Antigene Strategy in Breast Cancer Therapy: Rationales for Direct Targeting of erbB2/Her2 DNA with Polyamides

Abstract

The major goal of this project is to design and computationally evaluate most potent Pyrrole-Imidazole containing polyamide inhibitors of erbB2/Her2 oncogene transcription. We have used an original algorithm to identify the most suitable sequences within erbB2 promoter DNA and then focused our efforts on modeling and design of polyamides with high affinity and specificity to the target these DNA sequences. We have developed a fast and reliable algorithm to build 3-Dimentional molecular models of polyamide-DNA complexes from the corresponding sequences. In our modeling program, PolyGroove, the initial configuration of the complex is generated from standard B-DNA model and the polyamide chain, which is placed in the minor groove according to the specified polyamide-DNA pairing rules. The models are energy optimized with special distance restrains imposed by the modular nature of polyamide-DNA recognition, and then without any restrains. The algorithm has shown excellent performance in comparative NMR and modeling studies of ten-ring polyamide hairpins, with the control ab-inito model closely reproducing all NMR restrains. The PolyGroove program was successfully applied to automatically generate and predict binding energies of polyamide-DNA models with long binding sites (12 and 13 bp) within Erb2/Her2 promoter, using various topologies and a number of new functional groups. Ten most promising candidates for erbB2/Her2 gene-specific inhibition were selected for the further studies.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2002
Accession Number
ADA415582

Entities

People

  • Juan F. Recio
  • Vsevolod Katritch

Organizations

  • Scripps Research

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Alanine
  • Algorithms
  • Breast Cancer
  • Chemical Synthesis
  • Chemistry
  • Geometry
  • Inhibition
  • Mathematics
  • Molecular Biology
  • Molecules
  • Neoplasms
  • Recognition
  • Sequences
  • Solid Phases
  • Three Dimensional
  • Topology
  • Transcription Factors

Readers

  • Breast cancer cell signaling and growth regulation.
  • Molecular Genetics
  • Neural Network Machine Learning.