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Proteins are large, complex molecules that play many critical roles in the body. They do most of the work in cells and are required for the structure, function, and regulation of the body’s tissues and organs. Sometimes we design molecules (drugs) that can efficiently interact with proteins (one which are cause pf diseases) to stop its action. While for endogenous substances proteins do not change itself by mutations, unless there is a question of selectivity among same classes of proteins to perform specific function (A problem related to Evolution), but for foreign molecules like drugs, the targeted proteins can mutate randomly. This is called Drug resistance, which is one of the most serious health problems facing humanity in the 21st century. This problem represents an enormous challenge to rational drug design. The tremendous effort and money spent to develop new drugs, goes in vain, when we cannot predict the new set of mutations a protein would undergo and accordingly prepare drug to combat those changes. The accurate estimation of extent of favorable/unfavorable interactions between the proteins and small molecules helps to understand the mechanism of protein action as well as in designing new drugs. Towards this direction, researchers are in constant search of better methods to accurately [1] calculate the protein small molecular interaction and [2] explain the molecular basis of drug resistance.
Warshel and his colleagues suggest a paradigm shift in fighting drug resistance, by exploiting the fact that the survival of the pathogen depends on the ability of the targeted proteins to mutate to a form that will allow it to keep a high “vitality value” (by reducing the binding affinity of the given drug) while still retaining its catalytic function. We have been trying to attack this “Achilles heel” by developing effective computational approaches for evaluating the vitality values and the more general survival function that reflects additional constraints on folding and stability. These approaches should allow us to simulate drug resistance on a molecular level and to advance new inhibition strategies to which the pathogen will not easily adapt.
As a part of the main direction of fighting drug resistance we have also been working toward improving and validating the methods for evaluating the energetics of drug binding [3] [4].
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