Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.The online version of this article (10.1186/s13321-019-0346-7) contains supplementary material, which is available to authorized users. Fragment-based screening approaches have emerged as effective and complementary alternatives to high throughput screening (HTS), opening new avenues for drug design . A recenMolecular docking is a method of choice for the search for original hit compounds. For example, in a discovery effort of the A2A adenosine receptor, virtual screening provided new fragments although many ligands were already reported for this target protein . Other rMolecular docking can be thought of two separate but related phases. First is the “sampling” phase where the 3D pose of the ligand into the protein receptor is explored. Typically, many hundreds or thousands of potential poses are sampled. Phase two is the “scoring” phase in which a scoring function is used to order the sampled poses and ultimately produce the top set of predicted poses and their scores. These phases are interrelated, since the scoring function is also used to drive the sampling, but can be thought of as two separate problems. Docking programs typically generate multiple possible ligand poses with an associated score, but identifying the correct binding pose out of the set of possibilities is still an issue. In a recent example of fragment-based drug discovery, docking poses used to guide the design of PIM-1 kinase inhibitors have not been validated by X-ray crystallography  .Benchmarking studies have demonstrated that the use of experimental information on binding mode improves pose prediction of drug-like ligands –18. ReceScoring by similarity is fast but requires the 3D-structures of reference complexes. The power of the approach depends on the coverage of protein interactions by the reference molecules, and therefore it is desirable that the ensemble of reference molecules provides a comprehensive description of interactions made by the protein. Our recent analysis of the Protein Data Bank (PDB) suggested that fully mapping a pocket is achieved by nine different fragments or nine different drug-like ligands .Do the three methods have comparable performance levels ?Are the fragments more appropriate references than drug-like ligands?Are molecules chemically similar to the docked fragment better references?Here, we explore the rescoring performance on fragment pose prediction of three rescoring approaches based on the 3D-structure of reference complexes: similarity of interaction fingerprints (IFP) , graph mWe defined a fragment as an organic molecule which is small but not a crystallization additive (such as buffer or precipitant). Size selection rules were a molecular weight (MW) below 300 Da and a number of non-hydrogen atoms between 2 and 18. We looked for drug-like ligands in the sc-PDB , keeping3D-structures were downloaded from the RCSB PDB web site  and prephttp://www.eyesopen.com). RMSD values were computed on non-hydrogen atom coordinates using Surflex-dock v3066 [If multiple structures were available for the same fragment within the same protein site , different bound conformations were picked according to the root mean square deviation (RMSD) of the non-hydrogen atoms coordinates. In detail, duplicate fragments were identified by comparing canonical SMILES strings generated using the OpenEye Python2.7 API version 2017.Oct.1  . PLANTS The cavity center of a protein site was defined from the centroid of all the fragments and drug-like ligands bound to this protein. The cavity radius was set as the maximum distance between the cavity center and the atoms of all the ligands crystallized in the binding site (fragments and ligands), plus 2 Å. On average, the radius was equal to 11.2 Å. Ten poses were saved per docking run.The input conformation of docked fragment came from the crystal structure of a parent complex (see the above paragraph for the selection of coordinates when the fragment is present in more than one complexes). Of note, PLANTS “sampling” performances were not changed if fragment structures were generated ab initio  and the maximum π–π interaction distance was set to 5.0 Å. The similarity was computed using the grim module of IChem v5.2.9 (default settings).In the GRIM approach, the binding mode of a ligand to its protein is encoded into Interaction Pseudo Atoms , which evaluates the overlap of shape and pharmacophore features (-scoreonly option)  and 13.3 drug-like ligands (1 to 136 by HET codes) per protein . The number of poses generated for a fragment ranges from 20 to 1400 depending on the number of protein site structures. In about one third of the fragment/protein complexes studied, a docking solution close to the native pose is ranked first by the ChemPLP scoring function , interaction graphs (GRIM) and shape comparisons (ROCS). Statistics on the success rate in pose prediction suggest that the IFP method shows inferior performance than GRIM, which in turn is inferior to ROCS. This ranking coincides with the granularity of the encoding of the structural information used for the rescoring. IFP are based on the comparison of binding modes, but do not encode the geometry of interactions, nor their arrangement in space. In addition, the encoding per residue does not capture the number of interactions of the same type being established between the ligand and a protein residue. Like IFP, GRIM is based on the interactions between the ligand and the protein, but with a detailed encoding of their position and geometry. Moreover, GRIM is able to find the same motif  in two different binding modes while tolerating variations in the position of the protein atoms involved in the common interactions. ROCS uses only the information provided by the ligand atoms and therefore does not explicitly encode the interactions made with the protein. Nevertheless, the superposition of the pharmacophoric properties of the docked fragment and the reference molecule implies not only that interactions of the same type are formed, but also that the ligand atoms involved in these interactions occupy strictly the same position in the protein site.The three rescoring approaches have different strengths and weaknesses, and are therefore not necessarily applicable in the same situations. IFP requires consistent numbering of residues in all the protein structures, precluding comparison of binding modes involving incomplete or mutated binding sites. However, IFP is the fastest of those three methods. Moreover, IFP also has the advantage of being able to find the interactions which are conserved when the protein undergoes important conformational changes. It is important to note that in the work described here, this situation does not occur. On the other hand, GRIM allows the comparison of any complexes, including mutated or even homologous proteins. Again, this has not been investigated here. ROCS is the most restrictive method since all the reference complexes have to be 3D-aligned onto the input protein structure before rescoring. The quality of the alignment determines the rescoring efficiency, so that the approach is more suitable for rigid sites.Although IFP, GRIM as well as ROCS were able to recover the correct pose in a docking pose ensemble in many cases, rescoring failures also occurred. We thus wondered whether the rescoring performance depends on the physico-chemical properties of the fragment and protein binding site. Firstly, the performance of IFP, GRIM and ROCS are the same on the flexible and rigid binding sites. All the three methods are thus able to pick the correct pose in the appropriate protein conformation. The data also do not show a relationship between rescoring performance and the size of the binding site, or its surface polarity. By contrast, rescoring performance seems to change with fragment size. GRIM scoring accuracy increases when the fragment number of  non-hydrogen atoms increases but the variation is not significant  provides a first negative answer. The lead fragment 7-azaindole indeed showed multiple binding modes when crystallized in the ATP-binding site of the Pim-1 kinase . Our recIn the docking benchmark set presented here, we distinguished the different native poses of a fragment by hierarchical clustering based on RMSD with a 1.0 Å cut-off. We identified only six fragments with multiple binding modes (Additional file Interactions with the protein have already been considered in successful virtual screening campaigns. For example, hit rates of about 10% have been obtained in the search for human bromodomains inhibitors . Here weAdditional file 1. Figure S1. Effect of the fragment input conformation on PLANTS performance; Figure S2. IFP, GRIM and ROCS performance in scenario 1 and scenario 3; Figure S3. Comparing Tanimoto and Tversky similarities for rescoring using IFP and ROCS; Figure S4. Chemical similarity between the docked fragment and the reference molecule picked by GRIM or ROCS Tv-Combo; Figure S5. ROCS Tv-Combo rescoring performance versus fragment properties; Figure S6. Tc-IFP rescoring performance versus fragment properties; Table S1. Description of the proteins in the benchmark set; Table S2. Kolmogorov-Smirnov test on non-weighted RMSD distributions binned by fragment properties; Table S3. Intersect with the Astex diverse dataset; Table S4. Intersect with D3R datasets; Table S5. Intersect with the CSAR datasets; Table S6. Fragments with multiple crystallographic poses.


