During the past 15 years mass spectrometry (MS)-based analyses have become established as the method of choice for direct protein identification and measurement. Owing to the remarkable improvements in the sensitivity and resolution of MS instruments, this technology has revolutionised the opportunities available for the system-wide characterisation of proteins, with wide applications across virtually the whole of cell biology. In this article we provide a perspective on the current state of the art and discuss how the future of cell biology research may benefit from further developments and applications in the field of MS and proteomics, highlighting the major challenges ahead for the community in organising the effective sharing and integration of the resulting data mountain. Trends in Cell Biology, entitled ‘Cell Biology and the genome projects – a concerted strategy for characterizing multiprotein complexes by using mass spectrometry’ In 1997 a commentary in Fifteen years later, the efficient detection of cell proteins using MS has indeed become routine, and it is hard to imagine conducting biological research without access to complete genome sequences. The speed and resolution of mass spectrometers has increased dramatically, and we now have access to powerful software for automated analysis of raw spectra Now that the human genome is sequenced, together with the genomes of most common model organisms, it is tempting to assume that cell ‘proteomes’ – in other words a detailed inventory of the proteins present – can be deduced simply by reference to ORFs in the corresponding DNA sequence. In practice the situation is much more complex. For example, in higher organisms there is usually no simple ‘one-to-one’ relationship between genes and proteins. Instead, there are ‘one-to-many’ relationships, primarily because a single ORF can encode multiple protein isoforms.A range of mechanisms, including alternative splicing of mRNA precursors, cleavage and processing of polypeptide chains, and post-translational modifications (PTMs), contribute to generating multiple protein isoforms, with distinct or overlapping functions. To complicate the situation further, the same polypeptide chains can also form distinct functional pools of protein that are regulated independently. For example, the catalytic subunit of protein phosphatase 1 (PP1) forms many separate protein phosphatase enzymes by binding to an array of different targeting subunits. These distinct and independently regulated forms of protein phosphatases act on different substrates in different subcellular locations Thus, although genomic sequences reveal the protein-coding potential for a given organism or species, they do not reliably inform us about the many protein properties that correspond to the variables that are usually modulated during biological responses and regulatory mechanisms. The dynamic nature of these protein properties also means that they cannot be deduced from a static DNA genome sequence alone, either for a given cell cycle stage or at different times during response to a cell signalling event. Furthermore, in most cases measurements of transcript levels will not reveal dynamic protein properties, such as PTM patterns, interaction partners, or subcellular localisation, let alone the existence of distinct functional pools of protein. Indeed, recent studies indicate that transcript levels often do not reliably reflect the abundance of the cognate protein It is clear that proteomes change significantly in real time, making them highly dynamic. For example, the changing nature of an organelle proteome was demonstrated by comparing the protein composition of nucleoli isolated from cells following drug treatment to inhibit transcription One of the most successful MS-based strategies for time-lapse proteomics has been development of isotope-labelling methods that facilitate the quantitation and differential comparison of proteins, either at different times and/or under different conditions. This is a flexible approach for comparing a control sample with one or more varied experimental conditions, such as changes in protein levels and PTMs in response to drug treatment, viral infection, hormone stimulation, cell differentiation, or oncogene activation A. It hasin vivo, has been widely applied to cell biology. Thus, translation of proteins incorporates amino acids that have the natural ‘light’ isotopes of carbon, nitrogen, or hydrogen substituted with heavier isotopic forms such as 13C, 15N, and/or 2H. Usually this is carried out using isotope-substituted forms of arginine and/or lysine because subsequent trypsin digestion of the isolated proteins for MS analysis, which cleaves at basic residues, generates peptides with a single labelled amino acid, thereby simplifying analysis and quantification. The distinct ‘light’, ‘medium’, and ‘heavy’ forms of each peptide detected by MS reflect the relative amounts of the corresponding protein in each of the three ‘isotopically encoded’ cell populations.Some of the first examples of using metabolic labelling to incorporate stable isotopes into proteins for quantitative proteomics took advantage of labelling methods previously used by structural biologists for nuclear magnetic resonance (NMR) analyses The light, medium, and heavy forms of the same peptides can be resolved and quantitated simultaneously within the same sample by MS, thereby reducing potential sources of experimental variation between samples. A similar strategy has also been applied to a range of model organisms including yeast, bacteria, nematodes, plants, and mice Although the utility of the SILAC proteomics approach has already had a major impact in cell biology, it is worthwhile to consider its potential limitations. For example, although metabolic labelling is an effective way to uniformly incorporate isotopic tags into proteins, in practice it is not always feasible, as seen with clinical samples and some model organisms. Also, the ion intensity for each peptide quantitated by duplex or triplex SILAC is distributed between several, separate isotopic peaks in the spectrum B. This cAn alternative approach is to use a chemical, rather than metabolic, labelling strategy, which can be applied to any isolated protein samples, including human clinical material. Samples are quantified separately, as seen with a technique such as ICAT (isotope-coded affinity tags) Another variation on the chemical labelling strategy uses isobaric tags to label isolated peptides, as seen in techniques such as iTRAQ (isobaric tag for relative and absolute quantitation) In comparison with metabolic labelling, all of the methods described above that are based upon chemical attachment of tags to peptides introduce additional steps in the analytical strategy that are potential sources of variation and experimental error. By contrast, metabolic labelling methods allow separate cell samples to be mixed at an early stage and proteins isolated simultaneously, and this helps to improve the accuracy of analysis and quantitation. In this regard it is interesting that a new method, known as NeuCode SILAC It is reported that multiplexing by NeuCode SILAC can detect as many as 12 different isotopomer labels in a sample, and this could in principle be extended in future to detection of 20 or more multiplexed samples Most cellular proteins function as components of protein complexes rather than as single polypeptides. Therefore, characterisation of the composition and dynamics of multiprotein complexes is crucial in most areas of cell biology. A variety of methods and approaches are used to identify protein interaction partners, typically involving variations on immunoprecipitation (IP) and affinity pull-down procedures A, which We expect that the role of MS-based IP experiments using both label-free and isotopic labelling strategies will continue to expand in the future. There is also considerable scope to improve the methodology further, for example by incorporating additional information when evaluating whether a coprecipitated protein is likely to be a specific interaction partner or not. Nonetheless, affinity pull-down of interacting proteins also has inherent limitations – including limited ability to resolve multiple related forms of a complex that share some components and are all isolated together by IP, but which differ in the presence of specific subunits and/or different protein isoforms or PTMs. Affinity purification is also difficult and costly to adapt for the system-wide analysis of large numbers of complexes and for the study of changes in protein complexes during different cellular growth conditions and responses. For system-wide studies, new approaches that combine large-scale separation of protein complexes in cell extracts, using either size exclusion chromatography (SEC) There is likely to be continued growth in proteomics publications as the methods and scope for using MS-based approaches to address problems in cell biology develop further. We also anticipate the expansion of quantitative, ‘next-generation’ proteomics strategies for the systematic, quantitative analysis of protein properties – in other words the determination of ‘multi-dimensional proteomes’. The corollary is that the future value of proteomics in cell biology is closely linked with the need to develop effective, new strategies for multi-dimensional data analytics and to cope with the large volumes of proteomics data that will be generated. This includes the need to build new and more efficient data-processing pipelines which will be increasingly important to cope with the scale and complexity of future proteomics data arising from large-scale experiments.This brings forth several important challenges for the international research community if we are to exploit fully the opportunities that proteomics offers cell biology. We highlight in particular the need to tackle both effective data sharing, ideally through the creation of freely accessible, user-friendly, online data repositories and the related need to integrate proteomics data efficiently with other published information and with cognate large-scale genomic and transcriptomic data . The utihttp://chorusproject.org), we suggest that this alone will not meet the needs of the cell biology community. In addition to raw file repositories, cell biologists require convenient access to searchable databases providing complex, multi-dimensional proteomics data at the level of identified protein sequences, including the measured values of protein properties in different cell types and under different conditions. The value of such data will be further increased if entries are extensively tagged with consistent metadata that can be filtered and selected.There is currently a lack of any coordinated, international ‘open data’ strategy in the proteomics community and this is a situation that we hope will be addressed as a matter of urgency. Although public repositories are being developed for depositing raw MS data