We also consider correlation based on residue hydropathy rather than protein-specific interactions. Finally, in experiments of family classification tests, the modeling power of MIV was shown to be significantly better than the classic MI method, reaching the level where proteins can be classified without alignment information.We investigate methods of estimating residue correlation within protein sequences. We begin by using mutual information (MI) of adjacent residues, and improve our methodology by defining the


Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.Here we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved.The network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches. Protein-protein interactions are fundamental to many cellular processes , and a dRecently, the analysis of protein structures as small-world network systems has attracted significant interest -17. In sIn this work, we characterized unbound proteins as small-world networks for their use in docking. We used different topology measures and evaluated their use to predict protein binding sites. We then applied these descriptors to the scoring stage of protein-protein docking using the latest standard docking benchmark. These scoring functions were integrated in pyDock, a successful docking scoring algorithm based on physico-chemical terms .closeness and degree values were virtually the same for the two types of networks , with some differences in the clustering and betweenness parameters . In the next section we describe how we directly used these values for docking scoring, with no further optimization. But first, we have evaluated the capabilities of the network-based values to predict binding interfaces. With this only purpose, for each protein and network parameter, we defined as interface predictions an arbitrary number of residues (i.e. nodes) with the highest network topology values (see below) and evaluated whether they were present in the binding site of the known protein complex. For this purpose of interface predictions, only surface residues of the unbound protein were considered, defined as those having relative accessible surface area (ASA) > 0.1%. The positive predictive value (PPV) for each complex was calculated as the percentage of predicted residues that were part of the real interface (i.e. residues with at least one atom within 10 Å of the partner protein in the complex). Then we computed the mean PPV of all unbound proteins. Additionally, we used different cutoff values to restrain the selection of predicted residues. It should be noted that some proteins had no residues with values above certain cutoffs and, thus, no predictions were computed in these cases. The random PPV was calculated by randomly selecting surface residues of the unbound proteins. This was repeated 100 times for the different cutoff values.We modeled each of the unbound protein structures of the docking benchmark 3.0  as residcloseness parameter at different cutoff values . The average distance to all other nodes is expected to be always higher in larger proteins than in smaller ones. Thus, the higher closeness values were mostly found in the smallest proteins  were all large (i.e. more than 400 residues) and in some cases presented domains weakly connected with the protein core, like the receptor of the FH2 complex (PDB code 1Y64). By residue type, the largest average closeness values typically correspond to hydrophobic residues , probably due to their higher frequency in the protein interfaces, as well as in the protein core. In any case, the difference of the predictive success rates with respect to random was significant and increased with the cutoff values. The most successful predictions were found for the enzyme/inhibitor group, with a mean PPV of 63.3% when considering all proteins, and reaching 100% when applying cutoff values over 0.50 to the closeness values. In these cases, success rates were always clearly above random . This shows that predictions are not very sensitive to whether the networks are defined from the Cα or Cβ atoms, and for the rest of the analysis we will only use the networks defined with the Cα atoms.We first studied the results of the interface predictions based on the arbitrary number of four residues with the highest network degree network parameter showed a similar trend to those of closeness, where PPV improved at higher (i.e. more restrictive) cutoff values. The degree of a given node is the fraction of nodes to which such node is connected, so residues in small proteins will expectedly have higher degree values  were defined at different contact distances between all heavy atoms of the complex subunits (see Methods). Success rates improved when large docking interfaces were considered, reaching 12.6% when including residues up to 15 Å from the docking partner. However, using even larger docking interfaces worsened the top 10 success rate (e.g. 10.7% using 18 Å). Scoring with other network-based parameters showed similar trends . Finally, when both partners had incorrect interface predictions (49 cases), docking success clearly worsened (2.6%). The correlation between the success of the interface predictions and the docking scoring performance is thus evident.Additionally, we evaluated how the docking scoring performance depended on the success of the interface predictions for each partner. Notably, the 29 cases (out of 124) for which Scoring with topology-based network parameters was in all cases clearly better than the FTDock default scoring (top 10 success rate 1%), but still far from the performance achieved by state-of-the-art energy-based scoring functions like pyDock . This combined scheme was implemented in a new scoring function called pyDockCloseness. We performed a cross-validation test to discard any possibility of over-training in our scoring function (see Methods). In all cases the resulting optimal weight was the same (w = 0.45), which confirmed the robustness of the combined function. As a further test to prevent over-fitting, we validated our combined scoring function on the new cases of the recently released docking benchmark 4.0 [Closeness 15 Å and 26.3% for pyDockCloseness, confirming the improvement of the combined scored achieved in benchmark 3.0. Taking into account all the cases in benchmark 4.0 , FTDock found a near-native solution in 141 out of 176 cases and top 10 success rates were 22.0%, 10.6% and 29.8% for pyDock, Closeness 15 Å and pyDockCloseness, respectively . The scoring results on the enzyme/inhibitor group substantially improved with all parameters. For example, Closeness 15 Å had a remarkable 26.5% top 10 success rate, not far from that of pyDock (34.7%). Moreover, the combination of both scores clearly increased the top 10 success rate (44.9%). In the case of the complexes classified as "other", Closeness 15 Å performed poorly (2.7% top 10 success rate) as compared to pyDock (15.1%). However, their combination significantly improved the success rate to 23.3%. Given the poor interface predictions and docking results of the network-based parameters in antibody/antigen cases, we repeated the weight optimization between Closeness 15 Å and pyDock considering enzyme/inhibitors and "other" type of complexes only. The resulting weighting factor was the same as with the whole set of complexes, reinforcing the robustness of our pyDockClosenesss scoring function. In any case, antibody/antigen cases were discarded for the rest of the analyses.The ability of Closeness 15 Å behaved similarly, yielding top 10 success rates of 28.6%, 15.3% and 0% for the groups of proteins with small, medium and large conformational changes upon binding, respectively. In the cases with small changes upon binding (averaged unbound/bound RMSD for receptor and ligand < 0.5 Å), Closeness 15 Å contribution to the combined score could not improve the already excellent results of pyDock. On the contrary, in cases showing medium conformational changes (unbound/bound RMSD between 0.5 and 1.5 Å), pyDockCloseness top 10 success rate (30.6%) was considerably better than that of pyDock alone (20.0%). Interestingly, for the most difficult and challenging group of cases with high flexibility (unbound/bound RMSD >1.5 Å), the Closeness 15 Å contribution to the combined score made the success rate to improve with respect to pyDock (from 16.7% to 23.3%), regardless of the poor performance of the network-based scoring alone for the top 10 predictions (0%). Altogether, our coarse-grained network-based approach  seems to be especially helpful in cases with significant conformational changes, successfully complementing our all-atom approach whose predictions quickly degenerated in inaccurate geometries [The improvement of pyDockCloseness over pyDock was noteworthy in cases with significant conformational change  was due to the limited sampling of FTDock in these conditions [Closeness 15 Å individual scorings, reaching 37.5%, 36.6% and 16% top 10 success rate for the small, medium and large grid-size groups, respectively.Cases in the benchmark were classified according to the FTDock grid size, which is proportional to the sum of both protein radii . The topnditions . For proCloseness 15 Å scoring. Spherical cases  showed a poor performance, whereas prolate cases  and, specially, oblate cases  