Methyl phenlactonoates are efficient strigolactone analogs with simple structure.

Strigolactones (SLs) are a new class of phytohormones that also act as germination stimulants for root parasitic plants, such as Striga spp., and as branching factors for symbiotic arbuscular mycorrhizal fungi. Sources for natural SLs are very limited. Hence, efficient and simple SL analogs are needed for elucidating SL-related biological processes as well as for agricultural applications. Based on the structure of the non-canonical SL methyl carlactonoate, we developed a new, easy to synthesize series of analogs, termed methyl phenlactonoates (MPs), evaluated their efficacy in exerting different SL functions, and determined their affinity for SL receptors from rice and Striga hermonthica. Most of the MPs showed considerable activity in regulating plant architecture, triggering leaf senescence, and inducing parasitic seed germination. Moreover, some MPs outperformed GR24, a widely used SL analog with a complex structure, in exerting particular SL functions, such as modulating Arabidopsis roots architecture and inhibiting rice tillering. Thus, MPs will help in elucidating the functions of SLs and are promising candidates for agricultural applications. Moreover, MPs demonstrate that slight structural modifications clearly impact the efficiency in exerting particular SL functions, indicating that structural diversity of natural SLs may mirror a functional specificity.

An integrated structure- and system-based framework to identify new targets of metabolites and known drugs.

MOTIVATION: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. RESULTS: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called 'drugs'). For a given drug, our method uses sequence order-independent structure alignment, hierarchical clustering and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug's PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs-two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development. AVAILABILITY AND IMPLEMENTATION: The program, datasets and results are freely available to academic users at http://sfb.kaust.edu.sa/Pages/Software.aspx.