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HERO ID
4272680
Reference Type
Journal Article
Title
Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions
Author(s)
Brylinski, M
Year
2018
Is Peer Reviewed?
Yes
Journal
Chemical Biology and Drug Design
ISSN:
1747-0277
EISSN:
1747-0285
Volume
91
Issue
2
Page Numbers
380-390
Language
English
PMID
28816025
DOI
10.1111/cbdd.13084
Web of Science Id
WOS:000422952300004
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028597986&doi=10.1111%2fcbdd.13084&partnerID=40&md5=150e9a9aa14af9b250ef118a30d32706
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Abstract
The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.
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