The increasing ability to generate large-scale, quantitative proteomic data has brought with it the challenge of analyzing such data to discover the sequence elements that underlie systems-level protein behavior. Here we show that short, linear protein motifs can be efficiently recovered from proteome-scale datasets such as sub-cellular localization, molecular function, half-life, and protein abundance data using an information theoretic approach. Using this approach, we have identified many known protein motifs, such as phosphorylation sites and localization signals, and discovered a large number of candidate elements. We estimate that ∼80% of these are novel predictions in that they do not match a known motif in both sequence and biological context, suggesting that post-translational regulation of protein behavior is still largely unexplored. These predicted motifs, many of which display preferential association with specific biological pathways and non-random positioning in the linear protein sequence, provide focused hypotheses for experimental validation. Short amino acid sequences, typically 3 to 10 amino acids in length, play important functional roles in determining protein behavior Computational approaches have been developed to discover protein motifs and have led to fundamental observations related to the sequence determinants of protein behavior de novo protein motif-finding approach that seeks to address these challenges. The underlying algorithm draws on information theory, specifically the idea of mutual information Here we describe a new Similar to ongoing experimental and computational efforts to decode the regulatory genome motif profile denotes the presence or absence of the motif in each protein sequence. A motif is present in a protein if the amino acid sequence contains at least one exact match to its pattern. The protein behavior profile is derived from experimental data and indicates the behavior of each protein in the experimental data. Such behaviors can be direct measurements, e.g., protein abundance or half-lives, or derived from the experimental measurements using data analysis techniques such as clustering; in the latter case, a protein's behavior can be described as the label of the cluster to which it was assigned. The correlation between a protein motif and a protein behavior profile is determined using mutual information and assessed using non-parametric randomization tests. Highly informative motifs are predicted to influence the studied protein behavior.Motifs are defined as fixed-length patterns using a degenerate code of amino acids. For example, a motif may be defined as “L.[RK]”; in this motif, only “L” is allowed at the first motif position, any amino acid is allowed at the second position , and either “R” or “K” is allowed at the third position. Given a motif, the k-mer exploration step, where all abundant k-mers are evaluated and scored using mutual information, followed by motif refinement, where changes are made to the initial k-mers that increase both motif degeneracy and motif information. In the first step, a motif profile is created for each k-mer and the mutual information (MI) between this profile and the protein behavior profile is calculated. In the second step, informative k-mers are converted into more informative degenerate motifs using a greedy search procedure, in which sets of amino acids are tested at individual positions of the motif and changes that lead to more informative motifs are preserved. Thus, this two-step algorithm performs a comprehensive coarse-grained search of motif space and generates accurate motif representations.Informative motifs are discovered via a To aid in the interpretation of motif predictions, our framework also includes post-processing steps intended to assess statistical significance, minimize false positives, and determine the biological significance and functional roles of the predicted motifs. Motif significance is calculated through non-parametric randomization tests in which the protein behavior profile is shuffled and the mutual information is calculated between this shuffled profile and the motif profile. This procedure is repeated 10,000 times by default and a motif is deemed significant if its mutual information with the motif profile is greater than all 10,000 randomized information values. Biological significance of motifs is explored by analyzing the set of proteins containing the motif and the positions of predicted motif instances to determine GO enrichment, overlap with protein domains, and possible motif-motif interactions. A detailed description of the FIRE-pro framework can be found in S. cerevisiae and S. pombe, including discrete sub-cellular localization and protein-protein interaction data, as well as quantitative protein half-life and abundance data. Many of these analyses would have been difficult to carry out in existing protein motif discovery frameworks due to the large number of proteins being analyzed or the multi-class or quantitative nature of the data.We used FIRE-pro to discover motifs involved in a broad range of biological processes and functions. To this end, we compiled and analyzed >600 experimental protein datasets from Our analyses revealed a total of ∼6,900 protein motifs with an average of 11 motifs per protein dataset  co-occur with a domain. Of those motifs, 1531 or 22% of all motifs, have positive domain overlap scores and can be considered to be domain signatures . As an eAcross all profiles, 15% of discovered motifs are associated with a domain but lie near the domain rather than in the domain itself. This includes the cyclin-dependent kinase substrate “SP.[RK]” and the motif “V..[TSP]P”, which are consistently located near kinase domains. For example, in 85% (11/13) of Cdc28-interacting proteins containing a protein kinase domain and the “SP.[RK]” motif, the motif lies nearby rather than within the domain. This type of motif may impart a functional site to proteins with a common domain and may regulate domain function and specificity, perhaps by mediating interactions with other proteins.Many motifs, especially localization signals, tend to be positioned at the N- or C- termini of proteins FIRE-pro also determines motif pairs that co-occur within the same proteins and co-localize in the primary protein sequences . BrieflyOverall, our protein motif dataset contains ∼1,500 interacting motif pairs involving ∼2,000 individual motifs, indicating that ∼25% of the motifs are involved in motif-motif interactions. While some interactions represent neighboring domain signatures, others may mediate co-regulation of protein binding or post-translational modification. One example of potential co-regulation is a cluster of three co-localizing motifs found in proteins that interact with the ribosomal subunit protein Rps17b. Of the 63 proteins that contain the motifs “AR..[AR]”, “K.[RAK]A”, or “G[KMI]K[VAG]”, over half contain at least two of the three motifs. This observation suggests that interaction with Rps17B is mediated by several, possibly redundant protein motifs; alternatively it may indicate that additional proteins cooperate with Rps17b to regulate its targets.S. cerevisiaeMany protein motif analyses involve comparing two classes of proteins, e.g. CDC28-interacting proteins vs proteins that do not interact with CDC28. However, many protein behaviors involve more than two protein groups. This is the case for protein localization, where proteins can be localized to many distinct compartments, e.g. nucleus, ER, Golgi, cytoplasm, membrane and mitochondria. While each of these behaviors can in principle be analyzed independently, analyzing them simultaneously can be useful because the same protein motif can be associated with multiple protein groups and thus weak but consistent enrichment across multiple groups would result in higher and more significantly informative motifs. Due to its use of mutual information, the FIRE-pro framework can naturally process multiple protein groups simultaneously. Moreover, the combined heatmaps show motif over- or under-representation across all groups provides easier interpretation of protein motif function. As part of our global analysis, we applied FIRE-pro to a sub-cellular localization dataset obtained from ∼4,000 GFP-tagged proteins in Application of FIRE-pro to the resulting multi-class localization partition revealed sixteen motifs . The mosAn advantage of FIRE-pro over existing methods is its ability to discover motifs associated with quantitative protein measurements. This feature stems from the capacity of FIRE-pro to find motifs that are informative of multiple protein groups, as quantitative protein measurements are first discretized  prior to estimating the mutual information. The discretization process used in FIRE-pro is the same as the one used in FIRE It has previously been shown that functional instances of protein motifs tend to lie in intrinsically disordered regions of protein sequence To benchmark FIRE-pro's performance, we carried out a comparative study of existing methods Motif-x  on five A critical feature of our approach is that it returns very few motifs when given randomized input. To illustrate this, we randomly shuffled the protein behavior profiles of the five datasets mentioned above and applied FIRE-pro to the shuffled data with the same parameters as the original run. The number of motifs found per randomized dataset ranged from 0–3 with an average of 1.2 motifs as compared to a range of 3–17 and an average of 9 motifs per real dataset . Thus weAs the amount of available proteomic and genomic data expands, biologists increasingly rely on computational methods to extract key features and general principles from the data. We have therefore designed an approach to protein motif discovery that is capable of harnessing the information found in large-scale datasets, such as protein abundance, gene expression, localization, and post-translational modification. FIRE-pro facilitates the discovery of short sequence motifs informative of the global behavior of proteins. The use of mutual information provides a universal framework that can be applied to any type of biological data, be it discrete or quantitative, and the algorithm can be applied to proteome-scale data from any organism, including humans. The algorithm can simultaneously discover over- and under-represented motifs and has no requirement for an explicit background model. Given the increasingly quantitative nature of proteomics experiments, we believe that FIRE-pro is a valuable tool capable of revealing sequence elements that determine diverse protein behavior.The systematic application of our approach to a set of ∼650 proteomic datasets revealed several novel insights into post-translational regulatory networks. We discovered that many of the strongest motifs tend to be under-represented in specific groups of proteins just as they are over-represented in coherent groups of proteins in which the motif is thought to play a functional role. Context-dependent avoidance of specific motifs may represent a crucial constraint for the evolution of protein sequences and be an important parameter in successful design of custom proteins. It was also intriguing to discover a number of potential phosphorylation motifs informative of protein-protein interactions. While these motifs need to be further characterized and experimentally tested, the abundance of known and putative phosphorylation sites is not surprising as eukaryotic genomes contain hundreds of kinases that exert a profound influence on cellular activity. However, relatively little is known about substrate specificity for most of these kinases, and we anticipate that our framework and results will shed light on the structure of phosphorylation networks. Our study also underscores the fact that functional motifs tend to have a variety of non-random features including gene functional enrichment, position biases in linear sequence, relationships with protein domains, co-occurrence with other motifs, and associations with regions of protein. The comprehensive understanding of these features is