression dataset. Let ePPIg(i) denote the number of genes that are found to be significantly co-expressed with gene g in gene expression dataset i . The co-expressed protein-protein interaction degree (ePPID) of protein g is then defined as ePPIDg = max(ePPIg(i); i = 1,2,...,10). In addition, we tried other co-expressed protein-protein interaction degree measures to demonstrate the robustness of our results  in each protein interaction dataset. Similar to the concept behind the date and party hub definition in , we furtTo study the contribution of permanent and transient interfaces to protein evolutionary rate in the "SIN" dataset, the ePPID of hub proteins in the "SIN" dataset was calculated similarly, and the hub proteins were further grouped into four classes: non-co-expressed singlish-interface hubs, non-co-expressed multi-interface hubs, co-expressed singlish-interface hubs and co-expressed multi-interface hubs by taking intersections.APCC: average Pearson correlation coefficient; AP/MS: affinity purifications followed by mass spectrometry; CAI: codon adaptation index; dN: non-synonymous substitution rate; ePPID: co-expressed protein-protein interaction degree; FDR: false discovery rate; GO: Gene Ontology; PCC: Pearson correlation coefficient; PER: percent of all possible correlations; PPID: protein-protein interaction degree; Y2H: yeast two-hybrid.KP and XM conceived and designed the experiment; HS and XM supervised the experiment; KP, CC, ZX, HS and XM conducted the experiment and wrote the manuscript. All authors have read and approved the final manuscript.Supplementary texts, figures and tables. This file contains Supplementary Texts S1-S11, Figures S1-S4 and Tables S1-S38.Click here for file


Detoxification proteins are a class of proteins for degradation and/or elimination of endogenous and exogenous toxins or medicines, as well as reactive oxygen species (ROS) produced by these materials. Most of these proteins are generated as a response to the stimulation of toxins or medicines. They are essential for the clearance of harmful substances and for maintenance of physiological balance in organisms. Thus, it is important to collect and integrate information on detoxification proteins.To store, retrieve and analyze the information related to their features and functions, we developed the DetoxiProt, a comprehensive database for annotation of these proteins. This database provides detailed introductions about different classes of the detoxification proteins. Extensive annotations of these proteins, including sequences, structures, features, inducers, inhibitors, substrates, chromosomal location, functional domains as well as physiological-biochemical properties were generated. Furthermore, pre-computed BLAST results, multiple sequence alignments and evolutionary trees for detoxification proteins are also provided for evolutionary study of conserved function and pathways. The current version of DetoxiProt contains 5956 protein entries distributed in 628 organisms. An easy to use web interface was designed, so that annotations about each detoxification protein can be retrieved by browsing with a specific method or by searching with different criteria.http://lifecenter.sgst.cn/detoxiprot/.DetoxiProt provides an effective and efficient way of accessing the detoxification protein sequences and other high-quality information. This database would be a valuable source for toxicologists, pharmacologists and medicinal chemists. DetoxiProt database is freely available at  Living organisms are exposed to the external environment and are continuously confronted with a variety of chemical compounds and xenobiotics. Many of these naturally occurring substances or man-made chemicals can be deleterious when inhaled or absorbed, or even hazardous to organisms, (e.g. plant toxins to prevent herbivory). Artificial chemicals include anti-cancer drugs, antibiotics produced by fungi, insecticide, herbicide and other environmental pollutants . Some kiPhase I reactions include oxidation, reduction and hydrolysis. A major function of the Phase I reaction is the transformation of substrates so that they can be easily eliminated or modified by reactive groups for subsequent Phase II reaction. The most important Phase I system is the cytochrome P450 enzyme (CYP). It is an important class of heme-containing monooxigenase that mainly catalyzes the oxidative reactions of the adjunction of an atom from molecular oxygen into a substrate. This superfamily can be functionally classified into three groups, with more than 50 protein members have been identified in humans . The genDetoxification proteins have been found from invertebrates to vertebrates. More than one hundred genes encoding these proteins have been identified in humans. Large efforts have been made to understand the structural, functional and evolutionary basis of detoxification proteins, as well as their roles in disease development -20. ManyWe extensively searched various public databases and related literature to collect information on detoxification proteins. Data collection was performed by the following approaches: we first performed a systemic search for relevant literature and determined the precise classification of these proteins. Accordingly, 20 protein families were identified Figure . For eachttp://www.expasy.org/tools/protpar-ref.html). To provide fast protein classification, protein domains were predicted by various web servers including the Pfam [Extra information from other databases and literature, as well as the predicted protein features were incorporated into each detoxification record. Protein cofactors, tissue distribution and cellular location were retrieved from the references and publicly available databases such as NCBI and UniProtKB, while experimentally determined protein structures were retrieved from protein databank (PDB) . DetoxiPthe Pfam , Prositethe Pfam  and SMARthe Pfam . ExternaCaenorhabditis elegans, Danio rerio, Drosophila melanogaster, Gallus gallus, Homo sapiens, Mus musculus, Pan troglodytes, Rattus norvegicus and Xenopus laevis). The nomenclatures of the toxins were revised and unified according to MeSH and PubChem [Due to the importance of detoxification proteins for medicinal chemistry, we collected information from the literature the about the