ROSETTA protocols for comparative and de novo modeling and protein-protein docking. We demonstrate that the cross-link validation and visualization software Xwalk facilitates successful cross-link data integration. Besides the protocols we introduce XLdb, a database of chemical cross-links from 14 different publications with 506 intra-protein and 62 inter-protein cross-links, where each cross-link can be mapped on an experimental structure from the Protein Data Bank. Finally, we demonstrate on a protein-protein docking reference data set the impact of virtual cross-links on protein docking calculations and show that an inter-protein cross-link can reduce on average the RMSD of a docking prediction by 5.0 Å. The methods and results presented here provide guidelines for the effective integration of chemical cross-link data in molecular modeling calculations and should advance the structural analysis of particularly large and transient protein complexes via hybrid structural biology methods.Chemical cross-links identified by mass spectrometry generate distance restraints that reveal low-resolution structural information on proteins and protein complexes. The technology to reliably generate such data has become mature and robust enough to shift the focus to the question of how these distance restraints can be best integrated into molecular modeling calculations. Here, we introduce three workflows for incorporating distance restraints generated by chemical cross-linking and mass spectrometry into  Conventional structural biology techniques like X-ray crystallography or Nuclear Magnetic Resonance (NMR) spectroscopy solved the structure of a large number of macromolecular complexes Chemical cross-linking in combination with mass spectrometric analysis (XL-MS) has emerged as a viable tool for probing the structure of many protein complexes without such bias. Although XL-MS only provides comparatively low resolution data DSS) as a reagent. DSS has a spacer length of approximately 11.4 Å, but was experimentally found to bridge lysine residues of up to 30.0 Å and more . This distance restraint takes into account the length of two extended lysine side chains (∼5.5 Å each) and some conformational flexibility of the protein complex Xwalk  algorithm SAS) distance, which represents a more reasonable measure of cross-link distances in modeling calculations.The distance restraints generated by XL-MS can be used to guide molecular modeling simulations towards native-like conformations. However, two important points need to be addressed prior to the incorporation of XL-MS distances. First, cross-linker molecules are flexible and can covalently link lysine residues over a large range of inter-residue distances Xwalk algorithm in combination with the ROSETTA molecular modeling suite to affinity purified protein complexes from the Protein Phosphate 2A (PP2A) network We recently applied the de novo prediction of protein structures, comparative modeling of proteins and protein-protein docking. A demo version of the docking protocol is available in the ROSETTA protocol capture archive: XL_guided_protein_docking/run_demo.sh. We also introduce a database with literature-curated cross-links, which we exploited for computing probability distributions of cross-link distances. And finally, we demonstrate in a systematic study the association between the accuracy of a cross-link guided protein docking calculation and the number of employed inter-protein cross-links.Here we describe in detail three computational modeling workflows that we applied in the above study ROSETTA molecular modeling suite. Protocols applied within the workflows are available in the public release of ROSETTA starting from version 3.1, with the exception of the protocol nonlocal, which was downloaded from ROSETTA’s trunk at revision 42791. The reader is kindly referred to the help pages of the individual software programs for a detailed explanation of the employed application parameters. Parameter arguments shown in curly brackets require case specific input.All modeling calculations were performed using the All workflows have in common that they utilize XL-MS data to guide modeling calculations towards native-like conformations with the assumption that all identified cross-links were formed on native structures. The guidance was achieved by two means. Firstly, by incorporating the cross-link distance restraints into the ROSETTA scoring function where they penalize models with cross-link distances above the distance threshold of 30.0 Å, which leads to a preferential sampling of the conformational space around the native conformation. Secondly, by applying XL-MS distances as post-modeling filters in the candidate selection stage, where they removed models that violate cross-link data.The structure of a target protein can be predicted with comparative modeling, if experimental X-ray crystallography or NMR structures of a homologous template protein exist. Out of the 94 proteins that were purified from the PP2A interaction network http://toolkit.tuebingen.mpg.de/hhpred/) on the target sequence to determine the best template structure.1.1 Run HHpred http://www.pdb.org).1.2 Download the template structure from the Protein Data Bank.1.4 Generate fragment files for the target protein using the ROBETTA server http://bioinf.cs.ucl.ac.uk/psipred).1.5 Predict the secondary structure of the target protein using PSIPRED 1.6 Using the input files from above, run ROSETTA’s minirosetta application on each target protein using following command line flags:-database {rosetta_DB_dir}-run:protocol threading-run:shuffle-in:file:fasta {protein.fasta}-in:file:psipred_ss2 {protein.ss2}-in:file:template_pdb {template.pdb}-in:file:alignment {protein-template.aln}-out:overwrite-out:nstruct 200-out:shuffle_nstruct 200-cm:aln_format general-idealize_after_loop_close-out:file:silent_struct_type binary-loops:extended-loops:build_initial-loops:remodel quick_ccd-loops:relax relax-loops:frag_sizes 9 3 1-loops:frag_files {fragments9} {fragments3} none-frag9 {fragments9}-frag3 {fragments3}-relax:fast-relax:default_repeats 2-silent_decoytime-random_grow_loops_by 4-select_best_loop_from 1-in:detect_disulf false-fail_on_bad_hbond false1.7 Predict at least 200 models per target.Xwalk specific distance file format :1.8 Create a tab delimited text file (xls.txt) holding the list of all intra-protein cross-links and mono-links in a 1 protein.pdb LYS-1-C-CBLYS-2-C-CBwhere the 1st column is an incremental index, the 2nd column is the PDB file name and the 3rd and 4th columns are dash delimited PDB information about first and second cross-linked atom, respectively. The PDB information should list the residue name, residue number, chain ID and atom name. Mono-links are described with the first three columns only.Xwalkhttp://www.xwalk.org) on each model with following command1.9 Calculate the SAS distance for each intra-protein cross-link using $> java Xwalk -infile {model.pdb} -dist {xls.txt} –bb –radius 2.0–mono.1.10 Choose models that satisfy the largest number of intra-protein cross-links and mono-links.1.11 If multiple models satisfy the same number of cross-links and mono-links, or if none of the models satisfy any cross-link, select the model with the lowest Root Mean Square Deviation (RMSD) to the template structure as the best model.Alternatively, distance information from XL-MS data can also be exploited as distance restraints within the scoring function (see next section), which for the PP2A project was omitted to keep the modeling unbiased for validation purposes.de novo modeling. The human IgBP1 protein had no crystal structure available in the Protein Data Bank (PDB) de novo modeling workflow :2.2 In addition, create a AtomPair {atom name1} {residue number1, chain ID1} {atom name2} {residue number2, chain ID2} FLAT_HARMONIC {x0} {standard deviation} {tolerance}dist and three parameters that for a DSS cross-link were heuristically chosen to be 0 = x15.0, tolerance = 15.0 and standard deviation σ = 1.0:The flat harmonic function guarantees that models are penalized only if the Euclidean distance between two cross-linked atoms exceeds 30.0 Å. The function takes the Euclidean distance ROSETTA’s nonlocal application with the input files described above and the following command line flags:2.3 Run -database {rosetta_DB_dir}-in:file:fasta {protein.fasta}-in:file:psipred_ss2 {protein.ss2}-in:file:template_pdb {template.pdb}-in:file:alignment {protein-template.aln}-out:overwrite-out:nstruct {n}-cm:aln_format general-frag3 {fragments3}-frag9 {fragments9}-abinitio::relax-abinitio::no_write_failures-abinitio:increase_cycles 1-abinitio:rg_reweight 0.25-nonlocal:builder star-nonlocal:mode semirigid-nonlocal:gap_sampling_extension 5-jumps:ramp_chainbreaks-jumps:overlap_chainbreak-jumps:increase_chainbreak 0.5-constraints:cst_fa_file {template.cst}-constraints:cst_file {template.cst}2.4 Generate at least 100,000 models.Xwalk input file listing all cross-links and mono-links (see step 1.8) and run Xwalk (see step 1.9).2.5 Generate a 2.6 Choose top models as described in step 1.10. Choose the lowest scoring 500 models if more than 500 models should satisfy the largest number of cross-links.2.7 Calculate an all against all Cα coordinate RMSD matrix among the top models.RR script:2.8 Perform a hierarchical clustering with the complete clustering method using rmsd.dat<-read.tablermsd.dist<-as.dist(rmsd.dat)rmsd.hist = hclust(rmsd.dist)R script:2.9 Cut the hierarchical cluster tree at an RMSD threshold value of 10 Å in the same rmsd.cut<-cutreewrite.table2.10 Pick the lowest scoring model from the largest cluster as the best model. If the clustering should retrieve only singletons, pick models that have the smallest RMSD value compared to the template structure as the best models.The catalytic subunits of PP2A were docked against best full-length models of IgBP1 (see previous section) using the following workflow see also . The worROSETTA’s relax protocol with following command line flags:3.1.1 Prior to docking calculations, crystal structures from the PDB (e.g. PP2AA) need to be relaxed. For relaxation, run -database {rosetta_DB_dir}-in:file:s {protein.pdb}-relax:sequence-constrain_relax_to_start_coordsROSETTA constraint file holding a list of all inter-protein cross-links (see step 2.2).3.1.2 Prepare a ROSETTA’s docking_protocol application in low-resolution centroid mode using following command line