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All naturally produced terpenes are derived from two universal C5 diphosphate precursors, dimethylallyl diphosphate (DMAPP) and isopentenyl diphosphate (IPP). Various prenyl transferases use DMAPP to prenylate aromatic compounds, while others, in combination with IPP, lead to the enzymatic formation of geranyl diphosphate (GPP), farnesyl diphosphate (FPP), geranylgeranyl diphosphate (GGPP), and geranylfarnesyl diphosphate, the direct precursors of monoterpenes (C10), sesquiterpenes (C15), diterpenes (C20), and sesterterpenes (C25), respectively. FPP and GGPP are also the basis for the biosynthesis of triterpenes (steroids) and tetraterpenes (carotenoids), respectively. Nature has developed two biosynthetic pathways to produce DMAPP and IPP, the mevalonate (MEV) pathway and the methylerythritol phosphate (MEP) pathway. Both use compounds derived from glucose through glycolysis, and 18 enzymes are involved to generate both DMAPP and IPP. Here, we sought to simplify biochemical access to these two universal diphosphates using the two commercially and industrially available C5-OHs, dimethylallyl alcohol and isopentenol (IOH), as starting substrates, as well as two enzymes, selected from a diverse choice, able to carry out the double phosphorylation of these two C5-OHs at room temperature using ATP as a phosphate donor. The first phosphorylation is performed by a promiscuous acid phosphatase (AP), used in the reverse reaction mode, whereas the second is performed by the recently described isopentenyl phosphate kinase (IPK). We show the interest of this artificial biosynthetic terpene mini-path (TMP) by testing it in a three-enzyme cascade, leading to the formation of the cytotoxic prenylated diketopiperazine tryprostatin B (TB) from chemically synthesized brevianamide F (BF), using FtmPT1 prenyltransferase as a biocatalyst, in addition to the two previously mentioned kinases. We first performed the proof of concept of this simplified pathway in vivo (Escherichia coli), using already described enzymes, that is, an AP from Salmonella enterica and an IPK from Thermoplasma acidophilum. The complete conversion of BF (3.3 mM, 1 g/L) to TB was obtained after optimization of culture conditions and process parameters. Following this first success, we performed a screen in search of highly active phosphatases and IPKs to develop the TMP in vitro. A highly active AP from Xanthomonas translucens and an IPK from Methanococcus vannielii were selected from these screens, allowing the in vitro development of the TMP. Under optimized conditions, the three-enzyme cascade led to the total transformation of BF (10 mM, 3.3 g/L) to TB in less than 24 h, establishing the in vitro utility as well as the in vivo utility of the TMP. The implementation of this biosynthetic TMP offers thus the possibility to access virtually any terpene structure using two easily commercially and industrially available compounds in bulk, either in vivo or in vitro, and is thus a viable alternative to the natural MEV and MEP pathways for bioaccess to terpenes.

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The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better tradeoff between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the 210 J Intell Inf Syst (2012) 39:209-247 performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon test.