Human Interaction Dataset Research Articles

A topology-based metric for measuring term similarity in the gene ontology.

The wide coverage and biological relevance of the Gene Ontology (GO), confirmed through its successful use in protein function prediction, have led to the growth in its popularity. In order to exploit the extent of biological knowledge that GO offers in describing genes or groups of genes, there is a need for an efficient, scalable similarity measure for GO terms and GO-annotated proteins. While several GO similarity measures exist, none adequately addresses all issues surrounding the design and usage of the ontology. We introduce a new metric for measuring the distance between two GO terms using the intrinsic topology of the GO-DAG, thus enabling the measurement of functional similarities between proteins based on their GO annotations. We assess the performance of this metric using a ROC analysis on human protein-protein interaction datasets and correlation coefficient analysis on the selected set of protein pairs from the CESSM online tool. This metric achieves good performance compared to the existing annotation-based GO measures. We used this new metric to assess functional similarity between orthologues, and show that it is effective at determining whether orthologues are annotated with similar functions and identifying cases where annotation is inconsistent between orthologues.

Interactome-wide prediction of short, disordered protein interaction motifs in humans

Many of the specific functions of intrinsically disordered protein segments are mediated by Short Linear Motifs (SLiMs) interacting with other proteins. Well known examples include SLiMs that interact with 14-3-3, PDZ, SH2, SH3, and WW domains but the true extent and diversity of SLiM-mediated interactions is largely unknown. Here, we attempt to expand our knowledge of human SLiMs by applying in silico SLiM prediction to the human interactome. Combining data from seven different interaction databases, we analysed approximately 6000 protein-centred and 1600 domain-centred human interaction datasets of 3+ unrelated proteins that interact with a common partner. Results were placed in context through comparison to randomised datasets of similar size and composition. The search returned thousands of evolutionarily conserved, intrinsically disordered occurrences of hundreds of significantly enriched recurring motifs, including many that have never been previously identified (). In addition to True Positive results for at least 25 different known SLiMs, a striking number of off-target proteins/domains also returned significantly enriched known motifs. Often, this was due to the non-independence of the datasets, with many proteins sharing interaction partners or contributing interactions to multiple domain datasets. The majority of these motif classes, however, were also found to be significantly enriched in one or more randomised datasets. This highlights the need for care when interpreting motif predictions of this nature but also raises the possibility that SLiM occurrences may be successfully identified independently of interaction data. Although not as compositionally biased as previous studies, patterns matching known SLiMs tended to cluster into a few large groups of similar sequence, while novel predictions tended to be more distinctive and less abundant. Whether this is due to ascertainment bias or a true functional composition bias of SLiMs is not clear and warrants further investigation.

Human activity recognition using overlapping multi-feature descriptor

An efficient overlapping multi-feature descriptor and classification scheme for human activity recognition is introduced. The descriptor is constructed by overlapping global feature combinations of multi-frames using a Hankel matrix representation. The descriptor captures the local and temporal information while overcoming the limitations of global features using an overlapping combination scheme. In addition, a random forests classifier is used to cope with noise in the descriptor that can be obtained from no-activity frames in a video. Using this framework, it is shown that the approach outperforms the state-of-the-art methods using the KTH dataset and a much more complex human interaction dataset.

Proteome-wide Prediction of Signal Flow Direction in Protein Interaction Networks Based on Interacting Domains

Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.

Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery

Short linear motifs (SLiMs) are important mediators of protein-protein interactions. Their short and degenerate nature presents a challenge for computational discovery. We sought to improve SLiM discovery by incorporating evolutionary information, since SLiMs are more conserved than surrounding residues. We have developed a new method that assesses the evolutionary signal of a residue in its sequence and structural context. Under-conserved residues are masked out prior to SLiM discovery, allowing incorporation into the existing statistical model employed by SLiMFinder. The method shows considerable robustness in terms of both the conservation score used for individual residues and the size of the sequence neighbourhood. Optimal parameters significantly improve return of known functional motifs from benchmarking data, raising the return of significant validated SLiMs from typical human interaction datasets from 20% to 60%, while retaining the high level of stringency needed for application to real biological data. The success of this regime indicates that it could be of general benefit to computational annotation and prediction of protein function at the sequence level. All data and tools in this article are available at http://bioware.ucd.ie/~slimdisc/slimfinder/conmasking/.

