title: Tubulin inhibitors: pharmacophore modeling, virtual screening and molecular docking.
abstract: OBJECTIVE
  To construct a quantitative pharmacophore model of tubulin inhibitors and to discovery new leads with potent antitumor activities.
  
  
  METHODS
  Ligand-based pharmacophore modeling was used to identify the chemical features responsible for inhibiting tubulin polymerization. A set of 26 training compounds was used to generate hypothetical pharmacophores using the HypoGen algorithm. The structures were further validated using the test set, Fischer randomization method, leave-one-out method and a decoy set, and the best model was chosen to screen the Specs database. Hit compounds were subjected to molecular docking study using a Molecular Operating Environment (MOE) software and to biological evaluation in vitro.
  
  
  RESULTS
  Hypo1 was demonstrated to be the best pharmacophore model that exhibited the highest correlation coefficient (0.9582), largest cost difference (70.905) and lowest RMSD value (0.6977). Hypo1 consisted of one hydrogen-bond acceptor, a hydrogen-bond donor, a hydrophobic feature, a ring aromatic feature and three excluded volumes. Hypo1 was validated with four different methods and had a goodness-of-hit score of 0.81. When Hypo1 was used in virtual screening of the Specs database, 952 drug-like compounds were revealed. After docking into the colchicine-binding site of tubulin, 5 drug-like compounds with the required interaction with the critical amino acid residues and the binding free energies < -4 kcal/mol were selected as representative leads. Compounds 1 and 3 exhibited inhibitory activity against MCF-7 human breast cancer cells in vitro.
  
  
  CONCLUSIONS
  Hypo1 is a quantitative pharmacophore model for tubulin inhibitors, which not only provides a better understanding of their interaction with tubulin, but also assists in discovering new potential leads with antitumor activities.
authors.forename: Miao-Miao
authors.initials: MM
authors.lastname: Niu
authors.forename: Jing-Yi
authors.initials: JY
authors.lastname: Qin
authors.forename: Cai-Ping
authors.initials: CP
authors.lastname: Tian
authors.forename: Xia-Fei
authors.initials: XF
authors.lastname: Yan
authors.forename: Feng-Gong
authors.initials: FG
authors.lastname: Dong
authors.forename: Zheng-Qi
authors.initials: ZQ
authors.lastname: Cheng
authors.forename: Guissi
authors.initials: G
authors.lastname: Fida
authors.forename: Man
authors.initials: M
authors.lastname: Yang
authors.forename: Hai-Yan
authors.initials: HY
authors.lastname: Chen
authors.forename: Yue-Qing
authors.initials: YQ
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000737:chemistry* / Q000494:pharmacology*; D001940:Breast / Q000187:drug effects / Q000378:metabolism / Q000473:pathology; D001943:Breast Neoplasms / Q000188:drug therapy / Q000378:metabolism / Q000473:pathology; D045744:Cell Line, Tumor; D049109:Cell Proliferation / Q000187:drug effects; D015195:Drug Design; D005260:Female; D006801:Humans; D062105:Molecular Docking Simulation; D012984:Software; D013329:Structure-Activity Relationship; D014404:Tubulin / Q000737:chemistry / Q000378:metabolism*; D050257:Tubulin Modulators / Q000737:chemistry* / Q000494:pharmacology*
chemical_list: D000970:Antineoplastic Agents; D014404:Tubulin; D050257:Tubulin Modulators