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

摘要: Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown provide insight into the mechanistic link between drug therapies systems-level off-target effects while being expanded explicitly include three-dimensional structure proteins. The integration these molecular-level details, such as physical, structural, dynamical properties proteins, notably expands computational description biochemical network-level possibility understanding predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range scale from atomistic details an entire metabolic network. Using approach, can understand how genetic variation, impacts reactivity protein, influences both native drug-induced states. As proof-of-concept, study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, glyceraldehyde-3-phosphate dehydrogenase) their respective variants clinically relevant associations. all-atom molecular dynamic simulations enables sampling long timescale conformational dynamics proteins (and mutant variants) complex with metabolites or molecules. We find changes protein's due mutation protein binding affinity and/or molecules, inflicts large-scale metabolism.