Computational Biology Computer Aided Drug Designing Mutation

Effects of Mutation on Drug Binding

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The ability to predict how a mutation affects ligand binding is an essential step in understanding, anticipating and improving the design of new treatments for drug resistance, and in understanding genetic diseases.

Mutations could reduce the binding affinity of a drug to its target or render the target constitutively active. In another scenario, a mutation could cause disruption or aberrant interaction of a specific protein-protein interaction, resulting in drug resistance.

The wealth of information arising from second generation genome sequencing is showing that future responses to two major areas of human health and disease will very often depend on understanding the effects of missense mutations on ligand binding to proteins. In many genetic diseases such as in Mendelian disorders, mutations are observed to affect the binding of substrates or ligands in active sites. In a similar way drug resistance, which is frequently due to the effects of mutations on drug recognition of protein targets, is of growing significance not only to developing nations as a result of the use of antibiotics in tuberculosis, malaria and other infectious diseases, but also throughout the world due to the overuse of drugs in fast evolving cancers and antibiotics for infections, which will also have huge impacts on safety in surgery. Both genetic disease and drug resistance require initial characterization of changes in the individual human or pathogen genome sequence, allowing us to prioritize treatment strategies, and then use of this information to design better drugs as part of a new personalized or precision medicine.

Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods.

Marketed drugs sometimes perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed, worse, a significant fraction of patients experience adverse effects more severe than anticipated.

Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenomics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein–drug interactions and, ultimately, drug response. 

Changings in the disease causing genes show promising therapeutic targets in multiple cancer types. However, given the unique and complex signaling biology of the MAPK pathway, the diverse array of RAF and MEK alterations observed in cancer can possess distinct functional characteristics. 

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