yielded better predictions  for protein-ligand binding sites in non-globular proteins [The anisotropy of the proteins (i.e. the length of the most different axis divided by the mean length of the other two) played a crucial role in the success of the proteins . On the Closeness 15 Å, but dramatically dropped for the group of largest interfaces . To conbetweenness, closeness and degree) and the clustering coefficient. The closeness centrality of a node x is defined as follows:Within graph theory and network analysis, there are various node measures that determine its importance. In this work, we tested four widely used network parameters: three centrality measures  is the shortest path distance between node x and any other node y. Thus, the closeness of node x is the inverse of the average distance to all other nodes. The three remaining network parameters are defined as follows: for any node x, degree is the number of edges incident to that node, betweenness is the sum of the fraction of all shortest paths between any two nodes that pass through x and clustering is the fraction of contacts that exist between its neighbors (i.e. the number of triangles through x) relative to the maximum possible contacts between them. For this work we showed the inverted clustering value (1/clustering) so that higher scores correlate with protein binding sites.where We used the standard protein-protein docking benchmark 3.0  for (i) We used FTDock  with staCloseness score for a given docking pose P, the precomputed closeness values of all interface residues i, j (defined as those with a heavy atom within a threshold distance d from any heavy atom of the partner protein), from receptor and ligand respectively, were added up. Each residue value was added only once, regardless of the number of contacts that formed with the partner protein. Weighting the residue-level closeness values by the number of atomic contacts established with the partner protein worsened the scoring results. A possible reason is that we are scoring rigid-body docking poses, in which side-chains are not always in optimal conformation for binding, and therefore a coarse-grained scoring based on counting residues (not atomic contacts) is preferred. Perhaps flexible docking solutions might benefit from a scoring system based on the number of contacts, but this is beyond the scope of the current work. The same scheme was applied to the rest of network parameters. Different values for d, ranging from 5 to 15 Å were tested:We scored docking poses using the topology-based parameters precalculated on the unbound proteins (see above). Only the values of residues at the docking interface were used. For instance, to obtain the pyDock  is a scoP and a threshold distance d, we defined the pyDockCloseness score as the combination of Closeness and pyDock:We combined each network-based scoring and pyDock in a new scoring function. For instance, for a given docking pose w was calculated by minimizing the function F(w) on benchmark 3.0 [The value of mark 3.0 , a subsem, using w to balance the Closeness scoring in the pyDockCloseness function. Values ranging from 0.0 to 2.0 with a step of 0.05 were used to determine the lowest value of F(w).where w. In addition, we performed a leave-one-out cross-validation to ensure the optimized parameter was robust to permutations. The process consisted in calculating w using all the cases of the training set except one, which was then used for validation. This was repeated in a way that each case in the training set was used once for validation.In order to prevent overfitting, we validated the predictions on the subset of benchmark 4.0 that was not used for the training of CP performed the docking calculations. FG calculated the topological parameters of proteins and devised the concept. CP and FG drafted the manuscript. JFR devised the concept, directed the research and finalized the draft. All authors analyzed the results, read and approved the final manuscript.Supporting figures. this file contains all the supporting figures that are referenced in the text.Click here for file


Many protein domains bind to short peptide sequences, called linear motifs. Data on their sequence specificities is sparse, which is why biologists usually resort to basic pattern searches to identify new putative binding sites for experimental follow-up. Most motifs have poor specificity and prioritization of the matches is thus crucial when scanning a full proteome with a pattern.doremi.jensenlab.org, which allows for an easy application of the method. We show that this approach leads to improved predictions of binding partners for PDZ domains and the SUMO binding domain. This is consistent with the earlier observation that coupling sequence motifs with network information improves kinase-specific substrate predictions.Here we present a generic method to prioritize motif occurrence predictions by using cellular contextual information. We take 2 parameters as input: the motif occurrences and one or more of the interacting domains. The potential hits are ranked based on how strongly the context network associates them with a protein containing one of the specified domains, which leads to an increased predictive performance. The method is available through a web interface at  Physical interactions among proteins play a crucial role in cellular signaling. There are two types of binding: domain–domain and domain–motif. For the latter type, a conserved domain interacts with a short peptide sequence (linear motif), which may be subject to post-translational modification (PTM) . Many prThe computational task of dealing with motifs can be split into two separate tasks: the discovery of novel, not known motifs and the search for new instances of already known motifs. The discovery of novel motifs is challenging, nonetheless a number of techniques have been developed to address this problem . More reWhen searching for new instances, all occurrences of a motif should thereby be screened for their biological validity. Based on the protein sequence, predictors for protein folding, structure and disorder can be used to filter for potential motifs . As funcFurther improvements can be achieved by integrating the cellular context of the interactions. As we try to filter for binding-associated motifs, the information about protein–protein interactions between the substrates and a protein containing the binding domain is an obvious approach. As long as this information is available, the question of biological relevance can be coupled to the question of co-localization and association; a binding site that will never be in proximity to the corresponding domain is biological not relevant in the studied context.Previous work has shown that contextual information in the form of known physical interactions  or functdoremi.jensenlab.org.Here, we present DoReMi (Domain aided Regular Expression Mining), a tool that allows the user to specify any motif and the associated binding domain(s). By integrating the protein–protein association data into the scoring schema, we extend the usage from being a subsequently applied filter to a factor for ranking found potential instances. The algorithm is publicly available using the web interface at The algorithm takes a motif representation as input and scans a selection of representative sequences to find matching instances. To filter these hits, the score of a motif search gets combined with the protein–protein association scores derived from the STRING database to rank the occurrences . Previounn. The parameter β was set based on our experience in the NetworKIN project to a value of 0.34.The association scores are generated from the STRING confidence association scores. As the database only provides direct associations, indirect paths have to be calculated to cover also indirect interactions like e.g., scaffolding. Indirect interactions are computed by multiplying connecting edges on the path between the proteins of interest. To reduce the effect of over-connected nodes (hubs), we penalize for the connectivity by normalizing for the sum of all connecting edges of the node. The final score of an interaction can be described by the formula: After extending the network to cover indirect paths, the dataset covers a wide range of associations between single proteins, each represented by a single score. This score can be interpreted as the likelihood of two proteins interacting with each other.The potential interaction partners are recruited by selecting all proteins containing the interacting domain(s). To find these proteins, the Hidden-Markov-Model (HMM) representations provided by the PFAM database are used to scan the whole STRING sequence database for matching structures . For theThe result from the PSSM search and the derived contextual network score are combined using a weighting factor to be able to adjust the algorithm to different biological cases. To make the algorithm easily accessible and applicable, we developed a web service implementing the described workflow. The interface of this webservice allows for multiple different inputs for the motif finding part, which are subsequently enhanced with the pre-computed network information.To estimate the performance of our method, we selected two sets of binding sites, which have been