Handling Noise in Protein Interaction Networks.

Protein-protein interactions (PPIs) can be conveniently represented as networks, allowing the use of graph theory for their study. Network topology studies may reveal patterns associated with specific organisms. Here, we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the organization measurement (OM) method. The OM methodology was applied in the denoising of the PPI networks of two Saccharomyces cerevisiae datasets (Yeast and CS2007) and one Homo sapiens dataset (Human). To evaluate the denoising capabilities of the OM methodology, two strategies were applied. The first strategy compared its application in random networks and in the reference set networks, while the second strategy perturbed the networks with the gradual random addition and removal of edges. The application of the OM methodology to the Yeast and Human reference sets achieved an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast and Human reference set interactions resulted in an AUC of 0.71 and 0.62, whereas the random addition of 80% interactions resulted in an AUC of 0.75 and 0.72, respectively. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89%, when 80% more edges were inserted. The true positives identified and inserted in the network varied from 95%, when removing 20% of the edges, to 40%, after the random deletion of 80% edges. The OM methodology is sensitive to the topological structure of the biological networks. The obtained results suggest that the present approach can efficiently be used to denoise PPI networks.

Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction.

De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.

Computational methodology for predicting the landscape of the human-microbial interactome region level influence.

Microbial communities thrive in close association among themselves and with the host, establishing protein-protein interactions (PPIs) with the latter, and thus being able to benefit (positively impact) or disturb (negatively impact) biological events in the host. Despite major collaborative efforts to sequence the Human microbiome, there is still a great lack of understanding their impact. We propose a computational methodology to predict the impact of microbial proteins in human biological events, taking into account the abundance of each microbial protein and its relation to all other microbial and human proteins. This alternative methodology is centered on an improved impact estimation algorithm that integrates PPIs between human and microbial proteins with Reactome pathway data. This methodology was applied to study the impact of 24 microbial phyla over different cellular events, within 10 different human microbiomes. The results obtained confirm findings already described in the literature and explore new ones. We believe the Human microbiome can no longer be ignored as not only is there enough evidence correlating microbiome alterations and disease states, but also the return to healthy states once these alterations are reversed.

Uncovering microbial duality within human microbiomes: A novel algorithm for the analysis of host-pathogen interactions.

Microbial species thrive within human hosts by establishing complex associations between themselves and the host. Even though species diversity can be measured (alpha- and beta-diversity), a methodology to estimate the impact of microorganisms in human pathways is still lacking. In this work we propose a computational approach to estimate which human pathways are targeted the most by microorganisms, while also identifying which microorganisms are prominent in this targeting. Our results were consistent with literature evidence, and thus we propose this methodology as a new prospective approach to be used for screening potentially impacted pathways.

Computational prediction of the human-microbial oral interactome.

BACKGROUND: The oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome. RESULTS: We collected high-quality experimental PPIs from five major human databases. The obtained PPIs were used to create our positive dataset and, indirectly, our negative dataset. The positive and negative datasets were merged and used for training and validation of a naïve Bayes classifier. For the final prediction model, we used an ensemble methodology combining five distinct PPI prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. Performance evaluation of our method revealed an area under the ROC-curve (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10-7), leading to a set of 46,579 PPIs to be further explored. CONCLUSIONS: We believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. The dataset used for training and validation, the predictions obtained and the network final network are available at http://bioinformatics.ua.pt/software/oralint.

From protein-protein interactions to rational drug design: are computational methods up to the challenge?

The study of protein-protein interactions (PPIs) has been growing for some years now, mainly as a result of easy access to high-throughput experimental data. Several computational approaches have been presented throughout the years as means to infer PPIs not only within the same species, but also between different species (e.g., host-pathogen interactions). The importance of unveiling the human protein interaction network is undeniable, particularly in the biological, biomedical and pharmacological research areas. Even though protein interaction networks evolve over time and can suffer spontaneous alterations, occasional shifts are often associated with disease conditions. These disorders may be caused by external pathogens, such as bacteria and viruses, or by intrinsic factors, such as auto-immune disorders and neurological impairment. Therefore, having the knowledge of how proteins interact with each other will provide a great opportunity to understand pathogenesis mechanisms, and subsequently support the development of drugs focused on very specific disease pathways and re-targeting already commercialized drugs to new gene products. Computational methods for PPI prediction have been highlighted as an interesting option for interactome mapping. In this paper we review the techniques and strategies used for both experimental identification and computational inference of PPIs. We will then discuss how this knowledge can be used to create protein interaction networks (PINs) and the various methodologies applied to characterize and predict the so-called "disease genes" and "disease networks". This will be followed by an overview of the strategies employed to predict drug targets.

OralCard: a bioinformatic tool for the study of oral proteome.

OBJECTIVES: The molecular complexity of the human oral cavity can only be clarified through identification of components that participate within it. However current proteomic techniques produce high volumes of information that are dispersed over several online databases. Collecting all of this data and using an integrative approach capable of identifying unknown associations is still an unsolved problem. This is the main motivation for this work. RESULTS: We present the online bioinformatic tool OralCard, which comprises results from 55 manually curated articles reflecting the oral molecular ecosystem (OralPhysiOme). It comprises experimental information available from the oral proteome both of human (OralOme) and microbial origin (MicroOralOme) structured in protein, disease and organism. CONCLUSIONS: This tool is a key resource for researchers to understand the molecular foundations implicated in biology and disease mechanisms of the oral cavity. The usefulness of this tool is illustrated with the analysis of the oral proteome associated with diabetes melitus type 2. OralCard is available at http://bioinformatics.ua.pt/oralcard.