Most recently, bioinformatics is being employed to understand the organization and
function of the human genome. However, the use of bioinformatics to characterize
smaller, less complex genomes, notably bacteria and yeast, has preceded studies of the
human genome. For example, the Saccharomyces cerevisiae genome project, which
delivered the first complete eukaryotic genome with 16 chromosomes and 6200 genes,
provided a model for ways in which DNA sequence information could be used in the
systematic study of biochemical and functional processes.
Bioinformatics is proving invaluable in harnessing the power to study bacterial
genomes in the search for new antibiotics. Over the past four decades, the search for
new antibiotics has been essentially restricted to a relatively small number of well-
known classes of compounds. Although this approach yielded numerous effective com-
pounds, clinical resistance (i.e., antibiotic-resistant “superbugs”) ultimately arose
because of insufficient chemical variability. Bioinformatics-aided exploration of bacte-
rial genomes is providing opportunities to expand the range of potential drug targets
and to facilitate a shift from direct antimicrobial screening programs to rational target-
based strategies. By comparing the genes of a given type of bacteria with the human
genome it is possible to identify genes unique to the bacteria which may be targeted in
such a way as to reduce potential toxicity in humans. Moreover, by determining the
function of these bacteria-specific genes, it is possible to ascertain their usefulness as
targets in designing drugs that will be lethal to those bacteria. Thus, bioinformatics is
an extremely powerful tool for the future of theoretical drug design.
Cheminformaticsis the chemistry equivalent to bioinformatics and involves the tools
and techniques (usually computational) for storing, handling, and communicating the
massive and ever-increasing amounts of data concerning molecular structures. Like
bioinformatics, cheminformatics attempts to combine data from varying sources:
- Molecular modelling studies
- High-throughput screening results for molecules
- Structure-based drug design studies
- Small molecule compound libraries
- Virtual chemical libraries
There are many examples of applying cheminformatics to drug design. For instance, if
the pharmacophore for a particular class of compounds has been identified through QSAR
studies, then it is possible to search other families of molecules to ascertain whether this
pharmacophore is present in other classes of molecules. Various mathematical algo-
rithms are in place to permit overlapping of structurally different molecules to see
whether a common pharmacophore exists. In short, this is using cheminformatics to
discover other molecules with the same pharmacophore but with different “molecular
baggage” portions. A technique that is somewhat analogous to this pharmacophore
search application of cheminformatics is to use a docking algorithm to systematically
insert all molecules within a compound library into a known receptor site. By this strat-
egy, the three-dimensional structure of a receptor has been determined by X-ray crys-
tallography. Next, each molecule within an extensive library of molecules is docked
with this receptor via computer simulation. Molecules that fit into the receptor can be
identified and subsequently explored in an experimental setting.
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