***TASK***
the task is to simplify the input abstract of a biomedical literature

***INPUT***
the input is the abstract of a biomedical literature

***OUTPUT***
the output is the simplified abstract for the input abstract of a biomedical literature
***DOCUMENTATION*** 
***EXAMPLES*** 
Input:
Output:

***EXAMPLES***
Input: Functionally analogous enzymes are those that catalyze similar reactions on similar substrates but do not share common ancestry, providing a window on the different structural strategies nature has used to evolve required catalysts. Identification and use of this information to improve reaction classification and computational annotation of enzymes newly discovered in the genome projects would benefit from systematic determination of reaction similarities. Here, we quantified similarity in bond changes for overall reactions and catalytic mechanisms for 95 pairs of functionally analogous enzymes (non-homologous enzymes with identical first three numbers of their EC codes) from the MACiE database. Similarity of overall reactions was computed by comparing the sets of bond changes in the transformations from substrates to products. For similarity of mechanisms, sets of bond changes occurring in each mechanistic step were compared; these similarities were then used to guide global and local alignments of mechanistic steps. Using this metric, only 44% of pairs of functionally analogous enzymes in the dataset had significantly similar overall reactions. For these enzymes, convergence to the same mechanism occurred in 33% of cases, with most pairs having at least one identical mechanistic step. Using our metric, overall reaction similarity serves as an upper bound for mechanistic similarity in functional analogs. For example, the four carbon-oxygen lyases acting on phosphates (EC 4.2.3) show neither significant overall reaction similarity nor significant mechanistic similarity. By contrast, the three carboxylic-ester hydrolases (EC 3.1.1) catalyze overall reactions with identical bond changes and have converged to almost identical mechanisms. The large proportion of enzyme pairs that do not show significant overall reaction similarity (56%) suggests that at least for the functionally analogous enzymes studied here, more stringent criteria could be used to refine definitions of EC sub-subclasses for improved discrimination in their classification of enzyme reactions. The results also indicate that mechanistic convergence of reaction steps is widespread, suggesting that quantitative measurement of mechanistic similarity can inform approaches for functional annotation.
Output:  When species evolve, their genes duplicate and diverge to allow for adaptation of their functional repertoires to the changing environment. In this scenario, unrelated genes can convergently evolve to produce proteins with the same molecular function, termed “functionally analogous.” A quantitative determination of the reaction similarities among functionally analogous enzymes could provide insight about the different structural solutions nature has used to evolve similar catalysts. Bond changes between substrates and products, and between successive reaction intermediates, were used to compare the reactions catalyzed and the mechanisms of catalysis for 95 pairs of functionally analogous enzymes. Less than half of the reactions catalyzed by unrelated enzymes, but defined as similar by the Enzyme Commission (EC) classification, are similar in terms of bond changes, suggesting that this classification often fails to capture quantitative differences between many enzyme reactions. Furthermore, we addressed for the first time whether the chemical mechanisms by which similar overall reactions are achieved in functional analogs are also similar. We conclude that convergence of reaction is often accompanied by convergence of chemical mechanism. These results will be useful for classifying enzymes, guiding functional annotation of newly determined enzyme sequences and structures and for informing the engineering of enzymes with new functions.

Input:  Ontologies represent powerful tools in information technology because they enhance interoperability and facilitate, among other things, the construction of optimized search engines. To address the need to expand the toolbox available for the control and prevention of vector-borne diseases we embarked on the construction of specific ontologies. We present here IDODEN, an ontology that describes dengue fever, one of the globally most important diseases that are transmitted by mosquitoes.
We constructed IDODEN using open source software, and modeled it on IDOMAL, the malaria ontology developed previously. IDODEN covers all aspects of dengue fever, such as disease biology, epidemiology and clinical features. Moreover, it covers all facets of dengue entomology. IDODEN, which is freely available, can now be used for the annotation of dengue-related data and, in addition to its use for modeling, it can be utilized for the construction of other dedicated IT tools such as decision support systems.
The availability of the dengue ontology will enable databases hosting dengue-associated data and decision-support systems for that disease to perform most efficiently and to link their own data to those stored in other independent repositories, in an architecture- and software-independent manner.


Output:

The need for the construction of a dengue ontology arose through the fact that the incidence of dengue fever is on the rise across the world; the number of cases may be three to four times higher than the 100 million estimated by the WHO and a vaccine is still not available in spite of the significant efforts undertaken. Thus, control of dengue fever still relies mostly on controlling its mosquito vectors. Large amounts of entomological, epidemiological and clinical data are generated; these need to be efficiently organized in order to further our comprehension of the disease and its control. IDODEN aims to cover the different aspects and intricacies of dengue fever and syndromes caused by dengue virus(es). It contains more than 5000 terms describing epidemiological data, vaccine development, clinical features, the disease course, and more. We show here that it can be a helpful tool for researchers and that, in addition to allowing sophisticated search strategies, it is also useful for tasks such as modeling.
