Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures

Abstract

Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.

Figure 1:. Multiscale structure- and physics-based methods bridging from atoms to cells.

Emerging multiscale computational methods coupled with increasingly accurate structural data on biological and chemical systems enables the development of highly detailed and predictive models of drug action across spatial scales ranging from angstroms to microns and temporal scales ranging from femtoseconds to minutes. Such approaches can be gainfully used to address a number of outstanding challenges in drug discovery and design (Table 1).

Figure 2:. Multiscale Simulation Methods to Predict Drug Metabolism by Cytochrome P450 Enzymes.

Cytochrome P450 enzymes (CYPs) play a central role in metabolizing most drugs. Understanding their reactivity and selectivity is a central goal in predicting drug metabolism, and provides an example of a drug development challenge requiring multiscale simulation approaches. The flowchart shows a practical workflow for multiscale modeling of metabolic reactions of pharmaceuticals in CYPs. Mammalian CYPs are membrane-bound enzymes, but typically only the structures of the soluble portions are determined experimentally, lacking the membrane-anchoring helix, and the membrane. The intact CYP, in situ, can be modeled by adding the transmembrane helix and assembling the membrane around the protein, which occurs spontaneously in coarse-grained (CG) molecular dynamics (MD) simulations. CG methods allow MD simulations on timescales of microseconds to milliseconds, showing how the protein is oriented in the membrane and how drug molecules such as warfarin move through the membrane and associate with the protein. Understanding how the drug accesses and binds within the active site requires more detailed, fully-flexible MD simulations with an atomistic molecular mechanics (MM) representation in which every atom in the simulation is represented explicitly, in contrast to the representation of amino acids by a small number of ‘beads’ that group atoms together at the CG level. The CG model is converted into an atomistic (AT) model, which can be used for MD simulations of drug binding. Typical MM methods cannot be used to model chemical reactivity, therefore for potentially reactive poses of the enzyme/drug complex, the system is converted to a quantum mechanics/molecular mechanics (QM/MM) model, in which the reactive Compound I and the drug are included in the QM region for modeling of chemical reactions of the drug in the enzyme.