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4 CONCLUSIONS
The GST sequences of four Plasmodium species
were compared using multiple sequence alignments
and found to be highly conserved. When aligned
with members of each of the GST classes used in
this study our results show that the Plasmodium
GSTs appear to be highly related to the alpha and
sigma families of GSTs. Using phylogenetic analysis
we found that Plasmodium GST are members of the
sigma class. The 3D structure of PfGST was
compared, using structural alignments, with
members of several GST classes. We determined
that PfGST has a high degree of similarity with the
sigma class in the H-site, G-site and C-terminus of
the protein. Structural analysis and phylogenetic
analysis of PfGST revealed that this enzyme
represents a unique clade within the sigma class of
GSTs.
This work contributes to a better understanding
of the structure and classification of Plasmodium
GSTs. These results have the potential to enhance
our knowledge of the relevance of GST to
Plasmodium drug resistance. It has been recently
demonstrated that 7-nitro-2,1,3-benzoxadiazole
derivatives are a new class of suicide inhibitors of
GST that accumulate in tumor cells and evade the
extrusion mechanisms mediated by the multidrug
resistance associated protein pumps (MRP) (Ricci et
al., 2005). Studies in human cancer cells in-vitro
showed that the GST inhibitor 6-(7-nitro-2,1,3-
benzoxadiazol-4-ylthio)hexanol (NBDHEX) has
high antiproliferative activity and helped overcome
MRP mediated drug resistance (Federici et el., 2009;
Filomeni et al., 2008). In-silico studies will be
undertaken to evaluate NBDHEX’s potential as an
antimalarial drug. These studies could support the
use of this GST inhibitor in Plasmodium.
We expect to use the results presented here for
further studies using molecular biology and genetics
approaches in order to study the involvement of
GST in the Plasmodium life cycle and mosquito
transmission. These studies should be useful to find
alternative strategies for malaria control.
ACKNOWLEDGEMENTS
RGM, EECL, HBN, TW, AJP and this project were
partially supported by NIGMS grant T36-
GM008789 and acknowledge use of the Pittsburgh
Supercomputing Center’s National Resource for
Biomedical Supercomputing resources funded
through NIH NCRR grant P41-RR06009. AES and
EECL were partially supported by NIGMS/MBRS
award GM08224 and from the RCMI award
G12RR03051 of the NCRR/NIH.
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