important because it provides information regarding some of the mechanisms underlying post-translational regulation. A natural extension of our work is the systematic integration of these distinct features, e.g., using probabilistic weighting, in order to enable the recognition of functional protein motif instances and to facilitate the prediction of post-translational regulation directly from primary protein sequences.http://tavazoielab.princeton.edu/FIRE-pro. We expect that application of FIRE-pro to human protein data will lead to new insights about how mutations in protein regulatory motifs disrupt protein function and ultimately contribute to human disease.In summary, FIRE-pro is an approach to protein motif-finding suited for the proteomic era. Rather than finding motifs over-represented in a set of proteins relative to a background set, the algorithm seeks to discover motifs informative of measurements or behaviors associated with each protein. In addition to presenting an approach for motif-finding in large-scale data, we have presented a number of examples of known and novel predictions of protein motifs uncovered by FIRE-pro. In the future, such information could form the basis for a library of protein motifs to be used in synthetic biology, i.e., to engineer protein behaviors such as half-life, localization, and interaction partners. It is our hope that computational tools such as FIRE-pro will help advance the body of biological and biomedical knowledge and perhaps yield new organizing principles about post-translational regulation of protein function. To this end, the source code, datasets, and results are freely available at Text S1Supplementary methods and text(0.24 MB PDF)Click here for additional data file.Figure S1Mitochondrial localization motifs. (A) P-value heatmap of motifs enriched in mitochondrial-localized proteins. Columns correspond to classes of proteins and rows correspond to predicted motifs. The yellow/blue color-map indicates over/under-representation of a motif in a given group. (B) Position bias of a mitochondrial motif corresponding to the "RxxS" consensus sequence for the N-terminal mitochondrial signal peptide cleavage site. A histogram of normalized motif positions in mitochondrial proteins ("Enriched") reveals that the motif is highly enriched in the N-terminus relative to non-mitochondrial proteins ("Other").(0.08 MB PDF)Click here for additional data file.Figure S2S. cerevisiae  was clustered into six distinct localization patterns: "nucleus", "mitochondria", "cytoplasm", "nucleus & cytoplasm", "ER", and "cell periphery & ambiguous". Each row represents a protein, with each column representing a particular sub-cellular localization. Cluster 2 ("cell periphery & ambiguous") includes proteins localized to a variety of organelles such as the nuclear pore, vacuoles, and microtubules. (B) iPAGE enrichment analysis of clustered localization data. Columns correspond to clusters in panel A and to columns in Protein localization profile. (A) Sub-cellular localization data from (0.10 MB PDF)Click here for additional data file.Figure S3Enrichment analysis table for motifs associated with sub-cellular localization see . For eac(0.09 MB PDF)Click here for additional data file.Figure S4S. cerevisiae. The half-life values for ∼3,750 yeast proteins were sorted and binned into ten equally populated classes, with the shortest half-life proteins comprising the left-most columns and longest half-life proteins comprising the right most columns. The columns represent protein behavior classes and correspond to those in GO analysis of protein half-life and enrichment analysis of half-life motifs. (A) iPAGE analysis of quantitative half-life data in (0.10 MB PDF)Click here for additional data file.Figure S5S. cerevisiae. Protein abundance measurements from ∼3,800 TAP-tagged yeast proteins  were binned into ten classes, with low-abundance proteins on the left and high-abundance proteins on the right, and analyzed with iPAGE. Columns represent protein behavior classes and correspond to the columns in GO analysis of quantitative protein abundance data in (0.06 MB PDF)Click here for additional data file.Figure S6S. cerevisiae. (A) Protein abundance measurements from ∼3,800 TAP-tagged yeast proteins  were binned into ten classes and analyzed with FIRE-pro. Low-abundance proteins (left columns) were enriched for cytoskeletal proteins, DNA-binding proteins, and kinases, whereas high abundance proteins (right columns) were enriched for house-keeping proteins such as those involved in maintenance of localization, proteasome complexes, and ribosomes. FIRE-pro finds seven protein motifs informative of low protein abundance and one motif informative of high protein abundance. Similar to half-life results Click here for additional data file.Figure S7GO analysis of clustered PPI network profile. iPAGE analysis of clustered protein-protein interaction data from BioGRID. Clusters include groups enriched for biological processes such as RNA processing and protein sumoylation, cellular components such as nucleoplasm and proteosomal complex, and molecular functions such as transferase activity. The columns represent protein clusters and correspond to the columns in (0.06 MB PDF)Click here for additional data file.Figure S8Multiclass analysis of clustered protein interaction network. (A) Protein-protein interaction data from BioGRID was clustered using the MCL graph-clustering algorithm and cluster indices were used as input to FIRE-pro. Ten motifs are found in the protein interaction clusters compared to zero motifs found in a control analysis of genetic-interaction cluster data. A log p-value matrix shows a number of known and unknown motifs involved in various modules of the network. (B) Enrichment analysis of all proteins containing each motif.(0.09 MB PDF)Click here for additional data file.Figure S9S. cerevisiae proteome were determined by DisEMBL, putative instances of motifs or k-mers were identified, and the disorder score was defined as the percentage of motif instances across the entire proteome that lie in disordered regions. FIRE-pro motifs are found more frequently in regions of protein disorder than all 3-mers or 4-mers .Protein disorder analysis. Distributions of disorder scores for all 3-mers, 4-mers, and FIRE-pro motifs. Disordered regions of the (0.10 MB PDF)Click here for additional data file.Table S1Phosphorylation motifs found amongst kinase and phosphatase interactors(0.05 MB PDF)Click here for additional data file.Table S2Domain signature motifs(0.07 MB PDF)Click here for additional data file.Table S3Categorization into known, semi-novel, and novel motifs(0.06 MB PDF)Click here for additional data file.Table S4Motif-discovery algorithms used in comparison(0.05 MB PDF)Click here for additional data file.Table S5Summary of data sets used in algorithm comparison(0.04 MB PDF)Click here for additional data file.Table S6Results of algorithm comparison(0.06 MB PDF)Click here for additional data file.Data S1A catalogue of all motifs discovered by FIRE-pro(1.65 MB XLS)Click here for additional data file.


Structural studies are increasingly providing huge amounts of information on multi-protein assemblies. Although a complete understanding of cellular processes will be dependent on an explicit characterization of the intermolecular interactions that underlie these assemblies and mediate molecular recognition, these are not well described by standard representations.Here we present PICCOLO, a comprehensive relational database capturing the details of structurally characterized protein-protein interactions. Interactions are described at the level of interacting pairs of atoms, residues and polypeptide chains, with the physico-chemical nature of the interactions being characterized. Distance and angle terms are used to distinguish 12 different interaction types, including van der Waals contacts, hydrogen bonds and hydrophobic contacts. The explicit aim of PICCOLO is to underpin large-scale analyses of the properties of protein-protein interfaces. This is exemplified by an analysis of residue propensity and interface contact preferences derived from a much larger data set than previously reported. However, PICCOLO also supports detailed inspection of particular systems of interest.http://www-cryst.bioc.cam.ac.uk/piccolo.The current PICCOLO database comprises more than 260 million interacting atom pairs from 38,202 protein complexes. A web interface for the database is available at  Genomics provides the parts list for understanding cellular processes. However, as 70% of eukaryotic genes work through multi-protein systems , it is oStructural characterization yields the most information of any experimental method, yet the details of intermolecular interactions are not described explicitly in standard representations. A range of experimental and computational techniques has been used to study protein-protein interactions, each of which provides information of a different nature, resolution and quality. Computational methods can be broadly divided into methods that identify interaction partners, those that predict interaction surfaces and those that predict the structure of the complex ,5.A large number of databases recording structural aspects of protein-protein interactions has been described and will be reviewed briefly here. These resources vary considerably with respect to their scope, coverage, interface definition, granularity of interface description, consideration of quaternary structure, frequency of updates and availability. Some databases consider protein-protein interactions in the form they are deposited in the PDB  be comprehensive, ii) have an accurate and robust interface definition, iii) describe interfaces at atomic resolution, and iv) have the capacity to remove redundancy in an appropriate manner.PICCOLO interfaces were identified using a two-stage algorithm. An initial radial cut-off search identified atoms on different polypeptide chains that are in close proximity. A second step uses a library of residue-dependent atomic radii and a set of molecular interaction expressions comprising distance and angle criteria to flag each putative interaction from the first stage as being engaged in any of a range of defined molecular interactions. This process provides a more specific set of intermolecular atom-atom interactions than the radial cutoff method alone. Solvent accessibility calculations are then performed, allowing interface and non-interface residues to be annotated as being either buried or exposed. This enables the classification of all residues in a protein complex into one of four structural environments, depending upon whether or not they engage in interactions and their degree of solvent accessibility; residues engaging in intermolecular contacts are classed as "interface core" or "interface periphery" and all other residues simply as "core" or "exposed" as appropriate.i.e. patterns of relative abundance of each residue type within interfaces, and residue contact preference, i.e. what residue-residue interactions are observed to occur more or less frequently that would be expected by chance alone.The PDB is highly redundant. To prevent any subsequent analysis being heavily biased, a novel pairwise clustering scheme was devised in order to generate a non-redundant set of interfaces. The compilation of a comprehensive and non-redundant set of structurally characterized interfaces enables a range of unbiased analyses of the fundamental properties of protein interfaces to be performed. Here we describe analysis of residue propensity, PICCOLO and its sister databases require comprehensive, up-to-date reference information regarding all solved structures currently available in the PDB. This information is housed centrally in a shared hierarchical PDB database schema, capturing annotations at the level of deposited structures, macromolecular chains and individual residues (including residue-level mapping of each PDB polypeptide to its cognate UniProt record) with automatic updates synchronized with weekly updates from the wwPDB.Although the data found in PDB files are