inducer, inhibitor and substrate for each protein from nine model organisms  when the structure file is available. This tool would be particularly useful for protein structure investigation and drug development. While protein sequences may be individually download in FASTA format, the complete set of protein sequences may be retrieved from the “Download” page. Figure Different browsing methods are implemented in the database to allow users to navigate by specific criteria. DetoxiProt provides seven different browsing methods: browse all data or specific dataset including the classification, species, tissue distribution, substrates, inducers and inhibitors. Cascading browse styles from specific dataset to protein list, then to detailed information are provided. The “detailed information” page includes general information, protein information, predicted protein features, protein structure, external database links and references. The 3D illustration of a protein structure may be viewed in interactive mode via the Jmol applet  plug-in is required.The DetoxiProt may be accessed through The authors declare that they have no competing interests.XL and YZ conceived the concept of DetoxiProt. GL and LW collected and compiled data from literature and public databases. YY and ZY constructed the database and compiled the draft of the manuscript. LY incorporated protein structure manipulative tools for the database. YH, HW and LW participated in the design of the study and integrated protein data. RH and RR provided valuable suggestions and revised the manuscript. All authors read and approved the final manuscript.Database structure for DetoxiProt. Five major table were used to store data, including the Main Info, predicted Domain, Mutual Blast result, Gene Ontology and Reference.Click here for fileDisplay of the results for different search method. (A) Result for query result list, (B) Result for BLAST, (C) Result for peptide mapping, (D) Key word search result for protein domains, (E) Result for 3D-BLAST.Click here for fileMus musculus by the Archaeopteryx tree viewerIllustration of the phylogentic tree of Cytosolic sulfotransferases of . Multiple Sequence Alignment was performed by MUSCLE, phylogenetic tree was generated by Maximum Likelihood (ML) method embedded in RAxML. The nonparametric bootstrap test was performed for 100 replicates.Click here for filePhylogenetic distribution of detoxification proteins in model organisms. A relative small number of protein families were found in invertebrates than that in vertebrates.Click here for fileGene ontology enrichment analysis of human detoxification genes. This analysis was performed using GOEAST: http://omicslab.genetics.ac.cn/GOEAST/Click here for fileA bipartite network demonstrating the relationship between detoxification proteins and related toxins. This network comprises 1633 edges and 882 nodes. Red nodes represent detoxification proteins and green nodes are toxins. The nodes size represents degree of connectivity of the nodes. Key proteins  have highest degree of connectivity were labelled, with 86, 74, 74, 51 and 46 edges respectively.Click here for file


The biological function of proteins is closely connected to interactions with their ligands and substrates. Proteins acting as receptors and enzymes bind these small molecules. Knowledge of the molecular mechanisms of protein-ligand interactions, particularly in the spatial structure of the protein-ligand complex, is a prerequisite for understanding the structure-functional properties of proteins and their role in biochemical pathways in the living cell. The availability of such a structure serves as a basis for rational drug design projects and greatly assists the search for new inhibitors (the ligands of certain protein-targets in an organism).Experimental tools for determining the spatial structure of proteins and their complexes with ligands  have particular limitations. Even if the structure of a protein is available, determining the structure of its complex with ligands may be experimentally demanding. Problems with purification and crystallization become especially difficult in studies of transmembrane proteins, which include a biologically important class of G-protein coupled receptors. However, recent successes in determining the structure of beta-adrenergic and adenosine receptors  are causThe technical difficulties restraining experimental methods stimulated computational molecular modeling. One of them (molecular docking) is a method aimed at predicting the spatial structure of a protein-ligand complex by docking a ligand molecule into the known atomic-resolution structure of a protein-binding site and estimating the reliability of the results. Nowadays, molecular docking has become an integral part of both fundamental studies aimed at understanding the structure-functional role of protein amino acids and applied drug-design programs , 3.Docking approaches are further improved by implementing new algorithms of the conformational search and new scoring functions (methods to estimate the free energy of ligand binding). Scoring functions may include either components of molecular mechanics force fields  or empirOf all the various types of interactions in biomolecular complexes , the stacking of aromatic substances deserves special attention. Most drugs include aromatic fragments in their chemical structure, and stacking often plays a notable role in their recognition by protein-targets. We have recently shown that an explicit account of stacking in scoring functions increases the efficiency of ATP docking . The aroHowever, the range of these parameters, which corresponds to the presence or absence of a stacking contact, is still not very well defined and usually taken as arbitrary , 7. DefiOne well-known example of stacking interactions is the parallel packing of purine and pyrimidine nucleobases in DNA , 9. SomeTaking all that into account, we analyzed the distribution of geometrical parameters h, d, and α for contacts of adenine and guanine moieties of ligands with the aromatic side chains of receptor amino acids Phe, Tyr, Trp, and His, as well as with the positively charged guanidine group of Arg and amino group of Lys. The results obtained for guanine are presented in It can be seen that two distinct orientations are typical for