previously been used for training of SUMOylation predictors . The famα. We subsequently benchmarked the resulting predictor on the newer dataset by Matic et al., removing all sites that have been already used in the training. To compare the approach of creating a PSSM to a regular expression, we used the SUMOylation motif The older dataset from database .In addition, we trained a predictor based on the combined datasets and benchmarked its performance by five-fold cross validation. The results of the partitioning were slightly better than the results from training on one dataset and benchmarking on the other. This suggests that cross validation gives a realistic performance estimate.Given this, we also used cross validation to test the ability of DoReMi to predict binding partners for PDZ domains . PDZ domα to 0.4 and 0.8 for the SUMO and PDZ test sets respectively. We selected these values based on the conservation of the known sequences, as one can expect a greater influence of the cellular context, given the higher flexibility of the motif. Cross validation was repeated five times and the reported numbers are the median performance.The combined set for SUMOylation contained 1063 sites while the publication by When selecting an appropriate score cut off by fixing the false positive rate  we can show an improvement of the true positive rate by up to 15 percentage points for the PDZ domain and 10 for the SUMO binding domain. The smaller increase for the SUMO domain is explained by the already good performance of the motif-based predictor, which leaves less room for improvement. These examples use simple motif predictors; for comparison, we also applied the context information to an already existing SUMO predictor (SUMOsp) to investigate the possibility of improving on more advanced methods. We acknowledge that the result will be influenced by the fact that this benchmark uses true positives on which the predictor got trained. The shown improvement by using contextual information should be valid. Comparable to the first examples, we see an improvement of the true positive rate by up to 10 percentage points, which motivated the inclusion of this possibility into the web interface. A more detailed listing of the performance numbers can be seen in These results show that, although advanced methods like SUMOsp perform better than the basic approach of combining a RegEx or PSSM with contextual information, it can also be improved by the same approach. This shows the flexibility and versatility of this approach. Especially under the current state of lack of data that we see in the field of linear motif, it can be of great help in discriminating between true and false positive predictions. It also adds a non-sequence specific layer to many methods.The web interface allows the user to easily apply the algorithm in a simple two-step process. The first step allows the user to define the potential binding sites. The first input type is specifying a regular expression; further details on the allowed format options are shown in The second step allows for the selection of one or more potential binding domains. The interface allows the user to search for Pfam domains by their accession, id or description. From the list of search results, the user can select the relevant domains . If neceThe output page shows a brief summary of the analysis . This inOur work focused on making the integration of contextual information to enhance the search for motif occurrences into an easy to execute task. We show that the addition of protein–protein interaction knowledge can improve the predictive performance of existing methods, even when doing it in a fully automated manner. This can be especially useful in the field of linear motifs; as for these, due to lack of data, advanced methods are not easy to develop. Further performance enhancements can potentially be reached when improving the domain associations in a more confined way.For future enhancements, we plan to expand the usability of the method and web interface. Currently, only the human proteome is available; however, the method is in principle applicable to any fully sequenced organism. We thus consider expanding it to major eukaryotic model organisms. We are also working on integrating DoReMi with existing motif resources such as the ELM database.


Many manually curated databases have been developed to index point mutations but they are not sustainable with the ever-increasing volume of scientific literature. There have been considerable efforts in the automatic extraction of mutation specific information from raw text involving use of various text-mining approaches. However, one of the key problems is to link these mutations with its associated protein and to present this data in such a way that researchers can immediately contextualize it within a structurally related family of proteins. To aid this process, we have developed an application called MutationMapper. Point mutations are extracted from abstracts and are validated against protein sequences in Uniprot as far as possible. Our methodology differs in a fundamental way from the usual text-mining approach. Rather than start with abstracts, we start with protein sequences, which facilitates greatly the process of validating a potential point mutation identified in an abstract. The results are displayed as mutations mapped on to the protein sequence or a multiple sequence alignment. The latter enables one to readily pick up mutations performed at equivalent positions in related proteins. We demonstrate the use of MutationMapper against several examples including a single sequence and multiple sequence alignments. The application is available as a web-service at  The amount of pharmacological and physiological literature pertaining to proteins has increased enormously over recent years A key tool in the arsenal of the molecular biologist to study function is site-directed mutagenesis (SDM). The effect of a single mutation can have a range of effects on protein function; from completely non-functional to a slight change in affinity for a known ligand. Additionally, point mutations are known to be important in the appearance of many disease states. In addition to mutations found/made for a particular protein, there is much greater potential for scientific discovery if one can utilize information about mutations found at similar positions from related proteins (however that may be defined). Knowledge about the impact of all mutations at a conserved position within a series of related proteins would help our understanding of all of those proteins and may reveal undiscovered relationships or suggest structurally or functionally important positions.Traditional keyword information searching performed in PubMed is inefficient at retrieving mutational data; typically one retrieves too many abstracts to be useful. The alternatives to automatic retrieval are manually curated systems such as PMD To help circumvent some of these problems we adopted a different approach. Rather than depend directly on MESH terms or keywords, the input is a sequence  identified by the Uniprot ID or Accession code. This information is used to obtain information regarding protein names and synonyms from Uniprot which is subsequently used to query PubMed. Although the search terms will be similar to a conventional search, we have the additional knowledge at this point as to which proteins we are most interested in. Starting from this perspective also has the advantage that the user can supply their own multiple sequence alignment and make their own judgment about the value of the data that comes back from the query. Furthermore, it facilitates the discovery of new relationships between sequences that might not normally be compared to each other. Because we leave the final interpretation of the data to the user, the issue of data accuracy, although important, becomes less critical to the usefulness of the application.As mentioned above, the design philosophy for MutationMapper differs from previous text-mining applications. The starting point is the protein sequence(s) rather than the literature. The user can provide a single sequence for which mutational data is requested for, or the user can provide a multiple sequence alignment (MSA) for which mutations will be searched for across all the proteins and overlaid on top of the given alignment. Provision of an alignment is only needed in order to visualize mutations at equivalent positions. It does not effect retrieval or mapping in any way and is not necessary in order to obtain mutational data for a single protein sequence. The overall workflow is illustrated in The input to MutationMapper, via a web-interface, is a Uniprot ID or a single protein sequence or an MSA in FASTA format with Uniprot Id or Accession code as the sequence identifier. The sequence identifier is used to retrieve protein information from Uniprot, including protein names, gene names, commonly recommended names and synonyms. This information is used to query PubMed and retrieve abstracts. Once abstracts are retrieved and converted to raw text, the next stage is to extract possible mutation data. For that purpose we used the freely available MutationFinder program The critical step is to map a particular mutation that is found within