Mass spectrometry-based proteomics methods are finding increasing use in structural biology research. Beyond simple interaction networks, information about stable protein-protein complexes or spatially proximal proteins helps to elucidate the biological functions of proteins in a wider cellular context. To shed light on new developments in this field, the Göttingen Proteomics Forum organized a one-day symposium focused on complexome profiling and proximity labeling, two emerging technologies that are gaining significant attention in biomolecular research. The symposium was held in Göttingen, Germany on 23 May, 2019, as part of a series of regular symposia organized by the Göttingen Proteomics Forum. The one-day symposium “Mapping Cellular Microenvironments: Proximity Labeling and Complexome Profiling” organized by the Göttingen Proteomics Forum (GPF) was held at the Max Planck Institute of Experimental Medicine in Göttingen, Germany on 23 May, 2019. Following the example of past GPF symposia to focus on emerging topics in proteomics research, this year’s event was co-organized by the Collaborative Research Centre SFB1190, a local research consortium that focuses on functional protein clusters located at cell compartmental gates and contact sites. Witnessed by more than 100 participants, a range of invited and local speakers presented novel or updated approaches and applications for proximity labeling and complexome profiling, techniques that complement conventional affinity-purification mass spectrometry (AP-MS) for the characterization of biologically relevant protein-protein interactions. Beyond identification of physically interacting proteins, these techniques can provide information about proteins in spatial proximity of a protein of interest (proximity labeling) or the composition, stoichiometry and apparent molecular weight of stable protein complexes (complexome profiling). Both techniques have recently gained significant attention in biomolecular research. Complexome profiling was the topic of the morning session, encompassing a set of methods to characterize the composition of protein complexes on a global scale under near-native conditions. Complex proteome preparations are fractionated for the apparent molecular weight of stable protein complexes by either blue native polyacrylamide gel electrophoresis (BN-PAGE)  or size As first speaker, Uwe Schulte  presented the pioneering work of his group on complexome profiling by BN-PAGE-mass spectrometry . Using pIn direct continuation, Daniel Kownatzki-Danger , as a local speaker, presented an application-oriented approach where BN-PAGE-based complexome profiling was used to compare native cardiac protein complexes in membrane preparations from mouse ventricular cardiomyocytes, as well as in human ventricular tissue biopsies. BN-PAGE was previously proposed as a differential diagnostic tool to study, e.g., OXPHOS defects in cardiac disease . Daniel https://github.com/CCprofiler/. Differential comparison of multiple samples in a clinical context requires faster turnaround, restricting the degree of fractionation and the amount of instrument time available per sample. In addition, differential analysis of multiple samples demands for a very high degree of data completeness, i.e., sample-over-sample consistency in detection and quantitation of peptides. Data-independent acquisition mass spectrometry (DIA-MS) has emerged as a data acquisition strategy that addresses both of these challenges, provided that information about the protein composition of the sample exists to deal with false positive identifications . As the 2O2 activation for generation of reactive biotin-phenoxyl radicals that covalently bind aromatic amino acid residues at proximal proteins within minutes and below [The afternoon session addressed proximity labeling techniques with biotin in living cells. In contrast to complexome analyses, only proteins localizing proximal to a bait protein (tens of nanometers apart) are covalently labeled and subsequently enriched from cell lysates. Captured proteins are then identified with either antibodies (targeted) or mass spectrometry (unbiased). Gene fusions couple bait proteins to enzymes that serve as source of reactive biotin derivatives, either to an engineered soybean ascorbate peroxidase (APEX) or to a modified biotin-ligase (BioID) in order to pursue biotin-based identification of labeled proteins. APEX requires cellular uptake of biotin-phenol and timed Hnd below ,12. Mutand below ,14. MoreSaccharomyces cerevisiae: Ribosomal Gβ-like protein Asc1/RACK1 and membrane-associated PROPPINs. PROPPIN-containing protein complexes are difficult to enrich with classical immunoprecipitation experiments according to their membranous cellular context. Studying membrane-localized bait proteins is feasible using APEX or BioID approaches, as both techniques rely on covalent protein biotin-labeling within living cells, enabling the capture of biotinylated proteins from cell lysates under denaturing conditions afterward, e.g., in the presence of membrane-solving detergents. Both BioID projects made use of Strep-Tactin (IBA GmbH)-based biotinyl enrichment, SILAC-based enrichment-quantification, and unbiased protein identification with mass spectrometry. Strep-Tactin captures enable nondestructive elution of biotinylated proteins through high biotin dosages. Accurate relative enrichment-quantification  against the following two negative controls was emphasized as key requirement for subsequent candidate mining within lists of MS-identified proteins: 1. Biotinyl-enrichment from cells heterologously expressing the foreign biotin ligase birA* without fusion to the bait protein, to account for bait-unspecific biotinylation through birA*; 2. Biotinyl-enrichment from cells without expressing foreign birA*, to account for naturally birA*-independent biotinylated proteins. Biotin site-identification within significantly enriched proteins by mass spectrometry was considered as additional evidence for cellular bait proximity, with potential for an additional gain in structural information concerning the spatial orientation or the accessibility of the bait-ligase fusion protein.Oliver Valerius  and Lena Munzel  presented data from quantitative BioID approaches to characterize the head region of the 40S ribosomal subunit (hr40S) and to identify interaction partners of β-propeller-binding-polyphosphoinositides (PROPPINs) localized to endosomes, vacuoles and autophagic membranes, respectively. Both BioID projects targeted β-propeller proteins of the WD40-repeat domain family in the yeast The RACK1-proxiOME revealed ribosomal proteins known as direct neighbors from ribosome crystal structures, proving the feasibility of the experimental concept . Key feaJulien Béthune  and colleagues have developed a methodology that combines the proximity-labeling technique BioID with the concept of protein fragments complementation assays, referred to as split-BioID . The priAquifex aeolicus biotin ligase [Bacillus subtilis biotin ligase [Escherichia coli birA-R118S [Beyond studying the molecular mechanisms of miRNA-mediated silencing with split-BioID and many other biochemical and genetical approaches, the group of Julien Béthune further developed smaller biotin ligases with improved labeling kinetics that can be used in the split assay format. Already published engineered smaller and/or more efficient biotin ligase variants are BioID2, originated from an n ligase ; BASU, on ligase ; and turrA-R118S . MicroID2O2 results in APEX-mediated biotinylation of the VapB environment that is specific for its localization to the INM. Biotinylated proteins were enriched using neutravidin (deglycosylated avidin to avoid lectin enrichment), identified with mass spectrometry, and relatively quantified against essential controls using stable isotope labeling with amino acids in cell culture (SILAC).In the scientific context of nucleocytoplasmic transport, Ralph Kehlenbach  presented data that originated from a quantitative modified APEX approach utilizing an enhanced ascorbate peroxidase 2 (APEX2)-approach to map compartment-specific protein interactions of the vesicle-associated membrane protein-associated protein B (VapB) . Since VExpression system or strategy for expressing the bait-enzyme fusion protein, including codon-usage. Testing for functionality/localization of the fusion protein. Biotin/biotin-phenol uptake, dosage requirements and subcellular localization, toxicity. 1-based label-free quantitation (LFQ), DIA-MS-based label-free quantitation, in vivo stable isotope labeling with amino acids in cell culture (SILAC), or post-digestion chemical peptide labeling). Enrichment-quantification strategies and negative controls (MSCapture/elution strategies (destructive versus non-destructive) and corresponding protein digestion protocols (in-gel versus in-solution vs. on-bead). Stable versus short-lived protein interactions/proximities. Labeling activity versus background noise (sensitivity versus specificity). Overall proximity capture (unbiased screening) versus context-specific proximity capture (targeted proximity capture).The decision for either APEX, BioID, split-BioID or one of their variants strongly depends on the cellular system or organism used, and on the process/complex/cellular site analyzed. Proximity labeling experiments require significant method establishing and optimization efforts. The following aspects should be considered during that process: Taken together, the symposium provided a balanced overview of both complexome profiling and proximity labeling approaches, two emerging mass spectrometry-based technologies in structural biochemistry that complement traditional affinity purification approaches and thus allow for a much more fine-grained study of cellular organization and cellular function.


For more than a century, the precision at which any protein (or RNA) could be localized in living cells depends on the spatial resolution of microscopy. Light microscopy, even recently benchmarked laser‐scanning microscopy, is inherently liable to the diffraction limit of visible light. Electron microscopy that had existed as the only alternative for decades is, in turn, of low throughput and sensitive to processing artefacts. Therefore, researchers have looked for alternative technologies particularly with ever‐growing interest in resolving structural underpinnings of cellular heterogeneity in the human body. Computational (“in silico”) predictions provided only partial solutions given the incompleteness of existing databases and erroneous assumptions on evolutionarily conserved sequence homology across species. A breakthrough that facilitates subcellular protein localization came with the introduction of “super‐resolution” microscopy, which yields 20–60 nm resolution by overcoming diffraction‐limited technologies. The ensuing combination of “super‐resolution” microscopy with unbiased proteomics continues to produce never‐before‐seen gains by quantitatively addressing the distribution, interaction, turnover, and secretion of proteins in living cells. Here, we illustrate the power of this combined work flow by the example of transmembrane receptor localization at the neuronal synapse. We also discuss how dynamic analysis allows for inferences be made for cellular physiology and pathobiology. Subcellular localization of proteins provides pivotal information for the investigation and modelling of cellular processes and pathways controlled by protein–protein interactions. Despite decades of investment, most proteins have been inadequately or incompletely localized,Proteins identified within large proteomes are commonly assigned to presumed subcellular positions by high‐throughput prediction algorithms exploiting sequence homology and functional classifiers.In experimental cell biology, antibody‐based immunohistochemical methods became benchmarks of target discovery. For protein localization in tissues that contain diverse cell types, marker proteins are used to “landmark” specific cellular compartments, an approach compatible with multicolor immunofluorescence light microscopy. A quantum leap in the precision of protein localization by light microscopy came with the introduction of confocal microscopy that can maximally separate excitation wavelengths by use of wavelength‐tuned lasers, as well as exclude out‐of‐focus light by applying micrometer‐scale apertures (“pinholes”), which act as low‐pass filters for the light beam. Even with the most advanced systems, light microscopy is diffraction limited: it cannot resolve fine differences in compartmentalization for subwavelength structures ≲250 nm.Electron microscopy is the alternative localization approach that first came to prominence. When combined with the use of antibodies (termed immunoelectron microscopy), subcellular assignments can be called at ≈5–10 nm precision with the physical size of antibody molecules defining its boundary of stochasticity. However, immunoelectron microscopy is known to be sensitive to processes of tissue preservation, antigen accessibility , antibody impurities, embedding, washing, and contrasting conditions.Therefore, methods that preserve the benefits of both light and electron microscopy, namely versatility, speed of tissue processing, and quasi‐equal resolution in multiple colors are sought after when designing pipelines for protein profiling. Retaining considerable throughput grew significantly in importance recently given the increasing number of tissue‐specific proteomes in many mammalian species.To satisfy the above objectives, “super‐resolution” microscopy has recently been developed to aid the subcellular localization of proteins by overcoming the resolution cut‐off imposed by the diffraction limit of light.co‐localization studies since it aids single‐molecule detection upon high‐density multichannel labelling in large fields of view. Thereby, high‐resolution dSTORMHere, we focus on dSTORM by highlighting its experimental prowess when combined with global proteomics. As such, multicolor dSTORM is particularly amenable for subcellular dSTORM is an extension of photo‐activation localization microscopy (PALM) for the single‐molecule localization of fluorescent proteins.Here, we exemplify the usefulness of dSTORM in neurobiology with a particular focus on synaptic proteins that are indispensable for intercellular communication in the nervous system. Resolving protein distribution at specialized pre‐ or postsynaptic sites that form the junction of intercellular information transfer between connected nerve cells  is a central effort in neurobiology to decipher physiological processes, as well as to implicate specific signal transduction cascades in the pathogenesis of neuropsychiatric and neurological diseases. Notably, a myriad of proteins is selectively partitioned into either the presynaptic terminal or the subsynaptic dendrite to specify steps of chemical  neurotransmission. Routinely, subcellular fractionation by sequential ultracentrifugation is the standard to separate and enrich cellular organelles. For synaptic proteins, the separation of synaptosomes  cells is amenable to develop benchmarked discovery pipelines encompassing i) protein localization at specialized subcellular sites, ii) quantitation of protein content in physiological and disease settings, iii) resolving protein localization at pre‐ and postsynaptic sites , iv) localization of synapse contingents with specific features in the brain, v) causally integrating specific proteins in synaptic neurotransmission within diverse neuronal networks, and vi) interrogating transient interactomes at high precision, resolution, and throughput. Notably, dSTORM is a valid method not only as a secondary verification tool  but also as the choice of method to define subcellular protein organization when functional segregation exists at subcellular domains at the nanometer scale. In neurons, this is best illustrated by the immediacy of synaptic, perisynaptic, and nonsynaptic membrane partitions that host vastly different protein machineries, for example, vesicle fusion, CaWith the present rate of technical developments, mass‐spectrometry‐assisted proteomics can identify >8000 synaptic proteins (including >2000 intra‐ or transmembrane species) in synaptosomal fractions. Ensuing knowledge on protein localization, spurred by the recent completion of the Human Protein Atlas, heralds a new era for “neuroproteomics.” Since the Human Protein Atlas alone has produced antibodies for >20 000 human proteins and already generated refined maps for subcellular and pathological protein localization,The authors declare no conflict of interest.