clearly invaluable, high-throughput processing of every structure in the repository can be hampered by the inherently heterogeneous and inconsistent nature of certain aspects of the data. A small minority of troublesome structures often require an incommensurate degree of attention to negotiate them successfully. Many of the problems stem from the fact that the PDB is not a relational database . The sitThe atomic coordinates deposited in PDB files solved by X-ray crystallography reflect the contents of the ASU. The ASU is the minimal set of atoms which, when operated on by the crystallographic symmetry operations defined by the space group, generates the unit cell. In biological systems the space group symmetry operations are typically rotations and translations. As such, although the ASU can represent the biologically functional assembly of the protein, often it comprises multiple biological molecules or even a portion of a biological molecule. Proteins crystallize in highly non-physiological environments, at low temperatures, at artificial protein concentrations and in the presence of organic solvents and crystallization buffers, which can lead to the formation of extensive non-specific crystal packing interfaces. This has important implications for interface characterization when using ASU data. The presence of non-specific crystal contacts introduces false positive interactions. Conversely, where the ASU comprises a subset of the biologically functional oligomer, some genuine interactions will be absent, thereby introducing false negatives.In order to circumvent these issues two versions or flavours of the PICCOLO database were built. The first flavour is derived from PDB files as they are provided by the wwPDB that for X-ray structures will represent the ASU. The ASU reflects the choice of the crystallographer in selecting a basic structural unit from which to build the crystal structure. The second flavour considers assemblies generated by the EBI's PISA resource , which aTo generate the PISA flavour XML files containing all data pertinent to the predicted assemblies were downloaded from the PISA website, parsed and loaded into a relational database. As of June 2011 (using PISA software version 1.20) PISA comprised 164,359 assemblies in 147,439 assembly sets. Assembly sets may include more than one assembly in cases where multiple biological units are found in the ASU e.g. PDB 1c3h consists of two distinct homotrimers. The PISA procedure may identify multiple assembly sets for each PDB entry. Only the assembly set predicted to be the most stable was considered further, leaving 49,829 assemblies in 41,811 assembly sets . Often assemblies in the top-ranked set are not confidently predicted to be stable. 41,146 assemblies are labelled as "stable in solution" and this set was considered further - the remaining assemblies of lower levels of predicted stability were discarded. The relevant transformations were applied to the coordinates of the ASU of each structure. Prior to transformation any water molecules within 5Å of each polypeptide chain have their chain identifier set to that chain. A mapping is maintained between the polypeptide chain identifiers in the original ASU PDB files and the newly generated PISA-predicted assemblies. There may be more than one biomolecule for each PDB entry.n chains n(n - 1)/2 comparisons are performed i.e. for a PDB entry with four chains A, B, C and D, six comparisons are performed . Note that the chain pairs are always ordered alphanumerically, preventing duplication of pairwise contacts. For each atom in the first chain of each chain pair all atoms within a fixed search radius are identified. If any of these atoms belong to the second chain, the pair is flagged as a potential inter-chain contact, the details of the two atoms are logged and the inter-atomic distance is measured. A default radius value of 6.05 Å is used, the value chosen as the maximum length of a water-mediated hydrogen bond [kd-tree family of algorithms use efficient hierarchical space-partitioning data structures for recursively organizing points in a k-dimensional space. This gain in efficiency means that PICCOLO can be run over the entire PDB overnight on a Linux workstation of modest specification.To generate PICCOLO all PDB entries containing more than one polypeptide chain are identified. For each of these entries every unique pair of non-identical chains is examined. Therefore, for gen bond . Neighbogen bond . The kd-http://bioinfo.mbb.yale.edu/geometry/geom-mbg/data/README.htm). This set of radii has previously been used to calculate protein volumes [http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html) to structures of the 20 canonical residues, followed by manual inspection. Each of the 20 canonical residue types also has an extra negatively ionizable OXT atom defined, to include the acidic carboxyl group when the residue is chain terminating.Definition of contacts using radial cut-offs is a commonly used approach. However, the method is considered to be sensitive but not specific, in as much as many atoms within 6.05Å of one another are unlikely to be engaged in a direct energetically significant interaction. To resolve this issue, based upon the chemical nature of the pair of atoms and the distance between them, each of the potential inter-atomic contacts is classified into a series of specific interaction types. These interaction types are listed in Table  volumes . Flags iVan der Waals contacts, the most common type of interaction, are assigned as those pairs of atoms whose interatomic distance is less than the sum of the van der Waals radii plus 0.5Å ,25. No rUnlike all other interaction types, hydrogen bonds and water-mediated hydrogen bonds are identified by an external program, HBPLUS . The algOnly in a minority of very high-resolution (< 1.0Å) crystal structures can hydrogen atoms be resolved accurately. Typically, little or no difference can be determined between carbon, nitrogen and oxygen atoms. For structures solved at resolutions greater than 1.0Å, atoms in the majority of side-chains can be uniquely identified from the electron density map, but for asparagine, glutamine and histidine, whose side-chains appear symmetrical in the electron density, certain atoms can only be identified on the basis of their local structural context and in particular their hydrogen bonds. To resolve this issue HBPLUS implements an option (-x) to explore potential hydrogen bonds that would be formed if the CD2 of histidine were actually ND1, CE1 was NE2 and the nitrogens and oxygens of the asparagine and glutamine amide groups were exchanged. Note that some atoms are capable of acting as either hydrogen-bond donors or acceptors depending on the details of their local structural context .Water molecules are present in 80% of PDB structures. Although relatively rare in intra-molecular interactions, water-mediated hydrogen bonds make a significant contribution to inter-molecular interactions. Water can mediate between two hydrogen bond donors, between two acceptors or from a donor to an acceptor. One difficulty in identifying water-mediated contacts is that in many lower-resolution crystal structures water molecules may be inappropriately modelled into patches of electron density or added during refinement to improve the calculated structure factors. In this work "genuine" structured waters were distinguished by considering only those water molecules that engage in more than one hydrogen bond. Conveniently, any water molecule suggested by HBPLUS to be hydrogen bonded to two residues on different chains, by definition already meets this definition. Hydrogen bonds and water-mediated hydrogen bonds are further sub-classified as being between either two main-chain atoms, two side-chain atoms or between main-chain and side-chain.Hydrophobic interactions are those where both atoms are labelled as hydrophobic and the inter-atomic distance is less than 5Å . With reAromatic interactions are defined when two criteria are met. When a pair of aromatic atoms is within the appropriate distance threshold then the centroids of the two parent planar ring systems are calculated. If the centroids are also within the distance threshold, then the contact is considered aromatic. In generating PICCOLO's sister database, CREDO , a proceπ-cation interactions are defined when a cationic atom and an aromatic atom approach within 6.0Å threshold of one another . DisulphEven though these interaction definitions are not rigorous, they are each precedented, robust and rapid to calculate. Note that an exclusive classification of inter-atomic interactions would require artificial prioritization of one interaction type above another. In this work, interactions are classified equivocally so each atom pair can simultaneously exhibit the character of more than one interaction type. Each atom-pair can therefore be thought of as being represented as a binary interaction fingerprint. This results in overlaps between for example, van der Waals contacts and shorter hydrogen bonds, hydrogen bonds and shorter ionic interactions and hydrophobic and aromatic interactions. Such deliberate ambiguity arguably reflects the somewhat amorphous nature of molecular interactions. All atom pairs within the original 6.05Å distance threshold of one another are only considered as being in contact with one another if one of the above criteria are met . Atom pairs not meeting any of these criteria are still stored in PICCOLO as being proximal to one another, but are in general not considered in any further analyses.2, can be calculated from the atomic coordinates by the program NACCESS [Whilst solvent accessibility can be used to identify residues engaged in interactions, here we use solvent accessibility to annotate residues already identified by the interaction fingerprint radial cut-off method described above. The solvent accessible surface area (ASA) of a protein molecule, measured in Å NACCESS  implemen NACCESS . To calcEquation 1:X relative to that observed in an Alanine-X-Alanine tripeptide. Absolute ASA data are calculated and stored in PICCOLO for each pairwise interaction at the level of individual amino acid residues and complete polypeptide chains. Relative accessibilities are stored at the level of individual residues. Accessibility data are stored for all residues in protein complexes - not just those residues mediating interactions.Relative accessibilities can also be calculated by expressing the accessible surface of each residue i × Rj ≤ 25). No chain length filtering criteria were applied prior to generation of PICCOLO. This was a deliberate choice; interactions of proteins with small peptides are of interest for small molecule drug design and when considering the effects of mutations on protein function. However, for the purposes of systematically deriving properties of protein interfaces, our primary interest was the interaction surfaces of globular proteins. Small peptidic polypeptide chains of less than 15 valid amino acid residues were therefore removed for this analysis. Collectively these filters remove 28,152 interfaces or 21.6% of the original 130,336 interfaces (data corresponds to an earlier version of PICCOLO).The PDB is inherently redundant, with the same protein often solved multiple times under different experimental conditions, with different ligands, in different conformations and so forth, and the same is true of protein-protein complexes. Any analysis of interface properties that ignores such biases is likely to be skewed by over-represented systems. Therefore, before interface properties are analysed the data sets were filtered and clustered to provide a reliable non-redundant set. The procedure of applying PISA-derived rotation-translation matrices to generate biological assemblies removes artefactual non-specific crystal packing interfaces. Despite this a small number of insignificant interfaces remain in the PISA-derived assemblies. These typically comprise only a handful of residues, and manual examination reveals they are almost exclusively due to peripheral contacts of non-neighbouring chains