Work

Structural Determinants of Binding of Aromates to Extracellular Matrix: A Multi-Species Multi-Mode CoMFA Study

Year: 2006

Type: article

Abstract: For small molecules acting in tissues, including signaling peptides, effectors, inhibitors, and other drug candidates, nonspecific binding to the extracellular matrix (ECM) is a critical phenomenon affecting their disposition, toxicity, and other effects. A commercially available ECM mimic, forming a solidified layer at the bottom of the vials, was used to measure the association constants of 25 simple aromatic compounds to two forms of ECM proteins, solidified (s-ECM) and dissolved (d-ECM) in the buffer during the incubation. Except for small homologous series, the binding data did not correlate with the lipophilicity and acidity of the compounds, contrary to a common expectation for nonspecific binding. To elucidate the putative structures of averaged binding sites of s-ECM and d-ECM, comparative molecular field analysis (CoMFA) was applied in a modified version taking into consideration that multiple modes and multiple species may be involved. The method shapes a receptor site model from a set of grid points in which the interaction energies between a probe atom and superimposed ligands are calculated. Electrostatic and steric energies in the grid points are characterized by regression coefficients. The forward-selection nonlinear regression analysis was used to optimize the coefficients in the novel multi-species, multi-mode CoMFA models. These models showed satisfactory descriptive and predictive abilities for both s-ECM and d-ECM binding data, which were better than those obtained with the standard, one-mode CoMFA analysis. The calibrated models, defining the electrostatic and van der Waals regions of putative binding sites, are suitable for the prediction of ECM binding for untested chemicals.

Source: Chemical Research in Toxicology

DOI: 10.1021/tx060188l

Authors: Yufen Zhang, Viera Lukacova, Vladimir Bartus, Stefan Balaz

Institution: North Dakota State University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Center for Research Resources

Awards: G1416491957, G19893751, New Strategies for Targeting Protease in Disease, North Dakota INBRE: Health and the Environment