In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments.One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim ( Large-scale sequencing initiatives such as the 1000 Genomes Project  have proThe predominant source of genetic variations detected in human population comes from single nucleotide variants (SNVs), which imply one single base substitution in the genome. Among SNVs, those that result in amino acid substitutions at the protein sequence level might be associated with genetic diseases since they can alter protein stability, interfere with protein-protein interaction properties ,5,6, eliTherefore, studying the effects of protein sequence variants on molecular function is crucial, but experimental methods are costly, time-consuming and challenging, making it infeasible to analyze a large number of amino acid substitutions. Hence, computational tools that rely on conservation-related attributes reflecting structural and functional relevance, as well as on protein structure and stability-related properties following the relationship between structure and function, are used to estimate the phenotypic effect of these variants. Some examples of such reported methods are SIFT , CADD 11, PolyPhRecent studies show that mutations in protein-protein interfaces are over-represented among disease-causing mutations ,18,19. WRegarding disease-causing mutations at PPI interfaces, they can induce geometrical and physicochemical changes at interaction sites that may affect interface stability, interface conformation dynamics through disruption or stabilization of specific conformational states, and the direct interactions between partner protomers . Thus, tIn this study, we have characterized protein-protein interactions involving 58 proteins with pathogenic mutations related to diseases detected in newborn screening. Interpretation of mutational data in this set of proteins is of major clinical interest regarding the possibility of large-scale gene sequencing to detect disorders in newborn testing. We used the experimentally solved structures of the protein complexes when available, and when not, the protein-protein interface was predicted by an ab-initio docking approach. The distribution of disease-causing and neutral variants across the different interface regions, as well as the substitution susceptibility of distinct amino acids, was discussed.A total of 58 proteins with pathogenic mutations involved in diseases detected in newborn screening were analyzed . As manyAmong the 58 analyzed proteins, 50 of them had interaction data available in Interactome3D database . From the total of 389 PPIs found in such databases, only a small fraction (<12%) had available complex 3D structure. All these PPIs involved a total of 351 protein partners, the majority of which (75%) had available 3D structure. Among the partners with known 3D structure, 37% of them had good structural coverage (>80% of its sequence) in a single PDB file, while 40% of them have their structure split in separated PDB files.Protein-protein interfaces were divided into core and rim residues (see Methods). For a given protein, residues can be defined as surface, interface core or interface rim depending on the considered partner. In all the annotated PPIs involving the 58 analyzed proteins, there were a total of 11,199 residues, of which 6019 were found to be buried in at least one structure . Of the The energetic contribution to protein complex stability was not uniform across the interface, and for instance, interface residues that were estimated to be energetically relevant for the interaction  tended to be located at the interface core region more often than expected by random . This waA total of 2882 disease-causing mutations and 1552 neutral variants were mapped onto the 3D structures of the protein-protein interactions involving the 58 genes analyzed here . Around p < 0.0001), which is consistent with previous studies [Regarding the disease-causing variants, 36% of them 1040) occurred in solvent-accessible residues, of which 488 were found at the interface with at least one partner (with 337 of them at the core region in at least one complex). The odds of being located at the interface rather than at a non-interacting surface was 1.44 higher for pathogenic variants compared to the rest of residues . Moreover, for these neutral variants, the odds of being located at the interface core rather than the rim or the non-interacting surface was 0.32 and 0.24, respectively. As in the case of disease-causing variants, there was no significant difference between the location of neutral variants at the interface rim region and the non-interface protein surface.Regarding the neutral variants, 66% of them occurred in solvent-accessible residues, of which 261 were found at the interface region , and 767 at the non-interacting protein surface. Contrarily to disease-causing variants, the odds of being located at the interface rather than at a non-interacting protein surface was smaller for neutral variants compared to the rest of residues  was the most mutated residue in both neutral and pathogenic variants in protein-protein interfaces. This high mutability can be explained by the fact that four out of the six codons for R include CpG dinucleotides, which tend to mutate at rates 10–15 times higher than other dinucleotides in the DNA . ArgininMany of the proteins analyzed here are involved in protein-protein interactions for which there is no available complex structure. In these cases, we could apply docking simulations to identify potential interface residues, using a pyDock approach and NIP interface prediction module. First of all, in order to apply docking simulations, we needed to check whether we had a complete structure or a reasonable model for the interacting proteins. Available databases such as Interactome3D provide this information. However, there are several issues to consider here. For example, in many cases either a model or an experimental version of the overall structure of the target protein is available, but with incomplete structural coverage. Or, it may also happen that the overall structure is split between different PDB files, and then we would need to infer the global structure from these different parts, a non-trivial problem. In Given all the above considerations, we have selected one example case, HADHA, involved in 13 different protein-protein interactions for which there is no complex structure available. There are 116 neutral and 31 pathogenic mutations described in HADHA. To better understand the functional influence of genetic variants at the protein level, structural characterization of single amino acid variants and their interactions is one of the basic steps. In this regard, several structural databases of protein interaction data can be used, such as Interactome3D . HoweverMutations in the same gene can affect different phenotypic traits (pleiotropy). In this context, the number of interactions and interfaces in a protein is key to understand pleiotropic effects in disease genes. Recent studies show that SAVs located at distinct protein-protein interfaces of the same protein are prone to produce different disease phenotypes ,26,27. MIn this line, structural analysis of the case example hemoglobin subunit beta (HBB) showed that the same variant could affect the interaction with different partner proteins if their interface patches are the same, and different variants could perturb different partner proteins if these have distinct interface patches. The extensive analysis of protein interface residues shown here, combining complex structures and docking predictions, demonstrate that pathogenic variants are more likely to be located at the interface rather than at the non-interacting surface. More precisely, we found that they are more probable to occur at the interface core region rather than at the rim, in agreement with previous studies ,17,18,28The present study has some limitations, such as the low availability of 3D protein structures or the global consideration in the analysis of both transient and permanent PPIs, which are known to show very different mechanistic, structural and energetic properties. Despite these limitations, this study shows that the structural characterization of PPIs and the analysis of the location of pathogenic and neutral variants, together with the identification of the interface residues that are more prone to be mutated and lead to disease, can provide novel information on disease-causing variants. This can be useful in order to characterize protein sequence variants in future studies, interpret them at the molecular level, improve the accuracy of pathogenicity predictors on new mutations, and help to advance toward precision medicine by helping clinicians to provide adequate diagnosis and treatments.Further studies with more docking simulations will need to be undertaken. For partners with 3D structures split in different PDBs, a template could be used to model the missing amino acids and join the distinct protein fragments in a correct global 3D structure. Moreover, if these PDBs contain at least one complete domain, docking simulations could be done at the domain level. Homology models could be generated for those proteins without available 3D structure, so that docking can be run afterwards in order to find a possible protein-protein interface. This would help to achieve a better understanding of disease at molecular level since more PPIs could be characterized and more disease-causing and neutral variants could be mapped on the structural models.Human PPI data and structural information for both protein complexes and their individual components were retrieved from Interactome3D . In thisHuman pathogenic mutations were compiled by pooling variants obtained from UniProt , selectiAs a further analysis, protein structures involved in each interaction were characterized in more detail regarding sequence identity, structural coverage, domains, and biological assembly . SequencProtein-protein complex structures, when available, were retrieved from PDB based on Interactome3D information. Protein-protein interfaces were defined in a similar way as previously described . Prior tu) < 0.1, or surface if they had rASAu ≥ 0.1. Surface residues were classified as interface residues when the difference in rASA between the uncomplexed and complexed form (rASAu-rASAc) was > 0, or non-interface surface otherwise. Interface residues were further divided into core and rim. Core was formed by interface residues that were buried in the complex (rASAc < 0.1), and rim was formed by interface residues that remained exposed in the complex (rASAc > 0.1). The value rASA was computed as the ratio between the Accessible Surface Area (ASA) of a given residue, and the ASA of the corresponding residue type in the extended conformation of the Gly-X-Gly peptide. All (ASA) calculations were done with ICM-Browser (http://www.molsoft.com).Residues were defined as buried if they had relative Accessible Surface Area in the uncomplexed structure -based program FTDock 2.0 , and thepatch module in pyDock, implementing the pyDockNIP algorithm [From the resulting docking poses, a normalized interface propensity (NIP) was obtained per residue with the built-in lgorithm . A normalgorithm . Usuallylgorithm .bindEy and resEnergy modules. The bindEy module computes the total binding energy for a given protein-protein interaction, based on the complex structure or a model. The resEnergy module calculates the contribution of each individual protein residue to the binding energy for a given protein-protein complex structure.The energetic characterization of protein-protein interfaces was performed with the pyDock i is calculated as shown in Equation (1), where in is the number of variants observed in the protein region i, and iN is the total number of residues in that region. The likelihood of a variant to be in region i rather than in region j in the protein was expressed then in terms of odds ratio (ORij) (Equation (2)). The χ2 test was used to compare the observed number of variants in each region with the expected one if variants were distributed according to the number of residues in the different regions. A two-tailed p-value < 0.05 indicated statistical significance of the preference for variants to be in one region over another. Bonferroni correction was used to adjust p-value for multiple comparisons.The statistical analyses were performed using version 3.4.4 of the R statistical package . The prop-value < 0.05 implied statistical significance according to an “N-1” χ2 test.The amino acid substitution susceptibility to disease-causing variants or neutral ones at protein interfaces was calculated, and a two-tailed


Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date.M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l2-regularized logistic regression model.In this work, taking M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance.The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between The online version of this article (10.1186/s12864-018-4873-9) contains supplementary material, which is available to authorized users. Mycobacterium tuberculosis is the causative agent of tuberculosis, an infectious disease that causes millions of deaths each year [M. tuberculosis H37Rv has attracted much attention partly due to its co-infection with HIV [M. tuberculosis infection and human defence.Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. As an example, ach year . In recewith HIV  and drugwith HIV . From thhttps://string-db.org/) has curated massive PPI networks of 1678 bacterial pathogens such as M. tuberculosis, B. anthracis, F. tularensis, Y. pestis, etc. However, there are very few experimental studies on protein interactions between bacteria and their host. From a computational view of point, M. tuberculosis H37Rv has been extensively studied in recent years in terms of drug resistance analysis [M. tuberculosis H37Rv PPIs from the known PPIs of other source species. In [M. tuberculosis H37Rv PPIs are laid aside unused and instead the E. coli PPIs are used as training data to predict M. tuberculosis H37Rv PPIs. In [lostridium difficile are used to expand the known M. tuberculosis H37Rv PPI networks, and the expanded PPI networks are further used as training data to train a random forest model for the discovery of novel M. tuberculosis H37Rv PPIs.To date, most of the experimental work focuses on detecting protein-protein interactions within bacterial cells. The database STRING   has been anatomically co-evolving with modern humans for tens of thousands of years on the basis of the evidences of its origin in Africa, the congruence in phylogeography and the dating of major branching events. Moreover, the drug resistance of Mycobacterium tuberculosis (MTB) strains is also evolving with the host genome. In [Actually, the parasitic or co-evolution relationships between bacteria and host indicate that bacterial protein-protein interaction alone is sufficient for us to infer bacteria-host protein interaction without resorting to third-party distant species. Knowledge transfer between two co-evolving species is more credible than that between two evolutionarily distant species. The bacterial pathogen protein interaction networks of 1678 bacteria in STRING , 26, 27.nome. In , it has nome. In .SLC11A1, VDR and HLA genes) have been reported to be associated with susceptibility to MTB drug resistance. As reviewed in [Furthermore, the interaction between bacteria and host also somewhat contributes to bacterial drug resistance. As suggested in , the intiewed in , the amiM. tuberculosis H37Rv as an example, we propose a general computational framework that transfers the knowledge of bacterial pathogen protein interaction networks to predict pathogen targeted human genes and immune signaling pathways. Due to lack of experimental studies, we take advantage of the co-evolution relationship between M. tuberculosis H37Rv and Homo sapiens [M. tuberculosis H37Rv protein interactions alone. We confine the search of M. tuberculosis H37Rv ortholog genes within its human host without resorting to a third-party species. The interlogs derived in this way are presumably more reliable than those derived from remote species. Given two interacting M. tuberculosis H37Rv genes  and their corresponding Homo sapiens ortholog genes , we deem  and  as two interlogs, since the human ortholog gene products are functionally or structurally similar to M. tuberculosis H37Rv gene products. To ensure the quality of data, only the significant interlogs are used as training data, and the less significant interlogs need to be further validated by the trained model. Ortholog and interlog knowledge transfer are prone to introduce a certain level of noise as well as increase the computational complexity. To solve this problem, we adopt theoretically well-established l2-regularized logistic regression as the base machine learning model. Finally, we further conduct gene ontology (GO) and pathway enrichment analyses on the predicted interactions to provide insights into the machinery of M. tuberculosis H37Rv infection and host response. As a major concern in recent years, bacterial drug resistance is also discussed in terms of bacteria-host PPI inhibition to provide a potential alternative solution to M. tuberculosis H37Rv drug resistance.In this work, taking  sapiens , 26, 27 M. tuberculosis H37Rv PPI networks in STRING [M. tuberculosis H37Rv and human host to construct training data that are experimentally not available. The second phase is to construct feature representation, train an l2-regularized logistic regression model, and then predict interactions from less significant interlogs and non-interlogs. The final phase is to comprehensively analyse the reconstructed MTB-human PPI networks for further understanding of the machinery of bacterial infection and host response. Especially, we analyse the MTB-human PPIs related to M. tuberculosis H37Rv drug resistance. Proper selection or design of PPI inhibitors could potentially provide an alternative solution to bacterial drug resistance.As shown in Fig. n STRING . This phM. tuberculosis H37Rv protein interaction networks in STRING [M. tuberculosis H37Rv protein interactions, we take the following three measures. Firstly, we only choose the PPIs with experimental evidences. Secondly, we remove the proteins that have no gene names. Lastly, we only choose the well-studied genes that have been annotated with at least one specific gene ontology (GO) term of molecular function or biological process, except the generic root GO terms  in the GO directed acyclic graph (DAG). As results, we obtain 5224 well-studied M. tuberculosis H37Rv genes that correspond to 7835 gene products/proteins and 28,347 M. tuberculosis H37Rv protein-protein interactions. In the same way, we obtain 20,081 well-studied human genes that correspond to 60,126 gene products/proteins.The n STRING  contain n STRING , 25, 29.M. tuberculosis H37Rv PPI networks via orthologous relationships to construct the training data for MTB-human PPI predictions. To formally formulate the method of data construction, we denote the obtained M. tuberculosis H37Rv PPI networks as G, which contains 28,347 interactions and 1469 well-studied M. tuberculosis H37Rv proteins. After removing the M. tuberculosis H37Rv proteins that have no human orthologs, we obtain 1359 well-studied M. tuberculosis H37Rv proteins in total. We search the human ortholog genes in SwissProt [Now we exploit the knowledge of wissProt  simply uwissProt  with defwissProt . ActuallwissProt , 33, whiwissProt . For eacwissProt , we stilG, we derive the interlogs as follows. Given two interacting M. tuberculosis H37Rv genes , we use Hi, Hj to denote the sets of ortholog genes mi and mj, respectively. In particular, if an ortholog gene yields more than one ortholog protein, only one ortholog protein is randomly chosen as the interacting partner. Given the cutoff of ortholog significance δ, e.g. E-value of PSI-BLAST, we further split Hi, Hj into two subsets M. tuberculosis H37Rv genes , we create the positive training instances from the set of significant ortholog genes mi, g) or  denotes significant interlog. Formula (M. tuberculosis H37Rv gene mi(mj) functionally or structurally co-evolves with its human host ortholog genes mi with mj to a great extent indicates the interaction of mi with mj ‘s ortholog gene mj with mi ‘s ortholog genes From mentclass2pt{minimmi, g) or  denotes insignificant interlog. For each M. tuberculosis H37Rv gene mi, let Pi = {g| {mi, g} ∈ Upos} denote the set of its human partner genes, Mi = {g| {mi, g} ∈ G} denote the set of its M. tuberculosis H37Rv partner genes, and Mi. Then we randomly sample the human genes that potentially do not interact with genemi from the set non-ortholog genes Ni = {g| g ∉ All_orthi ^ g ∈ Homowell} to construct the negative training data, where Homowell denotes the well-studied human genes. To obtain well-balanced training data, we impose the constraint ∣Ni ∣  =  ∣ Pi∣ on the sampling of negative training data. Then the whole negative training set is defined as follows.M denotes the set of M. tuberculosis H37Rv genes in the M. tuberculosis H37Rv PPI networks G.From the evolutionary point of view, the less significant interlogs are not so reliable as the significant interlogs, so that they need to be further validated by the predictive model, which is trained on the significant interlogs. The set of less significant interlogs is defined as follows.Model evaluation is a hard problem in the case of lack of experimental data. Nevertheless, random sampling of a tiny fraction of data in the huge space of non-interlogs is convincingly to capture non-interactions with a large probability. Hence we obtain the negative data to estimate the model as follows. Let M. tuberculosis H37Rv gene mi, we sample the human genes from the set For each The prediction set is further reduced for the sake of computational complexity by imposing a constraint on the space as follows.M. tuberculosis H37Rv protein. The data and analyses are referred to the section Formula  means thi in the training set U, we obtain two sets of GO terms, one set denoted as State-of-art feature construction is a critical step of machine learning modeling in solving specific problems. Gene ontology (GO) has been widely used as features to predict protein-protein interactions , 35–40. i1, i2), the