files, such as the PRIDE (Proteomics Identifications) database http://peptracker.com/epd/). Expanding the EPD and other similar resources to integrate data from many different laboratories will require community-led initiatives to agree on common metadata standards. It will also require commitment from funding agencies to provide the resources needed to create and maintain such tools for effective data integration and sharing.We have initiated such a scheme by creating a proteomics open data resource, termed the ‘Encyclopedia of Proteome Dynamics’ (EPD) An advantage to sharing large datasets across the community in a searchable format, including detailed and consistent metadata annotations, is the opportunity to build collections of multi-dimensional data from many independent experiments. This can be organised to facilitate analysis of the whole dataset and cross-correlation between parameters and measurements recorded in many separate experiments. However, to integrate and mine datasets effectively it is imperative that the community agrees on definitions of metadata terms and how they are applied, using these terms consistently in publications and data collections. This would make it much easier and more accurate to combine large data collections. In this way the value of the combined data could be greater than the sum of the conclusions derived from the analysis of any individual set of experiments – because it allows conclusions to be reached through the comparison of different result sets. Thus, the combined data constitute a ‘super-experiment’ that can answer questions that potentially were not conceived during the generation of the individual experimental datasets contributing to it. For example, it can detect a difference in protein expression levels, or any other properties, which consistently correlate with genotypes across data from multiple different cell lines.http://peptracker.com/pfl/), which records data annotating all proteins from many coimmunoprecipitation experiments Multi-dimensional proteomics databases can be used to generate new hypotheses for future evaluation and to test the feasibility of new ideas against a large collection of available data. This may already either confirm, or disprove, a new hypothesis or research question without the need to perform additional experiments, thereby saving time and research funds. For example, if a model requires that two or more proteins interact to mediate a particular response or mechanism, this can be evaluated initially by checking whether these proteins are detected in the same subcellular compartments and whether they copurify and/or cofractionate by SEC analysis. These are data already available, at least for some human cell lines, in the EPD. Another example of a ‘super-experiment’ is the Protein Frequency Library (PFL) (in vivo signalling pathways MS-based proteomics technology, which is already of value for cell biology, has great scope for further expansion and is poised to deliver major new insights into biological responses and molecular regulatory mechanisms. Over the coming years we expect that the increasing availability of benchtop MS instruments and widening of the expertise base will allow proteomics to become a standard tool in many cell biology laboratories rather than remaining the exclusive domain of specialised facilities. Further improvements in the speed and resolution of MS instruments, combined with improved experimental design and new data analysis methods, should help to make proteomics widely available as an assay tool for quantitating a wide range of protein properties and responses, and not merely a means of protein identification. Furthermore, as well as comparing relative changes in protein levels under different conditions, MS-based methods are also being developed to allow absolute quantitation of protein levels; this will be extremely useful for building a detailed systems model of many cellular reactions and Proteomics is fast approaching the ability to sample a ‘complete proteome’, whereby a ‘complete proteome’ can be defined as detection of peptide data for all expressed genes Maximising the future value to the cell biological community of the burgeoning proteomics data mountain requires new initiatives to promote the effective sharing and integration of data. We look forward to such developments, and suggest it would be most effective if led directly by cell biologists themselves and managed at the international community level with stable long-term funding. In another 15 years, therefore, it may well be that cell biology students will need to be as familiar with MS instrumentation and computers as they currently are with microscopes.


Well-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues.http://www.bioinformatics.org/surpresi.Using a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at Probabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins. The integration of knowledge of the locations of binding sites with ligand screening or docking protocols improves initial stages of the rational drug design  over all protein atoms. In the header section, the file contains detailed information about clustering and ranking of clusters. The web server and source code are freely available at The presented approach quantifies polar and directional propensities of amino acids using the partition in the knowledge-based continuous gradient of hydrophobicity generated by the protein itself. It yields a middle level of description of hydrophobic preferences between (coarse-grained) scales of hydrophobicity and (fine-grained) residue-residue contact matrices, where more specific local effects such as homophilic, counterion or phenyl rings interactions can be expressed explicitly . It has The adopted view concentrates on the characterization of proteins not assuming any specific chemical properties of ligands. Although based on a statistical model parametrized assuming spherical shapes of proteins , the method works well for moderately aspherical macromolecules, allowing for not only descriptive but also predictive applications. We do not incorporate into the identification method any additional features, such as the solvent accessible area or evolutionary conservation; the direct distance to the centroid was used only for the ranking in order to enable fair comparison with the FOD method; our measure is assigned homogeneously and isotropically in the whole protein volume, thus allowing for the examination of the predictive potential of the sole unexpectedness.Favorable outcomes of our approach, especially when applied to enzymatic active sites, can be explained by analyzing the consequences of the requirement of the precise and resolute positioning of a ligand , which can be best fulfilled by the creation of a binding pocket . The burThe most unexpected atoms are usually found in the deep-set parts of the pockets. The atomic depth has been found to be correlated with residue conservation ,79  atoms but does not enumerate explicitly all possible interactions between them.The approach is useful for the construction of a generic method that quantifies the unexpectedness of occurrences of individual atoms in a given distance from the geometric center of a protein. It turns out that the characteristics can be applied to the recognition of binding sites of both small ligands (enzymatic active sites) and other proteins (protein-protein interfaces).MK conceived of the study, implemented the method, carried out computations, analyzed results and wrote the manuscript.Protein chains in the learning set.Click here for fileGeometric characteristics of the learning set and their dependencies.Click here for fileThe plot of the probability density function used in this work.Click here for fileParameters of atomic distributions.Click here for fileα and distal side chain atoms of 20 amino acidsProbability densities of C.Click here for fileα and distal side chain atoms of CysProbability densities of C.Click here for file48 test setDetails on the efficiency of the SurpResi applied to the LB.Click here for file


In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at  In response to various extracellular stimuli, protein complexes are transiently assembled within the nucleus of cells to regulate gene transcription in a context dependent manner. Here we analyzed data from 3,290 proteomics experiments that used as bait different member proteins from regulatory complexes with different antibodies. Such proteomics experiments attempt to characterize complex membership for other proteins that associate with bait proteins. However, the experiments are noisy and aggregation of the data from many pull-down experiments is computationally challenging. To this end we developed and evaluated several equations that score pair-wise interactions based on co-occurrence in different but related pull-down experiments. We compared and evaluated the scoring methods and combined them to recover known, and discover new, complexes and protein-protein interactions. We also applied the same equations to predict domain-domain interactions that might underlie the protein interactions and complex formation. As a proof of concept, we experimentally validated one predicted protein-protein interaction and one predicted domain-domain interaction using different methods. Such rich information about binary interactions between proteins and domains should advance our knowledge of transcriptional regulation by CoRegs in normal and diseased human cells. CoRegs are members of multi-protein complexes transiently assembled for regulation of gene expression To accelerate research in the area of CoRegs signaling, the Nuclear Receptor Signaling Atlas (NURSA) http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.The aforementioned work, and other similar prior studies, ranked predicted associations and provided probabilities for interactions between baits and preys, building on the explicit nature of bait-prey relationship in epitope-based purifications. However, due to secondary cross-reacting proteins, bait-prey relationships are rarely explicit in IPs carried out with primary antibodies. Hence, here we developed and compared different ways, coded into mathematical functions, to score prey-prey interactions from a large, recently published, HT-IP/MS dataset. The equations predict direct protein-protein interactions between prey proteins without considering the specific baits. We also used the same equations to predict domain-domain interactions underlying the protein-protein interactions. We evaluated the performance of these equations using known protein-protein and domain-domain interactions from the literature and validated one protein-protein interaction experimentally, and one domain-domain interaction using computational docking. By combining the data from the 3,290 IP-MS experiments collected by NURSA we predicted binary interactions between prey proteins and their domains. We offer a global view of CoRegs complexes in human