flags:3.1.3 Using the constraint file, run protein-protein docking calculations with -database {rosetta_DB_dir}-in:file:s {protein.pdb}-constraints:cst_file {inter-xl.cst}-out:overwrite-out:nstruct {n}-out:file:o {model.pdb}-docking:low_res_protocol_only-docking:randomize1-docking:randomize2-docking:spin-docking:docking_centroid_outer_cycles 10-docking:docking_centroid_inner_cycles 50-docking:dock_lowres_filter 10 13.1.4 Generate at least 100,000 models.Xwalk input file that holds all intra-protein and inter-protein cross-links and mono-links (see step 1.8). Standard docking software and cross-link guided docking protocols utilize only inter-protein cross-links. But Xwalk ability to mimic cross-links by its shortest path and SAS distance calculation, allows it to additionally employ intra-protein cross-links and mono-links as post-docking filters. Predicted docking models that bury intra-protein cross-links or mono-links within their binding interface are thus detected by Xwalk and removed (see Discussion).3.1.5 Prepare a Xwalk’s Euclidean distance measure and subsequently the SAS distance measure (see step 1.9) on each model.3.1.6 Assess the number of cross-links and mono-links satisfied by each model. To speed up calculations, apply first ROSETTA energy score.3.1.7 Select models satisfying to the highest number of cross-links. Should their number be higher than 500 or should none of the models satisfy any cross-link, select those 500 models with the lowest BSA):3.1.8 Analyze the binding interface size of the selected models, which is equivalent to the buried surface area .23.1.9 Select further only models with a sufficiently large binding interface with BSA(complex) ≥900 Å3.1.10 Choose a large number of models with the shortest mean SAS distance over all cross-links. . The shortest mean distance leads to a preference for models with overall shorter cross-link distances that in most cases show distance ranges similar to 3.1.11 Calculate an all against all Cα coordinate RMSD matrix among the top models. Note, that the RMSD should be computed only on the smaller protein (ligand) and not on the larger structure (receptor), as the latter remains fixed during the docking calculations and has an RMSD of 0 among all selected models. The RMSD among the smaller proteins is also known as the ligand RMSD (L-RMSD).3.1.12 Perform a hierarchical clustering as described in step 2.8.3.1.13 Cut the hierarchical cluster tree at a L-RMSD threshold value of 20 Å (see step 2.9).3.1.14 Pick the lowest scoring model from the largest clusters as best models.We would like to emphasize that the workflow described above is only one of two strategies for cross-link guided protein docking. It was developed to highlight the impact of XL-MS data on docking calculations and visualize the set of conformations that satisfy a large number of distance information from XL-MS. For a biophysically more meaningful prediction, the workflow above can alternatively be extended after step 3.1.6 with a high-resolution refinement docking stage as follows:3.2.1 Run steps 3.1.1–3.1.6.ROSETTA energy score.3.2.2 Select models that satisfy most cross-links. Should their number be higher than 500 or should none of the models satisfy any cross-link, select those 500 models with the lowest 3.2.3 Filter the 500 models by binding interface size (see step 3.1.8 and 3.1.9).Superimpose application (http://cleftxplorer.googlecode.com/files/cX.zip), which is part of the CleftXplorer software package 3.2.4 Apply Quality Threshold (QT) clustering on models passing the interface size filter. A QT cluster is defined by the translational and rotational distances between a reference and referred ligand structures, where the distance must be smaller than 3 Å and 8°, respectively. The distance thresholds correspond to translational and rotational perturbations that will be applied during local-refinement docking, and thus, in contrary to RMSD based clustering, will allow a seamless transition between global and local docking calculations. Rotational and translational distances can be calculated with the java –cp cX.jar:colt.jar:cdk-1.0.2.jar cX/Superimpose -dir <dir> -chain A:A -dock -xseq -ca –r –ta 3∶8JAVA libraries are required by the Superimpose application and can be downloaded from http://acs.lbl.gov/software/colt/colt-download/releases/colt-1.2.0.zip and http://sourceforge.net/projects/cdk/files/cdk/1.0.2/cdk-1.0.2.jar, respectively. The Superimpose application will read in all PDB files from the directory <dir> and output an all against all dissimilarity matrices. The dissimilarity matrix holds 0′s for similar model pairs whose translational and rotational differences are smaller than the threshold values, and 1′s for distinct models.The colt.jar and cdk-1.0.2.jar ROSETTA score. Repeat this step two additional times to obtain the cluster representatives of the largest three clusters from the global docking calculations.3.2.5 Complete the QT clustering by searching within the output file for the model that has the highest number of similar models (i.e. largest number of 0 in a row), declare it as the cluster representative and remove it and all its cluster members from the dissimilarity matrix. If two models have the same number of similar models, then prefer the one with the lower ROSETTA’s docking_protocol application in high-resolution mode with 3.0 Å translational and 8° rotational Gaussian biased random perturbations ROSETTA constraint file (see step 2.2):3.2.6 Run -database {rosetta-database}-in:file:s <models.pdb>-constraints:cst_file {inter-xl.cst}-docking:dock_pert 3 8-packing:ex1-packing:ex2aro-docking:dock_rtmin-docking:sc_min-out:nstruct {N}3.2.7 Generate at least 5,000 per cluster representativeXwalk on all models as described in step 3.1.6.3.2.8 Run 3.2.9 Select top 500 models as described in step 3.2.2 and 3.2.3.3.2.10 Cluster models as described in step 3.1.11 to 3.1.13.3.2.11 Pick the best models as described in step 3.1.14.We have chosen 6 proteins from a recent work of Leitner and co-workers de novo modeling workflow. Our choice for KPYM_RABIT was due to its multidomain structure. According to the Pfam database One of the reference proteins, the rabbit pyruvate kinase (UniProt Entry name: KPYM_RABIT) was also chosen as a reference for the ab initio docking and cross-link guided protein-protein docking. This benchmark data set features high-resolution protein complexes that are non-homologous and for which X-ray or NMR models in unbound conformations of the complex substituents exist. As docking methods struggle most with proteins that undergo large conformational changes upon association, we focused our performance analysis only on 16 binary protein complexes from the “medium difficult” and “difficult” category . Among all virtual cross-links  inter-protein cross-link distances were calculated on the bound conformation of all 16 protein complexes using the inks see , one to The unbound conformations of the 16 protein pairs were first relaxed and subsequently docked at low-resolution (see steps 3.1.1 to 3.1.4). Around 100,000 docking models were generated for each protein pair and subsequently tested for their ability to satisfy one to seven virtual inter-protein cross-links. As all complexes had more than seven virtual cross-links see , we geneXLdb, which is based on a Microsoft Excel sheet  and predicted the interface between both proteins using cross-link guided protein-docking calculations (see section Protein-protein docking). For the partial denovo prediction of IgBP1, Cα distance restraints from the N-terminal region of the mouse homolog and 65 intra-protein cross-link distance restraints from our XL-MS experiments were applied Xwalk’s SAS distance as a post-processing filter. From around 157,000 structural models that were predicted, over 113,000 models satisfied at least 60 cross-links by means of the Euclidean distance. In contrast, only around 190 models satisfied the same number of cross-links using the SAS distance measure (see Immunoglobulin binding protein 1 (IgBP1) interacts with the catalytic subunit of PP2A, rendering it inactive and preventing it from proteasomal degradation sure see . Neverthsure see . Howeversure see . These fde novo modeling) with only 14 experimental intra-protein cross-links produced models with RMSD values down to 5.93 Å to the native structure while satisfying all 14 intra-protein cross-links. In contrary to the IgBP1 calculation, the simulation for the pyruvate kinase did converge, producing 5 clusters with at least 24 members, of which the lowest scoring models are shown in A similar structure prediction for the pyruvate kinase from the reference data set . The boxplots in The application of the cross-link guided docking protocol to the IgBP1-PP2AA protein complex (see steps 3.1.x) revealed 4 large clusters of predicted complex models. Despite the high L-RMSD values among the cluster representatives see , all modde novo structure prediction and protein-protein docking and demonstrated the added value of the distance restraints on the computational predictions. Each workflow utilizes the ROSETTA molecular modeling suite at several steps in its calculation. The three workflows provide complementary predictions to other structure determination methods like X-ray crystallography and NMR spectroscopy. They have less restriction on the size and rigidity of proteins and are well suited to determine the topology of protein complexes. As XL-MS provides rather low-resolution information, it is particularly useful for gaining structural information on larger protein complexes. In addition, structural models of protein-protein binding interfaces can be generated in cases in which structural information of the subunits exist.Distance restraints derived from XL-MS experiments are useful for driving modeling calculations towards native-like conformations. In this manuscript, we have described three different computational protocols for comparative and de novo models for the binding partners are used. It is often possible to predict the topology of a complex with already two cross-links to an accuracy of less than 10 Å L-RMSD Click here for additional data file.Figure S2Probabilities for observing a cross-link between 0 and 34.0 Å SAS distance. The probabilities were calculated with an empirical cumulative distribution function that was applied to all cross-links from the cross-link database XLdb (see XLdb see  having a(TIF)Click here for additional data file.Figure S3Empirical cumulative distribution functions applied on the entire cross-link database XLdb see .