Role of Molecular Dynamics Simulations in Drug Discovery

Authors: Julie Fang (1), Esra Tiras (2), and Swetha Upadrasta (3)

Editor: Monsurat Lawal, Ph.D.

Author Affiliations: (1) Johns Hopkins University, (2) University of Virginia, (3) University of California Irvine

Introduction

Computational drug discovery can accelerate the challenging process of designing a new drug candidate. Over the past decade, computational structure-based drug design has significantly advanced the field of drug discovery. Developing faster architecture and improved algorithms has made high-level computations quick and affordable, addressing limitations such as high computational costs and the approximation of molecular forces (1). Herein, we highlight molecular dynamics (MD) simulations as a drug design and discovery method to provide background knowledge.

Keywords

Molecular Dynamics Simulations; Molecular Docking; Computational Structure-Based Drug Design; Protein-Ligand Interactions; Conformational Changes; Binding Affinity; Lead Compound Identification.

What are MD simulations?

MD simulations are essentially computer simulations that model the behavior of atoms and molecules over time. By applying the principles of classical mechanics and interatomic force fields, these simulations can provide valuable insights into the dynamics and interactions of molecules within a biological system. In the context of drug discovery, MD simulations are used to study the interactions between potential drug molecules and their biological targets, such as proteins or enzymes.

Why MD?

Experimental methods alone cannot thoroughly investigate the mechanisms and interactions critical for drug development. Molecular dynamics simulations are extensively used in modern drug discovery and delivery, providing valuable information into the dynamical structures of macromolecules and protein-ligand interactions (2). MD helps assess the binding energetics and kinetics of ligand-receptor interactions (Figure 1), guiding the selection of the most promising candidate molecules for further development (3). Researchers have found that MD overcomes a significant limitation of static structure-based drug design, which fails to sample the protein conformational rearrangements that occur during ligand binding (1). Molecular recognition and drug binding are not static but dynamic processes, and molecular dynamics simulations provide insight into the motions of proteins (3).

 MD simulations of complexes have processes and protocols, including molecular docking, another computational drug design approach versatile in several capacities. Besides its relevance in various computational drug discovery methodologies, molecular docking is an unavoidable pre-step to MD simulation of a protein-ligand interaction mechanism (Figure 1). Docking techniques can examine proteins' binding sites to determine their suitability, plus the binding affinity and specificity of small molecules for target proteins. Molecular docking helps prioritize potential drug candidates by assessing their binding affinities and interactions with the target proteins.

Application of MD simulations in drug design

The most common application of MD simulations is the study of the interactions of potential drugs and the intended targets to treat. MD simulations play an essential role by identifying protein druggable sites, optimizing their structures, validating or reassessing docking results by exploring protein conformations, and evaluating the effect of mutations on their structure and functions. Simulations can provide insights into the dynamic pathways through which ligands enter and exit binding sites and accurate prediction of how well a potential drug binds to its target, enhancing the efficiency of the lead compound identification process (4,5).

MD simulation can also assess how drugs permeate cells, a vital method to investigate targeting drugs with intracellular or transmembrane proteins. Endpoint binding free energy calculations, such as MM/PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) and MM/GBSA (Molecular Mechanics Generalized Born Surface Area), are used to estimate the binding affinity of ligands to their targets. By simulating the interaction of drugs with lipid bilayers in the cell or other barrier tissues, researchers can predict and optimize the absorption properties of potential therapeutics (6). Many drugs cross cell membranes via passive diffusion. MD simulations can predict the rate of passive diffusion by modeling the energy barriers encountered by the drug as it moves through the lipid bilayer (7). Lipophilicity is another factor that is crucial for drug absorption. The lipid-water partition coefficient is a measure of a drug's lipophilicity. Drugs with optimal lipophilicity are more likely to permeate lipid bilayers efficiently (8). Simulating the interaction of anticancer drugs with lipid bilayers helps optimize their delivery to cancer cells, improving therapeutic outcomes (11). Researchers have used MD simulations to model the interaction of anticancer drugs with liposomal membranes, optimizing the encapsulation efficiency and release profiles (12).

The future of MD simulations in drug discovery

As computational power increases and force fields become more sophisticated, MD simulations are poised to play an even more significant role in drug discovery. Through MD simulations integration with other computational and experimental techniques, researchers can develop a more comprehensive understanding of the drug discovery process, leading to the development of safer and more effective therapies. As the technology evolves, MD simulations will undoubtedly be crucial in bringing new and innovative treatments to patients.

Conclusion

Drug discovery is a complex and time-consuming process. Conventionally, it involves a series of experimental steps, from identifying potential drug targets to testing their efficacy and safety in clinical trials. However, the advent of computational tools like molecular dynamics (MD) simulations has revolutionized this process, offering a powerful approach to unveil the therapeutic potential of drug candidates. Incorporating MD simulations and other computational methods into drug discovery and development processes provides a powerful approach to overcoming traditional experimental limitations. By offering atomic-level insights into drug-target interactions, these simulations are helping researchers to identify and optimize promising drug candidates more efficiently.

Glossary

1. Molecular Forces: Forces that govern the interactions between atoms and molecules in a system, such as van der Waals forces, electrostatic forces, and hydrogen bonds.

2. Molecular Dynamics (MD) Simulations: These are computational techniques that simulate the physical movements of atoms and molecules over time. These simulations help understand macromolecules' dynamic behavior in a biological environment.

3. Protein-Ligand Interactions: Interactions between a protein (typically an enzyme or receptor) and a ligand (such as a drug molecule) that can affect the protein's function. Understanding these interactions is crucial in drug design.

4. Molecular Docking: A method used to predict the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Docking helps in predicting the binding affinity and specificity of drug candidates.

5. Mutations: Changes in the DNA sequence of a gene can lead to changes in the protein structure and function.

6. Endpoint Binding Free Energy Calculation: A computational method to estimate the free energy of binding between a ligand and a protein to provide insights into the stability and affinity of the interaction. Widely used techniques include MM/PBSA and MM/GBSA.

7. MM/PBSA: Molecular Mechanics Poisson-Boltzmann Surface Area, a method to calculate binding energies involving molecular mechanics energies combined with solvation terms calculated from the Poisson-Boltzmann equation.

8. Lipophilicity: A critical parameter in the study of drug absorption, distribution, metabolism, and excretion (ADME) properties that show the ability of a drug to cross cell membranes, which are primarily composed of lipid bilayers.

9. Passive Diffusion: A fundamental mechanism by which drugs cross cell membranes without needing energy input or transport proteins. It is driven by the concentration gradient of the drug across the membrane, moving from an area of higher concentration to an area of lower concentration until it reaches equilibrium.

10. Liposomal Membranes: These refer to the lipid bilayer structures that form the outer shell of liposomes. Liposomes are spherical vesicles with an aqueous core surrounded by one or more phospholipid bilayers, used in drug delivery to encapsulate therapeutic agents.

References 

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