cells, and provide the predicted networks for exploration on the web through a web-based application with downloadable tables freely available at A detailed description of the IP-MS procedure can be found in references To score prey-prey interactions from the HT-IP/MS data table, containing the ranks of proteins from the 3,290 IP-MS experiments, we evaluated existing and developed new equations implemented as algorithms in MATLAB and Java.A and MB are the sets of all experiments that reported either protein A, B or both as present in the lists of pulled prey proteins. MA,B are lists where both A and B are present.Sørensen similarity coefficient (Sor) provides a symmetric similarity coefficient for comparing two finite sets. The coefficient ranges between 0 and 1, where 0 denotes no similarity, and 1 denotes identical sets. The Sørensen coefficient is calculated as the ratio of the cardinality of shared members between two sets and the sum of the cardinalities of the same sets.A,B is the Pearson's Correlation coefficient between proteins A and B where Q denotes the reported ‘abundance’ which is SPC/MW . Pearson's Correlation coefficient (Pr) characterizes the linear dependency of two variables. Here we used the Pearson's Correlation coefficient to quantify the correlation the SPC scores of two proteins across all IP/MS experiments.j (Equation 3 (E3) was developed through an intuitive manual symbolic search for functions that perform well, based on benchmarking, using known protein-protein interactions. E3 calculates a ratio between the sum of the SPC scores in experiment The AB correlation was also developed through an intuitive manual symbolic search for functions that perform well based on benchmarking using known protein-protein interactions. The AB correlation computes the mean of the product of SPC scores normalized by dividing by the sum of mean SPC scores across all experiments.http://www.ncbi.nlm.nih.gov/homologene).To evaluate the predicted prey-prey protein interactions using the four equations, we used an updated version of the human literature-based protein-protein interactome we developed for the program Genes2Networks ftp://ftp.sanger.ac.uk/pub/databases/Pfam/releases/Pfam24.0/on November 1st 2010.To identify domains for proteins, we used the Pfam domain database release 24.0. The file ‘Pfam-A.full.gz’ was downloaded from: Domain-domain interactions (DDI) were obtained from the Domine database Antibodies for STRN, also called Striatin, are polyclonal rabbit, and were purchased from Millipore Corp. Antibodies for CTTNBP2NL were purchased from GeneTex. MCF-7 cells were lysed in immunopreciptation buffer containing Hepes , NaCl (150 mM), EDTA (1 mM), Tween-20 (0.1%), glycerol (10%) and protease inhibitors. The lysates were pre-cleared in the presence of rabbit IgG and protein A beads. The input sample was collected after pre-clearing. Samples were rotated overnight with IgG or Striatin antibody and subsequently incubated for two hours with Protein-A beads. The washed protein-containing beads were denatured and analyzed by Western blot.The MolSoft ICM software was used to perform the domain-domain docking simulation. ICM uses a two-step method: pseudo-Brownian rigid-body docking followed by biased probability Monte Carlo minimization of the ligand side-chains, to sample conformational space in order to identify the global energy minimum for a given interaction We analyzed the experimental data from 3,290 IP-MS experiments targeting 1,083 antigens (bait proteins) using 1,796 different antibodies. These experiments detected 11,485 non-redundant proteins . Some ofIP-MS proteomics profiling have several known experimental challenges that need to be considered when applying functional global analyses on such data. First, it is well established that the proteins identified in such experiments are enriched for highly abundant and “sticky” proteins. This results in numerous proteins appearing frequently in almost all pull-downs regardless of the cell type, cellular fraction or experimental conditions. To address this we used a list of “non-specific” proteins to filter protein identifications that appear frequently in many pull-downs . For allOur aim in this study is to assign confidence scores to binary prey-prey protein-protein and domain-domain interactions by integrating information from the 3,290 IP-MS experiments. The rationale for this approach is that the experiments, reporting lists of ∼30–200 proteins for each pull-down, taken together, provide enough information to reconstruct high-fidelity, small-sized complexes and potentially enough to recover direct physical interactions between pairs of proteins and domains. We reasoned that if we use all the information across all experiments to score each pair of proteins for potential direct interaction, we will be able to identify novel associations in addition to recovering known interactions better than by chance. In contrast with most prior methods that focused on scoring bait-prey interactions, our equations predict interactions between prey proteins that commonly reappear together in different pull-downs. Although the data collected for this study was aimed at the recovery of interactions between the intended antigens (baits) and other proteins, the majority of primary antibodies cross-react with multiple secondary antigens and those antigens interact with other proteins. This complicates bait-prey scoring of HT-IP/MS data. Yet, logically, if two proteins reappear together at the top of lists in many different pull-downs, we can guess that they may physically interact regardless of which baits were used to pull them down, making it possible to predict likely binary interactions by utilizing the spectral counts, not just co-occurrence. To encode such logic into mathematical functions we devised four scoring schemes, each attempting to address the problem in a slightly different way. To evaluate the performance of the four scoring schemes we used known PPIs we consolidated from online databases To compare the performance of the different scoring methods we visualized the results as either receiver operator curve (ROC) , random Next, we used ball-and-stick diagrams to visualize the results across all experiments. We first visualized all overlapping interactions listed in the top 10% of predicted protein-protein interactions by each method . This resulted in a network made of 2,509 proteins (nodes) and 28,886 interactions (edges) . Using CBefore analyzing all of the 3,290 IP-MS experiments published by Malovannaya et al Since PPIs are often the result of interactions between the structural domains of the interacting proteins, and since we know most of those domains for all pulled prey proteins based on their amino-acid sequences, we can use the scores for PPIs to also score and rank domain-domain interactions (DDIs). The scoring of domain interactions is slightly more complex since most proteins have several different domains and the domains can appear more than once within the same protein. To resolve this we used the score for PPIs containing domains between all possible domain pairs from each side of the PPI and normalized the score across all the domains see . The aggThe plots show that we can reliably recover known and predicted DDIs. In addition to the four equations used to score PPIs, we introduced another scoring scheme, λ, for scoring DDIs. λ is an index that counts the number of times two predicted interacting prey proteins have a domain on each side of the PPI. Such an index improves DDI predictions. In addition to the type of analysis we did for PPIs, we also attempted to further combine different prediction methods to optimize DDI predictions. Finally we visualize our predicted DDIs with known DDIs as a network diagram to visually explore interactions among all domains  and withhttp://maayanlab.net/HT-IP-MS-2-PPI-DDI/. This resource can help us advance the catalogue of transcriptional regulation by CoRegs in normal and diseased mammalian cells.In this study we combined results from 3,290 experiments that identified nuclear protein complexes in human cells using IP-MS. We implemented and evaluated four different equations assessing their ability to predict direct physical PPIs from the aggregated proteomics data using known PPIs from the literature. The highest scoring predictions were visualized as networks with many densely connected clusters that are likely made of real protein complexes. The prediction scores for potential interactions could be considered as surrogates to real affinity constants. However, since we do not know the exact quantities of proteins, it is not possible to compute exact dissociation constants. Such binding constants can be useful for dynamical simulations where we could stochastically trace the transient dynamics of CoRegs complex formation in-silico. Scoring PPIs by only using the prey measurements may become more robust as more IP-MS experiments are published. However, careful attention should be given to weighting the repetitiveness of experiments so interactions from similar pull-downs, if repeated, are not mistakenly given higher scores. Regardless of possible limitations, the ability to recover direct PPIs based on such a massive dataset is an important step toward utilizing HT/IP-MS datasets for reconstructing networks and generating hypotheses. In addition, we show that the equations can be extended to predict interactions between structural domains. We also demonstrated two ways to further validate predicted PPIs and DDIs, using experimental and computational approaches. In summary, our analyses explored new methodologies for scoring PPIs and DDIs using data from related IP-MS experiments, providing many hypotheses about mammalian CoRegs complexes formation, and allowing users to explore novel complexes, PPIs and DDIs online at Dataset S1Information on each IP/MS experiment, quantity of proteins purified in each IP/MS experiment, size of protein lists purified in each IP/MS experiment, list of sticky proteins.(XLSX)Click here for additional data file.Dataset S2Scores for top 1% predicted PPIs by each method.(XLS)Click here for additional data file.Dataset S3Scores for top 1% predicted DDIs by each method.(XLSX)Click here for additional data file.Figure S1Each node in the network represents a list of proteins identified in one of the 3,290 IP-MS experiments color coded according to the bait protein targeted by an antibody in a single experiment. An edge represents the similarity between two lists using the Jaccard distance. A node is preserved if it has at least one edge with Jaccard distance <0.7. The network contains 491 nodes and 2233 edges. The diameter of a node represents the size of a list from a specific experiment.(EPS)Click here for additional data file.Figure S2(A) Histogram of Jaccard distances between pairs of 3,290 experiments. (B) Histogram of the size of pull-down lists from all IP-MS experiments.