in high order multiprotein systems. These were filtered out by removing those interfaces where the product of the number of residues from each side was less than or equal to 25 :To deal with these issues the following clustering procedure was devised. All interfaces in PICCOLO are first grouped by the unique ordered combination of UniProt identifiEquation 2:M is the proportion of missing residues. This score is based on the score used by Chandonia et al. in deriving the ASTRAL compendium [where mpendium ,52. NoteiB) is defined, independently of structural environment, as follows for each residue type i:Previous studies on residue propensities in protein-protein interfaces have drawn somewhat contradictory conclusions -57. HoweEquation 3:i Fis the count of each residues type calculated using all residues found in PICCOLO structures, not just interface residues. The environment-dependent residue frequency (eiE) is defined as follows:where Equation 4:ei Fis the count of each residues type i in each structural environment e. The normalized environment-dependent propensity (eiR) is then the ratio of the environment-dependent frequency (eiE) to the background frequency (iB):where Equation 5:ijP) can be derived for the PICCOLO-derived non-redundant set:The frequency of pairwise residue interactions  reflect the amino acid composition of each residue type i is be defined as:where Equation 7:i Urepresents the number of residues engaged in contacts. If interfacial amino acid residues exhibit no preference as to which residues they contact across the interface, the expected frequency of any particular residue-pair interaction would be simply the product of the two individual residue frequencies (i × WjW). Any such interaction preference can be quantified by calculating the log odds ratio of the observed interaction frequency to the expected interaction frequency:where Equation 8:L, is defined in Equation 8 above, but with i Wreplaced as follows:This measure is commonly used  but it dEquation 9:A summary of the number of data points in both flavours of PICCOLO is shown in Table The non-redundant set used to generate the residue propensities and contact preferences was derived from an earlier version of PICCOLO and comprises 14,658 interfaces.Figure For the majority of residues the propensity for the interface core and periphery is intermediate between that of the protein core and the exposed surface, with the propensity pattern for the interface periphery being most similar to that of the exposed protein surface and that of the interface core most similar to that of the protein core. The exceptions to this scheme are methionine, glycine, alanine, histidine, tryptophan, tyrosine and arginine. Of these, alanine and glycine, the two smallest residues, are disfavoured at the interface periphery. Histidine and arginine, two positively charged residues, are favoured at the periphery - in fact this is the structural environment in which these residues are most enriched. Arginine is capable of multiple types of favourable interactions: it can simultaneously form up to five hydrogen bonds and an ionic salt-bridge with the positive charge carried on its guanidinium motif. Tryptophan, tyrosine and methionine, three large, hydrophobic residues that can engage in a range of interactions, are all favoured at the interface core, corresponding with the observations of Ofran and Rost . The enrA series of matrices used in the derivation of the contact preference matrix are shown in additional files PICCOLO explicitly describes protein molecular interactions that are not captured in standard protein structural data representations. We believe that PICCOLO has several key features not present in any single previously published resource. These include: i) the fundamental nature of protein-protein interactions is described at maximal resolution; ii) the detailed molecular interaction terms help provide a more specific interface definition than is possible using standard radial cutoff approaches as well as providing a richer annotation of the observed interactions and iii) comprehensive coverage of structurally characterised protein complexes (both ASU and quaternary assemblies) with automated monthly updates ensures maximal data availability. These features make PICCOLO a valuable resource for researchers interested in individual systems or general properties of interfaces.Here we have exemplified the value of PICCOLO as a platform for probing properties of protein-protein interfaces, by performing an analysis of residue propensity and residue contact preference. Aside from its value in aiding the understanding of the principles underlying molecular recognition, the residue contact preference has potential application as a source of restraints for protein-protein docking scoring functions.i) the likely impact of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on molecular interactions [ii) the degree to which protein-protein interactions contribute to the determinants of evolution of protein families [iii) the nature of interactions that small molecule inhibitors of protein-protein interactions engage [PICCOLO has also been used to pursue a variety of other questions. These include ractions ,62, ii) families  and iii)s engage . Indeed With large and complex data sets of this nature visualization tools to aid analysis are of tremendous value. PyMOL (Delano (2002)) is an open-source molecular visualization system that extends, and is extensible by the Python programming language (Van Rossum 2003)). This enables Python functions to be written that connect to the MySQL database and extract annotations from PICCOLO describing the atoms and residues that are involved in interactions, and for these to be highlighted in the PyMOL window. Furthermore, the interatomic interactions themselves can be visualized by using different colour and dash parameters to indicate different interaction types. Figure ). This eWhile PICCOLO focuses on protein-protein interactions, parallel sister databases dealing with protein interactions with other classes of molecules have been developed within the group. BIPA  concernshttp://www-cryst.bioc.cam.ac.uk/piccolo. The database comprises detailed descriptions of protein-protein interfaces at various levels of granularity for all structurally characterized complexes deposited in the PDB. Automated database updates are performed every month. In building PICCOLO our focus was on providing a resource to enable large-scale analysis of global properties of protein interfaces. To this end the entire database has been made available in the form of a MySQL dump file. However, some users may find it useful to use PICCOLO to help analyze individual systems. For example, PICCOLO could be used to identify hot-spot residues or candidates for mutagenesis based on the number and nature of intermolecular contacts. Similarly, these same properties could be used to assess determinants for evolutionary conservation. As such, a simple query interface has been provided, implemented in the popular scripting language PHP. The interface enables individual complexes to be retrieved and the details of the intermolecular interactions, described at the level of atoms, residues or polypeptide chains, to be downloaded in a range of formats.PICCOLO is available through a simple web interface at the following URL The PICCOLO database uniquely captures the details of structurally characterized protein-protein interactions at atomic level. Neither the recent efforts at achieving a systems level understanding of cellular processes, nor component-by-component reductionist approaches, offers a complete understanding of cellular processes in isolation. Rather, the reciprocal synthesis of the complementary "top-down" and "bottom-up" views of biology offers the best hope of providing true insight. Such integration requires comprehensive data describing the fundamental details of each component and its interactions. We hope PICCOLO can be useful contribution to this end.ASA: Accessible Surface Area; ASU: Asymmetric Unit; nsSNP: non-synonymous Single Nucleotide Polymorphisms; NMR: Nuclear Magnetic Resonance; PDB: Protein Data Bank; PQS: Protein Quaternary Structure; PICCOLO: Protein Interaction Collection Online; PISA: Protein Interfaces, Surfaces and Assemblies.GRJB developed the PICCOLO software, database and front end and drafted the manuscript. AH contributed with the automated update of the resource. TLB participated in the design and coordination of the database. All authors helped to draft and have approved the final manuscript.GRJB received his Masters Degree in Molecular and Cellular Biochemistry from Balliol College, University of Oxford in 1999. He subsequently gained an MSc in Molecular Modelling and Bioinformatics from Birkbeck College, University of London before joining Inpharmatica Ltd, applying structural bioinformatics to drug discovery. After five years at Inpharmatica Richard left to undertake a PhD at the Department of Biochemistry, University of Cambridge. He is currently a postdoc in the Medicinal Informatics group of Professor Andrew Hopkins at the University of Dundee.AH received her degree in Chemistry from Barcelona University, Spain, in 1999 and her Masters in Computational Chemistry from Bologna University, Italy, in 2000. Until 2008 she worked as a molecular modeller in De Novo Pharmaceuticals and later in UCB Celltech. Since 2007 she has been carrying out PhD studies in protein-protein interactions as drug targets in the Department of Biochemistry, University of Cambridge, UK.TLB is in Biochemistry, University of Cambridge. Until 2009 he was Sir William Dunn Professor of Biochemistry and Head of Biological Sciences in Cambridge. His research is focused on structural biology and bioinformatics and their applications to drug discovery and medicine. Most of his work has been on multi-component protein assemblies that mediate cell regulation and relevant to cancer. Tom Blundell was member of advisory group to the Prime Minister (ACOST) in 1980s; founding Chief Executive (1994-1996), and Chair (2009 to 2013), Biotechnology and Biological Sciences Research Council; Chairman, Royal Commission on Environmental Pollution (1998 to 2005): President of UK Biosciences Federation 2001 to 2005 and Biochemical Society from 2008 to 2011. He was Non-Executive Director of Celltech (1996 to 2005), science advisor with Pfizer, UCB and SKB and co-founder Astex Therapeutics (1999) with oncology drugs in clinical trials.Atomic properties for each residue used in generation of PICCOLO interaction fingerprints.Click here for fileResidue matrices used to derive the contact preference matrix (Figure x Figure . The matx Figure , expectex Figure  and pairx Figure .Click here for file


Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area. The main objective of mass spectrometry-based proteomics is to provide a molecular snapshot of the form , abundance level, and functional aspects  of each protein in a biological sample -3. Amongin silico spectra [peptide-spectrum matches (PSMs). Such a database search, depending on the parameters of the search and the MS/MS platform, can result in a large number of PSMs that are assigned scores indicating the confidence level of correct identification of the respective peptide. The next step is to assemble a list of identified proteins from all, or a subset of, PSMs and provide statistical confidence levels for each protein.In a standard computational pipeline, MS/MS spectra from a mass spectrometer are searched against spectral libraries -8 and/or spectra -14 correProtein identification is a special case of label-free protein quantification because, in an ideal scenario, each protein with a correctly inferred non-zero quantity (abundance) would be considered identified. However, label-free quantification has not yet reached the accuracy needed for the wide dynamic range of quantities observed in cellular or extracellular proteomes . In addipeptide detectability [degenerate or shared peptides [unique peptides, i.e. peptides that can be mapped to only one protein. (4) It is non-trivial to estimate the false discovery rates (FDRs) of identified peptides and proteins. Some approaches to estimating peptide-level FDRs involve construction of decoy databases or use unsupervised estimation of class-conditional distributions . However, a large number of low-scoring PSMs may create difficulties in determining the certainty of both peptide and protein identification. While methods for the estimation of peptide-level FDRs have been well-characterized, computing protein-level FDRs remains an open problem [Obtaining a list of identified proteins from a set of peptide sequences with identification scores may seem straightforward. However, there are several factors that combine to challenge such intuition: 1) Usually only a small number of peptide identifications, mostly unreliable, are available for each protein . This istability , see add Usually peptides ,21. It i problem ,23.protein inference problem [The process of identifying proteins that are present in a biological sample is now widely framed as a statistical inference problem, and has been referred to as the  problem ,21. To d problem ,35-37. W1. Rule-based strategies - methods that rely on a relatively small set of confidently identified (unique) peptides that are subsequently assigned to proteins.2. Combinatorial optimization algorithms - methods that rely on constrained optimization formulations of the protein inference problem resulting, for example, in minimal protein lists that cover some or all confidently identified peptides.3. Probabilistic inference algorithms - methods that formulate the problem probabilistically and assign identification probabilities for each protein in a database.In the following sections, we provide justification for the development of advanced protein inference algorithms and then review the major computational strategies. All combinatorial optimization techniques are presented using a framework of integer programming; on the other hand, probabilistic algorithms are summarized using Bayesian inference principles. Our focus is also on the intuition behind the algorithms, the types of solutions generated, and the strengths and limitations of each method. We believe this information is essential in order to understand commonalities among the algorithms as well as their principal differences. It is also important for the proper interpretation of outputs from the various protein inference tools already applied in bottom-up proteomics.Before discussing algorithmic details, it is important to introduce notation that will be used throughout this paper. Let us consider a set of tandem mass spectra andConfident peptide identifications can be determined in several ways, typically by using strict FDR thresholds on the top-scoring PSMs (per peptide) and are estimated using a decoy database  or toolsj pthat is confidently identified, e.g. using an FDR threshold of 0.01, will result in setting It is important to mention that variables In some situations it will be necessary to consider peptides with explicit designations of their parent proteins. In those cases, the Variable Homo sapiens [Mus musculus [Saccharomyces cerevisiae [Deinococcus radiodurans [D. radiodurans proteins extracted from GenBank (27-Aug-2009), respectively.Our first goal is to investigate the influence of degenerate peptides and to show that their presence is often a major factor contributing to the challenges in protein inference. We analyze several cellular and serum samples and characterize the peptide identification process. The data include cell line and plasma samples from  sapiens , a tissumusculus , as wellrevisiae  and Deiniodurans . The setiodurans  against D. radiodurans data sets contain only 18% and 1% of degenerate peptides, respectively. Figure D. radiodurans. Finally, in Figure D. radiodurans candidate proteins are distinguishable; however, for human and mouse samples, between 30% and 50% of protein groups contain multiple proteins.Figure This analysis provides evidence that protein inference is a non-trivial problem, especially for multicellular eukaryotes that are known to contain large numbers of paralogous proteins. It also emphasizes the importance of developing sophisticated protein inference algorithms.With a typical LC-MS/MS experiment resulting in a potentially large number of protein identifications, concerns were raised regarding the impact of misidentified proteins on biomedical science . In respThere is a good rationale for using the two-peptide rule. In principle, one correct unique peptide should be sufficient to correctly identify a protein. However, even for the low FDR associated with a set of peptides, many individual peptides in a large data set are incorrectly identified. Furthermore, proteins identified by single peptide hits are more likely to be incorrectly identified than proteins with higher peptide coverage . It was However, the two-peptide rule has been challenged ,52. FirsWith the help of protein-level FDR estimation (using a decoy database), better and more complex rules may be devised to achieve even higher sensitivity. For example, Weatherly et al. proposed setting separate score thresholds for proteins with different number of confident peptide identifications . They reDespite the relative simplicity of rule-based approaches, the performance of heuristic rules is fundamentally limited by the lack of rigorous treatment and proper combination of the peptide identification scores and prior knowledge.The input to this class of algorithms typically consists of a set of confidently identified peptides Minimum set cover (MSC) problem: Given a set of confident peptide identifications This protein inference formulation is identical to the classical computer science problem of minimum set cover, where given a set of elements (peptides) s Figure . Using gThe MSC approach has been implemented in the IDPicker software ,55. IDPiThe minimum set cover formulation is one of the most commonly encountered strategies in protein inference, and is recommended by the guidelines for publishing proteomics results ,48. Its c% of the identified peptides, where 0 <c ≤ 1 is a user specified parameter.Although the MSC formulation relies on a set confidently identified peptides, a subset of such peptides are expected to be incorrect identifications. This fact provides motivation for the partial set cover approaches where the goal is to find the minimum protein list that covers at least 100·Minimum partial set cover (MPSC) problem: Given a set of confident peptide identifications c (0 <c ≤ 1), find a protein list c% of identified peptides are assigned to the proteins from where nference .Both the MSC and MPSC problem formulations result in situations where it is not possible to distinguish among proteins identified exclusively by degenerate peptides  are the most detectable peptides in protein P1. On the other hand, these peptides are among the least expected peptides to be observed if protein P2 was in the sample. Thus, protein P1 is more likely to be a correct identification than protein P2. Note that the tie resolution was provided by considering unidentified peptides.To provide intuition for the MMP approach, let us consider the example in Figure uishable ; howeversolution to the protein inference problem in which different peptides from assigned to protein missed if Before formalizing the MMP approach, let us consider a particular iP. Thus, for effective detectability below a certain threshold, no peptides are expected to be observed. The MMP approach can now be formalized as follows.where Minimum missed peptide (MMP) problem: Given a set of confident peptide identifications N(i) is a set of peptides connected to where Alves et al. have shown that the minimum cover set problem can be reduced to the minimum missed peptide formulation . Thus, tAlthough the MMP formulation was the first protein inference technique capable of resolving indistinguishable proteins, it generally shares the limitations of other approaches based on combinatorial optimization techniques. That is, these algorithms do not provide probabilities for identified proteins, unless post-processing statistical models are used .Similarly to the previous classes of algorithms, probabilistic approaches to protein inference generally consist of two steps. First, PSM scores are converted to PSM probabilities using algorithms such as PeptideProphet . After tSeveral classes of probabilistic algorithms have been proposed so far ,61,63-71ProteinProphet is the first and most widely used probabilistic protein inference approach , with imIn order to obtain protein identification probabilities, peptide identification probabilities are needed as input. Here, the difficulty is to obtain one peptide identification probability from typically multiple spectra matched to a peptide. The solution used in ProteinProphet is to simply take the maximum value among the peptide-spectrum matching probabilities for peptide where approach .A key feature of ProteinProphet is that protein probabilities are computed by assuming peptide identifications to be independent pieces of evidence for the presence of protein where ij NSPrequires ijNSP, iterative updating is used until convergence  is the set of proteins that contain peptide ij w= 1 for all unique peptides and that ij w= 0 if peptide j cannot be mapped to protein i, i.e. when ij wand where ij wfor peptides in the NSP adjustment step, i.e. By combining these four steps, with a minor modification to include weights As the first probabilistic inference method for protein identification, ProteinProphet has been very successful and, as part of the Trans-Proteomic Pipeline , remainsP1 and P2 with identified peptides {p1, p2, p3} and {p1, p2, p4}, respectively. Suppose p1 and p2 are reliable identifications, but that p3 and p4 are not, with small identification probabilities. In the seven toy datasets (A-G) in Figure p3 and p4, and computed the protein probability using ProteinProphet. In data sets A and E, when the probabilities of unique peptides are not larger than 0.5, ProteinProphet considers proteins P1 and P2 indistinguishable, and only reports a group probability; in data set B, when probability of peptide p3 is slightly larger than p4 (which has probability 0.5 or less), ProteinProphet considers protein P1 as much more reliable than P2; in data sets C and G, when probability of peptide is (slightly) larger than p3 (which has probability 0.5 or less), ProteinProphet considers protein P2 as much more reliable than P1; in data set D, when the probabilities are both larger than 0.5, ProteinProphet considers both proteins to be reliable; while in data set F, when the probability of peptide p3 is 0.2 or less, ProteinProphet suggests that only protein P2 can be the true protein, despite the significant probability that peptide p4 is a random identification. This non-continuity of the inference results is counterintuitive. Naturally, one would expect the probability of protein P2 (P1) decreases (increases) gradually as the probability of peptide p3 decreases.Because ProteinProphet relies on certain strong assumptions, e.g. the parsimony-driven weighting  (both the identified ones and those that are not) from protein i asThe MSBayesPro model has an important property in that the peptide identifications are conditionally independent given the presence of the parent proteins Figure . This isi qis the abundance of protein iP.where MSBayesPro requires peptide identification likelihood ratios and a set of peptide detectabilities. The former is a required input to the method, and the latter, as required parameters of MSBayesPro, can be provided as an input, or ideally, peptide detectabilities should be estimated via a machine learning model from the same data set on which protein inference is carried out ,61.For peptide identifications, the input to MSBayesPro is the likelihood ratios rm prior ,61. Herestandard peptide detectability effective detectability iq, the quantity of protein iP, is estimated by the maximum likelihood or moment matching approaches. If a (degenerate) peptide j pis shared by multiple proteins, the network structure requires combining