the text to the correct protein. In other words we need to validate the position of the mutation with respect to a given protein. Essentially, the wild type residue is looked for within the amino acid sequence (as given by Uniprot). If a match is found, the position is marked as “mapped”. If the algorithm fails to identify any protein name or is not able to map to the identified protein the mutation is labeled as “non-mapped”. If a mutation is mapped on more than one protein sequence than the information is processed further to map it down to one sequence The results are presented as highlighted positions on the sequence or the alignment provided by the user , false positive (fp), false negative (fn) and true negative (tn) as follows:-We evaluated the efficiency of MutationMapper using various single sequences and a few multiple sequence alignments as test cases. The performance was measured in terms of recall, precision, accuracy and F-measure. All the abstracts with mutations determined by MutationFinder E. coliThe performance of MutationMapper was first evaluated against test cases involving a single sequence. We show here the results for three examples; the human phosphatase and tensin homologue (PTEN) protein In PTEN_HUMAN, out of 6001 abstracts retrieved, 297 were found to contain information concerning mutations with a total of 435 possible mutations. Out of the 435 mutations, we were able to map 125 mutations. Thus, 310 were not mapped. There were 121 true positives (i.e. mutations mapped back to the correct protein) and it mapped all the true positives with 100% recall. All the non-mapped mutations were true negatives, as they were not reported on PTEN_HUMAN, but for other proteins or other factors (see below). The false positives  were mutations mapped on the wrong protein. We had no false negatives. There was an accuracy of 99% and an F-measure of 98.3%. The number of true negatives was high and this reflects a number of issues surrounding the identification of mutations from abstracts: 1) A large proportion were gene mutations rather than protein mutations (e.g. PubMed ID (PMID) 19665071 where a gene has a “point mutation in exon 5 (A536G)”, PMID 16009891 which has a “major pathogenic effect at mRNA level for the mutant C1366T” and PMID 11597326 which has “an intronic variant of p53–G13964C”); 2) some were on another protein that was not the subject of the query. For example, PMID 16160475, where “six different beta-catenin mutations were found in 7 of 13 cases (54%)  however, no mutations of the PTEN or K-ras genes were identified”. In this example the mutation was performed on beta-catenin and not PTEN even though the sequence being queried against was the human PTEN sequence; 3) some were the names of cell lines. For example, “T47D cells” or “human glioblastoma cell line T98G” and 4) in some cases there was no specific protein mentioned. Similar performance was observed for other test cases (data not shown). In the context of our methodology presented here, these true-negatives are not a particular problem as we are able to trap them, but it does highlight the difficulty in being able to reliably identify genuine mutations with text-mining alone. In fact this exemplifies the key challenge because mutations are often not described in accordance with any standard nomenclature. For example, Wei et al. The use of a short-hand nomenclature based on motifs is quite common in the literature and creates difficulties for a solution that attempts to be generic. Typically, authors then refer to a position in a specific motif. For example, in NMDA receptors there is an invariant SYTANLAAF motif that has been investigated by mutational analysis. For convenience, these positions are sometimes referred to by their position with respect to the first S of this motif, so a mutation of the second tyrosine to phenylalanine might be described as Y2F  mutations being reported on another protein but which map onto the query due to a coincidental occurrence that the amino acid found at that position was identical in both proteins and; 2) mutations being reported on a closely related sequence. In glycophorin, we observed mutations reported on type B but since GLPA and GLPB are highly similar, it was mapped on type A (which was the subject of our query). There were no false negatives in the above examples. One of the reasons for false negatives is when the commonly used protein names in published text are not retrieved from Uniprot, hence the algorithm fails to identify the protein names in the abstract and is consequently unable to map it.Although mapping mutations to single sequences automatically is useful in its own right, the real power of the methodology is to map mutations to an alignment so that users can readily visualize data at common positions. We evaluated the performance on multiple sequence alignments using a set of P2X receptors and ionotropic glutamate receptors (iGluR) from rat as two distinct test cases  and 434 were not mapped. Out of 486 mutations mapped, there were 482 true positives and 4 false positives giving a precision of 99%  and 1015 were not mapped. Out of 239 mutations mapped, there were 206 true positives and 33 false positives giving a precision of 86%. Out of 1015 non-mapped mutations, there were 910 true negatives and 105 false negatives, so the recall was 66% . The trade off between these two options cannot readily be predicted and thus the simplest solution is to let the user explore the options for each individual case. Thus, we give the user the ability to browse the results of both algorithms. In high precision mode, the algorithm does an exact match for the protein expression. For example in the case of the GRIK5_RAT sequence, one of the protein expressions retrieved from Uniprot is “Glutamate receptor, ionotropic kainate 5″. Only if all of the words in the expression are found is the protein name used for mapping. In high recall mode, only two or more words are required from the expression for the protein name to be mapped. Using the high precision variant on the rat iGluR MSA gave a recall of 70% and a precision of 90%. In high recall mode, the recall was 99% and the precision was 33%  sequences, then the method cannot disambiguate them. One would expect this to occur more frequently the higher the percentage identity. However, in the context of the envisaged usage (i.e. looking for mutations performed across related proteins), we argue that this is in fact not a problem, because ultimately it is the reporting of a mutation at that position in the alignment that is of interest.To aid retrieval we also provide the option of including protein name expressions. As well as helping with ambiguity, it can also be useful if Uniprot does not include protein expressions that are commonly found in the published text. When a user runs a job, they are able to download the protein expression file that was used to search PubMed. The user can then modify the file to include additional expressions and rerun the job with those new expressions.An illustrative example is the case of the NMDZ1_RAT entry from Uniprot. The expression "NR1" is commonly used in the literature but this is not present as an expression in Uniprot. When this expression is included manually, the number of abstracts retrieved can be significantly increased  with a cAnother example is PEPT1, where the single Uniprot protein expression “Macrophage oligopeptide transporter PEPT1” retrieves only one abstract when queried against PubMed. But if we add an additional expression consisting of just “PEPT1”, 630 abstracts with 60 mutations are retrieved. Hence the addition of a user-informed expression can be particularly effective in such cases.Some protein expressions are similar to commonly used words or may only be a few letters long. The result is that the number of irrelevant abstracts retrieved is too large to be manageable. For example, for the matrix protein from influenza B (Uniprot entry BM2_INBMP), the gene name is ‘M’. This results in an enormous number of abstracts most of which are irrelevant. To avoid such problems and to increase the efficiency of the searches, we apply a filter. Words with two or less characters are ignored. Words with three characters are checked for the presence of a number. If a number is found, that string is used, otherwise it is discarded. Words with greater than three characters are used in the search.A commonly perceived view is that the most complete retrieval of mutation data can only be accomplished by searching the full text of articles. The retrieval of full-text presents additional problems http://mutationmapper.bioch.ox.ac.uk.We have developed a pipeline that can aid researchers in discovering literature pertaining to mutation data. The novel aspect of this pipeline is that it allows mutations to be analyzed in the context of a sequence alignment. Quite often a mutation is performed on one member of a protein family that might have structural meaning in the context of another. The searches against full-text were less informative than against abstracts only. Searches are likely to improve as textual data becomes more annotated. Although there is progress in this area File S1A single file containing five supporting information figures.(PDF)Click here for additional data file.


Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods. Saccharomyces cerevisiae, these complexes are comprised of many subunits that work in a coherent fashion. Therefore, protein complexes are important molecular entities in cellular organization, and are of great importance in unveiling the secrets of cellular organization and function.Protein complexes are groups of two or more associated polypeptide chains, which play a critical role in many biological processes. Many proteins are functional only after they are assembled into a protein complex and interact with other proteins in this complex. Even in the relatively simple model organism As protein complexes are groups of proteins that interact with each other, they are generally dense subgraphs in protein-protein interaction (PPI) networks To address these problems, other valuable resources are gradually being used for protein complex prediction. For example, several recent studies Gene Ontology (GO) is another useful resource, and is currently one of the most comprehensive ontology databases in the bioinformatics community In this study we determined how to predict protein complexes based on both the topological structure of PPI networks and GO annotation similarities. We proposed a novel method for protein complex prediction, called COAN, based on attribute graph clustering theory Some resources, such as gene expression data, have been used to assess the reliability of protein interactions. These methods usually assign a score to each protein pair. Unlike these methods, we integrated the PPI data and GO into a unified framework by constructing ontology augmented networks, based on attribute graph clustering theory The GO database is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. GO provides GO terms to describe gene product characteristics in the following three different aspects, (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. GO slims are cut-down versions of the GO ontologies containing a subset of GO terms. Compared with GO terms, GO slims give a broad overview of the ontology content without the detail of the specific fine-grained terms. GO slims are particularly useful for giving a summary of the results of GO annotation of a genome, microarray, or cDNA collection when broad classification of gene product function is required. The studies Given a PPI network The transition matrix When the transition matrix Where We use unified distance to measure protein pair closeness. One important difference between the unified distance on the ontology augmented network The COAN algorithm broadly consists of two phases. In the first phase, COAN generates seed cliques from all the maximal cliques. Firstly, COAN ranks the cliques based on the unified distance measure. Then, COAN chooses the top rank clique as the seed clique, and removes or prunes the others. This process is repeated until the candidate clique set is empty. In the second phase, COAN expands clusters starting from the seed cliques by adding the close neighbor proteins.As the existing PPI networks are usually sparse, enumerating all maximal cliques does not pose a problem In general, the maximal cliques overlap with each other. With COAN the seed cliques do not overlap as the overlapped cliques are removed or pruned. Given a candidate clique set ranked in descending order of their density value, denoted as In the second phase, COAN expands the seed cliques by adding the close neighbor proteins. We use the connectivity score to measure how strongly a protein In this section, we first describe the datasets and evaluation metrics used in our experiments, and then study the impact of the The two PPI datasets used were the DIP dataset The reference complex dataset was CYC2008 Overall, there are two types of evaluation metrics used to evaluate the quality of predicted complexes and compute the overall precision of the prediction methods.F1 which are commonly used in bioinformatics and machine learning. Let One type of evaluation metrics are Precision, Recall and F1 measure are defined as follows: If F1 measure provides a reasonable combination of both precision and recall, which can be used to evaluate the overall performance.Precision measures the fidelity of the predicted complex set. Recall quantifies the extent to which a predicted complex set captures the known complexes in the reference set. The Another type of evaluation metrics are sensitivity, positive predictive value (PPV) and accuracy which were recently proposed to evaluate the performance of the protein complex prediction methods Firstly, we kept F1 was 0.461, which is generally used to evaluate overall performance. When F1 all decreased. When As shown in F1, accuracy increased when In general, high sensitivity values indicate that the prediction has good coverage of the proteins in the reference complexes, while high PPV values indicate that the predicted complexes are likely to be true positive In this experiment, we compared COAN with the state-of-the-art methods: CMC F1 of 0.461, which was significantly superior to the other methods. Compared to COAN, COACH predicted more complexes, which was beneficial in achieving high recall and sensitivity. In contrast, MCODE only predicted 77 complexes, which resulted in the worst recall of 0.098 and F1 of 0.162. HUNTER predicted 92 complexes and achieved the highest precision of 0.685. MCL and CMC achieved the highest sensitivity of 0.555 and PPV of 0.566, respectively. In addition, we noticed that the size of the largest predicted complex by the four methods was very different. The largest predicted complex by MCL consisted of 498 proteins, which was far beyond the normal size protein complex.As shown in F1, PPV and accuracy. COACH predicted 345 complexes, and achieved highest recall of 0.343. HUNTER and MCL achieved best precision 0.865 and sensitivity 0.57, respectively. MCODE only predicted 72 complexes, and achieved worst recall 0.159.Next, we compared the four methods using the Krogan dataset. From F1 and accuracy.Overall, COAN predicted many protein complexes using the DIP and Krogan datasets, and outperformed other methods in the major evaluation metrics, In addition, Examples of predicted complexes using the DIP dataset are presented in In order to exploit GO to predict protein complexes in a PPI network, we have proposed a novel method which constructs an ontology augmented network based on a PPI network and GO annotation information. Ontology augmented networks can efficiently integrate the PPI data and GO into a unified framework through a unified distance measure. Using the ontology augmented network, we developed a clustering algorithm, COAN, to predict protein complexes, which was capable of taking into account the topological structure of the PPI network, as well as the similarity of GO annotations. Experimental comparisons on two yeast PPI datasets showed that our approach was better than or competitive with the state-of-the-art approaches. In particular, our approach provided a framework to integrate other valuable resources, such as gene expression data.In a complex, the GO annotations may have different importance. Therefore, they may have a different degree of contribution in the unified distance measure. In future work, we plan to explore a self-adjustment mechanism to determine the degree of contribution of different GO annotations. In addition, we will exploit other resources to improve the performance of COAN in protein complex prediction.Source Code S1The source code of COAN.(ZIP)Click here for additional data file.


Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification.A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics.The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes. Protein complexes are important molecular entities in cellular organizations. With large amounts of protein interactions produced by high-throughput experimental techniques ,2, proteGO annotation is a useful information resource to measure the reliability of protein interaction pairs. The GO project maintains three structured controlled vocabularies, which describe gene products in terms of their associated biological processes, cellular components, and molecular functions . The ontIn this paper, we attempt to make use of GO annotations and the ontology structure of GO terms to measure semantic similarity of GO terms and proteins. The similarity of two GO terms is measured based on their average distance to their lowest common ancestors in the ontology structure. Semantic similarity between proteins is computed as the similarity of two sets of GO terms, which annotate the two proteins respectively. PPIs in the network are then weighted by the similarity of interacting proteins for the filtering and clustering steps. As far as we know, most approaches filter the predicted complexes with low density or statistical significance in post processes ,9,11,12,In this section, we present, in detail, the two phases used in our approach. In the first phase, protein semantic similarity is computed based on their GO annotations. Following this, a core-attachment structure detection algorithm is applied to detect core and attachment proteins of complexes from the filtered PPI network. The flow of our method can be described in the following steps:(1) Computing protein semantic similarity for every pair of proteins with interaction in the PPI network.(2) Removing interactions with low similarity from the original network.(3) Finding cliques in the filtered network to form complex cores. Multiple highly overlapping cliques are merged to form one core.