As technology advances, new tools are available to study and understand the interaction between ligands and proteins. As a result, many methods to predict drug-target interaction have been developed and are used for the new drug discovery process.
Molecular Docking is a well-established in silico method widely used in structure-based drug design. It predicts the ligand-protein complex structure by modeling ligand and protein interactions at the atomic level. Through this process, we can gain useful insight into the ligand-binding modes and binding process, which can be applied to identify novel compounds of therapeutic interest.
In general terms, molecular docking aids in:
- Ligand and protein interaction modeling
- Prediction of ligand-protein binding structure
- Prediction of ligand activity on the target protein binding site
Ligand: Drug (Candidate)
Protein: Drug target (e.g. Enzyme, Receptor, Ion Channel, Transporter, etc)
Molecular docking method predicts the preferred relative orientation of one molecule (key) when bound in an active site of another molecule (lock) to form a stable complex such that free energy of the overall system is minimized.
The molecular docking process involves basically two main steps:
1) Sampling of different ligand conformations, positions and orientations within a particular binding site of the target.
2) Evaluation of the obtained docking poses through a scoring function in terms of a binding affinity
The protein docking problem can be defined as an optimization problem where the objective is to minimize the binding energy of the complex.
The following figure (Fig. 3) illustrates an example of the Drug and Drug-target complex. A Selective Serotonin Receptor Inhibitor (SSRI) bound to the 5-HT re-uptake transporter (5-HTT or SERT) inhibits the re-uptake of serotonin into the pre-synaptic neurons. This in turn, helps to maintain normal concentrations of serotonin in the synapses which is used in the treatment of depression.
The drug target can vary but they are mostly proteins such as an enzymes, ion channels, and transporters. Therapeutically popular drug-targets can change over time.
The variety of docking software available can be distinguished based on two basic components: the sampling algorithm the scoring function.
Sampling Algorithm
There are too many numbers of cases of ligand and protein active site binding. It is impossible to make all possible ligand conformation and compare. Therefore, there are three sampling algorithms that are commonly used.
- Pharmacophore based Algorithm
A pharmacophore is a description of molecular features that are essential for molecular recognition of a ligand by a specific target and can be responsible for a particular biological or pharmacological action.
This is a type of matching algorithm based on molecular shape where the protein and the ligand are represented as pharmacophores. Ligands are mapped into the active site of a protein in terms of shape features and chemical information.
2. Fragment-based Algorithm
This algorithm divides the ligand into several fragments that are docked separately. The largest fragment is most likely to have an important functional role, it is docked first. The remaining fragments are added incrementally afterward.
3. Stochastic Search Algorithm
These methods search the conformational space by randomly changing the ligand conformation. Ligand poses are generated through bond rotation, rigid-body translation or rotation. Poses are then selected based on an energy criterion. If the pose it passes the criterion, it is used to generate the next conformation. This iteration is repeated until the pre-defined quantity of conformations is reached.
Scoring function
The scoring function is used to evaluate the conformations generated by a sampling algorithm. It is used to discriminate the binders from the inactive compounds. They involve estimating the binding affinity between the protein and ligand and can be divided into force-field-based, empirical and knowledge-based scoring functions.
1. Force-field-based scoring functions
Asses the binding energy by calculating the sum of the non-bonded (electrostatics and van der Waals) interactions. The electrostatic terms are calculated by a Coulombic formulation and the van der Waals terms are described by a Lennard-Jones potential function. Cut-off distances are used to treat non-bonded interactions.
2. Empirical-based scoring functions
Binding energy is decomposed into several energy components, such as hydrogen bond, ionic interaction, hydrophobic effect, and binding entropy. Each component is multiplied by a coefficient and then summed up to give a final score. Coefficients are obtained from regression analysis fitted to a test set of ligand-protein complexes with known binding affinities.
3. Knowledge-based scoring functions
Use statistical analysis of ligand-protein complexes crystal structures to obtain the interatomic contact frequencies and/or distances between the ligand and protein. These frequency distributions are then converted into pairwise atom-type potentials. The score is calculated by favoring preferred contacts and penalizing repulsive interactions between each atom in the ligand and protein within a given cutoff.
References
[1] V. Khanna and N. Petrovsky, “Rational Structure-Based Drug Design,” Encycl. Bioinforma. Comput. Biol., pp. 585–600, Jan. 2019.
[2] X.-Y. Meng, H.-X. Zhang, M. Mezei, and M. Cui, “Molecular docking: a powerful approach for structure-based drug discovery.,” Curr. Comput. Aided. Drug Des., vol. 7, no. 2, pp. 146–57, Jun. 2011.
[3] S. Zarbafian et al., “Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes,” Sci. Rep., vol. 8, no. 1, p. 5896, Dec. 2018.
[4] L. Pinzi and G. Rastelli, “Molecular Docking: Shifting Paradigms in Drug Discovery,” Int. J. Mol. Sci., vol. 20, no. 18, p. 4331, Sep. 2019.
[5] A. Sethi, K. Joshi, K. Sasikala, and M. Alvala, “Molecular Docking in Modern Drug Discovery: Principles and Recent Applications,” in Drug Discovery and Development — New Advances [Working Title], IntechOpen, 2019.
