Molecular Docking

Manish Datt

Components of a docking program

Representation

Scoring function

Search method

ADFR encodes the docking problem into a list of variables describing a docking solution and optimizes it for the AutoDock4 force field using Genetic Algorithm.

Representation – Genome

Genome used by ADFR to encode the docking of a flexible ligand into a receptor with two flexible side-chains.

https://doi.org/10.1371/journal.pcbi.1004586

Scoring function

The AutoDock energy function is a weighted sum of terms representing van der Waals, hydrogen bond, electrostatic, and desolvation contributions, which are calculated between pairs of atoms.

\[ E = w_{vdW}\sum_{i,j}\biggl(\frac{A_{i,j}}{r_{i,j}^{12}} - \frac{B_{i,j}}{r_{i,j}^6}\biggr) + w_{hbond}\sum_{i,j}E(t)\biggl(\frac{C_{i,j}}{r_{i,j}^{12}} - \frac{D_{i,j}}{r_{i,j}^{10}}\biggr) + w_{elec}\sum_{i,j}\biggl(\frac{q_iq_j}{\epsilon \space r_{i,j}.r_{i,j}}\biggr) \\ + w_{sol}\sum_{i,j}(S_iV_j + S_jV_i)e^{\bigl(\frac{-r_{ij}^2}{2\sigma^2}\bigr)} \]

The ADFR score uses this energy function to independently score the interactions between the following three groups of atoms: Ligand atoms (L), Rigid Receptor atoms (RR) and Flexible Receptor atoms (FR).

\[ S_{ADFR} = E_{L-L} + E_{L-RR} + E_{L-FR} + E_{FR-FR} + E_{FR-RR} \]

https://doi.org/10.1371/journal.pcbi.1004586

Affinity maps

Without (A) and with (B) gradient.

The color gradient outside the protein surface indicates favorable interactions going from weak (green) to strong (blue). Inside the protein surface the color gradient indicates unfavorable interactions going from low (yellow) to highly unfavorable (red).

This protocol produces maps that facilitate the search by providing a gradient for resolving clashes and by removing buried favorable cavities too small to accommodate a ligand e.g., trapped water cavities.

https://doi.org/10.1371/journal.pcbi.1004586

Overall flowchart of ADFR

https://doi.org/10.1371/journal.pcbi.1004586

Genetic Algorithm

The ligand and receptor flexibility description is first used to assemble a list of variables (genome).

The initial population is then generated by creating a list of initial solutions.

The population is scored, sorted, and top-ranking solutions are clustered.

The GA seeds the next generation with the best solution of each cluster and completes it by crossing-over, mutating, and minimizing individuals from the mating population.

The optimization stops when one of the termination criteria (maximum number of generations or evaluations) is reached or the search converges, at which point the solutions within 1 kcal/mol of the best solution are written out.

Bender et al, Nat Protoc 16, 4799–4832 (2021)

ADFR vs Vina

Scaling of docking runtimes as function of the number of flexible receptor side-chains

https://doi.org/10.1371/journal.pcbi.1004586

Thank you!

manish@bioinfo.guru