A Structural Systems Biology Approach for Quantifying the Systemic Consequences of Missense Mutations in Proteins
Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation. Small changes in protein sequences, such as missense mutations resulting from genetic variations in the genome, can have a large impact on cellular behavior. Consequently, numerous studies have been carried out to evaluate the disease susceptibility of missense mutations by directly analyzing their structural or functional impact on proteins. Such an approach has been shown to be useful for inferring the likelihood of a mutation to be disease-associated. However, there are still many unexplored avenues for improving disease-association studies, due to the fact that the dynamics of biological pathways are rarely considered. We therefore explore the practicality of a structural systems biology approach, combining pathway dynamics with protein structural information, for projecting the physiological outcomes of missense mutations. We show that stability changes of proteins due to missense mutations and the sensitivity of a protein in terms of regulating pathway dynamics are useful measures for this purpose. Furthermore, we demonstrate that complicated mathematical models are not a prerequisite for mapping protein stabilities to network perturbation. Thus it may be more feasible to study the systemic impact of missense mutations associated with complex pathways.