target instance and the homolog instance are formally defined as follows:For each protein pair  shares the same GO term g, then the corresponding component in the feature vector GO term g, then the value is set 0; otherwise the value is set 1. The GO terms of the protein pair  that do not belong to the whole set of GO terms of the training set, formally defined as For each GO term O(n2). Here we adopt the l2-regularized logistic regression method [The existing interlog modeling methods –25 have n method , implemen method , to counxi, yi), i = 1, 2, …, l; xi ∈ Rn; yi ∈ {−1, +1}, linear regression attempts to derive a decision function f(xi) = ωTxi + b, which is further converted to probability via the logistic function p = 1/1 + exp(−yi(ωTxi + b)). The weight vector and bias  could be estimated by minimizing the negative log-likelihood 2-regularized logistic regression imposes a constraint on the l2-norm of the weight vector ω to solve the following unconstrained optimization problem [C denotes the penalty parameter/regularizer that balances the two terms in Formula (αi denotes Lagrangian operator and Given a set of instance-label pairs ( problem .10\documf(x) yields two outputs i1, i2), which are further combined into one final decision as follows.i1, i2) is defined as below.In the test and prediction phase, the decision function ζ is used to filter out those weak positive predictions.The threshold i1, i2) is represented with two instances, the target instance As described above, each protein pair , SE (sensitivity), SP (specificity), MCC (Matthews correlation coefficient) and F1 score, are used to evaluate the proposed model via 5-fold cross validation. The dataset is randomly split into five disjoint parts. For five folds, each fold treats one part as test set and the other four parts are merged as training set. For each test example, the true label and the predicted label are recorded into the confusion matrix  formula . Based o formula  and the  formula .14\docummentclasspt{minimaδ = 1e ‐ 50. To date, there is no commonly-accepted standard to choose PSI-Blast E-value cut-off. We are inclined to choose a small E-value cut-off to obtain quality interlogs. As results, we obtain 15,287 significant interlogs as the positive examples  is orthologous to the M. tuberculosis H37Rv protein I6YAZ7 (Rv3043c) with E-value equal to 4e-094. From GO enrichment analysis as partially provided in Table Rv2193, MT-CO1} both get involved in the common biological processes of oxidation-reduction process (GO:0055114) and hydrogen ion transmembrane transport (GO:1902600). In addition, the two genes are also both involved in aerobic cellular respiration, e.g. Rv2193 respiratory electron transport chain (GO:0022904) and MT-CO1 aerobic respiration (GO:0009060). Besides, the two orthologous proteins {Rv3043c|I6YAZ7, MT-CO1|P003951} are also involved in other highly similar biological processes  is orthologous to the M. tuberculosis H37Rv protein I6YCF3  with E-value equal to 5e-058. The GO enrichment analysis of the two genes {Rv2737c, ERCC6} is partially provided in Table Rv2737c protects microbial DNA from antibiotics , DNA damage , ultraviolet radiation , ionizing radiation , etc. Accordingly, the human gene ERCC6 gets involved in response to gamma radiation (GO:0010332), response to UV (GO:0009411), response to oxidative stress (GO:0006979), DNA damage response, signal transduction resulting in induction of apoptosis (GO:0008630), base-excision repair (GO:0006284), etc. The similar biological processes suggest that the two genes {Rv2737c, ERCC6} potentially interact.The drug-resistant gene The ROC curves of 5-fold cross validation are illustrated in Fig. Analysis of the constructed data, we have analysed the quality of the derived significant interlogs via GO enrichment analysis. To well interpret the good two-class separability, we conduct further GO enrichment analysis on the positive and the negative training data.Furthermore, the proposed method achieves quite well-balanced performance on the two classes, indicating that the positive class of significant interlogs and the negative class of non-interlogs are well separated. In the section As illustrated in Fig. At present there is no experimental data available as validation set to evaluate the proposed model. Nevertheless, the less significant interlogs would be more likely to be interacting partners than non-interlogs. For the reasons, we explicitly study the potential interactions from the less significant interlogs and the non-interlogs, partly to evaluate the proposed model as well. The predicted interactions from the less significant interlogs and the non-interlogs are provided in Additional files M. tuberculosis H37Rv drug-resistant genes and human host genes are of special interest to us. Moreover, inhibitors of pathogen-host PPI interface would be more therapeutically with less side-effect on other human genes and pathways. The predicted interactions from less significant interlogs related to drug resistance are illustrated in Fig. As reviewed in , host faRv0849|I6X9Y5, ABCB1|P08183} is derived from the known interaction {Rv0849|I6X9Y5, Rv1348|I6YAB3} [M. tuberculosis H37Rv protein I6X9Y5 with E-value equal to 6e-043. The interlog {Rv0849|I6X9Y5, ABCB1|P08183} is predicted to be a pathogen-host protein interaction with probability 0.9987  and participate the biological process of transport  .The interlog {|I6YAB3} , whereinRv1988|Q10838, PNP|P00491} is derived from the known interaction {Rv1988|Q10838, Rv0535|I6Y409} [M. tuberculosis H37Rv protein I6Y409 with E-value equal to 6e-034. The M. tuberculosis H37Rv gene Rv1988 is classified into the drug resistance type of target-modifying enzymes [M. tuberculosis H37Rv Rv1988 gets involved in the biological processes of rRNA modification (GO:0000154), rRNA methylation (GO:0031167) and response to antibiotic (GO:0046677), while the human gene PNP also gets involved in the biological processes of protein modifications, e.g. the catabolic processes of nucleobase-containing compound (GO:0006139), inosine (GO:0006148), purine nucleotide (GO:0006195), etc. In addition, PNP is involved in the biological processes of immune response  and response to drug (GO:0042493).The interlog {|I6Y409} . The hum enzymes . As showM. tuberculosis H37Rv and Homo sapiens, which is still incomplete since the prediction set is only a small part of the prediction space.The prediction set that contains 407,700 MTB-human protein pairs is derived from the huge space of non-interlogs according to Formula  what roles the targeted human genes play in the human PPI networks. The two concerns are actually about two kinds of degree distributions (1) the degree distribution of the M. tuberculosis H37Rv genes in the MTB-human PPI networks  and Rv1436 (LH57_07850) interact with 209 and 206 human genes, respectively. As shown in Fig. M. tuberculosis H37Rv genes target a small number of human hub genes. The human PPI networks are constructed from HPRD [M. tuberculosis H37Rv and Homo sapiens, we will discuss the patterns of M. tuberculosis H37Rv genes manipulating the human immune signaling pathways in the section Taking advantage of the predicted MTB-human PPI networks, we need to address two concerns (1) how many human genes a rom HPRD  and BioGrom HPRD . To furtTo date, there are very few experimental studies on protein interactions between bacterial pathogens and their hosts. The latest database STRING has curated 1678 bacterial pathogen protein-protein interaction networks, but no work has been reported to exploit these networks to predict pathogen-host protein interactions thus far. Pathogen-host protein interactions play a critical role of signaling cross-talks between pathogen PPI networks and host PPI networks, which is of significance to understand the underlying mechanism of bacterial invasive infection and host immune response.Mycobacterium tuberculosis is an obligate pathogenic bacterial species in the family of Mycobacteriaceae and the causative agent of tuberculosis. The physiology of M. tuberculosis is highly aerobic and requires high levels of oxygen. As primarily a pathogen of the mammalian respiratory system, M. tuberculosis mainly infects the lungs as well as other tissues. M. tuberculosis H37Rv has received much attention in recent years partly due to its co-infection with HIV and increasingly serious drug resistance. To date, the cross-talks or interactions between M. tuberculosis and H. sapiens proteins are much less understood than the individual genome of M. tuberculosis. To the best of our knowledge, there is no experimental study on protein interactions between M. tuberculosis H37Rv and Homo sapiens.Salmonella-human PPIs as templates to infer Salmonella-plant PPIs via plant-human ortholog mapping  are searched within the human genome space, presumably A’ and B′, respectively, then it is assumed that A interacts with B′ and B interacts with A’. The assumption is based on the accumulated evidences that the different strains of the obligate human pathogen M. tuberculosis have co-evolved, migrated, and expanded with their human hosts [The related computational methods generally infer interlogs via one or more third-party species. For instances, Kshirsagar et al.  use the see Fig. . Zhou etsee Fig. . Howeversee Fig. . As illuan hosts . KnowledM. tuberculosis H37Rv protein interaction networks in STRING [M. tuberculosis H37Rv and Homo sapiens. Among the experimental data in STRING [experimental data in STRING [It is noted that the quality of n STRING  directlyn STRING , only 32n STRING  that aren STRING  have tesSalmonella-plant interlogs from the known plant-human PPIs as the training data to train a KMM-SVM model for novel Salmonella-plant PPI predictions. As shown in Table Salmonella-human PPI networks is transferred in the first experiment to predict Salmonella-mouse protein interactions, achieving SE 0.937 and SP 0.517; and the knowledge of Salmonella-Ecoli PPI networks is transferred in the second experiment to predict Salmonella-human protein interactions, achieving SE 0.257 and SP 0.161. The results in the second experiment are obviously much poorer in that the gap of species between E.coli and human is much larger than that between mice and human. Even in the first experiment, the method [The interlog-only methods , 25 do ne method  neither M. tuberculosis H37Rv genes. As shown in Fig. M. tuberculosis H37Rv genes are inclined to target those human genes located in the cellular compartments of membrane (GO:0016020), integral to membrane (GO:0016021), cytoplasm (GO:0005737), nucleus (GO:0005634), mitochondrion (GO:0005739), etc. As shown in Fig. Figure M. tuberculosis genes interact with human defence system. For simplicity, the pathways IL1~IL11 in NetPath are merged into one IL signaling pathway, thus we totally obtain 27 human immune signaling pathways. The predicted MTB-human PPIs related to human immune signaling pathways are provided in Additional file M. tuberculosis H37Rv genes are inclined to target the human immune signaling pathways of AR (Androgen receptor), TNF-alpha , TGF-beta (Transforming growth factor beta receptor), IL (Interleukin), BCR (B cell receptor), TSH ,, etc. In most cases, many M. tuberculosis H37Rv genes are