INTEGRATION OF GENOMIC AND STRUCTURAL INFORMATION TO CHOOSE NEW THERAPEUTICAL TARGETS AND DEVELOP NEW DRUGS AGAINST MALARIA

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Publications

SitCon: binding site residue conservation visualization and protein sequence-to-function tool

Publication . Kairys, Visvaldas; Fernandes, Miguel X.

We introduce SitCon (SITe CONservation), a program designed to explore conservation of functionally important sites in a series of hypothetically homologous candidate protein structures, given amino acid sequence as an input. This can especially be useful when looking for an unknown function of a protein. SitCon exploits the fact that binding sites of proteins are preserved better than the overall residue sequence conservation. To test the capability of unknown function prediction, we randomly chose known function proteins from Caenorhabditis elegans genome. To imitate a behavior of an unknown function target, only the low homology proteins with 0.01 E-score 100 were analyzed as templates. Out of 29 enzyme targets, SitCon was able to provide various hints about their function in at least 69% of the cases. For the eight nonenzyme targets, the predictions matched in only 25% of the cases. SitCon was also tested for a capability to predict presence or absence of metal-containing heterogroups in the target enzymes with 80% success rate. Because this algorithm is not based on specific protein signatures, it may allow detection of overlooked relationships between proteins. SitCon is also very effective as a tool allowing visual comparison of binding site residue conservation between the target and homologous templates side-by-side.

2007Journal article

Open access

Using protein homology models for structure-based studies: approaches to model refinement

Publication . Kairys, V.; Gilson, M. K.; Fernandes, Miguel Xavier

Homology modeling is a computational methodology to assign a 3-D structure to a target protein when experimental data are not available. The methodology uses another protein with a known structure that shares some sequence identity with the target as a template. The crudest approach is to thread the target protein backbone atoms over the backbone atoms of the template protein, but necessary refinement methods are needed to produce realistic models. In this mini-review anchored within the scope of drug design, we show the validity of using homology models of proteins in the discovery of binders for potential therapeutic targets. We also report several different approaches to homology model refinement, going from very simple to the most elaborate. Results show that refinement approaches are system dependent and that more elaborate methodologies do not always correlate with better performances from built homology models.

2006Journal article

Open access

Quantitative structure-activity relationship models with receptor-dependent descriptors for predicting peroxisome proliferator-activated receptor activities of thiazolidinedione and oxazolidinedione derivatives

Publication . Lather, Viney; Kairys, Visvaldas; Fernandes, Miguel X.

A quantitative structure–activity relationship study has been carried out, in which the relationship between the peroxisome proliferator-activated receptor a and the peroxisome proliferator activated receptor c agonistic activities of thiazo lidinedione and oxazolidinedione derivatives and quantitative descriptors, Vsite calculated in a receptor-dependent manner is modeled. These descriptors quantify the volume occupied by the optimized ligands in regions that are either com mon or specific to the superimposed binding sites of the targets under consideration. The quantita tive structure–activity relationship models were built by forward stepwise linear regression model ing for a training set of 27 compounds and vali dated for a test set of seven compounds, resulting in a squared correlation coefficient value of 0.90 for peroxisome proliferator-activated receptor a and of 0.89 for peroxisome proliferator-activated receptor c. The leave-one-out cross-validation and test set predictability squared correlation coeffi cient values for these models were 0.85 and 0.62 for peroxisome proliferator-activated receptor a and 0.89 and 0.50 for peroxisome proliferator-acti vated receptor c respectively. A dual peroxisome proliferator-activated receptor model has also been developed, and it indicates the structural features required for the design of ligands with dual peroxisome proliferator-activated receptor activity. These quantitative structure–activity relationship models show the importance of the descriptors here introduced in the prediction and interpretation of the compounds affinity and selectivity.

2009Journal article

Open access