A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein’s structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.Author Summary: Structural systems pharmacology is an emerging field of computational biology research that aims to merge network and molecular views of biology. Genome-scale models are in silico, network models of metabolism, and by integrating the detailed knowledge we can gain from molecular simulations with these models, we can begin to understand whole cell phenotypes at a more complete scale. In this study, we use and integrate a variety of simulation tools at both the network and molecular levels to allow us to understand how a mutation can change an enzyme’s ability to bind to drugs or metabolites. We look at three different enzymes within red blood cell metabolism, and find that these computational tools reflect what we know about them relatively well, and also potentially serve as a workflow for understanding other traits in the overall theme of personalized medicine.