Phe: parallel and perpendicular to the guanine plane Interestingly, the distribution for the planar guanidine group of Arg very much resembles that of Tyr, Trp, and His, where the orientation parallel to guanine is predominant. For the amino group of the Lys side chain, two modes of contacts with guanine were observed: above the plane (cation-π interaction) and in the plane. The latter corresponds to the formation of a hydrogen bond to the heteroatoms of the guanine ring.Distributions for adenine are similar to those obtained for guanine (data not shown). The results of the presented analysis may be used in developing scoring criteria and applied to rescoring the results of docking or even during the docking procedure.We demonstrated the efficiency of an explicit account of stacking interactions in a scoring function along with reranking the results of GTP docking to the 14 different proteins that bind this ligand. All structures of GTP-protein complexes, which were generated with the docking procedure, were labeled as either correct or misleading by the value of root-mean-square deviation (rmsd) of the guanine atoms from the reference X-ray structure (see Methods). To estimate the validity of a docking pose, we constructed a number of scoring criteria in the form of a linear combination of the interaction terms. To do that, all GTP-protein complexes were divided into two equal groups: the training and the test sets. The weighting coefficients for these terms were fitted by the linear regression procedure to the binary function, which took on the value of 1 for the correct docking poses and 0 for the seven complexes of the training set. To test the robustness of the new scoring functions, the leave-one-out cross-validation procedure was performed. The relative error of all weighting coefficients of interaction terms was less than 30%, thus indicating the reliability of the results.New scoring functions were used to analyze the GTP docking poses generated for each complex. The efficiency of the goldscore  functionOf the proposed scores, the SF3 is the most efficient. The number of complexes for which the correct pose was ranked at the top by SF3 considerably exceeds that of goldscore. Also, the average best rank of the correct docking pose improves; i.e. the quality of ranking increases uniformly for all complexes. This can also be seen from the results of ranking for each complex The analysis of structural data for complexes of proteins with ligands containing adenine or guanine moiety yielded a more accurate definition of the geometrical parameters of stacking interactions with aromatic side chains of receptor amino acids. Reranking the results of GTP docking demonstrated that an explicit account of stacking in scoring criteria provides a more efficient estimation of the reliability of the structure of the protein-ligand complex predicted with molecular modeling approaches. The obtained results can be further applied to a broader class of nucleobase-containing ligands.The structures of complexes of proteins with adenine- and guanine-containing ligands were taken from the Brookhaven Protein Data Bank (PDB) . The PDBGTP docking was performed using the GOLD  program The surface area of the hydrophobic GTP-protein contact  was calculated based on the concept of Molecular Hydrophobicity Potential (MHP) using the PLATINUM web server . A MHP-sThe term Th-bond is a binary function that takes on the value of 1 when a hydrogen bond network of guanine and a particular motif is formed; if not, it takes on a value of 0. Such motifs were hydrogen bonds between the guanine atoms N1, N2, and O6 and residues i, i, i, or i, i, i-2.


Molecular co-evolution analysis as a sequence-only based method has been used to predict protein-protein interactions. In co-evolution analysis, Pearson's correlation within the mirrortree method is a well-known way of quantifying the correlation between protein pairs. Here we studied the mirrortree method on both known interacting protein pairs and sets of presumed non-interacting protein pairs, to evaluate the utility of this correlation analysis method for predicting protein-protein interactions within eukaryotes. We varied metrics for computing evolutionary distance and evolutionary span of the species analyzed. We found the differences between co-evolutionary correlation scores of the interacting and non-interacting proteins, normalized for evolutionary span, to be significantly predictive for proteins conserved over a wide range of eukaryotic clades . On the other hand, for narrower ranges of evolutionary span, the predictive power was much weaker. Proteins seldom act alone; rather, they tend to carry out their activities via interactions or networks. The detection of protein interactions can help to better understand the molecular machinery of the cell and expose biological processes and pathways that have not been characterized so far. Thus, to understand the mechanism of proteins; it's important to study their partners as well. In recognition of this importance, there are several public protein-protein interaction databases available online, for example DOMINE Traditionally, protein-protein interactions have been studied via wet-lab experimental methods, such as yeast two-hybrid v1.2-β2 complex using 9 mer sliding windows, to infer the composition of a control network interacting with the complex via domain-domain interactions.Methods for computational prediction of protein-protein interactions can be mainly classified to two different approaches: studies that use structural information Co-evolutionary analysis for whole proteins can be based on either codon usage or amino acid sequences. Fraser et al. The mirrortree method consists of the following steps: 1). find orthologs of the two proteins in multiple species, 2). align the ortholog sequences from the common species to get a multiple sequence alignment (MSA), 3). Create an evolutionary distance matrix either directly from the pairwise evolutionary distances between the aligned protein pairs or from a phylogenetic tree constructed from the MSA, and 4). Construct a linear correlation coefficient (Pearson's correlation) to determine the co-evolution of protein pairs and further predict possible interactions.Recent studies using mirrortree method to infer