Abstract
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.
Highlights
Synergistic advances in pharmacogenomics, genome-wide association studies (GWAS) and next-generation sequencing bring promise to future applications of personalized medicine
We were interested in quantifying the number of proteins in the human erythrocyte metabolism that (i) are known pharmaceutical targets and (ii) have been documented with both disease and non-disease causing mutations (Fig 2(A))
Genetic changes that occur in cells other than the erythrocyte are often manifested in the erythrocyte, assuming correct isoforms and similar genetic control [33,34,35,36]
Summary
Synergistic advances in pharmacogenomics, genome-wide association studies (GWAS) and next-generation sequencing bring promise to future applications of personalized medicine. While numerous harmful gene-drug associations have been identified from GWAS (and those with significant side effects have warnings on pharmaceutical labels [2]), screening genome-wide associations across the broad scope of available pharmaceutical compounds is currently limited by both the cost of carrying out such studies [3] as well as a lack of statistical power due to the rarity of deleterious mutations. To address these limitations, a number of recent studies have developed mechanistic, computational analyses and the construction of omics-based workflows that identify, for example, the mode of action of common drug side effects [4]. A recently updated version of the erythrocyte metabolic model (iAB-RBC-283), based on the global reconstruction of the human metabolic network (Recon 2) [7] has been used to study the response of the cell to deleterious single nucleotide polymorphisms (SNPs) as well as drugs with known targets [5,8,9]
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