(EPS)Click here for additional data file.Figure S3(A) Receiver operator curve (ROC) of the recovery of known interactions using the different scoring methods. Recall rate of known PPIs (y-axis) is computed and displayed as a ratio between ranked predicted PPIs by each scoring method and known PPIs. (B) Area under the curve (AUC) computed for each method.(EPS)Click here for additional data file.Figure S4Running-sum of the top 1,563,309 predicted PPIs, predicted with the equations: (A) E3, (B) AB, and (C) Pr. The running-sum increases by √((u−t)/t) units if it encounters a known PPI, and decreases by √(t/(u−t)) units otherwise. The magenta line in each chart shows the walk when incorporating the Sørensen similarity. u and t are counts of predicted and known interactions in the current dataset respectively. The running-sum for a random sample of scrambled ranks of the same set of interactions along with the mean of running-sums of 1000 random samples are also included in each chart.(EPS)Click here for additional data file.Figure S5Moving average of a window of 2,000 ranks predicted PPIs visualized as a line graph. Sørensen similarity between pairs of proteins combined with other scoring schemas. The inset in each chart shows the recall for PPIs with evidence of indirect interaction, i.e., one intermediate. (A) E3, (B) AB, and (C) Pr.(EPS)Click here for additional data file.Figure S6(A) Venn diagram showing the overlaps between the three different scoring methods for the top 10% of predicted interactions. (B) Overlaps of known PPIs from predicted interactions represented in .(EPS)Click here for additional data file.Figure S7Similarity graph created from a subset of 114 IP-MS experiments. Nodes represent baits and links represent similarity using the Jaccard index. Nodes are colored based on the bait. Most experiments used Estrogen Receptor α (ESR1) and nuclear receptor co-activator 3 (NCOA3), also called SRC3, as baits under different conditions.(EPS)Click here for additional data file.Figure S8(A) Hierarchical clustering of the quantities of identified proteins from the subset of 114 experiments. Only proteins that were present in three or more IP experiments were included. (B) Network of predicted complexes. Complexes are formed by visualizing predicted protein-protein associations ranked in the top 1000 by all three scoring schemes. All nodes with connectivity of one were removed. Edges are colored according by the following criteria: Light blue are predicted interactions that do not have reported direct or indirect interaction in the literature; Green are predicted interactions that have one or more reported indirect interaction; Red edges are recalled direct interactions. Dotted gray edges are direct interactions which were not ranked in the selected range by the methods but are present in the literature. Nodes with a pink circle around them represent members of previously characterized complexes from the Corum database; Blue nodes represent proteins that were also used as baits it at least one of the experiments.(EPS)Click here for additional data file.Figure S9Heatmap of the percent overlap between the five complexes predicted from the subset of 114 experiments (columns) and complexes from the Curom database (rows).(EPS)Click here for additional data file.Figure S10Left: Hierarchical clustering of the quantities of identified proteins from the subset of 114 experiments . Right: Zooming into two clusters to visualize the segregation of two complexes pulled by two different antibodies targeting the same bait.(EPS)Click here for additional data file.Figure S11(A) Recall rate for previously reported DDIs from DOMINE (y-axis) as a function of the ratio of predicted DDIs ranked by one or a combination of the scoring schemes. (B) Area under the curve (AUC) for the ∼728 K ranked DDIs  and AUC for the top 7 K predicted DDIs .(EPS)Click here for additional data file.Figure S12A comparative chart of running-sums, as described for (EPS)Click here for additional data file.Figure S13Network representation of the top 10% of predicted DDIs where nodes having 50 or more predicted interactions were removed for visualization clarity. The network contains 357 domains (octagons) and 773 edges (red and blue lines). Node sizes are proportional to their connectivity. Predicted and recalled DDIs are colored in light blue and red respectively.(EPS)Click here for additional data file.


Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH) with one or two binding sites, or multiple-interface hubs (MIH) with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs  or party hubs .Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB) protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.http://hybsvm.gdcb.iastate.edu.We provide a web server for our three-phase approach:  Proteins are the principal catalytic agents, structural elements, signal transmitters, transporters and molecular machines in cells. Functional annotation of proteins remains one of the most challenging problems in functional genomics, however, our evolving understanding of a proteins' interaction partners helps in functional annotation of proteins in vitro, without offering any insights into the physical basis of such interactions, or whether such interactions may actually occur in vivoOur current understanding of protein-protein interaction networks is quite limited for a variety of reasons. The challenge of reliable and complete determination of the interactome is far from being fully addressed due to the high rate of false positives. These false positives are associated with high throughput experiments, the low coverage of solved co-crystal structures in the Protein Data Bank (PDB), and the difficulty of obtaining reliable negative evidence that a protein does not interact with one or more other proteins. For example, Y2H experiments focus on pair-wise interactions between proteins and provide, at best, rather indirect evidence for higher order interactions e.g., those that require three proteins to come together to form a complex. Even in the case of pairwise interactions, Y2H experiments only provide evidence that a pair of proteins is likely to interact Protein-protein interaction networks are usually represented and visualized as graphs in which the nodes correspond to the proteins and edges denote their possible pairwise interactions. Such a representation is simply not rich enough to encode interactions that involve more than two proteins, nor do they distinguish between them. Furthermore, a single target protein can interact with a large number of partners: some of these interactions may be mutually exclusive because of competition between potential binding partners for the same interaction sites on the target protein. Other interactions may be simultaneously possible, and in many instances, even mutually cooperative, i.e., binding of one partner to the target protein may prepare the target for binding to a second partner essential for survival and perhaps as a consequence, more conserved across species with implications for determining the evolutionary rates for protein hubs. They also found that MIH proteins are more likely to be members of large stable structural complexes. SIH and MIH also differ in terms of network expansion during evolution: SIH appear to follow the canonical preferential gene duplication model whereas MIH do not Of particular interest in this study are hub proteins, i.e., proteins that interact with large numbers of partners Hub proteins can also be classified based on the kinetic mode of interaction. Han et al. Experimental characterization of hub proteins in terms of their structural and kinetic characteristics requires knowledge of the structures of complexes formed by such proteins in interaction with their binding partners There has been considerable work on machine learning approaches for distinguishing hub proteins from non-hub proteins Against this background, we introduce a three-phase machine learning approach See . Phase IIt should be noted that categorizing hub proteins into structural and kinetic classes presents many challenges. SIH and date proteins are defined by the absence of concurrent interaction partners or interaction sites. However, it is difficult to reliably determine the absence of interaction between a protein and one or more putative interaction partners because of the lack of experimental data under a broad range of conditions. It is thus possible that some proteins labelled as SIH in our dataset are in fact MIH where not all interaction partners have been identified. Conversely, because of the high false positive rates associated with high-throughput experiments, some proteins labelled as MIH or party hubs are in fact SIH. These sources for errors in the protein-protein interaction data need to be kept in mind in interpreting the results of our study as well as other similar analyses of protein-interaction data.http://hybsvm.gdcb.iastate.edu.A web server for the three-phase approach for automated PB/NPB, SIH/MIH, and date/party prediction is available at Our approach to classifying proteins based on binding patterns is a 3-phase approach: Phase I predicts if a protein is likely to bind with another protein, i.e., protein-binding (PB). Phase II determines if a protein-binding protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs, based on sequence information alone. We present results of experiments for each of the three phases.k-letter) subsequences (k-grams) of amino acids. In our experiments, we used values of k ranging from k = 1 (amino acid composition) through k = 4 . Larger values of k were not considered, because we run out of data to reliably estimate the model parameters. We use a range of standard machine learning methods implemented in Weka version 3.6.0: J4.8 version HybSVM, which uses an SVM to combine the outputs of a set of predictors. We compare the results of predictors trained using machine learning methods with two baseline methods: the first baseline method classifies proteins based on the number of SCOP In this study, we use a simple encoding of protein sequences using the probability distribution short , we assembled Dataset 1, which consists of 5,010 proteins including 3,418 proteins that bind to one or more proteins and 1,592 that bind to small ligands, but are not known to bind to other proteins. As mentioned in the k-gram used. The accuracies ranged from 74.4%  to 87.2% . Simply predicting each protein as belonging to the majority class yields an accuracy of 68.2% (see Domain-based method). Most of the methods were able to achieve accuracies well above 68.2%. The precision values ranged from 0% to 81%, recall from 0% to 93%, and correlation coefficient from 0.00 to 0.69. As shown in HybSVM for Phase I, which constructs a support vector machine (SVM) classifier that takes as input, for each protein sequence to be classified, the outputs of seven classifiers as well as the PSI-BLAST method and produces as output, a class label for the protein. The 7 classifiers used are: NB(1), NB(2), NB(3), NB(4), NB 