detectabilities ij dover all parent proteins of jp. Here, MSBayesPro assumes that Using peptide detectability is an important distinguishing feature of MSBayesPro. Detectability is required to build the conditional distribution tables between the Protein and Peptide layers and subsequently to compute the posterior probabilities for the proteins. However, to use detectability properly it is important to consider the impact from protein quantity (Box 1). Li et al.  proposeda posteriori configuration) is computed as With the Bayesian network model structure and parameters specified, it is in principle easy to exactly compute the joint posterior probability for the proteins, i.e.  general . MSBayesIt is important to note that MSBayesPro also reports estimated protein quantities and the marginal posterior probabilities for peptides, which provide better scores for measuring peptide confidence . Thus, iThe use of peptide detectability is both the strength and a limitation of MSBayesPro. The method requires good detectability predictions in order to achieve good performance . HoweverThe Fido model ,76 uses β is introduced to the model, which is the prior probability for a peptide to be identified from an artificial "noise" node. This addresses the situation where input peptide probabilities are not accurate (e.g. many incorrect peptides are assigned high probability). We believe this is a legitimate remedy for disasters that can happen during the peptide probability estimation. However, parameter β seems to be redundant given that j pto be identified from "noise". The authors indeed observed strong inverse correlation between the optimal values of α and β.There are two major differences in the Bayesian network models used by Fido and MSBayesPro. First, unidentified peptides are ignored in Fido and a sequence-independent parameter is used as a replacement for peptide detectability Figure . Hence, One limitation of the Fido model is that it requires a decoy (randomized) database to find the best values of the parameters  point of view . This woGerster et al.  recentlyOur main goal in this review was to present the challenges, intuition and proposed solutions to the protein inference problem. With increased throughput of proteomics experiments, the tools and approaches presented here will have increasingly more important applications to many problems in biology and biomedical sciences. These applications include inference and verification of gene models, identification of splice forms or post-translatioinally modified sites. Some of these problems can only be addressed using proteomics techniques and, as such, proteomics holds great promise in systems biology, biomarker discovery, diagnostics, prognostics and treatment monitoring.Undoubtedly, there is a need for more sophisticated methodology for protein inference, unbiased performance evaluation of these techniques, as well as stand-alone tools with graphical user interface that will facilitate transition from research environments to practice in biomedical sciences. We conclude this paper by discussing the current issues in evaluating protein inference algorithms and then speculating on the ideal protein inference approaches.Despite the development of computational protein identification methods, objectively evaluating the performance of the methods remains a problem. Two strategies are currently available: the use of standard samples (mixtures of known proteins) and the use of decoy protein sequences to estimate FDR at the protein level. Both approaches have limitations.To date, only a limited number of standard samples -80 contaThe second approach estimates protein-level false discovery rates with the help of decoy databases. Although the approach has been used in several studies ,52, two F nand Dn, and the total number of forward and decoy proteins in the databases be F Nand DN, respectively. Let the protein level FDR in forward database be P FDRand the rate of incorrect protein identifications from the forward and decoy database beThis problem can be addressed by correcting for the bias due to the number of true proteins in the forward database. Let the number of identified forward and decoy proteins be andrespectively. An assumption regarding a decoy database is that the rates of the false protein identifications are identical; hence, Note that there is a correction factor approach  developeWe would like to point out that, for probabilistic protein inference algorithms, theoretical protein FDR values can be computed based on the protein posterior probabilities. However, such theoretical FDR values are only accurate when the reported protein posterior probabilities are accurate. Hence, they need to be evaluated themselves, e.g. against the target/decoy-based empirical FDRs.The second and more serious issue for applying the decoy approach is related to the existence of protein families. In fact, to our knowledge, no solution has yet been proposed. Simply speaking, a randomized database cannot serve as a good decoy for evaluating methods on data sets that contain many degenerate peptide identifications. The reason is that such peptides are typically shared among forward proteins, which could be similar to each other due to biological/annotation reasons, but not with decoy proteins. As a result, a randomized protein database cannot provide indications whether the identifications made among homologous proteins are correct or not. For this reason, a randomized decoy database is expected to underestimate FDRs for eukaryotic samples, which have large number of shared peptides Figure . The proWe emphasize that both standard mixtures and the target/decoy approach for complex samples have their pros and cons in evaluating protein inference algorithms, and they are not mutually exclusive approaches. In fact, standard mixtures can be used to validate the target/decoy approach for protein FDR estimation. It is generally a good idea to use both strategies for a more complete and objective evaluation.Due to the complexity of protein inference, fair evaluation of the proposed methods has been challenging. This is due to two major aspects. First, reliable and objective validation of the protein identification results is itself a challenging problem, as the FDR estimation is still unreliable. In addition, it is not even obvious how to compare models whose outputs are considerably different, e.g. those that provide protein groups and those that resolve ties between all proteins. Second, due to the lack of agreed upon guidelines, avoidable unfair comparisons are sometimes seen in the literature . In otheIn order to address this situation, we tentatively propose the following principles for comparisons of protein inference algorithms. First, whenever possible, the same or equivalent peptide identification scores as input to different programs should be used. Second, effort should be made to provide inputs most appropriate to each algorithm considered. For example, algorithms that take all peptide identifications should be provided all scores, while programs that take only confident identifications should be provided such a subset. Third, at least one standard protein mixture data set should be used and all known proteins (whether they belong to "indistinguishable" protein groups or not) in such data sets should be included in the evaluation of the protein inference methods. This will allow the evaluation of protein inference algorithms on proteins identified without any unique peptides. Finally, and in an ideal scenario, large data sets from complex samples of unknown proteins should also be used to compare different programs; however, we caution that the current decoy database strategy may not provide reliable FDR estimates at the protein level .Despite the amount of published work, the protein inference problem is far from solved. We believe two aspects are crucial to the future approaches. First, the model should be probabilistic and with degenerate peptides treated in principled ways. Second, unidentified peptides should be exploited with peptide detectability incorporated into the model, perhaps adjusted to allow modeling peptide competition at the elution stage in a given sample. Despite the current limitations of peptide detectability predictions, especially for non-tryptic and modified peptides, it is believed that including detectability ,35,69,71Furthermore, we believe that better estimation of peptide/protein quantity might also help protein inference by, for example, improving the quantity adjustment of peptide detectability ,61, and Algorithmic development is equally important for rigorous and yet practical probabilistic inference. Serang et al.  proposedThe authors declare that they have no competing interests.Peptide detectability.Click here for file


Post-translational modifications (PTMs) regulate protein activity, stability and interaction profiles and are critical for cellular functioning. Further regulation is gained through PTM interplay whereby modifications modulate the occurrence of other PTMs or act in combination. Integration of global acetylation, ubiquitination and tyrosine or serine/threonine phosphorylation datasets with protein interaction data identified hundreds of protein complexes that selectively accumulate each PTM, indicating coordinated targeting of specific molecular functions. A second layer of PTM coordination exists in these complexes, mediated by PTM integration (PTMi) spots. PTMi spots represent very dense modification patterns in disordered protein regions and showed an equally high mutation rate as functional protein domains in cancer, inferring equivocal importance for cellular functioning. Systematic PTMi spot identification highlighted more than 300 candidate proteins for combinatorial PTM regulation. This study reveals two global PTM coordination mechanisms and emphasizes dataset integration as requisite in proteomic PTM studies to better predict modification impact on cellular signaling. Normal cellular functioning is maintained by a vast array of macro-molecular machines that control both core and specialised molecular tasks. These machines are in large part multi-subunit protein complexes that undergo regulation at multiple levels, from expression of requisite components to a vast array of post translational modifications (PTMs). PTMs such as phosphorylation, ubiquitination and acetylation currently number up to more than 100,000 in the human proteome yet how, or if, they coordinate remains poorly understood. Here we show two mechanisms of systematic modification coordination that likely combine to provide finer control of protein complex function. Firstly, individual modifications selectively target protein complexes to execute specific molecular functions. Secondly, highly modified subunits of these complexes further accumulate multiple distinct modifications and contain regions of dense modification patterns, termed PTM integration (PTMi) spots. Through multiple PTM inputs, PTMi spots represent key regions for integrating multiple signals within these complexes, allowing finer regulation of protein function. Here we highlight the large extent of coordinated PTM regulation of protein complexes, and hence cellular function. Systematic dataset integration revealed biological insight into PTM mediated cellular regulatory mechanisms and further provides a resource for future hypothesis-driven studies. Normal cellular functioning requires a broad range of rapid responses to both internal and external cues that are largely mediated through multiple proteins acting in coordination to undertake specific molecular tasks. These rapid responses are predominantly mediated through alteration of protein binding partner preferences, stability and activity via regulation by a vast array of post-translational modifications (PTMs).Human PTMs are known to number greater than 400 A further layer of PTM complexity is generated through interplay between modifications on the same protein. This interplay, or crosstalk, can either modulate the occurrence of distinct PTMs at the same or spatially separated sites, or act in concert to