(TIF)Click here for additional data file.Figure S4Cross-Link Guided Comparative Modeling on a Benchmark Data Set. The performance of the modeling calculations was assessed by the Cα RMSD similarity between the predicted models and the native protein structure (see ture see . Green c(TIF)Click here for additional data file.Figure S5Cross-Link Guided De Novo Modeling on the Benchmark Protein KCRM_RABIT. (A) ROSETTA energy score versus RMSD plot for full-length models of KCRM_RABIT. Grey empty circles are all 105,294 models. Black circles depict models that satisfy all 14 intra-protein cross-links by means of the SAS distance measure. The five red circles are the lowest scoring models from the 5 largest clusters after clustering the lowest scoring 500 black circled models with a 10.0 Å RMSD cut-off. Compared to the five blue circles that represent the 5 largest clusters in a non-guided de novo prediction, the mean RMSD value drops from 12.5 Å to 9.7 Å. (B) Structure of the native KCRM_RABIT structure (PDB-ID: 1U6R) is shown on the left, while the five best models are shown on the right. The structures are colored from blue to red between the N and C-terminus. The de novo modeled N-terminal domain is encircled, while the C-terminal domain for which a template structure was provided is shown in transparent surface representation. Note the co-localization of the de novo modeled N-terminal domain.(TIF)Click here for additional data file.Table S1List of cross-links in XLdb.(XLSX)Click here for additional data file.Text S1XLdb, a cross-link database.(DOCX)Click here for additional data file.Text S2Checklist for Cross-Link guided Protein-Protein Docking Demo.(TXT)Click here for additional data file.


We introduce a Knowledge-based Decision Support System (KDSS) in order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) coding the expertise about the proposed scenario, our KDSS is able to suggest both strategies and tools, according to the features of input dataset. Our system provides a navigable workflow for the current experiment and furthermore it offers support in the configuration and running of every processing component of that workflow. This last feature makes our system a crossover between classical DSS and Workflow Management Systems.We briefly present the KDSS' architecture and basic concepts used in the design of the knowledge base and the reasoning component. The system is then tested using a subset of Saccharomyces cerevisiae Protein-Protein interaction dataset. We used this subset because it has been well studied in literature by several research groups in the field of complex extraction: in this way we could easily compare the results obtained through our KDSS with theirs. Our system suggests both a preprocessing and a clustering strategy, and for each of them it proposes and eventually runs suited algorithms. Our system's final results are then composed of a workflow of tasks, that can be reused for other experiments, and the specific numerical results for that particular trial.The proposed approach, using the KDSS' knowledge base, provides a novel workflow that gives the best results with regard to the other workflows produced by the system. This workflow and its numeric results have been compared with other approaches about PPI network analysis found in literature, offering similar results. The biological activity of a cell is not defined by the proteins functions per se , what itA group of proteins that interact in order to regulate and support each other for specific biological activities is called a protein complex. Protein complexes are one of the functional modules of the cell: an example of this protein function modules are RNA-polymerase and DNA-polymerase.PPI). A large amount of PPI data have been identified for different biological species by using high throughput proteomic technologies. Of course experimentalists can take advantage of using different online databases containing a list of PPIs for each species (DIP [in vitro) as real interaction with an emerging function.The concerted action of different functional modules is responsible of major biological mechanisms of a cellular process such as DNA transcription, translation, cell cycle control, and so on. Since a protein could have several binding sites, each protein can belong to more than one complex and exhibit more than one functionality. The basic element of these modules is the protein-protein interaction , where nodes represent proteins and edges represent pairwise interactions: it allows us to exploit graph theory methods and solutions.Usually, in bioinformatics a collection of these interactions is modelled as an undirected graph, the protein-protein interaction network , makes use of local graph properties and it is aimed at finding densely connected regions in protein interaction networks. Another algorithm based on local search is the Restricted Neighbourhood Search Clustering Algorithm (RNSC) developed by [MCL) [A number of clustering-based approaches have been proposed to solve the protein complex prediction problem. A well know algorithm introduced by , the Molloped by . This alby [MCL) , that diFP) interactions from dataset obtained by online DBs. For example [It is possible to increase the reliability of the PPI data by means of preprocessing techniques. Some preprocessing strategies are aimed at eliminating false positive . Our KDSS, combining the knowledge extracted from research papers covering a lot of different strategies and methodologies, is able to suggest and run a novel workflow of tasks for the presented issue.As it will be highlighted in Results and Discussion Section, the suggested workflow, using a test dataset, gives the best results with regard to the other, not suggested, workflows produced by the system and moreover it provides comparable results with respect to some of the common workflows found in literature. From this point of view, our KDSS represents a valid and powerful instrument that can help an experimentalist to face and solve the problem of extracting protein complexes from a PPIN, supporting him in the choice, configuration and running of proper tasks.Knowledge-based DSS is a category of DSS built using an expert system . These sExamples of DSS in Bioinformatics are ProCKSI , a systeAlong with the development of Expert and Decision Support Systems, in recent years in bioinformatics a new type of tools, called Workflow Management Systems (WFMS) , have beThe most used and famous WFMS for bioinformatics is Taverna : it is aOther WFMS for bioinformatics are Biowep , that alCited WFMSs, however, do not have a knowledge base, nor make decision like KDSS; the KDSS we present, on the other hand, offers not only support in the choice of the proper strategy, tool and algorithm, but it helps the user to configure and to run them, step by step. For this reason our system can be seen as an ideal merging point between classical DSS and emerging WFMS. It provides both the tools/services needed to resolve a problem, and also the knowledge necessary to suggest a specific strategy and justify its choice.First basic ideas of the proposed system can be read in ,27: in tThe system core is represented by a rule-based expert system . The thrIn the middle part there is the Knowledge Base: it contains all the information, called facts, that encode the expertise of the system about a certain application domain. Facts are given a rigorous and organized structure by means of an ontology of concepts .In order to obtain a well formed Knowledge Base, we adopt a precise and robust organization which, at the same time, is shareable and easily expandable, presented in . In factIF <precondition>THEN <action>. The rules, acting on facts, have to be considered as the coding for heuristics, guidelines and strategies adopted by an expert of the domain. Both facts and rules can be provided by one or more experts of the domain or can be extracted from experimental and scientific papers, clinical guidelines and so on.Apart from the facts, KB also has a set of rules, in the typical form The Reasoner is an inference engine that uses the facts and rules of the KB in order to make decision: it selects the strategies and the related tools that accomplish the user request according to the actual problem and the input data.The decision taken by the Reasoner are suggested to the User that can either accept them or select other available strategies and tools. In any case, it is the Executor that actually runs algorithm, tools and services. It can be considered as a sort of interface between the Reasoner and the library of algorithms and processing tools available locally and over the Internet. The Executor also updates the KB with the processing results. New results produce new facts that eventually can trigger other rules.Facts and rules of the KB are arranged into a set of decision-making modules, as reported in . In thisThe decision-making activity of the system is based on an organization of facts and rules arranged in functional modules called decision making modules. Decision making activity is task oriented. Each module has knowledge and skills, takes care of a specific part of the reasoning process and it is responsible for making decisions about a well defined task. Facts could be shared among different modules, whereas each rule belongs to only a module. Finally, each module can activate a previously defined solver; the same solver could be activated by different modules, by using different rules.If one module has not the needed knowledge to resolve a part of its task, it can activate another module with the proper skill. This activation mechanism defines a tree structure among decision-making modules, where parent modules manage global and general tasks and children modules are responsible of the decision-making process regarding more specialized tasks.For example, we can have specialized modules for dealing with preprocessing, visualization, or clustering operations that can be activated by general modules which supervise global task execution. The tree structure of modules can be also represented through a treemap : in a trSystem implementation is based on Java technology; grace to its features, such as platform and location independence, portability, OS