protein-protein interactions include the following: Kann et al. The effect of species genome choice on the efficacy of mirrortree-like methods has been evaluated for bacterial and archaeal genomes The key relationship defining the correlation between two sets of protein orthologs is the Pearson's correlation coefficient, given in We used the Biogrid database because it contains large sets of functional related or directly interacting protein pairs categorized into different species. In particular we used datasets from human and Saccharomyces cerevisiae (Baker's Yeast) as a standard to define interacting protein pairs. To identify orthologs of the proteins, we use the results of the OMA project Common interacting protein pairs are retrieved from interacting datasets of human and S. cerevisiae species. A total of 1,062 common interacting protein-protein pairs were found in the Biogrid data base from human and S. cerevisiae. Of these, 311 protein pairs were found to have corresponding ortholog groups in OMA browser. Adding the criterion that each group to be compared should have 15 or more common species in the common ortholog sets reduced the membership of the set for analysis from 311 to 259.We created a second set of putative interacting pairs by including all human Biogrid interaction pairs whose members have S. cerevisiae orthologs plus all S. cerevisiae interacting pairs whose members have human orthologs. The difference between the first set and the second set is that in the first set both the human and the S. cerervisiae pairs are confirmed experimentally to interact, whereas in the second set the interaction needed to be confirmed experimentally in only one of the two. Both datasets were constrained by the requirement that every protein needed to have 15 or more common species in their OMA ortholog sets. The total number of pairs fulfilling the requirements for the second set was 5,616. Finally a set of 5,616 different non-interacting pairs were created by sampling the second set with replacement We also created a third dataset of human and mouse common interacting protein pairs, using a procedure exactly analogous to the procedure for creating Dataset 1 (human and yeast set). This procedure gives us a total of 1,375 interacting protein pairs. We created a corresponding non-interacting set of 5,630 pairs by sampling with replacement and filtering in the same fashion as we did for Dataset 2. This set of 1,375 putative interacting pairs plus the 5,630-member set of non-interacting pairs we call Dataset 3.For each protein from the interacting or non-interacting protein pairs, we retrieved its 1 to 1 ortholog groups containing different species from OMA database. For each protein pair we extracted the common species to the 2 ortholog groups and used the 2 ortholog groups with common species set for evolutionary distance calculations.All the protein pairs datasets were aligned using MUSCLE (Multiple sequence comparison by log-expectation) The pair-wise distances for sequences from different species for any protein were calculated using the protdist package Jones Taylor Thornton matrix Dayhoff Pam Matrix which uses Dayhoff's PAM 001 matrix Kimura model, in which distance is defined as:Categories Model We also explored the use of other measures for evolutionary distance, including unweighted direct sum (0 or 1 for same or different) of position-specific substitutions, and different PAM or BLOSUM matrices with various ways of treating gap penalties. All results were essentially independent of the type of evolutionary distance employed, so in results we report using only one distance measure, the Jones Taylor Thornton matrix.For further study of species coverage effects on correlation analysis, we started with the human and mouse interacting protein pairs' datasets (Dataset 3) with 1,375 interacting and 5,630 non-interacting protein pairs. Then we divided the data into three categories: 1) present only in chordates (834 pairs); 2) present in other metazoan as well as in chordates, but not in plants or fungi (349 pairs); 3) present in all the eukaryotic kingdoms (192 pairs). The results of analysis of this dataset will be shown in the To illustrate the differences of correlations of interacting versus non-interacting protein pairs, the correlation scores for interacting and non-interacting protein-protein pairs were plotted against each other. An AUC score of 0.5 would indicate no predictive power, while a score of 1.0 would indicate perfect predictive power. Therefore a score of .73 indicates significant, but not perfect, predictive power. The right side edge shows the cut-off correlation score for prediction, scaled by color. We can read the corresponding true positive and false positive rates from the curve by matching the color in the curve to the right side correlation score color scale.We then studied the interacting protein pairs from human or S. cerevisiae (Dataset 2), the only difference of Dataset 1 from Dataset 2 was that the interaction in Dataset 2 was not necessary conserved in both human and S. cerevisiae. Density plot and ROC plot were plotted here too shown in We also studied the common interacting protein pairs between human and mouse (Dataset 3). The correlation density plot and ROC curve were plotted and shown in For a single measure of the predictive power of the method, we elected to use the Matthews correlation coefficient (MCC). MCC is a more robust measure of effectiveness of binary classification methods than such measures as precision, recall, and F-measure because it takes into account in a balanced way of all four factors contributing to the effectiveness; true positives, false positives, true negatives and false negatives. A good review of methods for binary classification is given in Powers, 2011 TP is the number of true positivesTN is the number of true negativesFP is the number of false positivesFN is the number of false negatives.In In We note that for a set of completely random sequences the correlation scores will average zero. At the other extreme, for a set of identical sequences the Pearson's correlation score will be undefined. We accordingly wondered if, between these extremes, there would be any systematic dependence of correlation scores on total conservation of the pairs. To explore this, we started