generate combinatorial outputs. Directed studies have provided several functional paradigms for PTM interplay in a range of cellular processes from modification of histone tails in epigenetic control Systematic analysis and identification of novel candidates for combinatorial PTM regulation has been predicated by data paucity for multiple signaling PTMs. Identification of phosphorylation sites on both tyrosine (pY) or serine/threonine (pS/T) residues has been aided by the generation of biochemical affinity reagents Two recent studies utilised evolutionary conservation of PTMs to drive systematic investigation into modification co-evolution Dataset S1, for details of collation and dataset characteristics see Dataset S2, for details of collation and dataset characteristics, see All PTM data was collated into a single dataset of 100,391 modifications, distributed across 12,127 proteins ( and S1). The majority of protein complexes were poorly modified for any given PTM and there were only 2 complexes that showed both a high total number and high average level of modification, with one NFAT5 transcriptional regulatory complexes in the ubiquitination dataset and an SRRM1 containing, phospho-serine/threonine enriched, regulatory splicing complex . Interestingly however, two distinct groups of protein complexes can be observed that were separated from the majority distribution . For all four modifications we found complexes that tend towards a large total modification but low median, and those that had fewer modifications but a higher median .In analogy to individual proteins that can be functionally classified by overall modification status Figure S3). The enriched groups of complexes for each PTM showed a high modification state distinct from 99% of data generated through 100 dataset randomisations . Phospho-serine and phospho–threonine complexes were involved in multiple mitotic stages, metabolism and cellular spatial organisation . For example while there were 28 proteins only modified by pS/T in the acetylation enriched complexes, the total number of modified residues  is less than expected from random samples of the whole dataset .In support of the hypothesis that proteins with these regions represent candidates for complex PTM regulation, this scoring metric highlighted canonical examples of regulation through PTM interplay. TP53, POLR2A, multiple histones and RTKs (EGFR) were all present among proteins with regions of high PTM density . We therefore sought to investigate how stringently controlled multiple-PTM integration is within these regions using a permutation analysis. We randomised the PTMs annotated at specific sites across a protein sequence, controlling for both PTM density and type and then calculated the number of different PTMs present in 20AA windows across all proteins in 100 permutations. As can be seen in The highly dense PTM regions showed a similar contribution of each PTM to that present in all PTM modified windows , showing diversity in both density and length (Dataset S3). We therefore sought to operationally define and characterize high density regions with respect to overall protein architecture. Sequential 20AA windows across the entire protein sequence were utilised to ascertain the overlap between PTM dense regions and annotated protein domains (Dataset S3). Highly dense PTM windows were predominantly located outside of classically annotated functional domains . Therefore the dataset was binarised, regions with 0 or 1 mutation were treated as background and windows with 2 or more mutations were taken forward . The fraction of mutated 20AA windows in annotated protein domains was higher in comparison to windows covering either fringe or external regions of domains . The proteins showed diverse PTM density distributions with some dominated by a single-PTMi spot, such as the highly studied C-terminal tail of POLR2A crucial for transcriptional initiation and elongation, while others showed two or more (Dataset S4). PTMi spots provide regulatory focal points within these proteins as sites of single- and multiple-PTM integration and they preferentially accumulate in the highly modified complexes identified here , providing a second layer of PTM coordination within protein complexes.Taken together, identification of canonical examples of PTM interplay, stringent control of multiple-PTM integration, spatial separation from annotated protein domains into regions of protein disorder and the high cancer mutation frequency all support the notion that the highly dense regions of PTMs bear functionally relevant features and can thus be characterized as PTM integration (PTMi) spots. Recently, Beltrao et al. Figure S13 and Dataset S4), suggesting that this mechanism of PTM coordination exists proteome wide.Interestingly, while combining the PTM data with known protein complexes provided insight into two distinct layers of PTM coordination within protein networks, PTMi spot characteristics were also observed throughout the entire PTM dataset . However, the percentage of windows that were modified across STYK density varied only slightly between 20–27% . While the majority of these windows contained few modified residues, the PTM distribution shifted to more dense modification patterns with increasing STYK density . Therefore, as expected, the number of windows containing a PTM density of >0.3 initially increased, with higher STYK densities allowing more dense modification patterns. However, the total number of available windows becomes limiting at higher STYK densities, leading to the observation of a peak of highly modified windows between 0.4–0.5 local STYK density . Disordered windows were favored at high modification states at the STYK density of 0.4–0.5, showing a 2–3 fold increase . We therefore observed that the number of windows at a given STYK density guides the presence of highly modified protein sequence, and that within the optimal STYK density, disordered regions create much more favorable conditions, e.g. the structural flexibility, for multiple modifying enzymes to act.Therefore we sought to further characterize the PTMi spots independently of the protein complex data and initially investigated the influence of local disorder and STYK ratios on PTMi spots proteome wide in more detail. While the fraction of disordered windows increased as the STYK ratio increased, the total number of disordered 20AA windows actually decreased (Dataset S4), with the only characterized SLIM of the remaining three representing a PKB motif. The enrichment for target sites for serine/threonine kinases here is unsurprising given that the majority of single-PTMi spots were phosphorylated on serine or threonine residues (7) possible modification states, thus it is likely that phosphorylation either proceeds in a sequential manner or not all states have distinct functional outcomes Dataset S4) could regulate localization of target proteins. For example, in analogy to Ste5, the ARAF, RAF1, ARHGEF2 and DLC1 proteins are involved in signaling from the outer cell membrane (Disordered protein regions often contain common short (3–10 residues) linear motifs (SLIMs) that can provide mechanistic insight into protein function. We parsed the PTMi spot sequences using the SLIMfinder tool (2–5)PY motif downstream of either a hydrophobic or basic stretch of amino acids Figure S15). Phosphorylation of serine residues in the canonical HNRNPA1 PY-NLS has been previously reported to reduce binding affinity of karyopherins/importins and was also observed in each putative PY-NLS here. Interestingly, the tyrosines in the strictly conserved PY motifs were phosphorylated in the four candidate proteins. These tyrosines have been shown to be crucial for importin binding and make several hydrophobic interactions with karyopherin β Figure S15), suggesting distinct signals may integrate within these PTMi spots to regulate protein localization.The potential for single-PTMi spots to regulate protein localization then led us to investigate the potential for multi-PTMi spots to perform likewise functions. A sub-class of nuclear localization signals (NLSs) have been reported in disordered regions of protein sequences and require a R/H/KXDataset S4). This suggests that these PTMi spots may be hyperphosphorylation switches that mediate stability of many human protein targets.The yeast protein Sic1 provides a functional paradigm for phosphorylation and ubiquitination integration that can be further analyzed here. During the S phase of the cell cycle, lysine residues are ubiquitinated in response to proline directed hyperphosphorylation of adjacent serine residues, ultimately leading to Sic1 proteosomal degradation Besides localization and stability control, the 240 multi-PTMi spots identified proteome wide include the few well characterized examples of multiple PTM interplay in human cells. Functional paradigms provided by intensively studied proteins such as TP53, RNApol II subunit POLR2A and core histones demonstrates the regulatory potential of combinatorial PTMs Large scale proteomic studies have provided high quality information of protein phosphorylation of functional cellular snapshots, different cell compartments, cell types or specific organs. Recently, the development of novel antibodies has enabled the mass spectrometry based, global investigation of lysine acetylation This initial map provided insight into two distinct mechanisms of PTM coordination across protein complexes; firstly, complexes as entireties accumulate modifications through preferential binding profiles suggesting coordination of single PTMs at the protein complex level Figure 1Dataset S2). This suggests that while acetylation will almost certainly impact on molecular function, multiple inputs are likely required for stringent control of NuRD dependent transcriptional events. The prevalence of multiple PTM signals across all identified complexes would support the notion that greater biological insight can be gained through dataset integration, e.g. through prioritisation of highly, multiply modified components in further functional analyses.Similar to our findings, it has been previously reported that protein complexes selectively accumulate acetylation modifications through preferential interaction profiles A second layer of coordination was found through the preferential accumulation of proteins within these complexes that contain PTMi spots, integrating both single and multiple PTMs in short defined sequence spaces. We provide evidence to support the hypothesis that PTMi spots, i.e. dense clusters of modifications in defined amino acid sequence stretches, represent functional entities. We contrast PTMi spots to classically defined protein domains and suggest equal importance in normal cellular functioning Figure 3Two recent reports into global PTM analysis used distinct measures to identify putatively functional PTMs. Beltrao et al. identified and characterized individual PTM “hot spots” present within certain protein domain families The presence of multiple PTM signals in short sequence stretches could provide exquisite sensitivity in PTM signaling, for example through multivalent interactions with other cellular components in vivoThe strengths and weaknesses of this analysis are inherent in the data collation. This integrated modification map significantly advances our understanding of PTM signaling, with the modifications collated here likely representing the more prevalent PTMs as predicted by the number of components encoded in the genome to regulate them. However, not only are these datasets likely incomplete, over 400 modifications impact on protein function within the cell Here we have shown two distinct mechanisms of signal integration exist within human protein complexes; firstly multiple signals are integrated on individual subunits of highly modified PTM complexes, predominantly in low density or spatially separated regions. However, these complexes are enriched for PTMi spots with the capacity to integrate multiple PTMs in short defined sequence spaces. PTMi spots likely represent functionally important regions within proteins on a