independence, Java represents a good support for the proposed work. The system also contains a "Rule-Based System" to manage the knowledge-base; the rule based engine adopted is Jess , the RulThe knowledge base have been modeled using one of the largest adopted tool for building ontologies, that is Protege' . ProtegeThe application scenario focuses on the complexes extraction problem, that contains in turns two main sub-problems: the preprocessing and purifying of PPI data and the protein complex clustering.The rest of this Section is organized as follows: in the next sub-section we introduce the dataset used in the scenario; then we show how the proposed method system integrates aforementioned approaches and how it helps users to face the protein complexes extraction problem. Finally in the last sub-section the analysis of experimental results is discussed.DIP). The input dataset used in this scenario is a subset of Saccharomyces cerevisiae PPI-Network composed by 34 proteins and 90 interactions, as shown in Table DIP ID of the interaction between the previous pair of proteins. We chose this very simple dataset because it has been well studied by [in vitro) at biological interaction level. DIP also provides a subset of PPIs curated manually by experts, that are called core PPIs. A well studied small set of PPI allows us to better describe how the system works, and the choices it takes; obviously we know this dataset is not representative of a whole PPI Network, in facts it represents only a pretext for executing the system and obtaining some results comparable with the other papers in literature [In our experiments, among different available online databases of PPIs network, we use the Database of Interacting Proteins  in PPIN: the eated by . When thact1 and sla2. At this moment, the PPIN is composed by 34 proteins and 91 interactions; the user could either continue the experiment or execute another preprocessing tool (in cascade or restarting the preprocessing phase).When the user accepts the proposed value, the system executes the algorithm, that finds a new potential FN interaction between the proteins Betweenness Centrality algorithm from among three different tools available into the knowledge-base, because the system indicated this is the algorithm with the lowest computational cost. The result of Betweenness Centrality algorithm is a PPIN with 34 proteins, 88 interactions and 65 core-interactions; then the system advices the user to change strategy and/or modify parameters because two core-interactions have been deleted.If the user wants to try another solution before continuing the experiment and he does not want to accept the system advices, he could choose to follow the strategy to find and delete false positive PPIs. In this case, the system saves results obtained so far and it proposes to run one of those algorithms that satisfy the selected strategy. The user selects the PPI Complex Extraction module contains all the workflow elements; it supervises the main problem at highest abstraction layer, giving the other directives to Complex Preprocessing module. The latter is responsible of some strategies for verifying and purifying the network and have knowledge about tools used for data manipulation. At abstraction layer 1, the child module contains the strategies used in this experiment: in facts the user tried first to add new PPIs and then to delete false positive PPIs; obviously, both these strategies have the same PPIN as input, according to the user choices. All the tools used for processing data are shown at lower abstraction layer and their order in the figure follows the implementation timeline. When the user concludes the preprocessing phase and chooses the appropriate output to continue the experiment, then the Complex Preprocessing module ends its activity and gives the focus back to the parent decision module. At this point the PPI Complex Extraction module knows the data input has been preprocessed and gives focus to the child Complex Clustering. Also the latter module knows the preprocessing phase is done, thus it uses this information for suggesting an appropriate clustering strategy. The authors [MCODE is sensitive to noise in the PPIN and the preprocessing phase can increase the algorithm performance. Other authors [MCL and RNSC work almost in the same manner in terms of precision and recall, whether PPIN are noisy or purified. Since MCL algorithm is faster than the other algorithms and it work well with dense graphs, the system proposes to use this algorithm based on the flow simulation analysis for clustering. Moreover MCL algorithm has been widely used with protein-protein interaction networks belonging to the species Saccharomyces cerevisiae, so that the system can suggest standard parameters for this species. When the user accepts the advice and confirms proposed parameters, the system runs the MCL algorithm. Now the user can either end the experiment or execute another clustering tool, having as input the PPIN obtained through the preprocessing phase. If the user wants to try another tool, he can consider descriptions, pros and cons that are available for each strategy and algorithm contained into the system.Figure  authors ,43 demon authors ,15 noticThe whole workflow is shown in Figure Before concluding the experiment, the system proposes to visualize the output of MCL algorithm with the well know Cytoscape tool . VisualiGene Ontology Term Finder web service [In order to test the result obtained by the system running, the biological significance of each protein complexes is validated by means of the  service , that re service . This stProtein complexes obtained by system running are evaluated using two different criteria; in facts, some external and internal evaluation criteria have been analyzed.The external criterion is based on the comparison among the results obtained through the proposed system and two different approaches proposed by ,38, thatG2/M transition of mitotic cell cycle, Actin Filament Depolymerization, Actin Cytoskeleton Organization, Actin Polymerization or Depolymerization, Rho Protein Signal Trasduction). Table Rho Protein Signal Trasduction.Table With regard to the internal criteria, two different tests have been executed, aiming at demonstrating the workflow suggested by the system is the best than the other workflows the system could build, according to its knowledge. In particular, the first test reports a comparison among all the algorithms contained in the knowledge base of our KDSS for the complex clustering problem: MCL, RNSC and MCODE. It is worth remembering that the proposed scenario contains only three of the most common and high-performance algorithms for protein complex extraction, because they represent a simple set of tools able to demonstrate how good the system works. Results of the first test are shown in Table 1.93e-05) in the complex related to the Actin Cytoskeleton Organization GO term, therefore the system proposes once again the most appropriate algorithm to the best of its knowledge.The second test aims at investigating about the preprocessing phase suggested by the system. In facts the system, according to its knowledge, can deal with the complex extraction problem using a preprocessing of the input PPI-Network, by means of two strategies: finding the false negative PPI (adding edges to the network) or the false positive PPI (removing edges from the network). Since the MCL algorithm proposed by the system is not sensitive to all three of the alternative ways related to the network preprocessing, we test the result of the preprocessing phase over the MCODE tool, because a comparison of clustering algorithms for protein-protein interaction networks showed that MCODE is sensitive to noise in the network . For thiIn this paper, we presented a novel approach for the extraction of the protein complexes based on KDSS. The system interacts with the user using its expertise about PPINs. The system suggests to the user what are the strategies and algorithms suitable for the problem and, moreover, helps him providing the description, pros and cons of each available technique. Finally the system also runs the selected tools, suggesting to the user what are the most common parameters for the specific situation and, during the experiment it builds a workflow of executed operations, enabling the chance of backtracking for exploring alternative paths. The presented results show that the workflow suggested by the system gives the best results with regards to the other workflows produced by the system itself and, furthermore, that workflow offers similar results when compared to other PPI extraction methodologies found in literature.In the near future, we will give research community free access to our system, thanks to the migration towards a web service. Moreover we are working in order to enrich the Knowledge Base with skill regarding the proposed scenario and, at the same time, we will use our system architecture for facing other bioinformatics issues. Finally we will give support to the developer community in order to provide a simple editor so that it will be possible to insert into the system further knowledge and expertise.The authors declare that they have no competing interests.AF: software design, implementation, writing, assessment, discussions. MLR: software design, implementation, writing, assessment, discussions. RR: software design, discussions, writing. AU: software design, discussions, writing, funding. SG: project conception, software design, discussions. All authors read and approved the final manuscript.The publication costs for this article were funded by the CNR Interomics Flagship Project "-Development of an integrated platform for the application of "omic" sciences to biomarker definition and theranostic, predictive and diagnostic profiles".BMC Bioinformatics Volume 14 Supplement 1, 2013: Computational Intelligence in Bioinformatics and Biostatistics: new trends from the CIBB conference series. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/14/S1.This article has been published as part of


A large fraction of the proteome is made by proteins that are not permanently monomeric but form oligomeric assemblies, which can be either homo- or hetero-oligomeric. Here it is described that protomers of hetero-oligomeric proteins tend to resemble each other more than expected. This is verified by comparing the level of similarity of pairs of hetero-oligomeric protein protomers and of pairs of proteins that do not interact with each other. This observation, interesting per se, might reflect the evolution of hetero-oligomers from ancestral homo-oligomers, through gene duplication and paralogs divergence. However, other hypotheses cannot be excluded and the observed structural similarity might result from several causes.The online version of this article (doi:10.1186/2193-1801-3-680) contains supplementary material, which is available to authorized users. In some cases, it is obvious that different protomers of a protein-protein complex are responsible of different biochemical activities. For example, the TATA-binding protein (TBP), together with several transcription factors (TFs) and RNA polymerase II, can form the RNA polymerase II preinitiation complex  by means of the distance on the Proteomic Ramachandran plot (PRplot) (Carugo and Djinović-Carugo The Proteomic Ramachandran Plot (PRplot) is a generalization of the Ramachandran plot, where a protein structure is represented by the average values of the main-chain phi and psi torsion angles . On the one hand, I assumed that a polypeptide chain found in a hetero-oligomeric complex does not interact with a protein chain found in a homo-oligomeric complex. In the second hand, I assumed that a monomeric protein does not interact with another monomeric protein. Therefore, I constructed six sets of 1,000 pairs of random homo-hetero protomers (Random set 1–6) and six sets of 1,000 pairs of monomeric proteins (Monomer set 1–6)  when the two structures that are compared are protomers of the same hetero-oligomeric complex. This clearly indicates that, if two proteins are able to form a stable complex, their structures are more similar than those of two proteins that do not interact to form a stable complex. In agreement with what is described in the previous chapter about the analysis of the dpp values, it can be concluded that two proteins that interact in a hetero-complex tend to more similar to each other than expected.The CATH database has been established about fifteen years ago as a collection and a classification of protein structural domains . This clearly suggests that it is more probable that two protomers of the same hetero-oligomeric complex have the same fold than two non-interacting proteins. This result perfectly agrees with the analogous results based on the dpp distances on the proteomic Ramachandran plot and on the m-scores computed after optimal superposition of two protein structures.SCOP is another database and classification of protein structural domain. Like CATH (examined in the previous chapter), SCOP adopts a hierarchical classification scheme, albeit different from that of CATH.The first classification level is the “class”, like in CATH, the second is the “fold” and this corresponds to the topology level of classification of CATH. In SCOP, therefore, there is not an intermediate level of classification between the class and the fold (this intermediate classification level is the architecture node of CATH). After the fold classification level, there are further levels of clustering (“superfamily” and “family”), which include evolutionary information. Moreover, the definition of protein domain is slightly different, with the consequence that the protein domains tend to be larger in SCOP than in CATH.Here, like for the comparison based on CATH, I limit the attention to the fold level of structural classification of the protein domains. Two structures are then considered to be similar if they have the same fold and different if they do not. Table The present data indicate that protomers of hetero-oligomers tend to have three-dimensional structures surprisingly similar, much more than pairs of non-interacting proteins. Higher m-scores and lower dpp distances are observed when two protomers of the same hetero-oligomer are compared than when two unrelated and non-interacting proteins are compared. Two protomers of the same hetero-oligomer have the same fold, according to the classifications of CATH and SCOP, more frequently than two unrelated and non-interacting proteins.This is per se an interesting observation and several possible explanations can be proposed to explicate it, though none of them can be definitely proven just on the basis of data mining analyses.First, this might depend on the data paucity. In effect, the three-dimensional structures of only few thousands of homo- and hetero-protein protein complexes are available in the databases. This might seem surprising, given the tremendous increase of the number of new protein three-dimensional structures that have been determined in the last few years. However, only a small fraction of these new structural results involve supramolecular assemblies that have never been analyzed before. It is therefore possible that the higher than expected similarity between protomers of hetero-oligomeric complexes is not a genuine feature of Nature but only an observation that depends on the incomplete sampling of the protein universe.A second possible explanation is that everything is merely casual, in the statistical sense. In other words, there would be no evolutionary of physico-chemical restrictions that make protomers of hetero-oligomers more similar than expected. Although none can confute this hypothesis on the basis of theoretical considerations or on the basis of some clever experiment, most of us will not believe that such a curious observation is only accidental because of mere epistemological considerations.A physico-chemical approach can also be adopted to propose intriguing hypotheses. For example, it is possible that the protomers of the hetero-oligomers need a similar flexibility and it is reasonable to suppose that a similar flexibility can be provided by similar folds  the first chain in each hetero-oligomeric complex and the first chain in each homo-oligomeric complex and randomly build six ensembles of pairs of protein structures; each pair is formed by a hetero- and a homo-oligomeric protein chain; and each of the six ensembles contains 1,000 pairs of structures. These datasets were named “Random set 1”, “Random set 2” etc.The second strategy was based on the assumption that monomeric proteins do not interact with other monomeric proteins to form stable complexes. Therefore, all monomeric proteins (according to the quaternary status annotation of the Protein Data Bank) were downloaded from the Protein Data Bank  on phi/psi space of the Proteomic Ramachandran plot (PRplot) were computed as:Sequence alignments were performed with the Needleman-Wunsch algorithm .In agreement with previous studies, it is reasonable to suppose that this surprising similarity between protomers of hetero-oligomeric complexes is due to the evolutionary relationship between hetero-oligomers and earlier homo-oligomers, though gene duplication and paralogs evolution Additional file 1:


The three-dimensional structure of a protein is an essential aspect of its functionality. Despite the large diversity in protein structures and functionality, it is known that there are common patterns and preferences in the contacts between amino acid residues, or between residues and other biomolecules, such as DNA. The discovery and characterization of these patterns is an important research topic within structural biology as it can give fundamental insight into protein structures and can aid in the prediction of unknown structures.Here we apply an efficient spatial pattern miner to search for sets of amino acids that occur frequently in close spatial proximity in the protein structures of the Protein DataBank. This allowed us to mine for a new class of amino acid patterns, that we term FreSCOs , which feature synergetic combinations. To demonstrate the relevance of these FreSCOs, they were compared in relation to the thermostability of the protein structure and the interaction preferences of DNA-protein complexes. In both cases, the results matched well with prior investigations using more complex methods on smaller data sets.The currently characterized protein structures feature a diverse set of frequent amino acid patterns that can be related to the stability of the protein molecular structure and that are independent from protein function or specific conserved domains.The online version of this article (doi:10.1186/s13040-015-0038-4) contains supplementary material, which is available to authorized users. Proteins are primarily composed of a long chain of amino acids that is folded into a complex three-dimensional structure. This spatial structure of a protein is an essential component in its functionality and is thus subjected to evolutionary pressures to optimize the inter-residue contacts that support it. For example, the proteins of thermophilic organisms are known to contain specific adaptations in their amino acid configurations and content to stabilize the molecular protein structure in the high temperature living environment in which these species live ,2. DespiDifferent methods have been described to explore the amino acid content of a protein and their interactions for a variety of goals. Within the scope of molecular modelling, frequent representations of common inter-residue contacts involve protein contact maps (PCM) or adjacency matrices ,4. TheseThe RSCB Protein DataBank (PDB) is the primary collection of protein molecular structures . It contcohesion. This metric scores an item set based on the physical distance of the typical items within the set; i.e. items that are far apart can be said to have a low cohesion, while items that are proximal have a high cohesion [Frequent pattern mining is a data mining technique that was developed to identify elements that often co-occur within a data set. The archetypical usage case is the ‘market basket’ problem, where the goal is to identify which items are often bought together based on the transactions made at a supermarket. Despite the simplicity of this problem, creating an algorithm that can solve it in an accurate and rapid manner is not trivial. A significant amount of research has therefore gone into solving this problem as efficiently as possible and many algorithms now exist that address it. Many common biological challenges can be readily translated into the ‘market basket’ problem, and therefore frequent pattern mining has seen significant use in