You have ligand that you know binds to your receptor, since you’ve measured its affinity using isothermal titration calorimetry. You want to know the exact pose of the ligand in its binding pocket, so you use a computer simulation to dock it to the receptor structure. Surprisingly, your software shows the ligand on the protein surface, not in the pocket at all. What type of experiment could you do to confirm the location of the binding site and pose of the ligand?
In modern drug discovery, protein-ligand or protein-protein docking plays an important role in predicting the orientation of a ligand when it is bound to a protein receptor or enzyme using shape and electrostatic interactions to quantify. The van der waals interactions also play an important role, in addition to Coulombic interactions and the formation of hydrogen bonds. All these interractions are approximated by the docking score, that represents the potentiality of binding.
The earliest reported docking methods were based on the lock-and-key assumption proposed by Fischer, stating that both the ligand and the receptor can be treated as rigid bodies and their affinity is directly proportional to a geometric fit between their shapes.Later, the “induced-fit” theory proposed by Koshland suggested that the ligand and receptor should be treated as flexible during docking. Each backbone movement affects multiple side chains in contrast to relatively independent side chains. Thus, the sampling procedure in a fully flexible receptor/ligand docking is of a higher order of magnitude in terms of the number of degrees of freedom than in flexible docking with a rigid receptor. Consequently, these flexible docking algorithms not only predict the binding mode of a molecule more accurately than rigid body algorithms, but also its binding affinity relative to other compounds.
Over the last two decades, more than 60 different docking tools and programs have been developed for both academic and commercial use such as DOCK, AutoDock, FlexX, Surflex, GOLD, ICM, Glide, Cdocker, LigandFit, MCDock, FRED, MOE-Dock, LeDock, AutoDock Vina, rDock, UCSF Dock, and many others.
A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Considering the limitation of computer resources, docking has been performed with a flexible ligand and a rigid receptor for a long time, and remains the most popular method in use. Recently many efforts have been made to deal with the flexibility of the receptor, however, flexible receptor docking, especially backbone flexibility in receptors, still presents a major challenge for available docking methods. In few studies Local Move Monte Carlo (LMMC) approach is a potential solution to flexible receptor docking problems.
Theory of docking: Essentially, the aim of molecular docking is to give a prediction of the ligand-receptor complex structure using computation methods. Docking can be achieved through two interrelated steps: first by sampling conformations of the ligand in the active site of the protein; then ranking these conformations via a scoring function. Ideally, sampling algorithms should be able to reproduce the experimental binding mode and the scoring function should also rank it highest among all generated conformations.
Extensions of force-field-based scoring functions consider the hydrogen bonds, solvations and entropy contributions. Software programs, such as DOCK, GOLD and AutoDock, offer users such functions. They have some differences in the treatment of hydrogen bonds, the form of the energy function etc.. Furthermore, the results of docking with force-field-based functions can be further refined with other techniques, such as linear interaction energy and free-energy perturbation methods (FEP) to improve the accuracy in predicting binding energies.
Docking methodologies-
DOCK is the first automated procedure for docking a molecule into a receptor site and is being continuously developed. It characterizes the ligand and receptor as sets of spheres which could be overlaid by means of a clique detection procedure. Geometrical and chemical matching algorithms are used, and the ligand-receptor complexes can be scored by accounting for steric fit, chemical complementation or pharmacophore similarity. Within its improved versions, incremental construction method and exhaustive search are added to consider the ligand flexibility. The exhaustive search randomly generates a user-defined number of conformers as a multiple of the number of rotatable bonds in the ligand. With respect to scoring, the latest version DOCK 6.4 has included both an AMBER-derived force-field scoring with implicit solvent and GB/SA, PB/SA solvation scoring. This is one of the method as mentioned above.
You have ligand that you know binds to your receptor, since you’ve measured its affinity using...
A. You are performing experiments with your protein of interest and its ligand. You are attempting to mutate the binding site of your protein to disrupt binding to ligand to see what happens inside the cell. You make a series of mutations and one in particular gives you surprising results: you observe that the mutation that you made in an amino acid that sticks out into the binding pocket actually increases the protein’s affinity for the ligand. Give a molecular...
1. Suppose you modify a ligand to increase its ability to hydrogen bond with residues in the protein’s active site. Will this increase the binding affinity of that ligand to the protein. Explain your answer. 2. Describe how you know if a molecule is a bad choice for a substrate from PyRx results. Include one structural and one free energy argument.