Absolute Configuration of Dihydro-ß-agarofuran Sesquiterpenes from Maytenus jelskii and Their Potential Antitumor-Promoting Effects.

Chemoprevention of human cancer appears to be a feasible strategy for cancer control, especially when chemopreventive intervention is involved during early stages of the carcinogenesis process. As a part of our ongoing research program into new chemopreventive agents, herein are reported the isolation, structural elucidation, and biological evaluation of 10 new (1-10) and three known (11-13) sesquiterpenes with a dihydro-ß-agarofuran skeleton from the leaves of Maytenus jelskii Zahlbr. Their stereostructures have been elucidated by means of spectroscopic analysis, including 1D and 2D NMR techniques, ECD studies, and biogenetic considerations. The isolated metabolites and eight previously reported sesquiterpenes (14-21) were screened for their antitumor-promoting activity using a short-term in vitro assay for Epstein-Barr virus early antigen (EBV-EA) activation induced by 12-O-tetradecanoylphorbol-13-acetate (TPA). Six compounds from this series (4, 5, 11, and 13-15) were found to exhibit higher efficacies than ß-carotene, used as reference inhibitor for EBV-EA activation. In particular, promising antitumor activity was observed for compound 5, exhibiting inhibition even at the lowest concentration assayed (10 mol ratio/TPA). Preliminary structure-activity relationship analysis revealed that the acetate, benzoate, and hydroxy groups are the most desirable substituents on the sesquiterpene scaffold for activity in the EBV-EA activation assay.

RNA 3D structure prediction: (1) assessing rna 3D structure similarity from 2D structure similarity.

Computational techniques for 3D structure prediction of proteins, the holy grail of bioinformatics, have undergone major developments in recent years, geared by international cooperation and competition with CASP (Critical Assessment of Structure Prediction Techniques) like contests to improve and refine them. Although straightforward extrapolation of these methodologies for the prediction of the 3D structures of other similarly relevant bio macromolecules may not be too compelling due mostly to the intrinsic differences in constitution, nature, and function between them, the conceptual framework underlying most of those techniques applied to the development of similar computational techniques in structural biology can lead to efficient systems for prediction of the 3D structure of other bio-macromolecules. One of them is the development of rational methodologies to model RNA 3D structures from the sequence of nucleotides composing them. In this paper we establish the fundamentals of a methodology to thread a sequence of nucleotides into a set of 3D fragments extracted from a data base expressly developed for this purpose. The technique is based on a newly implemented algorithm for extraction of 3D fragments by comparison of secondary structures of RNA. The result is a highly efficient system to produce a set of fragments from which entire RNA structure for the given nucleotide sequence can be built.