The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers\' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked models in native-like solutions. The best performing clustering approaches we tested indeed lead to more than double the number of cases for which at least one correct solution can be included within the top ten ranked models.

BACKGROUND: Knowing the subcellular location of proteins is critical for understanding their function and developing accurate networks representing eukaryotic biological processes. Many computational tools have been developed to predict proteome-wide subcellular location, and abundant experimental data from green fluorescent protein (GFP) tagging or mass spectrometry (MS) are available in the model plant, Arabidopsis. None of these approaches is error-free, and thus, results are often contradictory.
RESULTS: To help unify these multiple data sources, we have developed the SUBcellular Arabidopsis consensus (SUBAcon) algorithm, a naive Bayes classifier that integrates 22 computational prediction algorithms, experimental GFP and MS localizations, protein-protein interaction and co-expression data to derive a consensus call and probability. SUBAcon classifies protein location in Arabidopsis more accurately than single predictors.
BACKGROUND: SUBAcon is a useful tool for recovering proteome-wide subcellular locations of Arabidopsis proteins and is displayed in the SUBA3 database (http://suba.plantenergy.uwa.edu.au). The source code and input data is available through the SUBA3 server (http://suba.plantenergy.uwa.edu.au//SUBAcon.html) and the Arabidopsis SUbproteome REference (ASURE) training set can be accessed using the ASURE web portal (http://suba.plantenergy.uwa.edu.au/ASURE).

There is significant interest in the development of methods with the potential to increase access to \'the interactome\' for both experimental and clinical applications. Immunoprecipitation detected by flow cytometry (IP-FCM) is a robust, biochemical method that can be used for measuring physiologic protein-protein interactions (PPI) in multiprotein complexes (MPC) with high sensitivity. Because it is based on antibody-mediated capture of protein complexes onto microspheres, IP-FCM is potentially compatible with a multiplex platform that could allow simultaneous assessment of many physiologic PPI. Here, we consider the principles of ambient analyte conditions (AAC) and inter-bead independence, and provide a template set of experiments showing how to convert singleplex IP-FCM to multiplex IP-FCM, including assays to confirm the validity of the experimental conditions for data acquisition. We conclude that singleplex IP-FCM can be successfully upgraded to multiplex format, and propose that the unique strengths of multiplex IP-FCM make it a method that is likely to facilitate the acquisition of new PPI data from primary cell sources.

The kinetochore is a supramolecular structure essential for microtubule attachment and the mitotic checkpoint. Human blinkin/human Spc105 (hSpc105)/hKNL1 was identified originally as a mixed-lineage leukemia (MLL) fusion partner and later as a kinetochore component. Blinkin directly binds to several structural and regulatory proteins, but the precise binding sites have not been defined. Here, we report distinct and essential binding domains for Bub1 and BubR1 (here designated Bubs) at the N terminus of blinkin and for Zwint-1 and hMis14/hNsl1 at the C terminus. The minimal binding sites for Bub1 and BubR1 are separate but contain a consensus KI motif, KI(D/N)XXXF(L/I)XXLK. RNA interference (RNAi)-mediated replacement with mutant blinkin reveals that the Bubs-binding domain is functionally important for chromosome alignment and segregation. We also provide evidence that hMis14 mediates hNdc80 binding to blinkin at the kinetochore. The C-terminal fragment of blinkin locates at kinetochores in a dominant-negative fashion by displacing endogenous blinkin from kinetochores. This negative dominance is relieved by mutations of the hMis14 binding PPSS motif on the C terminus of blinkin or by fusion of the N sequence that binds to Bub1 and BubR1. Taken together, these results indicate that blinkin functions to connect Bub1 and BubR1 with the hMis12, Ndc80, and Zwint-1 complexes, and disruption of this connection may lead to tumorigenesis.

Characterizing the subcellular localization of a protein provides a key clue for understanding protein function. However, different protein localization prediction programs often deliver conflicting results regarding the localization of the same protein. As the number of available localization prediction programs continues to grow, there is a need for a consensus prediction approach. To address this need, we developed a consensus localization prediction method called ConLoc based on a large-scale, systematic integration of 13 available programs that make predictions for five major subcellular localizations (cytosol, extracellular, mitochondria, nucleus, and plasma membrane). The ability of ConLoc to accurately predict protein localization was substantially better than existing programs. Using ConLoc prediction, we built a localization-guided functional interaction network of the human proteome and mapped known disease associations within this network. We found a high degree of shared disease associations among functionally interacting proteins that are localized to the same cellular compartment. Thus, the use of consensus localization prediction, such as ConLoc, is a new approach for the identification of novel disease associated genes.