(4) Adding attachment proteins to these cores with the expanding strategy in RRW algorithm.The GO database is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. The ontology structure of GO terms is organized as DAGs of three domains with terms as nodes and their relationships as directed edges. The GO terms are structured by two kinds of relationships to each other: "is-a" and "part-of", representing specific-to-general and part-to-whole relations respectively.x can be measured by the path length from the root node to x passing through its ancestors. In a similar way, given a term x, its relative semantic specificity from its ancestor a can be measured by the path length from a to x. Since there may be multiple paths from one node to another in DAGs, we define distance d as the average path length from term a to x, while a is one of ancestors of x. Two terms, x and y, are considered more similar if their distances to their lowest common ancestors are shorter, or their lowest common ancestors average distance to the root is longer. We define LCA as the set of lowest common ancestors of term x and term y. For the node set of common ancestors of × and y, a ∈ LCA if the paths from a to x and a to y do not pass through any other common ancestor. Based on the graph characteristics of GO terms, we define the similarity of two GO terms x and y Sim as follows:Semantic similarity of GO terms can be measured by their positions in the DAGs. In the task of semantic similarity computation, we attempted to design our GO semantic similarity measure based on a graph-based method measuring concepts in a taxonomy structure . In the root denotes a virtual node as the parent node of the three root nodes of three distinct DAGs  in GO. ad denotes the average length of paths from root to x passing through a, ad=d+d. Sim reaches its minimum value zero when x and y are terms in different domains, while it reaches its maximum value 1 when x and y are the same term.where By the definition of term-wise similarity, we can measure the similarity of two proteins annotated by two sets of GO terms. We calculate each pair of GO terms in annotation sets of two proteins, and use the best-match average approach  to evaluA Tand B Tdenote the term sets annotating protein A and B respectively. For every term x in AT, we find the most similar terms in B Tto calculate A and B, which is also a uniform result.PSim similarity to weight every pair-wise interaction in the PPI network. Considering the inaccuracy of interaction network, we remove the interactions with a PSim value no larger than a threshold filter_thres. Only high quality interactions are involved in the following complex identification steps.We use The core-attachment structure  providesNA defined as follows is above threshold merge_thres:In our algorithm, we first used the clique finding algorithm as described in  to identA Vand B Vdenote the node sets of clique A and B respectively. All of the merged clusters and cliques not involved in the merging form core set of the complex. Attachment proteins are added to each core by the expanding strategy of RRW algorithm [where lgorithm . RRW is lgorithm , Aloy [2lgorithm  and MIPSlgorithm  indicatew Edenotes the weighted edge set. After construction of the weighted network G', cliques are detected by algorithm [G', core) denotes the RRW expanding procedure starting from a cluster core. RRW computes affinity score between each protein to the given cluster based on the random walk stationary vectors generated from G'. The closet protein to the cluster is added to the cluster in each expanding step. This process is continued until no protein's affinity score reaches a given threshold. We collect only the maximal expansion of each cluster as a predicted complex, which is different from the original RRW algorithm.The flow of our algorithm is described by pseudo-codes in Figure lgorithm  in step We apply our algorithm on three datasets of yeast protein interactions: Gavin , Krogan http://www.geneontology.org/ with version 1.2028, dated 06/10/2011. The version of the annotation file of Saccharomyces cerevisiae is 1.1566 submitted on 06/18/2011.The GO resource we used can be downloaded from NA is above 0.2, which is used in most approaches.We evaluate the experimental result with six evaluation metrics: precision (P), recall (R), F-measure (F), sensitivity (Sn), PPV and accuracy (Acc), which are described in . A predifilter_thres, was set from 0 to 0.9 by an increment of 0.1 each time. To observe how filter_thres affected the result, the merge_thres was fixed to 1, which led to unavailable merging step. The precision, recall, and F-measure with Krogan-Combined datasets influenced by different filter_thres are shown in Figure Before comparing with other approaches, the influence of parameters was examined in our method. To optimize our method, the edge filtering threshold, i.e., filter_thres, the precision rises in general, indicating that high accurate complexes can be identified from high quality interactions. Therefore, removing interaction pairs with low similarity significantly improves the performance of complex identification. The GO semantic similarity measure we proposed is effective in estimating the quality of PPI. The F-measure reaches maximum when filter_thres is set to an optimal value 0.6, which is also validated by other combinations of network and benchmark datasets. In addition, we found that the number of predicted complexes is inversely proportional to filter_thres. This number is above 1,000 when filter_thres is less than 0.3, which seems unreasonable for a network with 3581 nodes. This is because the clique finding algorithm [With the increase of lgorithm  generatemerge_thres. As shown in Figure merge_thres set to 1. However, the F-measure is improved solely with the increase of precision, while recall keeps the same value when merge_thres changes from 0.5 to 1. This indicates that the overlapping cliques may introduce matching between multiple similar clusters and a single benchmark complex. According to the definition of precision [merge_thres.We present another experiment to find optimal recision , redundaWe compared our method with six well-known approaches: MCODE , CFinderSensitivity, PPV and accuracy are metrics evaluating the correspondence between the prediction and benchmark in micro level. Sensitivity represents the coverage of a complex by its best-matching cluster , while PPV measures how well a given cluster predicts its best-matching complex . Accuracfilter_thres set to 0.6. The parameters of minimum and maximum size in the original RRW algorithm are also set to 2 and 20 respectively. Figure In Table NA scores with their matching real complexes are also listed. As is shown in Table The predicted complexes of our approach are generated from high similarity interactions in networks. Therefore, they have high similarity in GO annotations. We present several examples of predicted complexes generated from Gavin dataset in Table Computational approaches for protein complex detection are often affected by false-positive interactions in large scale PPI data. In this paper, we identify protein complexes in PPI networks with a two-phase method. We first measure the semantic similarity of GO terms and proteins by the ontology structure to evaluate the reliability of PPIs. After removing unreliable proportion of interactions, a core-attachment based clustering method is applied to the filtered network for complex identification. The main contributions of this paper are: 1) proposing a graph-based GO semantic similarity measure to purify the PPI network, 2) designing a core-attachment detection algorithm making use of the RRW algorithm to detect complexes from the filtered network.By comparing with various approaches, our method outperforms the other approaches in overall evaluations. The graph-based similarity measure enhances the complex identification performance. Removing unreliable interactions before clustering improves the performance significantly. The strategy of expanding clusters by RRW algorithm is also effective to identify the attachment proteins in protein complexes. A future research can focus on the similarity measure of PPI in the network. Various measuring method can be applied to estimate the reliability of protein pairs to filter the false-positive interactions.The authors declare that they have no competing interests.JW carried out the identification of protein complexes studies, participated in the design of the experiments and helped to draft the manuscript. DX carried out the GO database studies, proposed the method of computing protein semantic similarity and draft the manuscript. HL guided the design of the study and participated in the experimental results analysis. ZY participated in the study of PPI netwoks and helped to revise the manuscript. YZ participated in the study of RRW algorithm, and performed the statistical analysis. All authors read and approved the final manuscript.


We consider the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. Additional tests with large decoy sets probe the efficacy of two published statistically derived scoring functions that contain a huge number of parameters. In contrast, we demonstrate that state-of-the-art machine learning techniques can enormously reduce the number of parameters required, thereby identifying the relevant docking features using a miniscule fraction of the number of parameters in the prior works. The present machine learning study considers a 300 dimensional vector (dependent on only 15 parameters), termed the Chemical Context Profile (CCP), where each dimension reflects a specific type of protein amino acid-nucleic acid base interaction. The CCP is designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. Our objective function is the Chemical Context Discrepancy (CCD), which is defined as the angle between the native system's CCP vector and the decoy's vector and which serves as a substitute for the more commonly used root mean squared deviation (RMSD). We demonstrate that the CCP provides a useful scoring function when certain dimensions are properly weighted. Finally, we explore how the amino acids on a protein's surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA. Interacting molecules convey information via their association that is driven by surface complementarity and chemical compatibility. However, predicting the docking process generally requires some structural knowledge , the treatment of the docking of nucleic acids (NAs) onto proteins lags far behind. The continually growing database of solved structures for NA-protein molecular complexes Our goal is two-fold. The first goal is to analyze the behavior of the rigid body