SAFE Software and FED Database to Uncover Protein-Protein Interactions using Gene Fusion Analysis

Molecular fusion events in carcinogenic organisms: a bioinformatics study for the detection of fused proteins between viruses, bacteria and eukaryotes

Molecular fusion events have a prominent role in the initial steps of carcinogenesis. In this study, a bioinformatics analysis was performed between four organisms that are known to induce cancer development in humans: two viruses, Human Herpesvirus 4, and Human T-cell leukaemia virus, one bacterium, Helicobacter Pylori, and one trematode, Schistosoma mansoni. The annotated proteomes from these organisms were analysed using the SAFE software to identify protein fusion events, which may provide insight into protein function similarities and possible merging events during the course of evolution. Based on the results, five fused proteins with very similar functions were detected, whereas proteins with different functions that might act in the same molecular complex or biochemical pathway were not found. Thus, this study analysed the above four well-known cancer-related organisms with de novo bioinformatics programs and provided useful information on protein fusion events, hopefully leading to deeper understanding of carcinogenenesis.

Antibodies as stratagems against cancer

Antibodies have been in the frontline of anticancer research during the last few decades, since a number of different ways have been discovered to utilize them as parts or main components of anticancer drugs. Antibodies are used as the only component of some anticancer drugs, but they can also be conjugated with a variety of substances. Antibody engineering methods such as humanization, chimerization and Fc engineering are applied in order to modify their properties according to the requirements of anticancer drug application. Given the continuous advances in biology and informatics, the role of antibodies in anticancer treatment is expected to be prominent.

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

Background

Gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur.

Results

Here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database at http://modelseed.org/projects/fusions/. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent.

Conclusions

Customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2782-3) contains supplementary material, which is available to authorized users.

Unraveling microalgal molecular interactions using evolutionary and structural bioinformatics

Microalgae are unicellular microorganisms indispensible for environmental stability and life on earth, because they produce approximately half of the atmospheric oxygen, with simultaneously feeding on the harmful greenhouse gas carbon dioxide. Using gene fusion analysis, a series of five fusion/fission events was identified, that provided the basis for critical insights to their evolutionary history. Moreover, the three-dimensional structures of both the fused and the component proteins were predicted, allowing us to envisage putative protein-protein interactions that are invaluable for the efficient usage, handling and exploitation of microalgae. Collectively, our proposed approach on the five fusion/fission alga protein events contributes towards the expansion of the microalgae knowledgebase, bridging protein evolution of the ancient microalgal species and the rapidly evolving, modern, bioinformatics field.

Gene fusion analysis in the battle against the African endemic sleeping sickness

The protozoan Trypanosoma brucei causes African Trypanosomiasis or sleeping sickness in humans, which can be lethal if untreated. Most available pharmacological treatments for the disease have severe side-effects. The purpose of this analysis was to detect novel protein-protein interactions (PPIs), vital for the parasite, which could lead to the development of drugs against this disease to block the specific interactions. In this work, the Domain Fusion Analysis (Rosetta Stone method) was used to identify novel PPIs, by comparing T. brucei to 19 organisms covering all major lineages of the tree of life. Overall, 49 possible protein-protein interactions were detected, and classified based on (a) statistical significance (BLAST e-value, domain length etc.), (b) their involvement in crucial metabolic pathways, and (c) their evolutionary history, particularly focusing on whether a protein pair is split in T. brucei and fused in the human host. We also evaluated fusion events including hypothetical proteins, and suggest a possible molecular function or involvement in a certain biological process. This work has produced valuable results which could be further studied through structural biology or other experimental approaches so as to validate the protein-protein interactions proposed here. The evolutionary analysis of the proteins involved showed that, gene fusion or gene fission events can happen in all organisms, while some protein domains are more prone to fusion and fission events and present complex evolutionary patterns.