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Genes & Disease
Bioinformatics and drug discovery (cont)
Sometimes we want to develop broad spectrum drugs that
are effective against a wide range of pathogenic species
while at other times we want to develop narrow spectrum
drugs that are highly specific to a particular organism.
Comparative genomics helps to find protein families that
are widely taxonomically dispersed and those that are
unique to a particular organism.
For example, when we want
to develop a broad spectrum antibiotic, we are looking
for targets that are present in a large number of bacteria
yet have no similar homologues in human. This means that
the antibiotic will be effective against many bacteria
killing them while causing no harm to the human. In order
to determine the role our potential drug target plays
in a particular disease mechanism we use DNA and protein
chips. These chips can measure the amount of transcript
or protein expressed by a cell at different times or in
different states (healthy versus diseased).
Clustering algorithms are used to organise this expression data into different biologically relevant clusters. We can then compare the expression profiles from the diseased and healthy cells to help us understand the role our gene or protein plays in a disease process.
All of these computational tools can help to compose a detailed picture about a protein family, its involvement in a disease process and its potential as a possible drug target.
Following on from the genomics explosion and the huge increase in the number of potential drug targets, there has been a move from the classical linear approach of drug discovery to a non linear and high throughput approach. The field of bioinformatics has become a major part of the drug discovery pipeline playing a key role for validating drug targets. By integrating data from many inter-related yet heterogeneous resources, bioinformatics can help in our understanding of complex biological processes and help improve drug discovery.
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