PeptoGrid-Rescoring Function for AutoDock Vina to Identify New Bioactive Molecules from Short Peptide Libraries

Abstract

Peptides are promising drug candidates due to high specificity and standout safety. Identification of bioactive peptides de novo using molecular docking is a widely used approach. However, current scoring functions are poorly optimized for peptide ligands. In this work, we present a novel algorithm PeptoGrid that rescores poses predicted by AutoDock Vina according to frequency information of ligand atoms with particular properties appearing at different positions in the target protein's ligand binding site. We explored the relevance of PeptoGrid ranking with a virtual screening of peptide libraries using angiotensin-converting enzyme and GABAB receptor as targets. A reasonable agreement between the computational and experimental data suggests that PeptoGrid is suitable for discovering functional leads.

Keywords: Danio rerio; docking; gabab receptor; peptides; rescoring.

Figure 1

Boostrap analysis of Spearman’s correlation coefficient. PeptoGrid values are in gray.

Figure 2

Occurence maps for the C.ar atom type (e.g., aromatic carbon) inside angiotensin-converting enzyme binding site for (A) AutoDock Vina, (B) PLANTS and (C) LeDock. Density increases from blue through yellow to purple. Phosphonic tripeptide from 2XY9 is shown in sticks model.

Figure 3

Distribution of PeptoGrid scoring values for GABAB receptor. (A) Histogram of PeptoGrid scores. (B) Comparison of PeptoGrid score with AutoDock Vina energies. Colors denotes peptides: green is for PYYA, yellow for PSYG and red is for LNPW.

Figure 4

Behavioral effect of the tetrapeptides. Open field test: (A) Time at the bottom and (B) Time at the top. Shoaling test: (C) Time out of social zone and (D) Number of transitions. All values are normed to control. 1 MG and 10 MG denote doses of 1 mg/kg and 10 mg/kg correspondingly.