bioinformatics applications . Relevancohesion . While ocohesion . We use We have shown that the patterns retrieved on such a protein data set mostly involve combinations of three or four amino acids that frequently occur in close proximity . Thus th−80 were considered redundant, contains 32 142 protein molecules from a large variety of organisms. As using more stringent redundancy cut-offs had little effect on the resulting patterns [see Additional file α atom for protein residues and the N1 atom for DNA bases to determine the coordinates for the given label, i.e. the type of amino acid or base.Two large collections of three-dimensional structures are used in this study. The first is the collection of all structures contained within the RCSB PDB database, obtained on the 3rd of May, 2013 . Only nocohesion. In brief, the algorithm considers all possible amino acid combinations and identifies those combinations that are both frequent in the set of protein structures and that consist of amino acids in close average proximity. Hereafter these patterns of frequent spatially cohesive amino acids will be referred to as ‘FreSCOs’. This method can be sped up by pruning the possible search space with little to no loss in accuracy [The algorithm used in this paper is based on the principles of frequent pattern mining and the data mining concept of accuracy . The nexv is a pair  consisting of an item a ∈ I, where I is the set of all possible amino acids and a coordinate c ∈ R3. We can then represent a protein as a data object d = {v1, …, vl}, where l is the number of amino acids in the protein sequence. The set of all proteins within a given data set is denoted by D. We assume that two points can never occur at the same position, i.e. with the same coordinate. On the other hand, an amino acid ai may occur many times at different positions in a protein dg with Vgi denoting the set of these points.The three-dimensional structure of a protein can be considered as a list of points where each point X = {a1, …, ak} ⊆ I is termed a pattern of length k. Within the scope of this paper, we are interested in patterns with both a high support, i.e. those that occur often, and with a high cohesion, i.e. where the items are, on average, in close spatial proximity. For a given pattern X, we denote the set of all data objects that contain all items of X as N(X) = {d ∈ D|∀a ∈ X, ∃ ∈ d}. The support of X in a data set D is then defined asA subset cohesion within the scope of data mining can be readily extended to three-dimensional space. Given a set of points V = {v1, …, vk}, let MB(V) denote the ball with the smallest radius that contains V, i.e. the smallest enclosing ball. It has been shown that MB(V) always exists and is unique [V to be in close spatial proximity if the radius of MB(V) is small enough. Given a pattern X = {a1, …, ak}, assume that each amino acid ai occurs ni times in a protein dg ∈ N(X). Within a single protein dg that contains X, Rg is then defined as the radius of the smallest enclosing ball for a combination of V = {v1, …, vk} that matches X = {a1, …, ak}. We then define the cohesive radius of X in D asThe concept of s unique . IntuitiThe pattern miner theoretically enumerates every possible amino acid combination for their support and cohesion radius value, and will return those patterns that exceed the given cut-off for both. The utilized Apriori-like algorithm  speeds uThe pattern miner reveals all FreSCOs that are both frequent and in close proximity. Intuitively, one realizes that patterns matching very common amino acids will have on average a smaller enclosing ball by chance than those that are less common. To compensate for this phenomenon, a background distribution is computed for each described pattern based on a permuted set of protein structures. In this background set, the amino acid labels have been randomized for each protein while keeping the overall structure (i.e. the coordinates) identical. The pattern miner is then applied to this permuted set to compute a new cohesion value. Due to the nature of the permutation, the frequency of the amino acids and the patterns will remain the same, as the protein amino acid content does not change. This permutation is repeated ten times and the resulting cohesion values are used to estimate a background distribution for the cohesion value of each pattern. As these background scores seem to follow a normal distribution [see Additional file To identify the known conserved domains present in the studied protein set, the sequence of the non-redundant protein structures extracted from PDB were run through the Pfam sequence search web tool . DomainsThe gene ontology assignments to each protein chain that exist within the PDB database can be used to assess the overlap between specific protein functional characteristics and FreSCOs. To this end, each GO term associated that is annotated to at least 10 protein structures in the non-redundant PDB data set was checked for enrichment of FreSCOs. Gene ontology terms from the molecular function, cellular component and biological process trees were considered. However as most FreSCOs have a frequency of more than 90%, only the most cohesive matches within a protein structure were regarded as a match in this analysis. To this end, only matches where the residues occurred within a minimal enclosing ball with a radius less than 3 Å were included. The P-value for enrichment was calculated based on a hypergeometric distribution against specific FreSCOs occurring in a set of protein structures with a given gene ontology by chance.The optimal growth temperature (OGT) for different prokaryotic species was retrieved from the BacDive database on the 17th of December, 2013 . Organisα atoms of two residues that are typically considered as interacting [The entire non-redundant PDB data set can be mined for FreSCOs, i.e. patterns of amino acids that frequently occur in close proximity. Amino acids in close proximity are defined by a maximum cohesive radius of 4.5 Å for the purposes of this analysis, which corresponds to the distance between the Ceracting . The superacting  and furteracting . These Feracting ,31. In aeracting ,33. ThisAs can be seen in Figure Many of the FreSCOs can be linked to interactions that contribute to the stability of the protein structure. This corresponds to previous studies on smaller data sets where similar observations have been made . As mentStarting from the 260 triplet FreSCOs described previously, we find that 49 were significantly correlated with lower temperatures and 95 with higher temperatures [see Additional file −283. The interesting aspect of this FreSCO is the presence of glycine, which only has a small uncharged side chain, yet has been previously described as forming a significant number of hydrogen bridges in protein-DNA complexes [Bacillus caldolyticus cold shock protein (PDB: 2HAX). Here the glycine residue is in a position to directly interact with the thymine base, while the typically positively charged guanidine group of the arginine residue can interact with the negatively charged phosphate DNA backbone of the nucleotide.Many of the described FreSCOs were significantly enriched in nucleotide-binding proteins [see Additional file omplexes . One canIn this paper, we describe the mining of an interesting and relevant new pattern class, which we term ‘FreSCOs’, in the largest data set of protein molecular structures that is currently available. The found FreSCOs consist of combinations of two or three amino acids and seem to represent common building blocks aiding in the stability of the protein structure. These patterns are not directly related to any specific type of protein function as they occurred in over 90% of the analyzed structures, nor are they limited to the conserved domains of the proteins. Many of the discovered FreSCOs feature combinations of glutamate and lysine. These FreSCOS are only significant in the triplet combinations and not in the doublet patterns, suggesting an essential synergetic relationship with a third amino acid, which was often an aliphatic residue. FreSCOs featuring glutamate and lysine are found to occur mostly outside of known conserved protein domains yet are a frequent feature in protein structures. Lastly they are highly enriched in organisms that live at higher temperatures, indicating an important role in the thermostability of proteins. In general, comparison to growth temperatures reveal that hydrophilic residues were mostly found to be related with low temperatures, and hydrophobic, acidic and basic residues to high temperatures. Studying these temperature relationships with FreSCOs allows description of their synergistic tendencies among different amino acids and provides some indication of positional context, as was seen for FreSCOs containing glutamate and lysine in combination with hydrophobic residues. Further the enrichment of specific FreSCOs at higher temperature supports earlier conclusions that the mined patterns play a critical role in protein structure stability. Inclusions of the DNA molecular structures into the data set allowed description of patterns related to the contact preferences of protein-DNA complexes. Here the majority of the found patterns involve a thymine base or an arginine residue, which matches known preferences. Many of the FreSCOs described in this case indicate complex interactions involving several bases or amino acids and thus go beyond a simple one amino acid to one base contact preference.The next step will be to extend the framework to account for the similarity between amino acids when mining these proximal patterns. The current implementation considers each amino acid type as a discrete entity, however it is well established that some amino acids are more similar than others. This can be directly integrated into the mining framework to improve the detection of important amino acid patterns. In addition, currently around half of all matches for a given FreSCO describe short-range interactions. The proposed cohesive miner can theoretically be constrained to exclude residue matches that exist in close proximity on the protein chain, which may result in more interesting and less evident patterns. Further the function analysis of the found FreSCOs supports their use in studies targeting a specific subset of proteins to discover common patterns that may exist beyond the amino acid singleton or duplet.