predicted to target more than one human immune signaling pathways  is predicted to target five signaling pathways . Partial GO enrichment analysis of the targeted human genes on TNF-alpha and IL signaling pathways are given in Table Bacterial invasion could induce host inflammatory response, for instances, TNF-α is thought to play a role in the activation of resting macrophages and inhibition of bacterial dissemination, and IL-10 might play a role in controlling the trade-off between the anti-microbial activity and host-derived tissue caseation , 51. We M. tuberculosis H37Rv is predicted to invade human TNF-alpha signaling pathway through 443 MTB-human PPIs and the targeted human genes {CASP10, BID, RUVBL2, DDX21, RPL4, BRINP1, PSMC2, PSMD1, FANCD2, MTIF2, PSMD2, PSMB5, RPS11, PSMC3, GLUL, PDCD2, KTN1} ; 16.67% of genes are involved in the biological process of oxidation-reduction process (GO:0055114); 2.14% of genes are involved in the biological process of transmembrane transport (GO:0055085). Besides the GO terms marked with “shared” in Table PSMC2 also gets involved in translation  and post-translational protein modification (GO:0043687). Especially, the targeted human gene PSMC2 plays an important role in the host immune response .The tumor necrosis factor alpha  is a pro-inflammatory cytokine that belongs to the TNF superfamily . M. tubene GO:001020; 16.6M. tuberculosis H37Rv is predicted to interact with human IL-1~ 11 signaling pathways through 157 MTB-human PPIs and the targeted human genes {BCL2L11, UNC119, IRS1, IL2, DOK2, PTPN6, BAD, IL11, VCP, IRS2} ; 27.27% of genes fulfil the molecular function of ATP binding (GO:0005524); 9.79% of genes are involved in oxidation-reduction process (GO:0055114); 3.50% of genes are involved in transport (GO:0006810); 9.79% of genes are involved in DNA repair (GO:0006281), etc. Besides ATP binding (GO:0005524) and transport (GO:0006810), the targeted human gene VCP gets involved in autophagy (GO:0006914), macroautophagy (GO:0016236) and cellular response to DNA damage stimulus (GO:0006974), etc.Interleukins are a group of cytokines  that were first seen to be expressed by white blood cells (leukocytes). The function of immune system depends in a large part on interleukins that promote the development and differentiation of T and B lymphocytes, and hematopoietic cells . M. tubeM. tuberculosis H37Rv and H. sapiens to derive significant interlogs, which are used as the training data to build a predictive model. The knowledge transfer model effectively solves the problem that no experimental bacteria-host protein interactions are available as training data. The predicted protein interactions provided in the Additional files promise to gain applications in the two fields (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.In this work, we provide a general computational framework to exploit the knowledge of the pathogen protein interaction networks in the database STRING for the rapid reconstruction of pathogen-host protein interaction networks. We take full advantage of the co-evolution relationship between Additional file 1:Text file contains the positive training data consisting of the derived significant interlogs. (TXT 1201 kb)Additional file 2:Text file contains the negative training data consisting of randomly sampled non-interlogs. (TXT 769 kb)Additional file 3:Text file contains the gene ontology analysis of the derived interlogs that get involved in drug resistance of cytochromes and other target-modifying enzymes. (TXT 1703 kb)Additional file 4:Text file contains the gene ontology analysis of the derived interlogs that get involved in drug resistance of SOS-response and DNA replication. (TXT 1157 kb)Additional file 5:Text file contains the predicted results on the positive independent test set consisting of less significant interlogs. (TXT 1055 kb)Additional file 6:Text file contains the predicted results on the negative independent test set consisting of randomly sampled non-interlogs. (TXT 80 kb)Additional file 7:Text file contains the gene ontology analysis of the validated less significant interlogs that get involved in drug resistance of antibiotic efflux pumps. (TXT 234 kb)Additional file 8:Text file contains the gene ontology analysis of the validated less significant interlogs that get involved in drug resistance of target-modifying enzymes. (TXT 21 kb)Additional file 9:Text file contains the predicted results on the prediction set consisting of randomly sampled non-interlogs. (TXT 552 kb)Additional file 10:Text file contains the summary of the derived or predicted M.TB-human PPIs. (TXT 2888 kb)Additional file 11:M. tuberculosis H37Rv genes are predicted to target. (TXT 106 kb)Text file contains the human cancer/immune signaling pathways that Additional file 12:M. tuberculosis H37Rv genes that target human cancer/immune signaling pathways. (TXT 21 kb)Text file contains the


From the fundamental design of instrumentation to applications of ion mobility and the study of protein folding and interactions, I am honoured to read the articles in this special issue. Not only does the breadth of the science strike me but also the tremendous progress that has been made over the last few decades. As an introduction to these articles, I trace briefly the history of the mass spectrometry of macromolecular complexes. In so doing, I acknowledge the outstanding efforts of my colleagues worldwide, as well as former group members, in defining and establishing the field of gas phase structural biology and making it the highly respected research area it is today.E. coli GroEL 14-mer [Back in the 1990s, it was considered counterintuitive to imagine that macromolecular assemblies could be preserved in the mass spectrometer. I still remember my initial excitement, when conducting experiments myself, upon finding conditions whereby it was possible to maintain and ‘fly’ intact non-covalent complexes of transthyretin and retinol-binding protein extracted from chicken plasma [L 14-mer . Today GThe Heck group was also working in this area, making major advances in studying macromolecular interactions including those between GroEL and the heptameric co-chaperonins (GroES and gp31) [During these early days of studying protein interactions in vacuum, I became aware of the pioneering work of Joe Loo and his studies of ligand binding . Previoum/z range, endowing enhanced selectivity and sensitivity to the fragmentation process.Inducing fragmentation or unfolding via collisions is relatively straightforward to achieve albeit sometimes difficult to interpret. New ways of activating molecules in more controlled ways are coming to the fore. Combining ultra violet photo dissociation (UV-PD) with ion mobility (IM), Perdita Barran’s group shows here how they can control the initial unfolding steps of three proteins. Compact structures are populated during controlled UV-PD resulting in less fragmentation than the more extended structures that emerge following progressive in-source activation. By maximising selective cleavage at aspartic acid and proline residues, Scott McLuckey and his group use a sequence of proton transfer reactions to control charge and concentrate the precursor ions into one peak within a predefined Early methods of probing folded structure relied heavily on applications of hydrogen deuterium exchange—an approach that it is still widely employed due to its exquisite sensitivity. In this issue, three former group members  advance both the practice and understanding of this methodology.A longstanding interest of the Benesch group has been to understand the asymmetric charge partitioning that takes place during the collision-induced dissociation (CID) of protein complexes . JoiningUsing HDX-MS to discriminate between native and non-native protein conformations, the Borysik group demonstrate remarkable correspondence between experimental and simulated data. A limitation that they highlight however is in the characterisation of homo-protein complexes. Knowledge of the native interface is required, from high-resolution structural information. By contrast, this is not a requirement for hetero-complexes wherein the degree of peptide sampling in the native interface can be inferred directly from differences in associated and free proteins. A need for more advanced algorithms for calculating protection factors is proposed. The work of Jürgen Claesen and Argyris Politis, in taking into account protein motions, and proposing a new method for calculating protection factors, is an important advance in this regard.The strength of many mass spectrometry approaches is enhanced considerably when paired with other techniques. Two influential pioneers in applications of IM, David Clemmer and Mike Bowers, demonstrate this admirably by coupling IM with circular dichroism (CD) and atomic force microscopy (AFM) respectively. Research from the Clemmer group highlights the ability to monitor cis-trans isomerisation of polyproline examining interconversion of no less than eight different conformers. Meanwhile, combining IM with AFM, the Bower’s group tests the efficacy of a small molecule to inhibit the formation of the toxic Aβ42 dodecamer. The small molecule was found to prevent further aggregation of Aβ42, to destabilise preformed fibrils, and to reverse Aβ42 aggregation.Continuing the theme of IM, I am grateful to my former group member Brandon Ruotolo. Following excellent graduate training from David Russell, Brandon convinced me that we should apply IM-MS to protein complexes. Following months of modelling, and coinciding with the construction of a commercial instrument, Brandon demonstrated that the overall topology of a ring-shaped protein complex could be retained in the gas phase . He wentDuring early applications of IM to protein assemblies, we considered at length how best to model protein assemblies and to assign the mass spectra of heterogeneous systems. Again, I am indebted to former group members  who transformed our ability to model complexes and to assign spectra respectively. Tara Pukala showed that by decomposing complexes into subassemblies, it was possible to construct models of interacting subunits and to assemble complexes of unknown topology . Herein,Central to all modelling strategies is the representation of protein subunits. Matteo Degiacomi proposes a new expression to describe the overlap between two spheres for coarse-grained modelling. Also, in this issue, Michael Marty builds on his powerful deconvolution software UniDec  with theWhile fundamental studies pave the way for everything we do, I continue to be excited by the novel insight and biological knowledge that can be attained from the study of intransigent systems using MS. The interplay between phosphorylation status and interactions is beautifully demonstrated in the research of Claire Eyres. For example, specific phosphorylation events are shown to regulate the functions of homodimers and DNA binding.1,3-diallylurea).Combining cross-linking with MS provides structural details for interaction partners. The ability to carry out cross-linking in a reproducible and informative way remains challenging largely due to the paucity of cross linkers and robust software for interpretation . Both thContinuing with