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.

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.

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.

Progress in predicting inter-residue contacts of proteins with neural networks and correlated mutations.

This article presents recent progress in predicting inter-residue contacts of proteins with a neural network-based method. Improvement over the results obtained at the previous CASP3 competition is attained by using as input to the network a complex code, which includes evolutionary information, sequence conservation, correlated mutations, and predicted secondary structures. The predictor was trained and cross-validated on a data set comprising the contact maps of 173 non-homologous proteins as computed from their well-resolved three-dimensional structures.

Prediction of contact maps with neural networks and correlated mutations.

Contact maps of proteins are predicted with neural network-based methods, using as input codings of increasing complexity including evolutionary information, sequence conservation, correlated mutations and predicted secondary structures. Neural networks are trained on a data set comprising the contact maps of 173 non-homologous proteins as computed from their well resolved three-dimensional structures. Proteins are selected from the Protein Data Bank database provided that they align with at least 15 similar sequences in the corresponding families.