2-gram, NB 3-gram, NB 4-gram. PSI-BLAST performs well on sequences with high sequence similarity whereas the NB(k) and NB k-gram methods perform well on sequences with high k-gram composition similarity. Logistic regression models are applied to the HybSVM classifier to get a probability score for each prediction. These scores are then used to evaluate the quality of each prediction.To examine this possibility, we built HybSVM classifiers for Phase I against other standard machine learning approaches. HybSVM had an accuracy of 94.2% (an improvement of 6% in absolute terms over NB 4-gram) and a correlation coefficient of 0.87 (an improvement of 0.15 over NB 4-gram). For each performance measure the HybSVM method had the highest value for Dataset 1. HybSVM for Phase I also outperforms the other methods over the entire range of tradeoffs between precision and recall on a ROC curve  identify hub proteins, (2) filter out non-hub proteins, and/or (3) flag proteins that have potential of being non-hubs. To evaluate this type of method, we assembled Dataset 2, consisting of 4,036 proteins including 1,741 hub proteins and 2,295 non-hub proteins. The dataset was derived from high confidence protein-protein interaction data from BioGrid We used a simple homology-based method to classify proteins into hubs and non-hubs. A protein is classified as a hub if each of the top 4 hits returned by PSI-BLAST To evaluate structural predictions on hub proteins, we created Dataset 3. The dataset consists of 155 hub proteins including 35 SIH and 120 MIH. The dataset is a subset of data originally compiled by Kim et al. k-grams. The accuracies ranged from 67.7%  to 81.2% . Several classifiers actually had accuracies below 77.4% . The precision values ranged from 0% to 86%, recall from 0% to 63%, and correlation coefficient from 0.00 to 0.41. To validate how well interaction sites of SIH and MIH can be predicted on Dataset 3, we ran a subset of the data through the interaction site predictor ISIS k-value is optimal for all methods; optimal values of k vary with the size and complexity of the dataset. Variables such as number of proteins, size of proteins, and homology between proteins all play an important role in developing an appropriate model for our classifiers. Therefore, it is difficult to design a single model or choose a single optimal value of k for any dataset without prior knowledge of the data. We also observe that proteins within a class are assigned different labels by classifiers that correspond to different choices of k. Our results show that a single classifier does not classify all the proteins correctly, yet a vast majority of the proteins (over 93%) have at least one classifier that correctly predicts its class. Again, we used the HybSVM method, this time for Phase III classifications, to take advantage of the complementary information provided by the individual classifiers.HybSVM outperforms all other individual methods on 5 of the 6 performance measures. For Dataset 3, HybSVM improved accuracy by 5.2% (89.0%) and correlation coefficient by 0.22 (0.69) over the previous best classifier, NB 4-gram. This method also had the highest AUC with a value of 0.85 . NB(k) had the highest recall value at 0.84 (it was able to correctly label more SIH proteins), but it came at the cost of a low precision (0.31) and lower correlation coefficient (0.44). The threshold can be adjusted for HybSVM to achieve a better recall based on HybSVM having very balanced precision and recall scores  and the highest f-measure (76.0). HybSVM did not always outperform the other methods over the entire range of tradeoffs between precision and recall, it did outperform the other methods for a specific range of false positive rates (from 0.0 to 0.4 for SIH and from 0.25 to 0.5 MIH). No single method significantly outperformed HybSVM. It is worth noting that HybSVM method is especially attractive if there is little tolerance for false positives. In contrast, each of the other methods (with the exception of domain-based method) works relatively well, in settings where there is greater tolerance for higher false positive rates.From the results shown in A closer examination of the results for Dataset 3 shows that many of the misclassified hub proteins are close to the arbitrary boundary between SIH and MIH. This raises the question as to whether the labels could be more reliably predicted if the arbitrary cut-off on the number of interfaces is altered See . For exaHybSVM method can be used in settings where a low false positive rate is desirable.To assess kinetic predictions on hub proteins, we created Dataset 4. Dataset 4 contains 199 hub proteins including 91 date hubs and 108 party hubs. Dataset 4 was originally created by Han et al. HybSVM approach we report here focused instead on the binary classification task of distinguishing date hubs from party hubs. Our method does not need functional annotations (GO terms) of proteins, their domain composition, or their sequence alignments with their homologs. Our method also provides probability scores for each prediction. These scores allow an investigator to trade-off the reliability of predictions against the coverage of the predictions. HybSVM runs quickly and is easy to implement and update, which are ideal characteristics to serve the method through a web server. A web server implementation of HybSVM can be found here: http://hybsvm.gdcb.iastate.edu.A study by Mirzarezaee et al. has recently proposed methods for distinguishing date hub proteins from party hub proteins HybSVM classifier for predicting protein-binding proteins (Phase I). The union of these three datasets included 900 yeast hub proteins. The Phase I HybSVM classifier predicted 99.7% of the proteins as protein-binding proteins. Only three multi-interface proteins were misclassified.To validate our three-phase approach, we tested each phase on additional datasets. Since Dataset 1 (PB versus NPB) was created independently of Dataset 3 (SIH versus MIH) and Dataset 4 (Date versus Party), we used these two datasets along with the data used in the Mirzarezaee paper (Date versus Party) as a test set for the HybSVM classifier to discriminate date hubs from party hubs (Phase III). The Phase II classifier correctly predicted 147 proteins as hub proteins and 116 as likely hub-proteins. The classifier misclassified 45 proteins as non-hub proteins and 23 as likely non-hubs proteins (12% error rate). All other proteins were labeled as being of unknown category. The Phase III classifier used to discriminate date and party hub proteins predicted 67.9% of the 546 proteins correctly with a correlation coefficient of 0.36. One of the advantages of our approach over the Mirzarezaee study is that a probability score is assigned to the predictions. In this example, a majority of the misclassifications had a probability score under 0.70. Predictions with higher scores are more reliable. For example, in the case of predictions with score greater than 0.70 (337 proteins), accuracy improves to 74.2% (0.46 correlation coefficient). The predictions with score greater than 0.90 (78 proteins) yield even more reliable results: 84.6% accuracy, and 0.54 correlation coefficient. These results show that investigators can benefit from our method, which needs only sequence information, to control the quality of the predictions by sacrificing the coverage of the classifier. SIH and MIH class labels were not readily available for the Mirzarezaee dataset, so the structural classifier of Phase III was not evaluated on this dataset.We also used the data from the Mirzarezaee study as an additional test set to predict hub proteins (Phase II) and for the We have demonstrated that it is possible to fairly reliably classify proteins in a three-phase approach: the first phase distinguishes protein-binding (PB) versus non-protein-binding (NPB) proteins; the second phase predicts if the protein is likely to be a hub; the third phase classifies protein-binding proteins into SIH versus MIH and date versus party hubs. Our approach uses only sequence information and therefore will be highly useful for the analysis of proteins lacking structural information. These classifications provide insights into the structural and kinetic characteristics of the corresponding proteins in the absence of interaction networks, expression data, three-dimensional structure, sequence alignment, functional annotations, domains, or motifs. We note that the performance of our classifier for predicting structural characteristics of hubs  is better than that of the classifier for predicting kinetic or expression related characteristics of hubs .Here we used four datasets for training and testing classifiers for different phases of prediction. Because protein interaction datasets tend to have high false positive rates, when building these datasets, our main goal was to use high-quality data. Our second goal was to remove sequence bias in the datasets. The first dataset consists of proteins that are involved in binding with other proteins (PB) and proteins that are not involved in PB (NPB). This dataset was used in the first phase of our prediction. The second dataset consists of hub and non-hub proteins. This set of proteins was used in the second phase of predictions. Datasets 3 and 4 were used by the third phase to distinguish singlish/multiple and date/party hub proteins .http://hint.yulab.org/)–HINT is a database of high-quality protein-protein interactions for different organisms, which was obtained by integrating protein-protein interaction data from various sources and filtered to remove low-quality interactions.The first dataset consists of two subsets of proteins. The first subset is generated using high-quality sets of proteins that are known to interact with other proteins. These proteins form a protein-binding (PB) subset. The second subset consists of proteins that are unlikely to bind with other proteins (NPB). To create the PB subset, 3,640 yeast proteins were downloaded from HINT http://www.bindingdb.org/bind/index.jsp). This includes the entire set of protein targets that bind to small-molecules. In order to filter proteins that are interacting with other proteins, these 8,443 BindingDB proteins were BLASTed http://thebiogrid.org/). The remaining set of non-interacting proteins was 4,567 proteins. To minimize sequence bias, we clustered the proteins in both subsets where at least 80% of the sequence shared 50% or more sequence identity. A representative sequence was randomly chosen for each cluster to obtain the final dataset. The resulting dataset, Dataset 1, consists of a total of 5,010 proteins including 3,418 proteins in the PB subset and 1,592 proteins in the NPB subset.The NPB subset consists of proteins that bind with small molecules, but not with proteins. Identifying such a subset is a challenging task, because the available protein-protein interaction data are incomplete at best. It has been estimated that the fraction of identified interactions of the full