former group members, I am delighted to see the contributed article from Carla Schmidt and her research group. Carla first introduced me to the powers of cross-linking showing how differential cross-linking could be used to distinguish different populations of complexes—with or without phosphorylation for example . Here, sIt is, however, integral membrane proteins and their properties and structures in the gas phase that have always fascinated me. My adventures in this challenging area began over a decade ago first with Leopold Ilag  and lateLipids were discovered in these membrane protein complexes in detergent micelles and through my interactions with Min Zhou and Nina Morgner, we uncovered roles for lipids in rotary ATPases . Researcfinally be possible.Through my interactions with Art Laganowsky and others, we learnt much about the modulation of protein structures through specific lipid interactions . The masI am proud that many of my former group members have gone on to forge careers at least in part influenced by their time spent in my laboratory. It is equally rewarding to see former group members embark on entirely new directions. Leopold Ilag while in my group looked at early applications of MS to membrane proteins . Here, iWhether it is proteins from Arctic aerosols, the cell or its membrane, or the design of a new instrument, over my research career, I have witnessed great progress in the mass spectrometry of protein interactions, events which turned early scepticism to international acceptance. Moreover, researchers in this field continue to uncover new insights, to inspire novel research and to prompt new hypotheses. It remains for me to thank colleagues, friends, and current and former group members who have, over the years, played such an important part in inspiring my own work and have been instrumental in ‘capturing biology in flight’.


Evolutionary information contained in the amino acid sequences of proteins specifies the biological function and fold, but exactly what information contained in the protein sequence drives both of these processes? Considerable progress has been made to answer this fundamental question, but it remains challenging to explore the potential space of cooperative interactions between amino acids. Statistical analysis plays a significant role in studying such interactions and its use has expanded in recent years to studies ranging from coevolution-guided rational protein design to protein folding in silico.β2-adrenergic receptor (β2AR) was used to demonstrate that those ’blocks’ contribute the rational design for specifying functional residues. Sibe also provides folding modules based on both the positionally conserved couplings and well-established statistical potentials for simulating protein folding in silico and predicting tertiary structure. Our results show that statistically inferences of basic evolutionary principles, such as conservations and coupled-mutations, can be used to rapidly design a diverse set of proteins and study protein folding.Here we describe a computational tool named Sibe for use in studies of protein sequence, folding, and design using evolutionary coupling between amino acids as a driving factor. In this study, Sibe is used to identify positionally conserved couplings between pairwise amino acids and aid rational protein design. In this process, pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several ’blocks’, which could contribute to driving protein folding and design. A human The developed software Sibe provides a computational tool for systematical analysis from protein primary to its tertiary structure using the evolutionary couplings as a driving factor. Sibe, written in C++, accounts for compatibility with the ’big data’ era in biological science, and it primarily focuses on protein sequence analysis, but it is also applicable to extend to other modeling and predictions of experimental measurementsThe online version of this article (10.1186/s12859-019-2984-1) contains supplementary material, which is available to authorized users. A protein’s biological function and structure are evolved properties and are stabilized by thousands of weak interactions . That inTo better understand what coevolutionary information encoded in protein sequences is necessary and sufficient for protein folding or design, computational and statistical approaches have been applied to study effects of mutations, especially co-variations that may result in altered protein function and conformational changes –10. Two Protein design has been a long-standing challenge to test computational approaches used in protein sequence analysis, folding, and structure prediction –21, AlsoOur ability to reliably detect coevolutionary information will benefit from the development of additional systematic and well-packed tools that can efficiently and rapidly extract evolutionary information from protein sequences. Accordingly, we developed an end-to-end platform, termed Sibe, to investigate how positionally conserved couplings inferred from sufficiently large and diverse multiple sequence alignments (MSAs) can be used for specifying a protein’s structure and function and to build an improved version of the protein. As a general framework, Sibe aims to reduce the gap between sequence analysis and protein folding and design. Sibe provides an easy and rapid method for protein design and folding in silico using analytical and computational inferences based on protein sequences and estimated ‘residue blocks’ identified from highly correlated coevolution.Sibe utilizes a combination of mathematical principles underlying SCA- and DCA-based methods for detecting patterns of structural contacts and functional couplings within sequence alignments to identify functional and physical interactions between amino acids . In addiIn this report, we provide two instructive examples to show the capabilities of Sibe for protein sequence analysis, protein design, and folding predictions. In the first example, we use Sibe to statistically analyze an MSA of a eukaryotic signal transduction protein, a G-protein coupled receptor (GPCR) , for detThe general procedure for launching Sibe is to define a set of protein sequences and then align them in order to estimate variation frequencies in the sequence alignment. Before the initial statistical analysis, we must obtain the sequences of a given protein we are interested in, and then analyze the multiple sequence alignment for capturing the coevolution. Generally, the sequences are the output of searching the query against the UniRef90 database . In thisϕ, ψ) by a convolution neural network model [Sibe incorporates statistical potentials derived from protein sequences (energy-like coevolution) and structure information  for prork model , we perfrk model  on a setN sequences by L positions, denoted as δ=1 if sequence k has amino acid A at position i, otherwise δ=0. Similarly, a joint frequency of amino acid between a pair of positions is formulated as In this study, the use of Sibe was focused on coevolution at the residue level including positionally conserved couplings and statistical potentials derived from the site bias of residues and the pairwise couplings of residues. We perform sequence statistics on a multiple sequence alignment and apply Sibe to capture the amino acid covariances and conservation for evolutionary inferences, then compute residue blocks. Given an MSA of β2AR protein is used to show how Sibe can capture the couplings among residues and generate an energy-like potential derived from site bias of residues and pairwise couplings of residues. First, we compared the sequence of the target protein with the UniRef90 database [β2AR protein sequences, and demonstrated how the statistical energetic potentials derived from the MSA can be used as evolutionary constraints for protein design.Here, the example of the human database  and obtaA at position i would be if A randomly occurred with an expected probability distribution [To capture couplings between pairs of residues, we employ the Kullback-Leibler relative entropy  to measuribution . The defp(·) is the background probability.where β2AR protein. Given the MSA, we can easily compute the single site frequency fi(Ai) and joint frequency fij. To maximize the entropy of the observed probabilities, we can calculate the effective pair couplings and single site bias to meet the maximal agreement between the distribution of expected frequencies and the probability model of actually observed frequencies. To capture partial interactions, a global statistical model  Maximizing the entropy of the probability model, we can get the following statistical model, Z is a normalization constant, eij is a pairwise coupling, and hi(·) is a single site bias.where Evolutionary information based methods have been reported to be correlated with protein stability and can help design targeted single-point mutations , 34, 35.hi and couplings eij can be estimated from the same MSA used for inferences of the positional conservation. Accordingly, positionally conserved couplings are computed from a combination of relative entropy from Eq.  protein, which is a critical eukaryotic signal transduction protein that communicates across the lipid bilayer via recognition and binding of diffusible ligands for significant biological activities [β2AR protein and demonstrate that Sibe can identify significant positionally conserved couplings and important structural features that have been linked to ligand binding activities.Written in C++, Sibe allows for the rapid analysis of long protein sequences and captures the evolution-based information for protein folding, design, and structure prediction . This challenge creates two major questions we approached using Sibe. First, how can we explore the large sequence space to capture key mutations that relate to the functional roles of a protein? Second, how can evolutionary information contained within amino acid sequences contribute to protein evolution (e.g. via establishing kinetic and thermodynamic stability [A great testing ground for the sequence to structure paradigm is protein design . Howevertability ) and howβ2AR protein [β2AR protein was computed from its MSA, as the information calculated from the relative entropy can remarkably reduce the potential complexity of the protein-design problem [To address these questions, we used Sibe to facilitate protein design and attempted to uncover the biophysical rules governing protein folding. In this section, we will use the human  protein  to illus problem . In Fig. problem ) to consE=Es+α·Ec, where Es, Ec and α are site bias energy , coupling energy (contribution of pairwise amino acids), and a weight factor, respectively. According to stability analysis on the thioredoxin family (15 proteins) in ref. [α). Based on the calculation, we got a maximum correlation factor approximately -0.74 when optimized α equals -0.43, as shown in Fig. First, energy . We generated an MSA using human β2AR homologous proteins consisting of 221,306 sequences  constraints for each protein were used for analysis [To assess structure prediction by Sibe, we carried out calculations on an instructive example consisting of three representative