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 lowresolution 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

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. The predictors are trained to learn the association rules between the covalent structure of each protein and its contact map with a standard back propagation algorithm and tested on the same protein set with a cross-validation procedure. Our results indicate that the method can assign protein contacts with an average accuracy of 0.21 and with an improvement over a random predictor of a factor >6, which is higher than that previously obtained with methods only based either on neural networks or on correlated mutations. Furthermore, filtering the network outputs with a procedure based on the residue coordination numbers, the accuracy of predictions increases up to 0.25 for all the proteins, with an 8-fold deviation from a random predictor. These scores are the highest reported so far for predicting protein contact maps.

We have previously developed a method for predicting interresidue contacts using information about correlated mutations in multiple sequence alignments. The predictions generated with this method were clearly better than random but not enough for their use in de novo protein folding experiments. We assess the possibility of improving contact predictions combining information from the following variables: correlated mutations, sequence conservation, sequence separation along the chain, alignment stability, family size, residue-specific contact occupancy and formation of contact networks. The application of a protocol for combining these independent variables leads to contact predictions that are on average two times better than those obtained initially with correlated mutations. Correlated mutations can be effectively combined with other types of information derived from multiple sequence alignments. Among the different variables tried, sequence conservation and contact density are particularly relevant for the combination with correlated mutations.

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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.

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. The method could assign protein contacts with an average accuracy of 0.21 and with an improvement over a random predictor of a factor greater than 6, which is higher than that previously obtained with methods only based either on neural networks or on correlated mutations. Although far from being ideal, these scores are the highest reported so far for predicting protein contact maps. On 29 targets automatically predicted by the server (CORNET) the average accuracy is 0.14. The predictor is poorly performing on all alpha proteins, not represented in the training set. On all beta and mixed proteins (22 targets) the average accuracy is 0.16. This set comprises proteins of different complexity and different chain length, suggesting that the predictor is capable of generalization over a broad number of features.

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.

A structural model is presented for family 32 of the glycosyl-hydrolase enzymes based on the beta-propeller fold. The model is derived from the common prediction of two different threading methods, TOPITS and THREADER. In addition, we used a correlated mutation analysis and prediction of active-site residues to corroborate the proposed model. Physical techniques (circular dichroism and differential scanning calorimetry) confirmed two aspects of the prediction, the proposed all-beta fold and the multi-domain structure. The most reliable three-dimensional model was obtained using the structure of neuraminidase (1nscA) as template. The analysis of the position of the active site residues in this model is compatible with the catalytic mechanism proposed by Reddy and Maley (J. Biol. Chem. 271:13953-13958, 1996), which includes three conserved residues, Asp, Glu, and Cys. Based on this analysis, we propose the participation of one more conserved residue (Asp 162) in the catalytic mechanism. The model will facilitate further studies of the physical and biochemical characteristics of family 32 of the glycosyl-hydrolases.