DNA and non-DNA targets in the mechanism of action of the antitumor drug trabectedin.

We have analyzed the DNA binding properties of the antitumor agent trabectedin (ET-743, Yondelis) and different analogs, namely, ET-745, lacking the C21-hydroxyl group, and ET-637, ET-594, ET-637-OBu, with modifications at the trabectedin C domain, versus their effects on cell cycle, apoptosis, and gene expression. ET-745 failed to bind DNA, highlighting the importance of the C21-hydroxyl group for DNA binding. Analogs ranked trabectedin >> ET-637 approximately ET-594 > ET-637-OBu >> ET-745 for their DNA binding capacity; ET-637 and ET-594 display very different biological activities. Drugs were clustered in three major groups showing high (trabectedin, ET-637), intermediate (ET-637-OBu), and low (ET-594, ET-745) cytotoxic activity and similar transcriptional profiling responses. C21-hydroxyl-deficient analogs of the above-mentioned compounds showed a dramatic decrease in biological activity. Our data suggest that trabectedin interacts with an additional non-DNA target to raise an effective antitumor response, and that this interaction is favored through trabectedin-DNA complexes.

MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison.

Advances in structural genomics and protein structure prediction require the design of automatic, fast, objective, and well benchmarked methods capable of comparing and assessing the similarity of low-resolution three-dimensional structures, via experimental or theoretical approaches. Here, a new method for sequence-independent structural alignment is presented that allows comparison of an experimental protein structure with an arbitrary low-resolution protein tertiary model. The heuristic algorithm is given and then used to show that it can describe random structural alignments of proteins with different folds with good accuracy by an extreme value distribution. From this observation, a structural similarity score between two proteins or two different conformations of the same protein is derived from the likelihood of obtaining a given structural alignment by chance. The performance of the derived score is then compared with well established, consensus manual-based scores and data sets. We found that the new approach correlates better than other tools with the gold standard provided by a human evaluator. Timings indicate that the algorithm is fast enough for routine use with large databases of protein models. Overall, our results indicate that the new program (MAMMOTH) will be a good tool for protein structure comparisons in structural genomics applications. MAMMOTH is available from our web site at http://physbio.mssm.edu/~ortizg/.

Prediction of heterodimerization interfaces of G-protein coupled receptors with a new subtractive correlated mutation method.

Recent studies employing differential epitope tagging, selective immunoprecipitation of receptor complexes and fluorescence or bioluminescence resonance energy transfer techniques provide direct evidence for heterodimerization between both closely and distantly related members of the G-protein coupled receptor (GPCR) family. Since heterodimerization appears to play a role in modulating agonist affinity, efficacy and/or trafficking properties, the molecular models of GPCRs required to understand receptor function must consider these oligomerization hypotheses. To advance knowledge in this field, we present here a computational approach based on correlated mutation analysis and the structural information contained in three-dimensional molecular models of the transmembrane regions of GPCRs built using the rhodopsin crystal structure as a template. The new subtractive correlated mutation method reveals likely heterodimerization interfaces amongst the different alternatives for the positioning of two tightly packed bundles of seven transmembrane domains next to each other in contact heterodimers of GPCRs. Predictions are applied to GPCRs in the class of opioid receptors. However, in the absence of a known structure of any GPCR dimer, the features of the method and predictions are also illustrated and analyzed for a dimeric complex of known structure.