CHARMM-GUI PDB Reader and Manipulator: Covalent Ligand Modeling and Simulation

Overview

Journal J Mol Biol

Publisher Elsevier

Specialties Microbiology
Molecular Biology

Date 2024 Sep 5

PMID 39237201

Authors

Lingyang Kong

Sang-Jun Park

Wonpil Im

Affiliations

Soon will be listed here.

Abstract

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

Citing Articles

CHARMM-GUI for Protein-Ligand Docking of Multiple Reactive States along a Reaction Coordinate in Enzymes.

Suh D, Schwartz R, Gupta P, Zev S, Major D, Im W J Chem Theory Comput. 2025; 21(4):2118-2128.

PMID: 39950957 PMC: 11866752. DOI: 10.1021/acs.jctc.4c01691.

References

Lee J, Cheng X, Swails J, Yeom M, Eastman P, Lemkul J . CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput. 2015; 12(1):405-13. PMC: 4712441. DOI: 10.1021/acs.jctc.5b00935. View

Park S, Lee J, Patel D, Ma H, Lee H, Jo S . Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank. Bioinformatics. 2017; 33(19):3051-3057. PMC: 5870669. DOI: 10.1093/bioinformatics/btx358. View

Jo S, Lim J, Klauda J, Im W . CHARMM-GUI Membrane Builder for mixed bilayers and its application to yeast membranes. Biophys J. 2009; 97(1):50-8. PMC: 2711372. DOI: 10.1016/j.bpj.2009.04.013. View

Jo S, Song K, Desaire H, MacKerell Jr A, Im W . Glycan Reader: automated sugar identification and simulation preparation for carbohydrates and glycoproteins. J Comput Chem. 2011; 32(14):3135-41. PMC: 3188666. DOI: 10.1002/jcc.21886. View

Gao M, Moumbock A, Qaseem A, Xu Q, Gunther S . CovPDB: a high-resolution coverage of the covalent protein-ligand interactome. Nucleic Acids Res. 2021; 50(D1):D445-D450. PMC: 8728183. DOI: 10.1093/nar/gkab868. View

Pantsar T . KRAS(G12C)-AMG 510 interaction dynamics revealed by all-atom molecular dynamics simulations. Sci Rep. 2020; 10(1):11992. PMC: 7371895. DOI: 10.1038/s41598-020-68950-y. View

Kim S, Lee J, Jo S, Brooks 3rd C, Lee H, Im W . CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules. J Comput Chem. 2017; 38(21):1879-1886. PMC: 5488718. DOI: 10.1002/jcc.24829. View

Bauer R . Covalent inhibitors in drug discovery: from accidental discoveries to avoided liabilities and designed therapies. Drug Discov Today. 2015; 20(9):1061-73. DOI: 10.1016/j.drudis.2015.05.005. View

Lee J, Patel D, Stahle J, Park S, Kern N, Kim S . CHARMM-GUI Membrane Builder for Complex Biological Membrane Simulations with Glycolipids and Lipoglycans. J Chem Theory Comput. 2018; 15(1):775-786. DOI: 10.1021/acs.jctc.8b01066. View

10.

. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 2018; 47(D1):D520-D528. PMC: 6324056. DOI: 10.1093/nar/gky949. View

11.

Berman H, Henrick K, Nakamura H . Announcing the worldwide Protein Data Bank. Nat Struct Biol. 2003; 10(12):980. DOI: 10.1038/nsb1203-980. View

12.

Hayek-Orduz Y, Vasquez A, Villegas-Torres M, Caicedo P, Achenie L, Gonzalez Barrios A . Novel covalent and non-covalent complex-based pharmacophore models of SARS-CoV-2 main protease (M) elucidated by microsecond MD simulations. Sci Rep. 2022; 12(1):14030. PMC: 9386674. DOI: 10.1038/s41598-022-17204-0. View

13.

Awoonor-Williams E, Walsh A, Rowley C . Modeling covalent-modifier drugs. Biochim Biophys Acta Proteins Proteom. 2017; 1865(11 Pt B):1664-1675. DOI: 10.1016/j.bbapap.2017.05.009. View

14.

Jo S, Kim T, Im W . Automated builder and database of protein/membrane complexes for molecular dynamics simulations. PLoS One. 2007; 2(9):e880. PMC: 1963319. DOI: 10.1371/journal.pone.0000880. View

15.

Adeniyi A, Muthusamy R, Soliman M . New drug design with covalent modifiers. Expert Opin Drug Discov. 2016; 11(1):79-90. DOI: 10.1517/17460441.2016.1115478. View

16.

Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J . CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem. 2009; 31(4):671-90. PMC: 2888302. DOI: 10.1002/jcc.21367. View

17.

Cheng X, Jo S, Lee H, Klauda J, Im W . CHARMM-GUI micelle builder for pure/mixed micelle and protein/micelle complex systems. J Chem Inf Model. 2013; 53(8):2171-80. DOI: 10.1021/ci4002684. View

18.

Hopkins C, Le Grand S, Walker R, Roitberg A . Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. J Chem Theory Comput. 2015; 11(4):1864-74. DOI: 10.1021/ct5010406. View

19.

Herbst R, Schlessinger J . Small molecule combats cancer-causing KRAS protein at last. Nature. 2019; 575(7782):294-295. DOI: 10.1038/d41586-019-03242-8. View

20.

Qi Y, Lee J, Klauda J, Im W . CHARMM-GUI Nanodisc Builder for modeling and simulation of various nanodisc systems. J Comput Chem. 2019; 40(7):893-899. DOI: 10.1002/jcc.25773. View