GM-DockZn: a geometry matching-based docking algorithm for zinc proteins

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>GM-DockZn is freely available at www.qmclab.com/ for academic users.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>

Modeling Substrate Coordination to Zn-Bound Angiotensin Converting Enzyme 2

“…To dock the desired ligands from Ang II or mutant peptides to zinc within the ACE2 structure, GM-DockZn (Wang et al 2020) was used. This program determines potential coordination sites on the ligand molecule and docks each site to the zinc ion.…”

MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions

“…[7][8][9][10][11] To date, versatile docking programs have been accessible, including traditional ones [12][13][14][15][16][17][18][19][20] and deep learning-based ones. [21][22][23] However, only a few of them, such as FlexX, 24 AutoDock Zn , 25 MpsDock Zn 26 and GM-Dock Zn , 27 are specially developed for metalloproteins due to the intricate coordination geometries derived from metal ions, and most of them are predominantly specic to zinc metalloproteins. In a majority of existing docking programs, a metallic energy term is considered in the design of scoring functions.…”

Modeling Substrate Coordination to Zn-Bound Angiotensin Converting Enzyme 2

“…The experimentally derived structures for Ang II and ACE2, 1N9V 34 and 1R42 9 respectively, were obtained from the RCSB PDB (rcsb.org) 35 . Initial Ang II mutants' structures were derived through use of PDB manipulator 36 found in CHARMM-GUI [37][38][39] to induce single-point mutations. To improve these structures, Visual Molecular Dynamics (VMD) 40 was used to add a water box with a padding of 5 Å as well as add Na + and Clions to neutralize charge and establish a NaCl concentration of 0.15 M. The mutants then underwent minimization under constant temperature (310 K) and pressure (1 atm) for 10 ps using Nanoscale Molecular Dynamics (NAMD) 41 .…”