The database is freely available at http://www.codnas.com.ar/.

It is well established that the conservation of protein structure during evolution constrains sequence divergence. The conservation of certain physicochemical environments to preserve protein folds and then the biological function originates a site-specific structurally constrained substitution pattern. However, protein native structure is not unique. It is known that the native state is better described by an ensemble of conformers in a dynamic equilibrium. In this work, we studied the influence of conformational diversity in sequence divergence and protein evolution. For this purpose, we derived a set of 900 proteins with different degrees of conformational diversity from the PCDB database, a conformer database. With the aid of a structurally constrained protein evolutionary model, we explored the influence of the different conformations on sequence divergence. We found that the presence of conformational diversity strongly modulates the substitution pattern. Although the conformers share several of the structurally constrained sites, 30% of them are conformer specific. Also, we found that in 76% of the proteins studied, a single conformer outperforms the others in the prediction of sequence divergence. It is interesting to note that this conformer is usually the one that binds ligands participating in the biological function of the protein. The existence of a conformer-specific site-substitution pattern indicates that conformational diversity could play a central role in modulating protein evolution. Furthermore, our findings suggest that new evolutionary models and bioinformatics tools should be developed taking into account this substitution bias.

PCDB (http://www.pcdb.unq.edu.ar) is a database of protein conformational diversity. For each protein, the database contains the redundant compilation of all the corresponding crystallographic structures obtained under different conditions. These structures could be considered as different instances of protein dynamism. As a measure of the conformational diversity we use the maximum RMSD obtained comparing the structures deposited for each domain. The redundant structures were extracted following CATH structural classification and cross linked with additional information. In this way it is possible to relate a given amount of conformational diversity with different levels of information, such as protein function, presence of ligands and mutations, structural classification, active site information and organism taxonomy among others. Currently the database contains 7989 domains with a total of 36581 structures from 4171 different proteins. The maximum RMSD registered is 26.7 Å and the average of different structures per domain is 4.5.

Immortality is one of the main features of cancer cells. Tumor cells have an unlimited replicative potential, principally due to the holoenzyme telomerase. Telomerase is composed mainly by dyskerin (DKC1), a catalytic retrotranscriptase (hTERT) and an RNA template (hTR). The aim of this work is to develop new inhibitors of telomerase, selecting the interaction between hTR–DKC1 as a target. We designed two models of the human protein DKC1: homology and ab initio. These models were evaluated by different procedures, revealing that the homology model parameters were the most accurate. We selected two hydrophobic pockets contained in the PUA (pseudouridine synthase and archaeosine transglycosylase) domain, using structural and stability analysis. We carried out a docking-based virtual screen on these pockets, using the reported mutation K314 as the center of the docking. The hDKC1 model was tested against a library of 450,000 drug-like molecules. We selected the first 10 molecules that showed the highest affinity values to test their inhibitory activity on the cell line MDA MB 231 (Monroe Dunaway Anderson Metastasis Breast cancer 231), obtaining three compounds that showed inhibitory effect. These results allowed us to validate our design and set the basis to continue with the study of telomerase inhibitors for cancer treatment.

BackgroundNon-synonymous coding SNPs (nsSNPs) that are associated to disease can also be related with alterations in protein stability. Computational methods are available to predict the effect of single amino acid substitutions (SASs) on protein stability based on a single folded structure. However, the native state of a protein is not unique and it is better represented by the ensemble of its conformers in dynamic equilibrium. The maintenance of the ensemble is essential for protein function. In this work we investigated how protein conformational diversity can affect the discrimination of neutral and disease related SASs based on protein stability estimations. For this purpose, we used 119 proteins with 803 associated SASs, 60% of which are disease related. Each protein was associated with its corresponding set of available conformers as found in the Protein Conformational Database (PCDB). Our dataset contains proteins with different extensions of conformational diversity summing up a total number of 1023 conformers.ResultsThe existence of different conformers for a given protein introduces great variability in the estimation of the protein stability (ΔΔG) after a single amino acid substitution (SAS) as computed with FoldX. Indeed, in 35% of our protein set at least one SAS can be described as stabilizing, destabilizing or neutral when a cutoff value of ±2 kcal/mol is adopted for discriminating neutral from perturbing SASs. However, when the ΔΔG variability among conformers is taken into account, the correlation among the perturbation of protein stability and the corresponding disease or neutral phenotype increases as compared with the same analysis on single protein structures. At the conformer level, we also found that the different conformers correlate in a different way to the corresponding phenotype.ConclusionsOur results suggest that the consideration of conformational diversity can improve the discrimination of neutral and disease related protein SASs based on the evaluation of the corresponding Gibbs free energy change.