with the set of interacting and non-interacting protein pairs from human and S. cerevisiae species (Dataset 1). For each protein-pair's ortholog sets, we calculated the degree of conservation as the average identity for each pair of aligned sequences within each ortholog set, and then the average of the two means. The correlation score for each specific protein pair was calculated as stated in the method part. We further tested how evolutionary span affects the correlation scores. To do this we divided the results of Dataset 3 according to the evolutionary span of the common species used in the orthology pairs. In For another representation of the relationship of correlation score with evolutionary span, we plotted in Normalize the evolutionary span among the protein pair orthologous sets to be tested.Use as wide an evolutionary span as is available.Take into account independent evidence of conservation of interaction, if available.If prediction of a binary classification is desired, the peak of the Matthews correlation coefficient is a reasonable default choice for threshold, but the user may shift the threshold up or down depending on whether sensitivity or specificity is more valued.This study was aimed at assessing the mirrortree method for inference of protein-protein interaction, with the goal of understanding how to use it to achieve the most reliable predictions. The major results of our studies are:Over a wide range of degrees of conservation, correlation scores are independent of degree of conservation. However we see lower correlation scores for ortholog pairs that have very high or very low degree of conservation see .Overall correlation scores are higher when wider evolutionary spans are used in the analysis, as shown in The method will be more reliable when the particular proteins have a wider evolutionary span, because the signal to noise ratio will be more favorable, as demonstrated in For Dataset 1 we find a standard AUC score of the method to be over 0.7, higher than estimated by Hakes et al


Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA.APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis.In this study, we developed a rapid and cost-effective method to analyze variants in APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis.Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human  MUTYH, OGG1, APEX1, XRCC1 and PARP1APE1 along with environmental exposures has paved a way to identify polymorphic variants that have the potential to cause cancer risk APE1 result in conformational change of the protein, which in turn affects regular interaction with DNA. Altered protein function causes deficit in repair capacity, which may lead to genetic instability and carcinogenesis APE1 gene and their disease associations were discussed extensively. The current limitations in the experimental based approaches such as cost and time emerged the use of in silico methods and specifically molecular dynamics simulations in SNP analysis. Recently, few in silico studies were carried out in identifying the possible functional and deleterious nsSNPs in APE1 gene in silico methods have been developed recently, whose goal is to extrapolate the functional and deleterious nsSNPs from the neutral ones based on sequence and structure based approach Base Excision Repair (BER) is one of the DNA repair systems which are essential for the maintenance of genome integrity. Major genes involved in the BER pathway include in silico analysis presented in this work is to develop molecular dynamics simulation methodology that provides the direct link between deleterious nsSNPs at both functional and structural level. In this present study, we focused on variants in APE1 gene associate with disease susceptibility and their impacts on APE1-DNA complex dynamic behavior. The scope of this work is twofold. On one hand, it addresses the application of publicly available in silico tools such as Sorting Intolerant From Tolerant (SIFT) APE1 gene. On the other hand, structural analysis was carried out to know, how the APE1-DNA interacting function gets affected with respect to the identified deleterious nsSNPs in APE1 through Molecular Dynamic (MD) approach using GROningen MAchine for Chemical Simulations (GROMACS). In this study, investigations of structural consequences of these novel APE1 mutations I64T and P311S were explored using molecular dynamics simulations. Separate simulations were conducted to investigate the conformational behavior of the APE1-DNA complex in two different states namely, native and mutant.Current interest in molecular genetics is focused on disease-gene association that is, identifying which DNA variation or a set of DNA variation is highly associated with a specific disease at structural level. Knowledge of the three dimensional (3D) structure of a gene product will aid in understanding the function within the cell and its role in causing disease. Proteins with mutations do not always have the 3D structures that are analyzed and deposited in Protein Data Bank (PDB). Therefore, it is necessary to construct 3D models by locating the mutation positions and the residues in 3D structures. This is a simple way of detecting what kind of adverse effects that a mutation can have on a protein at structural level. It has been understood Protein-DNA complexes play multiple important roles in cellular processes that involve DNA transactions, such as transcription, replication, recombination, and DNA repair as well as the packaging of chromosomal DNA APE1 gene investigated in this work was retrieved from dbSNP database APE1 gene a total of 59 SNPs were found in database, out of that 10 (0.17%) were nsSNPs, 11 (0.19%) occur in the mRNA 3′UTR, 14 (0.23%) occur in the mRNA 5′UTR, and (0.17%) occur in intronic region. We selected nsSNPs for our investigation. We applied our method to evaluate the APE1 gene association with disease, and its related information for biomedical literature was taken from OMIM (Online Mendelian Inheritance In Man) APE1 gene was obtained from Swiss-Prot database The data set for analysis of potential impact of polymorphism in APE1 nsSNPs can be assessed by evaluating the importance of the amino acids they affect. Identifying the nsSNPs conferring susceptibility or resistance to common human diseases should