No abstract

Glucose-6-phosphate dehydrogenase (G6PD) is the first enzyme in the pentose phosphate pathway and is highly relevant in the metabolism of Giardia lamblia. Previous reports suggested that the G6PD gene is fused with the 6-phosphogluconolactonase (6PGL) gene (6pgl). Therefore, in this work, we decided to characterize the fused G6PD-6PGL protein in Giardia lamblia. First, the gene of g6pd fused with the 6pgl gene (6gpd::6pgl) was isolated from trophozoites of Giardia lamblia and the corresponding G6PD::6PGL protein was overexpressed and purified in Escherichia coli. Then, we characterized the native oligomeric state of the G6PD::6PGL protein in solution and we found a catalytic dimer with an optimum pH of 8.75. Furthermore, we determined the steady-state kinetic parameters for the G6PD domain and measured the thermal stability of the protein in both the presence and absence of guanidine hydrochloride (Gdn-HCl) and observed that the G6PD::6PGL protein showed alterations in the stability, secondary structure, and tertiary structure in the presence of Gdn-HCl. Finally, computer modeling studies revealed unique structural and functional features, which clearly established the differences between G6PD::6PGL protein from G. lamblia and the human G6PD enzyme, proving that the model can be used for the design of new drugs with antigiardiasic activity. These results broaden the perspective for future studies of the function of the protein and its effect on the metabolism of this parasite as a potential pharmacological target.

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Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.