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      Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach.

      Journal of Computer-Aided Molecular Design
      Artificial Intelligence, Binding Sites, Computer Simulation, Crystallography, X-Ray, Drug Design, Heterocyclic Compounds, chemistry, Heterocyclic Compounds, 1-Ring, Humans, Ligands, Models, Molecular, Protein Binding, Pyridines, Receptors, Adrenergic, beta-2, Receptors, CXCR4, antagonists & inhibitors, Sequence Alignment, Sequence Homology, Amino Acid, Structural Homology, Protein, Structure-Activity Relationship

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          Abstract

          CXCR4 is a G-protein coupled receptor for CXCL12 that plays an important role in human immunodeficiency virus infection, cancer growth and metastasization, immune cell trafficking and WHIM syndrome. In the absence of an X-ray crystal structure, theoretical modeling of the CXCR4 receptor remains an important tool for structure-function analysis and to guide the discovery of new antagonists with potential clinical use. In this study, the combination of experimental data and molecular modeling approaches allowed the development of optimized ligand-receptor models useful for elucidation of the molecular determinants of small molecule binding and functional antagonism. The ligand-guided homology modeling approach used in this study explicitly re-shaped the CXCR4 binding pocket in order to improve discrimination between known CXCR4 antagonists and random decoys. Refinement based on multiple test-sets with small compounds from single chemotypes provided the best early enrichment performance. These results provide an important tool for structure-based drug design and virtual ligand screening of new CXCR4 antagonists.

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