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Cited by 47Computational Drug Discovery MethodsAdvanced Chemical Physics StudiesProtein Structure and Dynamics

On-the-fly QM/MM docking with attracting cavities.

Chaskar, Prasad, Zoete, Vincent, Röhrig, Ute F.·2017·Journal of Chemical Information and Modeling
DOI 10.1021/acs.jcim.6b00406
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Abstract

We developed a hybrid quantum mechanical/molecular mechanical (QM/MM) on-the-fly docking algorithm to address the challenges of treating polarization and selected metal interactions in docking. The algorithm is based on our classical docking algorithm Attracting Cavities and relies on the semiempirical self-consistent charge density functional tight-binding (SCC-DFTB) method and the CHARMM force field. We benchmarked the performance of this approach on three very diverse data sets: (1) the Astex Diverse set of 85 common noncovalent drug/target complexes formed both by hydrophobic and electrostatic interactions; (2) a zinc metalloprotein data set of 281 complexes, where polarization is strong and ligand/protein interactions are dominated by electrostatic interactions; and (3) a heme protein data set of 72 complexes, where ligand/protein interactions are dominated by covalent ligand/iron binding. Redocking performance of the on-the-fly QM/MM docking algorithm was compared to the performance of classical Attracting Cavities, AutoDock, AutoDock Vina, and GOLD. The results demonstrate that the QM/MM code preserves the high accuracy of most classical scores on the Astex Diverse set, while it yields significant improvements on both sets of metalloproteins at moderate computational cost.

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Crossref · 10.1021/acs.jcim.6b00406

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  • Results obtained using QM or hybrid QM/MM-based methods are very encouraging compared to standard scoring functions, principally when dealing with metalloproteins.

    “The results obtained using QM or hybrid QM/MM-based methods are very encouraging when compared to the standard scoring functions, principally when dealing with metalloproteins (Chaskar et al., 2017; Pecina et al., 2018).”

    From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Chaskar et al., 2017

  • The use of quantum mechanical methods can improve the description of protein-ligand interactions and, in principle, could provide a more accurate binding affinity.

    “The use of quantum mechanical methods can improve the description of protein–ligand interactions and, in principle, could provide a more accurate binding affinity (Raha and Merz, 2005; Chaskar et al., 2017; Crespo et al., 2017; Cavasotto et al., 2018).”

    From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Chaskar et al., 2017

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Authors
Chaskar, P., Zoete, V., & Röhrig, U. F.
Affiliations
SIB Swiss Institute of Bioinformatics (CH)
Corresponding author
  • Zoete, Vincent
  • Röhrig, Ute F.
ORCID
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2016-12-16
Volume
57
Issue
1
Pages
73-84
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/acs.jcim.6b00406
OpenAlex
W2560065989
Semantic Scholar
22c3f98b844da19f066f9cadf9da64d5701a8917
Cited by
46
References
49
Citations by year
2026: 2 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 5 · 2021: 3 · 2020: 13 · 2019: 5 · 2018: 9 · 2017: 3
FWCI
5.52
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
  • IRIS (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
Funders
Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung (135460)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
SCC-DFTB, CHARMM force field, polarization, metal-protein interactions, zinc metalloproteins, heme proteins, electrostatic interactions, hydrophobic interactions, redocking, AutoDock Vina, gold
MeSH terms
Ligands, Protein Binding, Protein Conformation, Proteins, Quantum Theory, Molecular Docking Simulation

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