become increasingly feasible with improved in silico tools. In this analysis, we employed four in silico tools for determining the functional significance of ten nsSNPs in APE1 gene. APE1 gene by these in silico methods. SIFT predicts whether an amino acid substitution affects the protein function based on sequence homology and the physical properties of amino acid. This tool calculates a score for every substitution and predicts the functional effect. Three nsSNPs with dbSNP ID rs61730854, rs33956927 and rs1803120 having a tolerance index score of 0.00, 0.02 and 0.00 respectively were identified to be deleterious by SIFT. Rest of the seven nsSNPs were predicted to be tolerant by SIFT exhibiting tolerance index score range of 0.006–1.00. We further analyzed these ten nsSNPs using PolyPhen based on empirically derived rules. PolyPhen uses information about the structure of the protein , available structure data from Protein Data Bank, and multiple sequence alignment. Based on Position Specific Independent Count (PSIC) score difference, variant is predicted to be probably damaging or possibly damaging or benign. However the multiple sequence alignment is generated by PSIC software which assigns a score that indicates the probability of a given amino acid occurring at a particular position against any random position. Based on the calculated alignment score and differences in structural parameters, the PolyPhen designates the mutation as being “benign”, “possibly damaging”, and “probably damaging” with the respective scores of <1.5, >1.5 and >2.0 respectively. Two nsSNPs with an IDs rs61730854, and rs1803120 having score 2.286, and 3.123 were predicted to be probably damaging the protein structure and function. While three nsSNPs with IDs rs34632023, rs2307486, and rs1048945 showed a score 1.734, 1.811 and 1.539 were designated as possibly damaging to protein function. Remaining five nsSNPs with a score range of 0.465 to 1.272 were designated as benign by PolyPhen. To validate the prediction of SIFT scores, we used HMM based evolutionary approach PANTHER to validate its impact on protein function upon single point mutation. Out of these ten nsSNPs taken for our analysis, three nsSNPs rs61730854, rs1803120 and rs1803118 are having score −5.56072, −7.92488 and −3.68399 were designated as deleterious. Other seven nsSNPs exhibited a subPSEC score range of −1.63595 to −2.93478. In order to improve prediction accuracy of structure based approach tools, we used support vector machine based tool I-Mutant 2.0. The protein stability change due to single point mutation was predicted using I-Mutant 2.0. A score less than “0” means the mutation decreases the stability. The smaller, the score more, the more confident is the prediction. Conversely, a score more than “0” means mutation increase the protein stability. Seven nsSNPs with dbSNP IDs rs33956927, rs2307486, rs1803120, rs1803118, rs1048945, rs61730854, rs113056798 showed the negative DDG value −0.10, −1.13, −1.00, −0.33, −1.73, −0.01, −0.62 Kcal/mol, were considered to be least stable and deleterious. By comparing the prediction scores of all the four in silico tools, nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were designated as highly deleterious and functionally significant. The I64T polymorphism is the result of nucleotide change from C to T and results in the substitution of hydrophilic threonine for hydrophobic isoleucine at amino acid 64 of APE1 gene, thus affecting hydrophobic interactions. P311S involves change of non polar hydrophilic residue proline to polar neutral residue serine. It is well-known that during folding of the polypeptide chain, the amino acids with a polar (hydrophilic) side chains are found on the surface of the molecule, while amino acids with non polar (hydrophobic) side chains are buried in the interior. Thus, this variation may lead to change in the folding pattern of the protein.The functional impact of Modeling of predicted deleterious nsSNPs I64T and P311S on protein structure with PDB ID 1DE8 was performed using Swiss-PDB Viewer Molecular dynamics simulation approaches pay the way for in depth analysis on structural effects upon mutation in APE1-DNA complex. The DNA strands in the co-crystal with APE1  has a 30Hydrogen bonds are by far the most important specific interactions in biological recognition processes. Non synonymous SNPs can affect wild type protein function by affecting hydrogen bond formation between patterns The solvent accessible surface area (SASA) of a bimolecular is that, accessible to a solvent and it can be related to the hydrophobic core. It is typically calculated using the ‘rolling ball’ algorithm In silico methods especially can aid in identifying disease-causing mutations by helping in the selection and prioritization of likely candidates from a pool of data. Most of the disease causing deleterious nsSNPs may lead to alterations in the structure, folding, or stability of the protein product, thereby altering or preventing the function of the protein. Yue and Moult in silico methods have been developed to predict whether disease-related nsSNPs is deleterious or benign. Evolutionary information is commonly considered to be the most important feature for such a prediction task. In this analysis, we employed four widely-used computational tools SIFT, PolyPhen, I-Mutant 2.0 and PANTHER for determining the functional significance of nsSNPs in APE1 gene. These methods differ in the properties of the variant they take into account in the prediction, as well as in the nature and possible training of the classification method used for decision making. All the variation tolerance methods analyzed in this study follow a similar procedure in which a missense variant is first labeled with properties related to the damage it may cause to the protein structure or function. The basis for predicting the impact of mutations in these four algorithms are different and we would expect that the outcomes to be in some ways, dissimilar. However, the mutations that overlap the four predictions should provide greatest reliability to behave similarly.Owing to the application of high-throughput sequencing technologies, the number of identified genomic variants especially SNPs in the human genome is growing rapidly. This has yielded massive amounts of