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Final Progress Reports: Boston University: Bioinformatics and Molecular Modeling Core

Superfund Research Program

Bioinformatics and Molecular Modeling Core

Project Leader: Stefano Monti
Co-Investigator: Sandor Vajda
Grant Number: P42ES007381
Funding Period: 2000-2021

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Final Progress Reports

Year:   2016  2004 

The Core supports research projects investigating environmental pollutants that bind to specific protein receptors or enzymes, affecting pathways involved in reproduction and development. Dr. Vajda and his research team provide hardware and software tools and expertise in bioinformatics, molecular modeling and visualization, prediction of protein structures by homology modeling, and docking and the analysis of receptor-ligand interactions.

The research team has developed several computational tools used by BU-SBRP investigators:

  1. Computational solvent mapping, which is used to determine binding sites computationally rather than experimentally. The method places molecular probes (small molecules or functional groups) on a protein surface in order to find the most favorable binding positions. Since these small molecules prefer binding in the active site, computational mapping can be used to identify and characterize enzymes’ functional sites. The method, which has been applied to cytochrome P450s, revealed interesting differences between bacterial and mammalian molecules. One difference explains why mammalian P450s can oxidize a structurally diverse range of substrates.
  2. Cocking to nuclear receptors, a method used to discriminate agonists from partial agonists by performing calculations for a panel of different structures. Although x-ray structures are available for many nuclear receptors, docking of small molecules to these proteins is far from easy, because the binding of an agonist, partial agonist, or antagonist leads to specific conformational changes that affect cofactor binding and gene activation.
  3. Consensus, an algorithm that consistently provides a high-quality alignment of the core regions for comparative modeling.

The Bioinformatics and Molecular Modeling Core provides direct support to several projects with data on xenobiotic ligands such as dioxins, PCBs, phthalates in bacteria, fish, mouse and human models.

In support of Dr. John Stegeman's research, Dr. Vajda’s group used available sequence and structural information on cytochrome P450 enzymes to predict their structure. They employed homology modeling, using known structures of related sequences to predict the enzyme shape, then applied robust docking methods, computationally simulating the fit of a ligand into the active site of the predicted structure. The results suggest that species may exhibit differences in oxidative metabolism by P450s and that non-human models may not be appropriate to assess the risks that particular compounds pose to human health.

In collaboration with Dr. David Waxman, they investigated the binding of phthalates in peroxisome proliferators activated receptors (PPARs). Known to interact with PPARs, phthalates are industrial plasticizers and established reproductive toxicants. The researchers first docked 15 phthalates with known values of PPAR activation and showed that the calculated binding free energies agree with experimental data. The same docking and scoring calculations were then applied to 70 phthalate monoesters with no activation data. Some of the compounds with the lowest calculated binding free energies were known to be weak activators of PPAR and some others will be evaluated experimentally for PPAR activation.

The Core’s work is an example of interdisciplinary collaboration; as new and innovative engineering techniques shed light on biology, the biology in turn drives technical development. Such combinations of experimental and computational methods to identify ligands of proteins and enzymes will facilitate the integration of computational approaches into more traditional toxicity screening programs for environmental contaminants. These studies will help identify environmental chemicals that warrant closer scrutiny as potential ligands for human receptors.

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