PRINCESS, a Protein Interaction Confidence Evaluation System with Multiple Data Sources

Advances in proteomics technologies have enabled novel protein interactions to be detected at high speed, but they come at the expense of relatively low quality. Therefore, a crucial step in utilizing the high throughput protein interaction data is evaluating their confidence and then separating the subsets of reliable interactions from the background noise for further analyses. Using Bayesian network approaches, we combine multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein-protein interactions identified by high throughput experiments. This method shows high sensitivity and specificity to predict true interactions from the human high throughput protein-protein interaction data sets. This method has been developed into an on-line confidence scoring system specifically for the human high throughput protein-protein interactions. Users may submit their protein-protein interaction data on line, and the detailed information about the supporting evidence for query interactions together with the confidence scores will be returned. The Web interface of PRINCESS (protein interaction confidence evaluation system with multiple data sources) is available at the website of China Human Proteome Organisation.

Systems Biology Analysis of Human Primary T Cells Identifies SAP145 as Rate Limiting for the G1→S Phase Transition.

We identified a G0→G1 commitment point in primary human T cells that controls entry into the cell cycle from quiescence. We demonstrated proof of principle that cellular pathways regulating cell cycle progression and effector functions that normally coincide during CD3/CD28 stimulation can be uncoupled experimentally. We have now used systems biology approaches to identify nuclear protein networks in primary human T cells that are regulated during the transition from quiescence into the cell cycle (G0→G1→S-phase). First we sequenced proteins that became bound to chromatin & nuclear matrix in G1 but were not bound in G0 and vice versa by mass spectrometry. Bioinformatic analysis identified 76 proteins specifically bound in G0 not G1 and 254 bound in G1 not G0. 179 of the 254 proteins bound in G1 not G0 (i.e. dynamic protein changes) were mapped to the 55,000 human protein interaction dataset. These are involved in numerous cellular functions, including epigenetics, transcription, RNA splicing & transport, and others. Cell cycle regulated chromatin/matrix binding of a subset was verified by western blotting (2/2 bound in G0 not G1 and 22/23 bound in G1 not G0). One of the proteins induced and bound in G1 was SAP145 (SF3B2). This is a component of the ubiquitous SF3b RNA splicing complex, involved in both major (U2-type) and minor (U12-type) spliceosomes. Since SAP145 is induced during G1 we investigated whether there was a role for SAP145 in regulating cell cycle progression. T cells depleted of SAP145 by siRNA enter G1 from G0 but progress poorly through S phase and die, probably by apoptosis. The same occurs if another component of the SF3B complex, SAP49 (SF3B4) is depleted with siRNA, indicating that the effect is due to depleting the complex rather than the individual SF3B protein. Proteins that are induced during G1 by CD3/CD28 stimulation e.g. cyclin D3, Cdc6 and cdc2 are produced normally when SAP145 is depleted, suggesting that their pre-mRNAs are spliced normally. In contrast, the expression of p107 and cyclin A2 are reduced markedly when SAP145 is depleted. Therefore, a systems biology approach to analysing cell cycle transitions identifies the splicing protein, SAP145 as rate-limiting for the G1 →S phase cell cycle transition but not for the transition from G0→G1.

Probabilistic prediction and ranking of human protein-protein interactions

BackgroundAlthough the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%.ResultsThe prediction of human protein-protein interactions was investigated by combining orthogonal protein features within a probabilistic framework. The features include co-expression, orthology to known interacting proteins and the full-Bayesian combination of subcellular localization, co-occurrence of domains and post-translational modifications. A novel scoring function for local network topology was also investigated. This topology feature greatly enhanced the predictions and together with the full-Bayes combined features, made the largest contribution to the predictions. Using a conservative threshold, our most accurate predictor identifies 37606 human interactions, 32892 (80%) of which are not present in other publicly available large human interaction datasets, thus substantially increasing the coverage of the human interaction map. A subset of the 32892 novel predicted interactions have been independently validated. Comparison of the prediction dataset to other available human interaction datasets estimates the false positive rate of the new method to be below 80% which is competitive with other methods. Since the new method scores and ranks all human protein pairs, smaller subsets of higher quality can be generated thus leading to even lower false positive prediction rates.ConclusionThe set of interactions predicted in this work increases the coverage of the human interaction map and will help determine the highest confidence human interactions.

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online).

BackgroundThe significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level.ResultsHere, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs.ConclusionThis architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome

In order to consolidate the known human proteins interactions two tests were developed to measure the relative accuracy of the available interaction data. In addition, 6,580 interactions among 3,737 human proteins were recovered from Medline abstracts and combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing data sets.