human interactome is between 5% and 13% Manna et al. http://sin.gersteinlab.org. We filtered non-hub proteins out and applied the same sequence filtering that we used for Dataset 1 and Dataset 2. The resulting dataset, Dataset 3, consists of 155 hub proteins with 35 SIH and 120 MIH proteins.Previously Kim et al. Han et al. It is worth noting that the SIH-MIH and Date-Party classes show some overlap. For each class within our dataset we built a binary classifier that predicts if that protein belonged to that class or not. The reported accuracy estimates are based on stratified 10-fold cross validation. Each of the individual classifiers is described below:A support vector machine (SVM), given a training set, that is linearly separable in a kernel-induced feature space, implements a linear decision boundary that maximizes the margin of separation between the classes The Naive Bayes multinomial classifier models each sequence by a bag of letters sampled from a fixed alphabet. In our case, the bag of letters is the amino acid composition of a protein sequence. Thus, each element (amino acid) of the sequence is assumed to be independent of the other elements in the sequence given the class label. Based on this assumption, a multinomial Naive Bayes classifier can be built over all of the sequences for a given class. This is similar to the bag of words approach previously used for text classification k-grams (NB k-grams) method k along each sequence to generate a bag of k-grams representation of the sequence. Much like in the case of the Naive Bayes classifier described above, the Naïve Bayes k-grams classifier treats each k-gram in the bag to be independent of the others given the class label for the sequence. Given this probabilistic model, the previously outlined method for classification using a probabilistic model can be applied. A problem with the NB k-grams approach is that successive k-grams extracted from a sequence share k-1 elements in common. This grossly and systematically violates the independence assumption of Naive Bayes.The Naive Bayes k -1. It modifies the Naïve Bayes model to explicitly model the dependencies (of order k-1) between the letters of a sequence. It is easily seen that when k = 1, Naive Bayes k-grams as well as Naive Bayes (1) reduce to the Naive Bayes model. The relevant probabilities required for specifying the above models can be estimated using standard techniques for estimation of probabilities using Laplace estimators NB(k) k-grams and NB(k) models were constructed and evaluated on the dataset with k ranging from 1 to 4. Values of k larger than 4 were not considered because at higher values of k, the available data are insufficient to obtain reliable probability estimates.Naïve Bayes (NB) HybSVM classifier. Given a query sequence to be classified, we used PSI-BLAST to compare the query sequence against the training set. In 10-fold cross-validation, we ran PSI-BLAST with the query sequences in a given fold against the reference database comprised of the remaining nine folds. We repeated this process for each of the ten folds. For HybSVM, a class was assigned to the query sequence based on the top-scoring hit  from the PSI-BLAST results. The resulting binary prediction of the PSI-BLAST classifier for class c is 1 if the class label for the top scoring hit is c. Otherwise, it is 0. An e-value cut-off of 1×10−4 was used for PSI-BLAST, with all other parameters set to their default values. For predicting hub proteins, the four top-scoring hit were used. If there was a consensus among the top four hits then the class label of the four hits is assigned to the query sequence. If three of the four top-scoring hits had the same class, the query sequence is labeled as ‘likely’ belonging to that class. In addition to the prediction, we report the number of interaction partners belonging to the top hit, the range of interaction partners of the top four hits, and a weighted average (based on the BLAST score) of number of interaction partners of the top four hits.The homology-based tool PSI-BLAST The domain-based method builds a classifier by using a class-conditional probability distribution based on the frequency of SCOP k-gram feature that partitions the data to maximize the information gain between classes. The process is recursively repeated on the new partitions until no more information gain can be achieved. Additional techniques are performed  to help prevent overtraining. For these experiments we used the commonly used decision tree algorithm C4.5 A Decision Tree builds a predictive model by recursively partitioning the dataset based on choosing features that provide the most information gain. In our example, the feature set is the observed k-gram composition of amino acids given a class. For binary classes , the decision tree algorithm chooses a A multi-layer perceptron, often referred to as a multilayer artificial neural network HybSVM classifier that is a support vector machine (SVM) classifier that assigns the class label to a target sequence based on the class labels output by the Naive Bayes (NB), NB k-gram, NB(k) classifiers, and an additional attribute, the output from the PSI-BLAST classifiers. Since there are eight classifiers Naive Bayes, NB 2-gram, NB 3-gram, NB 4-gram, NB(2), NB(3), NB(4), and PSI-BLAST, the input to the HybSVM classifier consists of a 8-tuple vector of class labels assigned to the sequence by the 8 classifiers. A SVM is trained to predict the class label for each sequence based on the 8-tuple of class labels predicted by the eight individual classifiers.We introduce accuracy, precision, recall, f-measure, and correlation coefficient are used to evaluate the performance of our machine learning approaches Accuracy is the fraction of overall predictions that are correct. Precision is the ratio of predicted true positive examples to the total number of actual positive examples. Recall is the ratio of predicted true positives to the total number of examples predicted as positive. The F-measure (F1 score) is the harmonic mean of precision and recall. The F-measure has a range between 0  and 1 . Correlation coefficient measures the correlation between predictions and actual class labels. The correlation coefficient has a range of -1  and 1 .The performance measures M represents the total number of classes and N represents the total number of examples. TP, TN, FP, and FN are the true positives, true negatives, false positives, and false negatives for each of our classification problems. For example, when predicting SIH proteins: TP refers to a SIH correctly predicted, FP to MIH predicted as SIH, FN as SIH predicted as a MIH, and TN to MIH correctly predicted.Where possible, we used the area under the receiver operating characteristic (AUC) curve. The ROC curve plots the true positive rate versus false positive rate for a binary classifier system (a protein belongs to a given class or not) as its discrimination threshold is varied. The area ranges from 0 (worst) to 1 (best); the value of 0.5 refers to the expected value of a random method.Figure S1The accuracy curve of predicting singlish-interface and multiple-interface hub proteins as a function of the number of interaction sites. The curve shows the prediction accuracy for proteins with number of interactions sites less than the given maximum threshold. For example, the value of 5 on the x-axis refers to all hub proteins with 5 or fewer interfaces and the value on the curve (83%) at x = 5, represents the accuracy of this set.(DOCX)Click here for additional data file.Figure S2The sensitivity curve of predicting singlish-interface and multiple-interface hub proteins as a function of the number of interaction sites. The curve shows the prediction accuracy for proteins with number of interactions sites more than the given minimum threshold. For example, the value of 5 on the x-axis refers to all hub proteins with 5 or more interfaces and the value on the curve (97%) at x = 5, represents the sensitivity of this set.(DOCX)Click here for additional data file.Table S1Accuracy, precision, recall, and correlation coefficient (CC) of classification for the protein-binding versus non-protein-binding dataset are presented for internal machine learning methods. For each machine learning approach, values of k ranged from 1 to 4. The performance of the results were estimated using cross-validation. The highest performing value(s) for each performance measure is highlighted in bold.(DOCX)Click here for additional data file.Table S2Accuracy, precision, recall, and correlation coefficient (CC) of classification for the protein-binding versus non-protein-binding dataset are presented for standard machine learning methods. For each machine learning approach, values of k ranged from 1 to 2. The performances of the results were estimated using cross-validation. The highest performing value(s) for each performance measure is highlighted in bold.(DOCX)Click here for additional data file.Table S3Accuracy, precision, recall, and correlation coefficient (CC) of classification for the multi-interface versus singlish-interface dataset are presented for internal machine learning methods. For each machine learning approach, values of k ranged from 1 to 4. The performances of the results were estimated using cross-validation. The highest performing value(s) for each performance measure is highlighted in bold.(DOCX)Click here for additional data file.Table S4Accuracy, precision, recall, and correlation coefficient (CC) of classification for the multi-interface versus singlish-interface dataset are presented for standard machine learning methods. For each machine learning approach, values of k ranged from 1 to 3. The performances of the results were estimated using cross-validation. The highest performing value(s) for each performance measure is highlighted in bold.(DOCX)Click here for additional data file.Table S5Accuracy, precision, recall, and correlation coefficient (CC) of classification for the date versus party dataset are presented for internal machine learning methods. For each machine learning approach, values of k ranged from 1 to 4. The performances of the results were estimated using cross-validation. The highest performing value(s) for each performance measure is highlighted in bold.(DOCX)Click here for additional data file.Table S6Accuracy, precision, recall, and correlation coefficient (CC) of classification for the date versus party dataset are presented for standard machine learning methods. For each machine learning approach, values of k ranged from 1 to 3. The performances of the results were estimated using cross-validation. The highest performing value(s) for each performance measure is highlighted in bold.(DOCX)Click here for additional data file.Table S7The formula for binary classification for each of our five performance measures is provided. TP, TN, FP, FN are the true positives, true negatives, false positives, and false negative predictions.(DOCX)Click here for additional data file.