Proteins play a special role in bioinformatics. The surface shape of a protein, which is an important characteristic of the protein, defines a geometric and biochemical domain where the protein interacts with other proteins. The similarity analysis among protein models has become an important topic of protein analysis, by which it can reveal the structure and the function of proteins.In this paper, a new protein similarity analysis method based on three-dimensional protein models is proposed. It constructs a feature matrix descriptor for each protein model combined by calculating the shape index (SI) and the related salient geometric feature (SGF), and then analyzes the protein model similarity by using this feature matrix and the extended grey relation analysis.We compare our method to the Multi-resolution Reeb Graph (MRG) skeleton method, the L1-medial skeleton method and the local-diameter descriptor method. Experimental results show that our protein similarity analysis method is accurate and reliable while keeping the high computational efficiency. Protein similarity analysis is an important topic in bioinformatics. With it, we can help understand the structure and the function of proteins. The protein shape analysis plays an important role in medical research, computer aided molecular design, protein structure retrieval and prediction, among others. However, the analysis is highly challenging due to the complexity of a protein’s three-dimensional surface shape, which can deform significantly enough to change the topological structure during molecular interactions .Many researchers have contributed to similarity analysis methods for comparing protein shapes. Via et al.  gave a sOther shape similarity methods based on topology are also widely studied. For example, Hilaga et al.  proposedMotivated by the salient theory of a three-dimensional model proposed by Hoffman and Singh , and by A three-dimensional protein model can be represented by the form with the triangular mesh. We first estimate the curvature of each vertex on a protein model surface, and calculate the shape index (SI) and the salient geometric feature (SGF) based on the shape index of each vertex. Then, we construct the protein model’s feature matrix through the shape index and the salient shape index. Finally, we do the similarity analysis for protein models by the matrix-based grey relation analysis. The main process of our algorithm is shown in Fig. The concept of shape index was proposed in . It is ak1 and k2 denote the maximum and the minimum principal curvatures, respectively. From the above formula, we know SI is between -1 and 1. When the shape index is close to 1, it indicates the convex shape of the given vertex on the protein surface. On the contrary, when the shape index is close to -1, it indicates the concave shape of the given vertex on the surface. When k1 = - k2, the shape index is 0.where kG and the discrete mean curvature kM. Then, the maximum principal curvature k1 and the minimum principal curvature k2 are obtained bySI relates to the curvature estimation of each vertex on the model surface. We use Dyn and Hormann’s method  to estimFor the 1HLB protein model in Fig. Salient geometric feature is built on the theory of salience of visual parts proposed by Hoffman and Singh . They reF is a cluster consisting of each vertex i, w1 and w2 are the weights, we set them as 0.5. Area(i) is the area of the patch associated with vertex i relative to a cluster size, N(SI) is the number of local minimum(s) or maximum(s) shape index in the cluster, Var(SI) is the shape index variance in the cluster, SI(i) is the shape index associated with vertex i.where For the 1HLB protein, we give its salient geometric feature model in Fig. n-dimensional vector (where n is the number of model vertices), respectively. Because the number of vertices on each model surface is not the same for different proteins, these vectors cannot be directly compared and analyzed. In our approach, we cluster all feature values into the same group number through a clustering algorithm T and use QTQ to represent a K × K (K = 48) feature matrix, then do the similarity analysis for the protein models with this feature descriptor. We give the feature matrix figure of 1HLB protein model in Fig. In order to better reflect the shape feature of a three-dimensional protein model, the method based on the feature matrix expression has become a popular method for the model shape analysis . Here, wFor the shape analysis of a protein sequence or its surface model, common methods use distance measurements such as Euclidean distance, Manhattan distance, angle cosine method and correlation coefficient method, etc . One proFor our similarity analysis of three-dimensional protein models, because we construct a matrix-based feature descriptor, the previous vector-based method is not directly applicable for our measurement. At the same time, we hope to advocate the use of a scalar value of similarity directly between 0 and 1, where higher values represent greater similarity between two protein models. Here we popularize the vector-based grey relation analysis  to the mX and Y are matrices with the same m rows and n columnsSuppose that kth row, we produce the image of zero starting point of matrix X and YFor the X and Y for each rowThen, we compute the grey relation degree of matrix εijn(k) of matrix X and Y for the kth column. Finally, we obtain the grey relation degree of matrix X and Y bySimilarly, we get the grey relation degree From the above calculation process, it is easily known that the grey relation degree is between 0 and 1, and the degree indicates the high similarity of two models when it is close to 1.The algorithm presented in this paper is implemented on a Intel(R) Core(TM) i3-3110 M CPU @2.5 Ghz desktop computer with 4GB RAM running MS Windows 7. The software environment of the experiment is based on Mathworks’ MATLAB R2010a.We first chose four protein models from the Protein Data Bank , which aThen, we chose three groups of protein models from the Skolnick dataset , which aNext, we chose 10 protein models from the Chew–Kedem dataset , which aWe also demonstrate a total running time including searching the most similar protein model for 5 protein models in the Chew-Kedem dataset. In Table To increase the robustness of our method, we added another testing dataset as Skolnick’s dataset from R for the Finally, we compared two proteins (1BAR and 1RRO) that have a similar shape surface but have completely different secondary structure elements  Fig. 8)8). The aOur method is based on the surface analysis and the advantage is that the running time is fast because it does not need to conduct the skeleton extraction. The disadvantage is that it can not be applied for other protein models such as protein CPK models. The advantage of the skeleton based method is that it can be applied for both the protein surface (triangular mesh model) and the protein CPK model (point cloud representation), the disadvantage is the skeleton extraction requires a time-consuming process.In this paper, we propose a three-dimensional protein model’s similarity analysis algorithm based on salient shape index. We first calculate the shape index (SI) and salient geometric feature (SGF) of the protein models. And then we construct the matrix-based feature descriptor by SI and SGF information. Finally, we compare the similarity of protein models by the matrix-based grey relation degree. Experimental results show the effectiveness of our protein similarity analysis method.Currently, we only consider the shape index (convex and concave properties of the protein surface) and the salient geometric feature to analyze the similarity of the protein models. We do not take account of the physical properties of the protein molecules. In fact, these properties such as pH, polar and non-polar, hydrophilic, also affect the structure and the function of the protein molecules. How to combine these factors for the protein shape similarity analysis will be our future research. For the clustering in our similarity analysis, we find the cluster size is normally not equal and the clustering is sometimes dominated by several big clusters. The size of the clusters might be highly relevant in describing the global shape of the protein model. This also gives us an interesting work for our future research.