data on human SNPs, and this information investigates human complex diseases such as cancer. It has been estimated that the NCBI contains ∼25 million human entries in the release of build 130 APE1 gene using in silico tools such as SIFT and PolyPhen Deleterious nsSNPs can alter normal protein function, by means of geometric constraint changes in silico tools, we identified two nsSNPs I64T and P311S as highly deleterious based on the scores. To better understand how these deleterious nsSNPs affect the structural behavior of APE1 gene, we incorporated molecular dynamic approach using GROMACS force field 43a1. On 8ns simulation trajectory, different parameters were applied to investigate the molecular behavior of native and mutant (I64T and P311S) complexes. Structural validations for all three complexes were done by RMSD and RMSF analysis. RMSD analysis results inferred that native complex reach final deviations of ∼0.2 nm while the mutant complexes I64T and P311S showed high deviation value of ∼0.25 nm. In RMSF analysis, I64T and P311S shows maximum fluctuation value of ∼0.35 nm on the residue range of ∼110 and ∼27 respectively, on other hand native complex have low fluctuation value ∼0.1 nm and ∼0.25 nm on the same residue range. Based on RMSD and RMSF analysis, we confirmed that I64T and P311S showed different path of deviation and fluctuation to I64T and P311S complexes, which in turn leads to conformational change in mutant complexes. Structural mutations were found to affect buried residues in the protein core, causing changes in amino acid size, amino acid charge, hydrogen bonds, salt bridges, S-S bridges APE1 gene could affect APE1 protein natural behavior which may be used as suitable bio markers for detecting people at risk for certain diseases.By applying all four APE1 gene was obtained from SWISS-Prot database APE1 gene were retrieved from National Centre for Biotechnology Information (NCBI) database The protein sequence for APE1 gene. Sequence  and structure based methods  are the most common approaches used in SNP prediction tools. SIFT, PANTHER and I-mutant give results in two prediction categories-tolerated or deleterious effects, while PolyPhen gives results in three categories-benign (probably lacking any phenotypic effect), possibly damaging, and probably damaging (should affect protein function). Sequence-based prediction includes all types of effect at the protein sequence level and can be applied to any human protein with known relatives. Structure-based approach is feasible to implement for the proteins with 3D structures. Tools that integrate both sequence and structure information have the added advantage of being able to assess the reliability of the generated prediction results by cross-referencing the results from both approaches. Tools that combine these approaches use different algorithms and methodologies for prediction, thereby having a wider cover-age of the different aspects of SNP analysis. SIFT predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids Each nucleotide substitution has the potential to affect protein function. We used two diverse approaches, Empirical and Support Vector Machine (SVM) to determine functional SNPs in − ion was added to both native and mutant structures. It results in substitution of random water molecule with one chlorine ion in order to obtain neutralized system. The temperature was kept constant by using Berendsen thermostat Molecular mechanics potential energy minimization and MD simulations were carried out using the program package GROMACS 4.0.5 The trajectory files were analyzed through g_rmsd and g_rmsf GROMACS utilities in order to obtain the root-mean-square deviation (RMSD) and root-mean square fluctuation (RMSF) values. Number of distinct hydrogen bonds formed between protein and DNA during these simulations were calculated using g_hbond utility. Number of hydrogen bonds determined on the basis of donor-acceptor distance smaller than 3.9 nm and of donor-hydrogen-acceptor angle larger than 90 nm Table S1Summary of intra-molecular hydrogen bonds formation in native and mutant models of APE1 protein.(DOC)Click here for additional data file.


Medicago truncatula Gaertn. is a well-established model for studying legume biology, but little is known about the protein complement of mitochondria from this species. An initial characterization of the mitochondrial proteome of M. truncatula  was published recently. In the frame of this study, mitochondrial protein complexes were characterized using Two-dimensional (2D) Blue native (BN)/SDS-PAGE. From 139 detected spots, the “first hit” (=most abundant) proteins of 59 spots were identified by mass spectrometry. Here, we present a comprehensive analysis of the mitochondrial “complexome” (the “protein complex proteome”) of M. truncatula via 2D BN/SDS-PAGE in combination with highly sensitive MS protein identification. In total, 1,485 proteins were identified within 158 gel spots, representing 467 unique proteins. Data evaluation by the novel GelMap annotation tool allowed recognition of protein complexes of low abundance. Overall, at least 36 mitochondrial protein complexes were found. To our knowledge several of these complexes were described for the first time in Medicago. The data set is accessible under http://www.gelmap.de/medicago/. The mitochondrial protein complex proteomes of Arabidopsis available at http://www.gelmap.de/arabidopsis/ and Medicago are compared.Legumes  are unique in their ability to carry out an elaborate endosymbiotic nitrogen fixation process with rhizobia proteobacteria. The symbiotic nitrogen fixation enables the host plants to grow almost independently of any other nitrogen source. Establishment of symbiosis requires adaptations of the host cellular metabolism, here foremost of the energy metabolism mainly taking place in mitochondria. Since the early 1990s, the galegoid legume  Arabidopsis, potato, rice, and pea mitochondria have been analyzed extensively by gel-based and gel-free proteomic approaches  system, which transfer electrons to molecular oxygen. Coevally, a proton gradient is generated across the membrane. The backflow of protons into the mitochondrial matrix space mediates phosphorylation of ADP by the ATP synthase complex. A special feature of plant mitochondria is the presence of