Protein-DNA interactions are important for many cellular processes, however structural knowledge for a large fraction of known and putative complexes is still lacking. Computational docking methods aim at the prediction of complex architecture given detailed structures of its constituents. They are becoming an increasingly important tool in the field of macromolecular assemblies, complementing particularly demanding protein-nucleic acids X ray crystallography and providing means for the refinement and integration of low resolution data coming from rapidly advancing methods such as cryoelectron microscopy.We present a new coarse-grained force field suitable for protein-DNA docking. The force field is an extension of previously developed parameter sets for protein-RNA and protein-protein interactions. The docking is based on potential energy minimization in translational and orientational degrees of freedom of the binding partners. It allows for fast and efficient systematic search for native-like complex geometry without any prior knowledge regarding binding site location.We find that the force field gives very good results for bound docking. The quality of predictions in the case of unbound docking varies, depending on the level of structural deviation from bound geometries. We analyze the role of specific protein-DNA interactions on force field performance, both with respect to complex structure prediction, and the reproduction of experimental binding affinities. We find that such direct, specific interactions only partially contribute to protein-DNA recognition, indicating an important role of shape complementarity and sequence-dependent DNA internal energy, in line with the concept of indirect protein-DNA readout mechanism. Protein-DNA interactions regulate many cellular processes involving gene expression, DNA replication and repair. The recognition process can either be specific or non-specific depending on functional requirements-6. AlthoFor a large fraction of known and putative interactions structural knowledge is still lacking. In addition, gene regulatory processes often involve weak and transient protein-DNA interactions for which experimental structure determination using X-ray crystallography can be difficult or impossible. It is desirable to be eventually able to answer the question if a protein may interact with DNA and how, without performing the experimental structure determination.Computational docking methods can provide structural models of complexes in cases where it is difficult or impossible to obtain an experimental complex structure. It is a predictive method based on the structures of the individual partners. Several methods for protein-protein docking have been developed and used extensively for the prediction of protein-protein complexes,13. TheiOne major challenge to any docking algorithm is to handle the size of the macromolecules, as the computational time for evaluating all relevant binding geometries increases rapidly with the number of involved atoms. The computational demand can be reduced by the use of a simplified representation of the macromolecules. In such approach, biomolecules are represented by pseudo atoms that represent geometric centers of complete chemical groups instead of single atoms. Such approach has already been successfully used for the analysis and prediction of biomolecule complexes-21.One possibility to obtain the necessary interaction parameters is to relate them to the propensities of the considered pseudo atoms to stick together extracted from experimentally known structures. Gathering statistics for all possible pairs of the introduced atom types and using Boltzmann inversion to convert the probability of their finding together to (pseudo) energy functions, one can construct so called knowledge-based force field. In principle, such force field can not provide a valid representation of the free energy: the strIn the present study we have developed a distance-dependent, knowledge-based coarse grained force field for evaluating protein-DNA interactions during docking. The scoring function is compatible with the coarse-grained models for proteins and RNA, implemented in the ATTRACT docking program,21, and The force field is an extension of previously parametrized knowledge based potential for protein-RNA docking. The intσij and εij govern bead-bead interaction range and strength respectively. The above potentials are assumed to account for all effective protein – nucleic acid interactions, hence no additional electrostatic terms are introduced.Pairwise specific parameters The coarse-graining scheme Figure and inteσ parameters were optimized to provide possibly low root mean square deviation (RMSD) of protein Cα atoms after rigid body energy minimization of native complexes. The optimization was performed using Monte Carlo-like approach in which trial parameter changes were accepted or rejected based on changes in RMSD score. Such score increasingly favored minimized solutions with low RMSD, but deemed unimportant RMSD variations, if RMSD was below 1 Å. The optimization procedure was repeated until no further change was observed among the optimized σ values.In line with protein-RNA force field development scheme, the new parameters were obtained using a two stage procedure. First, starting from the existing values of their RNA counterparts , ε parameters, and was performed in a similar manner. Here, the goal was to enhance scoring of native-like complexes with respect to generated decoys (200 for each training set structure). The acceptance of new ε values was driving towards the maximum number of properly ranked (i.e. with rank 1) native-like complexes, and maximum score favoring possibly high ranks of the remaining, non-optimal cases. After every 10 cycles of trial changes covering each ε value, a systematic docking search was performed for the test set structures. ε optimization was stopped after no further improvement in docking results was observed.The second optimization stage involved adjustments of ε parameters, governing the interaction strength between two given pseudoatoms, were equalized to an average ε value from the full protein-DNA force field. At the same time, σ parameters, governing the interaction range, were left unchanged.In order to disentangle the role of specific bead-bead interactions from the effect of shape complementarity in docking experiments, a non-specific force field variant was also prepared. The values of all The PDB was scanned for entries representing protein-DNA complexes. About 588 entries reporting X-ray structures at resolution 3.0 Å or better and including both a polypeptide chain of 50 or more amino acid residues and a polyribonucleotide of 5 or more nucleotides were found. In order to remove redundancy, when the protein components in two entries had more than 30% identity, we kept the one having better resolution and more structural completeness for further analysis. We have also visually checked each structure with molecular visualization software, and when there were two identical molecules in one asymmetric unit we kept only the one.The final list of 117 complexes is reported in Additional fileAll docking simulations were carried out with the ATTRACT program. Both biSystematic docking search was performed as series of energy minimization runs, starting from positions evenly distributed around the receptor at distances precluding receptor-ligand overlaps. For each such position, a set of multiple initial ligand orientations was considered. For the results presented herein, a spacing of ∼12 Å between starting points, and 208 orientations per starting point were used, providing a reasonable compromise between sampling accuracy and computational time. Each energy minimization was divided into 5 stages, with distance cutoff for pairwise interactions decreased in a stepwise manner from 50 to 8 Å.fNC). The native interface was defined as consisting of protein and DNA beads found within 8 Å to each other in the crystal structure. A docked geometry was considered as a “hit”, with iRMSD ≤ 2 Å and fNC ≥ 0.3, or iRMSD ≥ 1 Å with fNC ≥ 0.5. Such criteria are equivalent to “hit” being “high” or “medium” quality solution according to the CAPRI challenge[fNC ≥ 0.5) was determined.Converged solutions were clustered according to their pairwise RMSD, and scored according to their potential energy. The evaluation of docking results quality was based on interface RMSD (iRMSD) between the assembled and native complex, and the fraction of reproduced native contacts  , was calculated between experimental and crystallographic sequences, and cases with similarity lower than 0.5 were excluded.Experimentally derived protein-DNA affinities, used for comparison with calculated binding energies, were taken from ProNIT database. In ordeΔG was calculated.Such procedure resulted in 15 crystallographic protein-DNA complexes with matching experimental binding free energies-43  for n ∈ {1.100}. The AUC of 1.0 would be equivalent to all native-like solutions having the best rank (the lowest energy) among their corresponding decoys, and AUC of 0.0 would mean no hits at all within 100 lowest energy solutions.During the second parametrization stage εparameter changes, AUC for the training set was still increasing after 10 optimization rounds  in attraction towards nonpolar amino acids such as Val, Leu, Ala, and Trp. Second important difference between RNA and DNA – the absence of C2’ ribose hydroxyl group – also led to the expected changes in interaction parameters for GS2 bead: an increase in attraction towards hydrophobic Leu, Cys, and Trp, and a stronger repulsion towards hydrogen