docking methods when beginning with either the bound (apo) or unbound (holo) crystal structures for the molecules to be docked. The second goal is to assess the performance of two published statistical scoring functions, commonly known as statistical potentials, and to compare them with our new statistical potential that is based on machine learning methods and that contains orders of magnitude fewer parameters than the published statistical potentials. The study further aims to provide reasonable performance using only the holo forms of the molecules and to devise scoring functions that are able to discern credible or native-like docking poses.We use van Dijk and Bonvin's protein-DNA benchmark set composed of 47 complexes whose PDB IDs are summarized in 10 docked conformations or poses can be generated. But the analysis of such a large decoy set is challenging. Hence, most applications including ours use far fewer poses.Rigid body docking methods have garnered tremendous attention because of the development of fast geometric algorithms using fast Fourier transform calculations of surface complementarity 5 models from the entire 1010 models produced for each protein-NA complex by FTdock 5. Models also are ranked by the root-mean-squared-deviation (RMSD) calculated between the model and native DNA conformations after the proteins have first been aligned.We retain the best 103 decoys from the 105 set) are retained. The degree of success is defined as the fraction of the best 20 RMSD scoring decoys in this smaller subset. This procedure allows us to examine whether the subset of decoys with the best surface complementarity also contains poses with low RMSD to the native pose.In order to further restrict the size of the decoy set, only poses with SCS ranks below a threshold . Consequently, we use 105 poses in the following tests.5 decoys are generated for each of the N = 47 complexes, yielding a total of 23.5×106 decoys. Three goodness-of-fit metrics quantify the performance: 1) The RMSD between the DNA molecules of the native and model once the proteins are aligned. 2) The fraction of native contacts, as defined in the CAPRI experiment 1/2. Because the MCC only considers the protein residues in contact with DNA, regardless of the DNA nucleotide, the MCC provides a less stringent than the measure of fraction of native contacts.Next, we inquire how well rigid body docking describes protein-DNA interactions. The algorithm is challenged with five docking trials that utilize the four combinations of the bound and unbound conformations of the protein and DNA molecules, as well as an extra trial using the bound form for the DNA and protein but with the protein's side chains rebuilt using the SCWRL algorithm in order to study contributions from side chain reorganization. For each trial, 10When the docked conformations of the molecules are used, a pose with a DNA RMSD≤4 Å is present for 80% of the N = 47 benchmark complexes . This rebest≤4 Å, while only 50% can be rebuilt to within 10 Å when using unbound conformations. Interestingly, the overall performance is slightly better when the protein is in the holo state. Potentially, docking using various DNA shapes could be a quick alternative to test a new target , while the other produces an entirely different set of interactions . The situation can be even worse when the molecule has high order symmetry such as the near palindromic DNA example. Furthermore, the RMSD often fails to capture the essence of the docking, which is best viewed as a search for a given set of molecular contacts between moieties of particular chemical character.This situation contrasts with that for protein-protein and protein-ligand docking where the RMSD Accordingly, we introduce a 300-dimensional vector termed the Chemical Context Profile CCP, see  to descrβ atom. Within this distance, the interaction energy is assumed to be constant  = 1/max, where r is the separation (in Å).We now describe the energy function to be used in conjunction with the CCP. Protein-NA interactions include long-ranged electrostatic energies. However, their effective range is an open issue constant , while br interaction is allayed by the fact that the Onsager length is 140 Å in a medium of low dielectric permittivity ε = 4, such as that inside a biomolecule. The binding interface between the protein and the nucleic acid is assumed as being largely desolvated. Because the rigid body docking approach only generates approximate models, we anticipate that the 1/r dependence should be adequate for our study.A possible concern about the long-range 1/The Chemical Context Discrepancy (CCD) is a measure introduced to guide the learning algorithm in selecting decoys with a native-like chemical context rather than a low RMSD. To emphasize our point, consider three DNA duplexes of increasing palindromic character. This increase results in the growth of a second minimum at ∼25 Å in plots of the CCD versus RSMD . A learnBecause we seek to score based on poses having a native-like chemical context, docking configurations are considered as native-like when they have a similar CCP as the native pose. The difference in CCP profiles of the decoy and the native is quantified by the angle between the CCP vectors for the native and decoy poses, as determined using the dot-product A frequently used scoring function is a statistical potential that is constructed from a database of protein-NA complexes, ideally utilizing an appropriate reference state k is the importance of this particular interaction in influencing the score. These interactions implicitly encompass factors such as electrostatics, hydrogen bonding, desolvation, London dispersion, polarization, ionization, and other effects. The 1/r dependence of our energy function mimics the function form of Coulomb's law although we do not explicitly account for the individual contributions of these factors to the total score, nor do we assume that the various weights can be uniquely decomposed into discreet components (as in Coulomb law's dependence on the product of charges Q1Q2). The sign of the weight indicates whether the interaction is attractive (negative) or repulsive (positive).Our CCP vector offers a straightforward solution for training based on known complexes. By weighting each one of the 300 interacting pair types in the CCP, a linear combination of the entries in the CCP vector serves as the dominant term in our scoring function, r Coulombic energy between the negatively charged phosphates with the positively and negatively charged amino acids. Without loss of generality, the contribution from the total Coulomb interaction has a weight of Even though there are 300 different types of NA-protein pairwise interactions, we seek find a much smaller subset that can still identify native-like docking poses. As a first step, we no longer distinguish between the NA residue type for the score calculation . Fifteen of the 17 components shown in 5 poses generated for each possible protein-DNA pair. Certain dimensions of this vector are more heavily weighted than others to produce a net score S (defined earlier) that is favorable only for likely protein-NA poses, with minimal false positives and negatives; i.e., the best weights vector ω is the one that maximizes the separation between the scores of known DNA-binding proteins from randomly chosen proteins from the PDB not known to bind DNA  to greatest. The best 50 decoys for each complex are identified using the scoring function S defined previously, producing the training set of 1700 decoys (50×34 complexes). The weight vector ω is adjusted to select for the most native-like among the 1,700 poses where the most native-like is determined by our CCD parameter which examines whether the chemical context of the pose is similar to that of the native pose . Specifically, we optimize the objective function,These weights are determined using a training set with 34 complexes. For each complex, the 10The optimization of ω includes proteins with both low (<7) and high (>7) isoelectric points (pI) because the net charge of the protein, either negative or positive, respectively, is a significant factor in identifying authentic complexes . A potenOur method is now compared to two statistical potentials aimed at identifying protein-DNA complexes. The first approach only considers contacts The first challenge “Does this protein bind DNA?” is pursued using three sets of proteins. One set contains the 47 proteins in the benchmark set , while t5 models, the statistical significance of a true positive hit  is high.The second challenge “Where would the DNA bind on the protein?” is addressed using the DNA's RMSD by considering the fraction of the native contacts reproduced and the Matthews correlation coefficient for the protein residues , rows. TBoth our scoring function and the distance-dependent function recognize native-like docking conformations when both the protein and DNA are in the bound forms, with 80% of the benchmark complexes having RMSDs below 5 Å . Even thEven if performance is assessed using less stringent criteria, such as the fraction of native contacts  within the DNA, probing a protein's surface with a “test” moiety enables highlighting the favorable and unfavorable regions for that test moiety, a procedure similar to the use of a test charge to probe an electrostatic field. Each of the known DNA-binding proteins yields a favorable score for the Phosphate at the sites where the DNA docks the proteins . The CCP also can provide an estimate of the magnitude of various types of interactions  provided that the entries in the CCP are designed accordingly.The introduction of the Chemical Context Profile (CCP) of the docking interface represents the chemical complementarity of the protein-NA interface for a given pose. The magnitude of the CCP correlates with the loss of surface area upon binding. Profiles from different poses can be compared with one another by their similarity to the native CCP, where the similarity is defined using the Chemical Context Discrepancy (CCD). Models with low RMSD to the native also have low CCD values, but the CCD measure is robust to DNA palindromic