additional “alternative” oxidoreductases in the OXPHOS system  Blue native (BN)/SDS-PAGE is an excellent system for the separation of mitochondrial protein complexes in their native forms and subsequent resolution into their subunits  for purified mitochondrial fractions were established by Dubinin et al.  root cell suspension cultures as described by Dubinin et al. Swiss Prot, using the following parameters: trypsin/P; one missed cleavage allowed; fixed modifications: carbamidomethylation (C), variable modifications: acetylation (N) and oxidation (M); precursor ion mass tolerance, 30 ppm; peptide score >24; charges 1+, 2+, 3+. Protein and peptide assessments with MASCOT scores above 25 were considered. Identified proteins were further analyzed for their sub-cellular localization using their homologous Arabidopsis accessions (according to TAIR 10 db) queried against the SUBA III database  and in-gel digested with Trypsin as described by Klodmann et al. . Trypticx- and y-coordinates by the Delta 2D (4.2) software  . An Excel (Microsoft) file containing this information and the corresponding gel image (.jpg) were then imported into GelMap. MS/MS results were uploaded as well. Detailed information on building a GelMap is available under http://www.gelmap.de/howto.After protein identification, the reference map was visualized using the GelMap platform . Among them are three legume-specific proteins involved in symbiosis to Rhizobial bacteria: a legume lectin (ID 101) and two nodulins , as well as a prefoldin protein (spot 77) which is supposed to be also legume-specific. The majority of plant lectins possess a signal peptide and thus are targeted via the secretory pathway into the vacuolar and extracellular compartments  have no homologoues in Arabidopsis.All 158 protein spots encircled in Figure n et al. , proteinMedicago truncatula Gene Index , the TAIR accession from the Arabidopsis homologous protein, protein name and origin, the protein database where the protein was identified, its protein complex identity, physiological function, and sub-cellular localization according to the SUBA III database . Since this database collects experimental data and in silico predictions of the localization of proteins in Arabidopsis, the corresponding TAIR homologs of each identified Medicago protein were used to assess the intracellular whereabouts of the Medicago proteins. At least for the “first hit” identifications, prediction data are available (except for one of the 158 “first hit” proteins). From overall 157 first protein hits, 145 proteins (92%) are assigned to mitochondria. Considering all 467 unique proteins, sub-cellular localization information is available for 413 proteins. The percentage of mitochondrial proteins in this dataset is lower (287 proteins = 69.5%). Twenty-five proteins (=6%) represent cytosolic proteins according to SUBA evaluation. A considerable number of proteins are assigned to other cellular compartments: 6% to plastids, 5% to the nucleus, 3.6% to membrane structures , and 1.2% to the cells vacuoles. For 17 proteins (4%), no SUBA predictions are available because of a lack of experimental data. These proteins are labeled as “NEW mitochondria” in our GelMap since they represent candidates for mitochondrial proteins. Considering that the “first hit” proteins, 92% of which are of predicted mitochondrial origin, are on average significantly more abundant than the proteins of lower MASCOT scores within each spot, we estimate that the overall purity of our mitochondrial fraction was in the range of 85%.In order to assess the purity of isolated mitochondrial fractions, the sub-cellular localization of all proteins identified was evaluated Two-dimensional BN/SDS reference maps generated with GelMap enable annotation and assignment of all proteins identified that belong to one certain functional protein complex, for example the complexes of the OXPHOS system , which was recently described to form part of three assembly intermediates of complex I in Arabidopsis  can be found at 600 kDa on the 2D BN/SDS gel. Thirteen distinct subunits were identified. As described previously by Dubinin et al. (2) is of higher abundance in mitochondria isolated from Medicago cells than in Arabidopsis.– n et al. , the comalternative oxidoreductases of the plant OXPHOS system were identified: AOX1a, AOX2, AOX3, NDA1, NDA2, NDB1, and NDB4. The NDA and NDB subunits are present at 160kDa and probably form a protein complex as reported for Arabidopsis  in the second gel dimension, but at much higher MWs in the native first dimension (100–200 kDa), indication for an association with other proteins  is given.– Since citric acid cycle also are involved in forming protein complexes: aconitase forms a putative dimer at 150 kDa in the native gel dimension, NAD-dependent malic enzymes was described to form a heterohexamer in Arabidopsis at 369 kDa  were found at MWs of 1000 and 1500 kDa. In the same MW range, also TIM subunits were found , suggesting the presence of a large TIM/TOM translocon supercomplex in Medicago.– VDAC oligomers were identified between 90 and 500 kDa, which form either distinct complexes or artificially aggregated during solubilization or electrophoresis of the native gel dimension.– Several HSPs form protein complexes in M. truncatula mitochondria: HSP60 complex is present in its main form at a MW of 600 kDa, where also other HSPs  were detected.– prohibitin complexes were found at 160, 300, and 1200 kDa. Increased abundance of mitochondrial prohibitins in M. truncatula was reported previously  gel dimension than on the denaturing gel dimension.– Many further enzymes listed under “further metabolic pathways” much likely form part of A short overview and characterization of the major M. truncatula. Generally, our GelMap presents protein candidates that may form protein complexes. It does not provide final proof for the presence of novel complexes: if a protein complex is described here for the first time, its occurrence should be verified by further independent experiments.This GelMap was built to systematically define the mitochondrial protein complex proteome of the model legume Medicago mitochondria were already identified and characterized in mitochondria from Arabidopsis cell suspension cultures .Click here for additional data file.