A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein-DNA interfaces. Protein-RNA interactions play key roles in many biological processes including protein synthesis, DNA repair, DNA replication, regulation of gene expression, and viral replication –5. BecauPSSM profiles of proteins are generated using the PSI-BLAST program, which is part of the NCBI BLAST package . Given aD, a BLAST database of protein sequences  If so, how can one obtain the smallest possible subset of D that meets our criterion?; (iii) How does the decrease in the size of the reference database of sequences used by PSI-BLAST translate into corresponding reductions in the memory and run time needed by PSI-BLAST ? To the best of our knowledge, this is the first work that systematically studies the pairwise relations between the size of the BLAST database and the performance of PSI-BLAST (in terms of memory usage and run time), the quality of the generated PSSM, and the accuracy of the developed PSSM-based protein-RNA interface predictor (respectively). Based on our results, we developed and implemented FastRNABindR, an improved version of the original RNABindR protein-RNA interface prediction server [http://ailab.ist.psu.edu/FastRNABindR/. Unlike RNABindR which limits submission to a maximum of 20 sequences, FastRNABindR accepts up to 500 proteins per submission and returns prediction results within approximately an hour. This research sets the stage for significantly speeding up a broad range of protein sequence classification and sequence labeling tasks that make use of PSSM based representation of the query sequences, including protein-DNA interface residue prediction [Against this background, we explore an alternative approach to reducing the run time of PSI-BLAST, namely, reducing the size of the BLAST database used to construct the PSSM profiles. In this work, we address the following questions: (i) Given database ), is then server . FastRNAediction , 28, proediction –32, protediction , 34, andediction , 36 to fhttp://ailab1.ist.psu.edu/RNABindR/rb198seq.txt. For running 5-fold cross-validation experiments [For cross-validation experiments, we used the benchmark dataset, RB198  in May 2eriments , we splihttp://ailab1.ist.psu.edu/RNABindRPlus/rb44.txt.For independent test evaluations, we used the benchmark test set, RB44 , 9. RB44For comparing our final model, FastRNABindR, with other protein-RNA interface prediction servers, we used the RB111 benchmark dataset . Like RBIt is worth noting that although the two independent test sets, RB44 and RB111, are non-redundant at 30% sequence identity, the sequence identity between any test sequence from RB44 or RB111 and the sequences in our training dataset, RB198, is less than 40% . In ordeTo the best of our knowledge, there is no gold standard for defining interface residues in a protein-RNA complex. Computational methods reported in the literature for predicting protein-RNA interface residues have used a range of distance cutoffs from 3.5 to 7 Å for determining whether an atom from a protein molecule (and hence an amino acid residue) interacts with an atom from some RNA molecule (and hence a nucleic acid residue) . FollowiFor extracting evolutionary features of protein sequences , we ran PSI-BLAST  against PSI-BLAST takes as input a query protein sequence and compares it to a protein database, using the gapped BLAST program . The outQ denote a query protein of L amino acids, P1 and P2 be two profiles of the query protein Q generated by running PSI-BLAST to compare Q to databases D1 and D2, any two BLAST databases considered in our experiments. We can define the distance between proteins P1 and P2 we use the distance between their respective PSSM profiles [Let profiles , 43. In e-value of 0.001) against the UR100 database. Then, we normalized values in the PSSM matrix using the logistic function. Specifically, each element in the PSSM matrix, x, is replaced with For each protein sequence in the data set, we generated a PSSM profile by applying PSI-BLAST to carry out three iterations of search (using an RNABindR ) with thSn), Specificity (Sp), and Mathew Correlation Coefficient (MCC) measures defined as follows [We experimented with three machine learning algorithms that have been widely used for developing biomolecular sequence labeling tools: Naive Bayes (NB) ; Random  follows , 50:ACCY axis and the false positive rate is plotted on the X axis. Each point on the ROC curve represents the behavior of the classifier at a specific choice of the threshold. The area under ROC curve (AUC) is equivalent to the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example. Any AUC score higher than 0.5 is considered better than random guessing. The ideal classifier will have an AUC equals 1. In the Results section, we limit our discussion to the AUC and report other threshold-dependent metrics in the Supporting Information  curve  describeormation .We assessed the performance of the PSI-BLAST program by recording the total running time taken to generate PSSM profiles for a given dataset  and the maximum amount of memory used during the entire execution period for a given dataset. Time and memory measurements are taken using the Linux utility commands, time and top. All profile generation experiments (as well as sequence similarity reduction using KClust ) were coIn light of the results presented in the previous section, it is natural to ask whether we can identify an optimal UniRef database, i.e., the one with the smallest number of protein sequences, and hence the fastest time for running PSI-BLAST and computing PSSMs that could be used to develop a classifier with the best predictive performance. Results in So far, we have shown that using UR100 database for extracting proteins PSSM profiles does not provide classifiers with the best predictive performance in terms of AUC estimated using both cross-validation and independent test experiments and there exist subsets of UR100 database that lead to improvements in classifiers performance. In this section, we address two interesting research questions: i) What is the best way to generate subsets of UR100?; ii) How does the decrease in the database size affect the computation performance of PSI-BLAST (in terms of computation time and memory)? To address the first question, we generated subsets of UniRef database see  section Another interesting observation from In summary, we have shown that the run time as well as the maximum memory used monotonically decrease with the decrease in the size of the reference database used by PSI- BLAST. We also showed that reference databases obtained by randomly sampling UniRef data yield larger number of hits for constructing PSSM profiles, and hence yield more representative PSSM profiles than those obtained from similarity reduced UniRef databases of the same size.The results summarized in the preceding section set the stage for implementing a protein-RNA interface prediction web server that can process large numbers of query sequences and return predictions in a reasonable amount of time. Our experimental results  are regularly updated to improve their coverage. Currently, NCBI nr BLAST database has more than 78 million protein sequences and this number is expected to further increase as ongoing sequencing projects generate additional data. The generation of PSSM profiles is an important application of homology search and PSSM encoding of protein sequences is a widely used feature representation for developing protein functional site predictors. Due to the large size of the BLAST databases, generating PSSM profiles is a computational bottleneck for many bioinformatics tools. In this work, we experimented with two approaches to reduce the size of the BLAST database, random sampling and similarity reduction, and showed that random sampled databases provide better PSSM profiles in terms of number of hits used to generate the profile and the distance between that profile and the corresponding one generated using the full BLAST database. Based on our findings, we developed and implemented FastRNABindR, a tool for accurate and fast prediction of protein-RNA interface residues. FastRNABindR uses only 1% of UniRef100 data to generate PSSM profiles. With this substantial reduction in the size of the BLAST database, we report more than 100-fold improvement in computation time while the predictive performance is better than that obtained using the entire UniRef100 data or at least as good as the best performance observed using eight more variants of UniRef100 considered in our experiments.In this work, we assessed the quality of the PSSM profiles generated using PSI-BLAST search against UR100 database and its variants using three performance metrics: PSI-BLAST run time; PSI-BLAST memory usage; and the predictive performance of the resulting protein-RNA interface predictor developed using PSSM profiles as input features. It would be interesting to analyze the protein sequences (PSI-BLAST hits) used to generate the PSSM profiles. Such analysis might help inform the development of methods to improve the quality of the PSSM profiles to be used as input features for protein-RNA interface predictors. Work in progress is aimed at: i) Exploring more sophisticated approaches  to determine the optimal BLAST database for a given classification task; ii) Applying the proposed methodology to develop reliable yet computationally efficient methods for related amino acid sequence labeling  and sequence classification . (iii) Exploring whether there is a single optimal BLAST database that can be used across multiple tasks  or whether the optimal BLAST database is task-dependent; iv) Developing parallel or distributed implementations and/or advanced data structures to further reduce the run time and memory usage of the methods in order to support very high throughput analyses.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.