sequences. The CCD can be used as a substitute for RMSD in machine-learning techniques The use of machine-learning techniques, more specifically sequential feature selection, can greatly reduce the parameter space in the derivation of a scoring function. This compression identifies the principal components of protein-DNA interactions. The compression process is implemented with a training set containing a small number protein-DNA complexes (N = 34), indicating that the method can be used when there are only a few structures. Very large decoy sets encompass many native-like complexes as true positive examples and provide far more true negative ones to score against. A total of only 15 components are identified as the principal components out of 300. This relative low number of parameters performs as well as a sophisticated statistical potential with several thousands of components, allowing for a quick complex scoring while preventing parameter over fitting.Rigid-body docking is examined to see whether it can provide rudimentary “initial contact” protein-DNA docked complexes from the holo or unbound forms of both molecules. With a scoring function that is able to identify these complexes, rigid body docking can be used in all-atom refinement procedures that allow flexibility of both protein and DNA. These refinement procedures already exist, e.g., the HADDOCK approach When the unbound states of both protein and DNA molecules are used, the performance of all the scoring functions tested herein is not much better than random. This situation likely reflects a “best case” actual genome-wide screening scenario because one or both structures generally are unknown. Hence, further research is required to increase the power of the scoring functions to identify early-encounter complexes. Unfortunately, no such structural database exists from which to extract any information. Potentially coarse-grain, Langevin or Brownian dynamics could be of use We map a protein's surface to assess its potential to bind DNA. This ability is particularly important because electrostatic models of proteins are often inadequate to identify the DNA-binding properties of proteins An existing protein-DNA benchmark α and Cβ for proteins and P, C2′ and C4 for DNA. All three DNA atoms appear in any nucleotide, regardless of type . The P atom is situated in the DNA backbone, C2′ in the DNA sugar ring, and C4 is in the nucleobase. This allows for easy computations of the RMSD between DNA molecules containing the same number of nucleotides but different sequences.The root-mean-squared-deviation (RMSD) calculation includes the following heavy atoms: C5 retained. Because FTdock converts the molecules into coarse grids and allows for some overlap between them, it implicitly accounts for some induced fit upon docking. In the FTdock algorithm, one molecule is held in place while the other is scanned across its surface. The static molecule has a core in addition to an interfacial surface region. We choose the proteins to be the static molecule to account for side chain flexibility. The great number of decoys offers a reference state, similar in spirit to “decoys as a reference state” or DARS The computer program FTdock β moieties of the alanines  in the protein, and major groove moieties (M) of the adenosines (A) in the DNA. The last term is between the valine  Cβ and the thymine (T) phosphate groups (P). The moieties, or interacting centers, for proteins are centered at the Cβ atom, while those for nucleic acids are presented in β atom, so that a residue interacts only with the closest groove. The total number of entries in the vector is 300: the 20 amino acid types times 3 NA moieties times 5 nucleotide types. The score is obtained by summing f(r) for each interacting pair, where the distance-dependent function is taken as f(r) = 1/max and where the average extent of the amino acid side chains <e> are presented in f(r) increases with the number of a given interacting pair type and with shorter separations.We define the Chemical Context Profile (CCP) as a multi-dimensional vector:ccp and ωarea are optimized using machine learning (as described below) to recognize native-like docking conformations in large sets of decoys. The weight Docked conformations are scored by weighting the Chemical Context Profiles, nat and mdl refer to native and model docking poses, respectively. The dot symbolizes the vector dot product, while vertical brackets indicate the vector norm, or magnitude. The CCD is used here as a substitute of the root mean squared deviation (RMSD) and is shown to correlate with low-RMSDs (Given a native and predicted (model) docking pose and their associated CCPs, the Chemical Context Discrepancy (CCD) is defined as the angle between the profiles,ow-RMSDs  while tan×m parameter optimization trials for a matrix with n by m elements, repeated over many training and test sets . Because our interaction matrix contains 20 * 3 * 5 entries, a total of 2300 experiments would be required. This number is computationally unfeasible even on a super computer. In practice, however, when the question is posed concerning a specific matrix (with many parameters), one can rely on the sequential feature selection (SFS) method 60, this protocol is a tractable way to locate significant features or parameters. To derive the most important features, we simply count the number of times a feature appears after the feature selection round; those that are selected early appear more often as they are permanently activated, so they are deemed the most important. This step is performed to identify which elements in the scoring matrix have the most impact and, hence, require the most optimization  method 5 decoys are generated for each member. The decoys are taken from the bound/bound states of the protein/DNA because they offer the best quality decoys with the lowest CCDs (or RMSD).The optimization program begins by loading the Chemical Context Profiles into memory for each of the decoys. The training set has 34 members  and reprd is updated using the formula defined earlier. Then, the decoys are sorted by their scores from best (lowest) to worst (greatest) for each PDB ID. The best 50 decoys are identified and represent the docking set solution. Two objective functions discussed in the text are optimized. The best weights vector ω is the one that maximizes the separation between the known DNA-binding proteins and those proteins with low (<7) or high (>7) isoelectric points . The selection process has identified 15 interacting pairs. Other models can readily be devised: the twenty amino acids could be grouped into six  and Surface Complementarity Score . Three decoy sets are used: known DNA-binding proteins , proteins with pI<7  and >7 . Left panes show how the protein-DNA complexes are scored in relation to the isoelectric point of the protein. Right panes show how the scores of the three decoy sets overlap (blue area) with one another; a perfect scoring function would separate the scores of authentic DNA-binding proteins from those that do not bind DNA.(PDF)Click here for additional data file.Figure S2Forward sequential feature selection. The method is applied to find the most important interacting pairs. (a) As the number of variables increases, the performance on the training set (open circles) also increases, but the performance on the test set (closed circles) degrades when too many variables are used; this is the learn-by-heart phenomenon. We thus cap the number of variables to 17 ; since two of the variables are fixed (Coulomb and |CCP|), 15 will be picked from the 20×3 = 60 interaction matrix. (b) The relative importance of the 60 pairs (left). The 15 most important pairs are highlighted in grey (right).(PDF)Click here for additional data file.Table S1List of PDB codes that are part of the N = 47 protein-DNA benchmark database.(PDF)Click here for additional data file.Table S2List of PDB codes that are part of the N = 34 protein-DNA training set database.(PDF)Click here for additional data file.Table S3List of PDB codes that features N = 40 proteins with isoelectric point lower than 7, and are assumed to not bind DNA.(PDF)Click here for additional data file.Table S4List of PDB codes that features N = 41 proteins with isoelectric point greater than 7, and are assumed to not bind DNA.(PDF)Click here for additional data file.Table S5Amino acid extent beyond theatom. Values extracted from 201 protein-DNA complexes. The magnitude reported is the average extent plus one standard deviation (PDF)Click here for additional data file.Table S6Performance of rigid body docking. The performance is measured on a benchmark comprising 47 different complexes. Five docking trials using various combinations of bound and unbound states of the docked molecules: bound/bound (B/B), bound, but with rebuild side chains/bound (S/B), unbound/bound (U/B), bound/unbound (B/U), and unbound/unbound (U/U). A total of 105 docked conformations are generated for each complex and docking trial. For each decoy set, decoys are sorted by RMSD from lowest to greatest, and the RMSD of the 20th best decoy is reported. The RMSD is taken between the DNA positions in the native and the docked complexes, once the proteins have been superimposed. All values reported here are in Angstroms, and are plotted in (PDF)Click here for additional data file.Table S7Performance of various scoring functions. The performance is tested using the bound states of the protein/DNA molecules. The scoring functions are our work (CCP), contact only statistical potential (C-S) and distance-dependent statistical potential (D-S). The RMSD column is the same as in table S6, B/B case. All values are in Angstroms, and are plotted in (PDF)Click here for additional data file.Table S8Summary of  A checkmark is put whenever the DNA RMSD is lower than 10 Angstroms. The FFT column relates to the ability of the FFT-based docking method to provide for good decoys, while the last three columns to the ability of the various scoring functions to identify them. Rows marked with an asterix have been left out while training, and as such constitute the test set.(PDF)Click here for additional data file.