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

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.

Recon3D enables a three-dimensional view of gene variation in human metabolism

“…We used Recon3D to map missense mutations from Single Nucleotide Polymorphism (SNP) database (dbSNP) 30 , UniProt 24 , PharmGKB 31 , among others, to the metabolic network using a previously established pipeline 20 ( Figure 3(a) ). We chose to focus on SNPs that were known to be deleterious or potentially harmful.…”

“…The ability of Recon3D to integrate multiple layers of biological data will provide a tool for obtaining a meaningful and coherent understanding of variation and the influence it exerts at both the level of individual proteins and within complex pathways. The inclusion of these multiple disparate data types offers new opportunities for network reconstruction: (i) it introduces atomic scale properties, such as ligand binding interactions; (ii) it provides new avenues for precision medicine by exploring human variation 14 , 15 , and (iii) it enables the probing of genetic variation via changes in the molecular properties of proteins 20 . In this way, individual sequence variations can be explicitly represented and the functional connections among disease, genetic perturbation, and drug action can be probed systematically.…”

“…The Recon3D knowledge-base is instrumental to gene variation analyses as it provides a framework for integrating structure-function relationships, and assessing specific and proteome-wide effects of sequence variation. Integrated frameworks like Recon3D enable understanding of how mutations or binding events lead to downstream responses and could aid in the identification of novel targets when coupled to structural bioinformatics 16 , molecular dynamics simulations 20 , 49 , and kinetic modeling 50 . In contrast, current metabolic models are not able to contextualize the effect of a sequence variant (beyond gene deletions) and therefore cannot be used to study disease-relevant mutations.…”

“…In one study, protein structures were mapped to the metabolic network of Escherichia coli , to reveal the role of ribosome pausing in co-translational protein folding 19 . In another study, human population variation was studied by integrating protein structures with the human erythrocyte metabolic network to understand the adverse effects of drugs on genetic variants 20 and identify new pathways related to drug perturbation. These studies highlight the value of integrating different types of data to address complex biological questions.…”

Expanding the uses of genome‐scale models with protein structures

“…As a proof‐of‐concept, molecular modeling tools were used to analyze the impact of coding mutations on drug binding in the human red blood cell (Fig D; Mih et al , ). Docking and molecular dynamics simulations enabled predictions of differences in the binding affinities of small molecules due to a mutation in selected proteins.…”