Both Neisseria meningitidis and Neisseria gonorrhoeae recruit the alternative pathway complement inhibitory protein factor H (fH) to their surfaces to evade complement-dependent killing. Meningococci bind fH via fH binding protein (fHbp), a surface-exposed lipoprotein that is subdivided into three variant families based on one classification scheme. Chimeric proteins that comprise contiguous domains of fH fused to murine Fc were used to localize the binding site for all three fHbp variants on fH to short consensus repeat 6 (SCR 6). As expected, fH-like protein 1 (FHL-1), which contains fH SCR 6, also bound to fHbp-expressing meningococci. Using site-directed mutagenesis, we identified histidine 337 and histidine 371 in SCR 6 as important for binding to fHbp. These findings may provide the molecular basis for recent observations that demonstrated human-specific fH binding to meningococci. Differences in the interactions of fHbp variants with SCR 6 were evident. Gonococci bind fH via their porin (Por) molecules (PorB.1A or PorB.1B); sialylation of lipooligosaccharide enhances fH binding. Both sialylated PorB.1B- and (unsialylated) PorB.1A-bearing gonococci bind fH through SCR 18 to 20; PorB.1A can also bind SCR 6, but only weakly, as evidenced by a low level of binding of FHL-1 relative to that of fH. Using isogenic strains expressing either meningococcal fHbp or gonococcal PorB.1B, we discovered that strains expressing gonococcal PorB.1B in the presence of sialylated lipooligosaccharide bound more fH, more effectively limited C3 deposition, and were more serum resistant than their isogenic counterparts expressing fHbp. Differences in fH binding to these two related pathogens may be important for modulating their individual responses to host immune attack.

The identification of near native protein-protein complexes among a set of decoys remains highly challenging. A strategy for improving the success rate of near native detection is to enrich near native docking decoys in a small number of top ranked decoys. Recently, we found that a combination of three scoring functions (energy, conservation, and interface propensity) can predict the location of binding interface regions with reasonable accuracy. Here, these three scoring functions are modified and combined into a consensus scoring function called ENDES for enriching near native docking decoys. We found that all individual scores result in enrichment for the majority of 28 targets in ZDOCK2.3 decoy set and the 22 targets in Benchmark 2.0. Among the three scores, the interface propensity score yields the highest enrichment in both sets of protein complexes. When these scores are combined into the ENDES consensus score, a significant increase in enrichment of near-native structures is found. For example, when 2000 dock decoys are reduced to 200 decoys by ENDES, the fraction of near-native structures in docking decoys increases by a factor of about six in average. ENDES was implemented into a computer program that is available for download at http://sparks.informatics.iupui.edu.

Coagulation factor VIII interacts with several members of the low-density lipoprotein receptor family including low-density lipoprotein receptor-related protein, low-density lipoprotein receptor, and very low-density lipoprotein receptor. The present study was aimed to compare the mechanisms of factor VIII interaction with low-density lipoprotein receptor-related protein, megalin, low-density lipoprotein receptor, and very low-density lipoprotein receptor in order to reveal a general mode of these interactions. Binding of plasma-derived factor VIII and its fragments to recombinant soluble ligand-binding domain of low-density lipoprotein receptor (sLDLR1-7) and purified megalin was studied in solid phase and surface plasmon resonance assays. Full-length factor VIII and its light chain bound to the receptors with similar affinities (KD = 260 +/- 9 and 156 +/- 4 nmol/l, respectively, for megalin and KD = 210 +/- 3 and 174 +/- 13 nmol/l, respectively, for sLDLR1-7). Von Willebrand factor inhibited factor VIII binding to both receptors. In contrast to the light chain, exposure of the high-affinity receptor-binding site within the heavy chain (KD = 22 +/- 4 nmol/l for megalin and 17 +/- 3 nmol/l for sLDLR1-7) required proteolytic cleavage by thrombin. This site was mapped to the A2 domain residues 484-509, based on the inhibitory effects of anti-A2 monoclonal antibody 413, and is shared by all four receptors. Using a panel of A2 mutants, we identified key amino acid residues- positively charged K466, R471, R489 and R490, and hydrophilic residues Y487 and S488- which form the frame of this \'consensus\' binding site. We conclude that interaction of factor VIII with the members of the low-density lipoprotein receptor family follows the general mode, requires dissociation of factor VIII from von Willebrand factor, and is activation sensitive.

Dual-color fluorescence cross-correlation spectroscopy (FCCS) allows for the determination of molecular mobility and concentrations and for the quantitative analysis of molecular interactions such as binding or cleavage at very low concentrations. This protocol discusses considerations for preparing a biological system for FCCS experiments and offers practical advice for performing FCCS on a commercially available setup. Although FCCS is closely related to two-color confocal microscopy, critical adjustments and test measurements are necessary to establish successful FCCS measurements, which are described in a step-by-step manner. Moreover, we discuss control experiments for a negative cross-correlation artifact, arising from a lack of detection volume overlap, and a positive artifact, arising from cross-talk. FCCS has been applied to follow molecular interactions in solutions, on membranes and in cells and to analyze dynamic colocalization during intracellular transport. It is a technique that is expected to see new applications in various fields of biochemical and cell biological research.