proteins following the steps shown on the left side of Fig. analysis . We presanalysis  and coevPhsior [ϕ and ψ located in As each potential confirmation of a protein is drawn from the same Ramachandran map distribution according to the given amino acid sequence, the conformations generated by the Markov Chain Monte Carlo method are partially correlated with each other. In each folding simulation, starting from a query sequence, we generated five hundred initial conformations to trigger structure predictions by iteratively biasing the prediction from constraints. For example, Sibe was used to iteratively predict the tertiary structure of the YwlE protein using the constraints of residue-contacts inferred from its MSA and predicted torsion angles (which are used to increase probabilities of on the Ramachandran map distribution) predicted by Phsior . After 5mentclass2pt{minimWe then compared the predicted models for the target proteins in comparison to the crystal structures  provides an analytical and computational tool for protein sequence data analysis, in silico protein folding and design. All modules in Sibe are implemented in the C++ programming language, and Sibe employs extensible application programming interfaces (APIs) in both C++ and/or Python, which allows rapid analysis of large amounts of protein sequence data for boosting abilities in protein design and folding predictions. Through two instructive applications, we have demonstrated the capabilities of Sibe in extracting meaningful information hidden behind ’big data’ to infer coevolutionary information encoded in amino acid sequences of proteins. In the first example, we applied Sibe to analyze protein sequences for studying the relationships between protein sequence and thermostability, with potential applications in rational design of proteins. We highlight the statistical potential of positionally conserved couplings (PCCs) among residues that are accelerated by graphics processing unit (GPU) computing. In the second application, we demonstrate how Sibe can simulate protein folding using PCCs as a driver and biases that are predicted by machine learning. We account for the overwhelming nature of simulating protein folding by iteratively fixing the residue-contacts and constraints of torsion angles.Generally, Sibe’s power and simple architecture are dependent on expressive and functional modules, which focus on extending methods specifically designed for scientific applications. Understanding the coevolutionary process from metagenome sequence data provides thermodynamic insights into a protein’s evolution, which can aid in the design of more efficient proteins. We hope that the methodology of protein design will have future applications in chemistry, bioremediation, drug design, and drug discovery.Additional file 1Supplementary material. (PDF 1535 kb)


Given the vital functions of these macromolecular machines, their identification and functional characterization is foundational to our understanding of normal and disease biology. The new CORUM 3.0 release encompasses 4274 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 4473 different genes, representing 22% of the protein coding genes in humans. Protein complexes are described by a protein complex name, subunit composition, cellular functions as well as the literature references. Information about stoichiometry of subunits depends on availability of experimental data. Recent developments include a graphical tool displaying known interactions between subunits. This allows the prediction of structural interconnections within protein complexes of unknown structure. In addition, we present a set of 58 protein complexes with alternatively spliced subunits. Those were found to affect cellular functions such as regulation of apoptotic activity, protein complex assembly or define cellular localization. CORUM is freely accessible at  Accordiet al. , even moet al. , is now Due to the importance of the topic, several endeavors were undertaken in order to unravel the cellular complexome. The first large-scale screens for protein complexes were performed in budding yeast ,5 and dihttp://mips.helmholtz-muenchen.de/corum/.For many years, the composition of thousands of protein complexes has been analysed in individual experiments and published in the scientific literature. In addition to the identification of subunit composition, which is the standard in high-throughput experiments, these complexes are also characterized with respect to their cellular function, association with diseases and sometimes stoichiometry. In order to provide a high-quality resource of information on mammalian protein complexes, we generated the comprehensive resource of mammalian protein complexes (CORUM) with 1750 complexes in the first release . The COROur goal was the generation of a reference dataset of protein complex information from mammalian organisms. In order to obtain a high quality and reliability of the data, we only include protein complexes that have been isolated and characterized in individual experiments. Although protein complex information from high-throughput approaches provides an invaluable resource of novel information, we do not include it in CORUM as these data are usually not corroborated by experiments unraveling the biological function of complexes. As of note, our novel core set from CORUM 3.0 (3512 protein complexes) and the hu.MAP dataset share only 29 identical protein complexes , the majSince the previous CORUM release, basic annotation topics such as protein complex name, identification of subunits based on UniProt identifiers, references of used articles and comments remained unchanged. For functional annotation of protein complexes, we offer a mapping to Gene Ontology (GO) terms since CORUM 2.0. In the meantime, we switched manual functional annotation of novel complexes completely to GO terms Figure .Compared to the previous CORUM publication , the conIn recent years, the CORUM dataset was used in a large number of analyses as reference dataset, for benchmarking computational models and high-throughput experimental data. Examples are results from three pioneering high-throughput investigations that were combined in order to present a comprehensive complexome of human cells . In addiBeside the analysis of experimental data, CORUM is also used by other data resources such as ‘Interactome INSIDER’  or the ‘To generate a high number of different gene products from comparably small genomes, mammals make use of processes such as alternative splicing to produce more than 82 335 distinct mRNAs e.g. in humans, according to the Gencode release 28 . AccordiThe effect of alternatively spliced subunits of cytoskeletal protein dystrophin can be illustrated for DAPCs (dystrophin-associated protein complexes). Duchenne muscular dystrophy is caused by the absence of dystrophin . The DAPThe splice variant dataset illustrates that functional properties of particular protein complexes can only be represented by isoform-specific variant annotation. The splice variant complexes can be downloaded as a separate dataset.Although there is a growing number of protein complexes with structure information, characterization of complexes is usually restricted to the identification of the subunits and biomedical information such as cellular function or association with diseases. Structural information such as stoichiometry of individual subunits is rarely discovered. In articles used for CORUM, we found only 288 protein complexes with stoichiometry data. For detailed structural information discovered by x-ray crystallography or nuclear magnetic resonance, information is even more sparse. In the new CORUM dataset 3.0, we have annotated 109 protein complexes with the PSI–MI interaction method, MI:0114 x-ray crystallography’. Preliminary information about protein complex structure with respect to neighboring proteins can be performed by serial protein interaction experiments. This was successfully applied for elucidation of the chaperonin Tric/CCT .Homo sapiens, Rattus norvegicus and Mus musculus. The largest fraction of PPI information was obtained from Homo sapiens (238 841 entries), followed by mouse (15 138) and rat (3962).In order to provide users with the option to inspect putative interactions between complex subunits, the new CORUM release provides a graphical tool that integrates known PPI information. A large publicly available collection of protein–protein interactions is provided by the IntAct database which includes data from different resources . For theFor visualization of interactions between protein complex subunits we use Cytoscape. Cytoscape.js is a javascript-based graph-visualization library that we embedded in version 3.2 on our website. For each protein complex that contains at least one interaction we show all protein–protein interactions according to the IntAct dataset in a graph below the list of search results. For Fanconi anemia FAAP100 complex (complex 6884) for example, nine interactions between subunits and two self-associations were found Figure . All forThe fact, that a protein–protein interaction was discovered in an experiment is no proof that it also exists in a protein complex. However, it is tempting to assume that for the majority of protein complexes, PPI data provide a reliable prediction of intramolecular associations.CORUM is a publicly available, centralized database for mammalian protein complexes based on manually curated information from scientific literature. Basic and translational researchers are provided with extensive search options to look for complexes containing their genes of interest, exhibiting a specific biological function or displaying other features. Computational biologists can download entire datasets in different formats for advanced studies. The importance of protein complex data is demonstrated by hundreds of studies that used the CORUM dataset during the last decade. These include basic research such as the inventory of the mammalian complexome as well as applied biomedical research, in particular of cancer. The CORUM 3.0 release provides a substantially enlarged dataset of mammalian complexes which is accompanied by a wider coverage of gene products that serve as subunits of protein complexes.In recent years, a growing number of studies has demonstrated that variants of gene products may have substantial effects on protein complex function. Here, we present for the first time a dataset that covers the impact of splice variants on cellular processes and diseases.In addition to the larger dataset, the CORUM 3.0 release also presents a Cytoscape-based tool for the graphical representation of known protein–protein interactions of subunits. As structural information about large protein complexes is sparse, this approach allows the prediction of structurally adjacent proteins within these large complexes.andreas.ruepp@helmholtz-muenchen.de.An important goal of CORUM for the future will be to obtain an even more complete representation of experimentally characterized protein complexes. To achieve this goal, we welcome the input of other researchers sending us information about novel published protein complexes not yet included in the dataset. Please contact us at