proteome wide level. These emergent features of the PTM landscape within interaction datasets highlight the requisite for dataset integration in future HTP proteomic studies to gain a better understanding of the context of observed cellular signaling states.Dataset S1). In the case of histone proteins where multiple very similar protein products are annotated across many RefSeqs and gene IDs, the most highly annotated protein in each class was taken forward for further analysis as representative of the protein sub-family. The most highly modified RefSeq annotated sequence for each gene ID was then utilised for further analysis giving a final dataset of 100,391 uniquely mapped 13mers annotated across 12,127 unique proteins (Dataset S1).Data for each PTM was obtained from PhosphositePlus Figure S16A). With the exception of acetylation, the frequency of modification generally increased with increased protein size . Acetylated proteins showed no difference between distinct bins of protein size suggestive of highly stringent modification control. Approximately 59% of proteins are modified by more than one PTM , with multiply modified proteins showing only a small tendency for increased protein length . Protein length is not however directly predictive of either high or multiple PTM modification as equally sized proteins can be poorly modified for one modification but highly modified for another, while smaller proteins can be modified by multiple PTMs .In analogy to protein-protein interaction data each PTM dataset showed an approximate scale free distribution highlighting the presence of modification hubs (Domain annotation was downloaded from HPRD in tab delimited format (version 072010) with only the annotation taken forward for the specific RefSeq identifier utilised in the PTM analysis.Each RefSeq protein sequence annotated for post translational modification was analysed using the Iupred disorder prediction software http://www.sanger.ac.uk/genetics/CGP, v58_150312. Mutations were filtered for only protein coding or frame-shift mutations that would likely affect functional protein domains. These mutations were then mapped to the annotated RefSeq protein sequences with only mutated amino acids annotated in the same position as on the protein sequence taken forward for further analysis giving a high confidence dataset of 182,581 mutations.The data was obtained from the Wellcome Trust Sanger Institute Cancer Genome Project web site, The 1258 genes annotated as tumor suppressor/oncogene were obtained from Memorial Sloan-Kettering Cancer Center CancerGenes dataset Figure S18A). Protein frequency in the dataset shows a similar distribution to binary interaction networks, with most proteins present very few times and a minority showing a high frequency in the dataset . Similar to protein length, while protein frequency in the dataset is not directly predictive of total modifications there is a slight positive correlation .Complexes were downloaded from ConsensusPathDB Random protein complex dataset generation was untaken via PTM annotation permutation performed at the protein level, keeping all modifications across an identifier linked when shuffled. Annotation permutation was performed within 16 individual bins of approximately 200–350 identifiers, each bin contained proteins within the same size and complex dataset frequency quartiles to account for slight correlations observed in the dataset characterisation. 100 annotation randomised datasets were generated before collating all data together and ascertaining the data distribution for the total and median levels of the randomized complexes for each modification . Only identifiers present in complexes that were enriched for the designated PTMs alone were taken forward to the GO analysis.Figures S5, S6, S7, S8, S9), therefore to obtain a value for signal enrichment we calculated the standard Z-score for each of the 16 values obtained for each group of complexes.To observe the origin of the PTM enrichment in protein complexes a Z-Score was used as an approximation of signal enrichment for each dataset. The proteins in each group of complexes was first sub-divided into groups dependent on their number of distinct modifications , and the total signal for each modification calculated, giving 16 values for each enriched complex group. The same number of complexes as present in each group was then randomly sampled 1000 times from the whole protein complex dataset and the total signal for each modification was obtained. The randomly sampled data predominantly followed approximate normal distributions  was binned as follows; 0.5>x>0 representing low disorder, 1>x≥0.5 representing medium levels of disorder and 1 representing high levels of disorder. For cosmic mutations, the number of mutations annotated in a 20AA window was calculated prior to binarising as described in the main text.Random PTM permutation analysis was performed on individual protein sequences keeping the type, frequency and density of modifications across the protein sequence constant, only shuffling the specific PTM annotation at each residue. This represents the most stringent randomisation analysis to determine if distinct modifications are in proximity solely through accumulation of PTMs in defined sequence spaces.An enrichment analysis of short linear motifs in the PTMi spots was undertaken using the SLIMfinder software From the PTMcode dataset Dataset S1Collated post translation modification dataset.(XLSX)Click here for additional data file.Dataset S2Annotated protein complex dataset.(XLSX)Click here for additional data file.Dataset S3Dataset of local PTM density across individual proteins.(RAR)Click here for additional data file.Dataset S4Annotated PTMi spots.(XLSX)Click here for additional data file.Figure S1Protein complex data distribution for each PTM. 2D density distribution for total and median modification of each complex for each PTM. For each plot blue to black regions represents increasing number of complexes in the dataset with the 500 most outlying protein complexes plotted as individual data points. Data points may be overlaid preventing visualisation of each complex values. 2D density distribution plot for (A) acetylation, (B) tyrosine phosphorylation, (C) ubiquitination and (D) serine/Threonine phosphorylation. Both x- and y-axis for (D) have been truncated for ease of visualisation excluding extreme outliers.(PDF)Click here for additional data file.Figure S2Characterisation of PTM enriched complex size. The two outlying groups of complexes were characterized for both total protein length and number of unique subunits present; (A) acetylation, (B) tyrosine phosphorylation, (C) ubiquitination and (D) serine/threonine phosphorylation. Cut-offs were utilised to generally characterize both sets of subgroups and are indicated in the top corner of each box plot.(PDF)Click here for additional data file.Figure S3Schematic representation of network randomisation workflow. Each unique protein is first binned based upon its frequency in the protein complex dataset. The proteins in each bin are then further divided into 4 sub-bins based on the protein size. Finally the annotations for the ∼200–350 proteins in each of these 16 separate bins are randomized.(PDF)Click here for additional data file.Figure S4Large scale figure for complex-subgroup functional enrichment. As for (PDF)Click here for additional data file.Figure S5Signal enrichment determination for acetylation enriched complexes. (Addendum to Main (PDF)Click here for additional data file.Figure S6Signal enrichment determination for phospho-tyrosine enriched complexes. (Addendum to Main (PDF)Click here for additional data file.Figure S7Signal enrichment determination for ubiquitination enriched complexes. (Addendum to Main (PDF)Click here for additional data file.Figure S8Signal enrichment determination for phospho-serine or phospho-threonine enriched complexes. (Addendum to Main (PDF)Click here for additional data file.Figure S9Signal enrichment determination for complexes enriched in 3 or more PTMs. (Addendum to Main (PDF)Click here for additional data file.Figure S10Modifications in 20AA windows. (A) The number of non-overlapping PTM density peaks (identified with 20AA windows) associated with each combination of modifications present across the proteins in the enriched complexes. (B) The percentage of signal for each modification in high density PTM windows in the enriched complexes in comparison to all windows over the complex dataset. (C) The distribution of distinct PTM density local peaks across proteins within the PTM enriched complex data.(PDF)Click here for additional data file.Figure S11PTM window mutational analysis. (A) Frequency of 20AA windows across a protein sequence that are frequently mutated in cancerous cells, based on their protein domain annotation (coloured bars). The frequency of 20AA windows outside of protein domain annotations that are frequently mutated in cancerous cells, based on their PTM density (Grey bars). In contrast to Main (PDF)Click here for additional data file.Figure S12Distribution of PTMi spots across the protein complexes. Bar-chart showing the percentage of complexes with PTMi spots across either the entire complex dataset or the PTM enriched complexes.(PDF)Click here for additional data file.Figure S13PTMi spots identification and characterization across the modified proteome. Same analysis as presented in Main (PDF)Click here for additional data file.Figure S14Proteome wide STYK ratio, disorder content and PTM density analysis. (A) Upper panel: Bar graph; Total number of 20AA windows spread across all proteins above a local STYK density of 0.3. Red line; Total number of medium or high disordered 20AA windows at each STYK density. Lower panel: Percentage of total 20AA windows in each disorder bin at a given STYK ratio. (B) PTM density distribution of the modified windows at a given STYK density. (C) Upper panel: Bar graph; Total number of 20AA windows with a PTM density of >0.3 at each STYK density of 0.3. Lower panel: Percentage of high PTM density 20AA windows in each disorder bin at a given STYK ratio.(PDF)Click here for additional data file.Figure S15PY-NLS sequences present in PTMi spots. Top: Amino acid sequence of the canonical PY-NLS present in HNRNPA1. The tripartite NLS motif is highlighted with transparent boxes over the protein sequence. pS residues annotated in this analysis and known to reduce karyopherinβ affinity highlighted. Bottom: Four PY-NLS sequences present in PTMi spots in this analysis. Modified amino acids and the tripartite motifs surrounding the NLS sequence highlighted.(PDF)Click here for additional data file.Figure S16Individual PTM dataset analysis. (A) Log:Log Distribution of each individual PTM data. Box plots of protein modifications binned by protein size for (B) acetylation, (C) tyrosine phosphorylation, (D) ubiquitination, (E) serine/threonine phosphorylation and (F) total PTMs. Y-axes are truncated to exclude outliers for ease of visualisation. Size ranges are approximately equal bins representing small (<297AAs), med-small (298–494AAs), med-large (494–757AAs) and large proteins (>757AAs).(PDF)Click here for additional data file.Figure S17Basic overlap PTM dataset analysis. (A) Overlap analysis of proteins shown to be modified by each individual PTM. (B) Box plots of total protein length binned by number of distinct PTM modifications, y axis has been truncated to 10,000 AAs for ease of visualisation. (C) Three tables providing examples of group of proteins that are equal in size together with the number of distinct modifications as examples for specific PTM states.(PDF)Click here for additional data file.Figure S18Complex subunit dataset analysis. (A) Histogram of protein length present in the protein complex dataset (black bars) compared to all PTM proteins (white bars). (B) Histogram of subunit frequency in the protein complex dataset. (C) Box plot of total PTM modification binned by frequency in the protein complex dataset, Y axis has been truncated to 100 modifications for ease of visualisation.(PDF)Click here for additional data file.