bond acceptors such as His or Asp. It should be noted, that the actual physical nature of interactions was not taken into account in our knowledge-based potential, and the observed changes in interaction preferences resulted solely from the selection pressure towards efficient scoring imposed in the force field optimization procedure.Systematic docking searches were not able to find any hit for around 15% of structures Table from botIn principle, a reliable docking method should not only allow distinguishing between bound geometries and their corresponding decoys, but should also provide proper ranking of native complexes according to their absolute binding free energies. In order to test this ability, force field energy values obtained upon energy minimization of native complexes were compared with experimental affinities. Such data is available for a number of protein-DNA interactions, howeverin vitro assays. Even, if a complete binding site is preserved, the influence of missing parts, such as flanking DNA segments, is impossible to estimate, albeit substantial in some cases. In an effort to minimize those effects, a strict selection process was introduced for matching structural and experimental data (see Methods). The already available dataset[One needs to standardize multiple factors affecting experimental results such as: temperature, pH, ionic conditions, the presence of cofactors, or experimental method used. Furthermore, one should bear in mind that structures depicted by X-ray crystallography usually represent only fragments of macromolecules used for  dataset, used toThe correlation (Pearson’s correlation measure is used here and in the following) between calculated and experimental binding free energies was at the level of 0.69 Figure, A. ThisSuch results indicate that the correlation to experimental binding free energy is generally a poor measure of force field performance, in line with similar observations from other studies. It may at al.[ΔGmeasurements for different mutants (see Methods).A more indicative estimate of the actual force field performance can be obtained by analyzing differences in affinities for a number of DNA mutants targeting the same protein partner. Here, one can assume limited variability of uncontrollable factors within each system of interest, and hence, the cancellation of their effect on relative free energies. A set of experimentally determined binding free energies with matching structural data was taken from the work of Morozov at al.. It compAn average correlation between experimental free energy differences and estimates obtained upon potential energy minimization in protein-DNA force field was 0.20 Figure. This isThe non specific potential and contact-based estimate of interaction energy showed no predictive power in sequence specificity test, yielding average correlations with experimental data at levels of −0.05 and −0.17, respectively Figure. It demoProper docking of macromolecules based on their unbound conformations requires the prediction of local and global structural rearrangements that likely occur upon binding. Several approaches have been adopted in order to tackle this problem, such as the use of multiple starting conformations, multiplfNC ≤ 0.3, or fNC ≥ 0.3, in line with CAPRI classification.A set of benchmark structures, containing 47 pairs of binding partners in their unbound conformations along with the corresponding assembled complexes, was taken from the work by van Dijk and Bonvin. This seAs a reference, bound-bound docking of all benchmark structures was also performed. “Hits” were found for almost all complexes (98%), and in 72% of the cases they corresponded to the best-ranked solutions Figure. The staNall). On the contrary, scoring in this scenario was less efficient, with no top ranked “hits” nor “acceptable” solutions at all . Nonetheless, there was a group of complexes with unbound protein components having an average RMSD of 3.5 Å, and the fraction of top ranked “acceptable” solutions still around 30%, while none of UD:BP cases, with even lower RMSD for unbound DNA, had a top ranked native-like solution.In mixed bound-unbound docking, two scenarios were considered: unbound DNA - bound protein (UD:BP), and bound DNA - unbound protein (BD:UP). The total fractions of generally found “hits” and “acceptable” solutions were very similar in both cases: ∼50%, and ∼75% respectively Figure, Nall. Ol Figure, N1. NonIt indicates that structural distortions of the two binding components may have different effects on the docking process. Unbound DNA appears to be generally more effectively recognized by a protein binding partner preserved in its bound conformation, but scoring of the resulting complexes among the very best solutions (with rank up to 10) is less frequent. It may be a consequence of the fact that regular DNA secondary structure, with exposed phosphate backbone, may provide enough contacts to find a native-like binding mode, but its favorable scoring among other backbone-driven geometries may require some degree of sequence dependent deformation[The statistics for unbound - unbound docking was expectedly worse than for mixed cases. High or medium quality solutions were generally found for 20% of cases (“acceptable” solutions for 60%) and for only ∼10% of cases “hits” were among 100 top ranking geometries (“acceptable” solutions for 35% of cases). Interestingly, the overall performance with respect to high or medium quality solutions for the same unbound benchmark set was quite similar to the one of the HADDOCK docking program working in rigid docking mode. Two-star solutions  were generally found for 20% of cases, and roughly half of them  was scored preferably, that is with at least 10% two-star geometries among the considered best ranking 400 . The reported results for “acceptable” solutions were somewhat better in the case of HADDOCK , however, it should be noted that an ensemble of 5 rigid protein geometries  was submitted to docking for each complex, instead of a single, crystallographic geometry used here.The analysis of results as a function of average RMSD for the two binding partners Figure, indicatA comparison with the results obtained for non-specific force field , i.e. rR|>0.2) was found between the tendency of “hit” rank to remain unchanged in spite of sequence perturbation (which could indicate the domination of indirect readout mechanism) and descriptors such as deviation from ideal B-DNA conformation, the amount DNA backbone involved in complex formation, or interface size. Nonetheless, the observed “limited” role of direct sequence readout at least to some extent reflects the complex nature of protein-DNA recognition, and thus should not be attributed solely to force field deficiencies.Unfortunately, no general correlation (with |A coarse grained force field for protein-DNA docking was presented. It is compatible with previously developed parameter sets for protein-protein and protein-RNA docking, all suitable to use within the ATTRACT docking protocol. The force field was parametrized in a knowledge-based manner on a set of available protein-DNA complexes. Its ability to reconstruct native complexes based on systematic docking of bound components was tested on an independent test set, yielding very good results. The quality of predictions in bound-unbound and unbound-unbound docking scenarios was expectedly worse, depending on the level of structural deformation of the binding partners. It is worth stressing, however, that the overall performance was similar to the one acheived by methods relying on the knowledge of the true interface, even though no such information was utilized here.The energy score for docked complexes was shown to correlate with experimental binding free energies, however, the correlation could be likely explained solely by taking into account the number of protein-DNA contacts, without truly meaningful input from specific amino acid - nucleotide interactions. The role of such specific interactions was further investigated by comparing experimental and calculated effects of DNA mutations on binding free energy, as well as monitoring the sensitivity of docking results on random DNA sequence alterations. Both tests revealed certainly notable, yet relatively limited importance of sequence specific interactions both for the reproduction of experimental effects and favorable scoring of native sequences.The extent to which it reflects true aspects of protein-DNA recognition, in which considerable effect is attributed to sequence specific DNA deformation and not only to specific intermolecular contacts, remains an open question. Certainly, however, the above findings pose a serious challenge to flexible protein-DNA docking algorithms. In order to avoid false positive shape complementarity the allowed conformational changes would have to remain strictly in physically sound regime, and furthermore, their associated free energy change would have to be adequately quantified and included into the scoring scheme. Addressing those challenges is within the scope of our current efforts.The authors declare that they have no competing interests.PS parametrized the force field, collected experimental data, carried out docking, interpreted the results, was involved in writing the manuscript, RB compiled the set of protein-DNA complexes, was involved in writing the manuscript, MZ designed the project, was involved in writing the manuscript. All authors read and approved the final manuscript.Supporting information. Supporting information in pdf format contains the list of all protein-DNA complexes used for parametrization and testing.Click here for file