Question

Computational methods can be applied at the early stages of the drug design process. It is use current technology to provide valuable insights into the understanding of chemical system in a virtual manner. By integrating the TWO (2) computational methods; molecular docking and molecular dynamics simulation, explain how these TWO (2) methods can be integrated in computational drug design.

Long answer in essay form for 25 Marks

Answer 1

Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.

Molecular docking has been developed and improving for many years, but its ability to bring a medicine to the drug market effectively is still generally questioned. In this chapter, we introduce several successful cases including drugs for treatment of HIV, cancers, and other prevalent diseases. The technical details such as docking software, protein data bank (PDB) structures, and other computational methods employed are also collected and displayed. In most of the cases, the structures of drugs or drug candidates and the interacting residues on the target proteins are also presented. In addition, a few successful examples of drug repurposing using molecular docking are mentioned in this chapter. It should provide us with confidence that the docking will be extensively employed in the industry and basic research. Moreover, we should actively apply molecular docking and related technology to create new therapies for diseases.

Molecular dynamics (MD) simulations are frequently used in SBDD to give insights into not only how ligands bind with target proteins but also the pathways of interaction and to account for target flexibility. This is especially important when drug targets are membrane proteins where membrane permeability is considered to be important for drugs to be useful.

Molecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug–target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug–target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand–target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization.