Research articleInvestigation on the isoform selectivity of novel kinesin-like protein 1 (KIF11) inhibitor using chemical feature based pharmacophore, molecular docking, and quantum mechanical studies
Graphical abstract
Ligand based pharmacophore model, docking and density function approaches were employed to reveal the lead candidate for novel kinesin like protein-1.
Introduction
“Kinesin-like protein (KIF11) is the member of kinesin superfamily, which move along the microtubule tacks in the cell with the help of nanomotors. Named from studies in the early days of discovery, it is also known as Kinesin-5(Eg5) (Mandelkow and Mandelkow, 2002). It has a variety of functions and is responsible for the transporter of vesicles and organelles (Miki et al., 2005). KIF11 is also required for establishment and maintenance of cell polarity, spatial organization of microtubules, chromosomes in the mitotic spindle, microtubule stabilization, and chromosome–microtubule interactions (Schmidt and Bastians, 2007). Removal of KIF11 prevents centrosome migration and it controls mitosis through bipolar spindle formation and chromosome separation (Huszar et al., 2009). KIF11 defects are linked to the disease of microcephaly with or without chorioretinopathy, lymphedema, or mental retardation (Kaestner and Bastians, 2010). KIF11 is over expressed in many proliferative tissues including leukemia as well as solid tumors such as breast, lung, ovarian, bladder and pancreatic cancers. Furthermore, Eg5 is not expressed in the adult peripheral nervous system; and hence Eg5 inhibitors may not cause neuropathic side effects commonly associated with agents that primarily target tubulin (Blangy et al., 1995, Ostergaard et al., 2012). Inhibitors of KIF11 have been developed as chemotherapeutic agents in the treatment of cancer. The first KIF11 inhibitor, monastrol was discovered in a chemical screen of a large library of cell permeable compounds (Sakowicz et al., 2004). Many specific inhibitors of Eg5 have been discovered including monastrol, S-trityl-l-cysteine (STLC) and ispinesib. These inhibitors exert their action through binding to an allosteric site located between helix α3 and loop 5 of the Eg5 domain (Jackson et al., 2007). A biphenyl-type inhibitor PVZB1194 binds to the helix α4/helix α6 allosteric pocket, which is located 15 Å from the ATP binding pocket. Binding of the inhibitor to the allosteric pocket induces the deformation of the ATP binding pocket through the Tyr104 residue. The biphenyl-type inhibitor suppresses the binding of ATP, while the conventional Eg5 inhibitors inhibit the release of ADP (El-Nassan, 2013, Luo et al., 2007). Recent studies evidenced that the biphenyl-type inhibitor PVZB1194 binds to the α4/α6 allosteric pocket 15 Å from the ATP-binding pocket that differs from conventional allosteric inhibitors that bind to the allosteric L5/α2/α3 pocket of Eg5. The conformations of α1, β3, P-loop, and α2 are unique for Eg5-PVZB1194. PVZB1194 binds to Eg5, the inhibitor pushes Tyr104 and conformations around the ATP binding site change to affect the binding of ATP (Yokoyama et al., 2015). The majority of human KIF11 inhibitors are selectively binds to a drug ‘hot spot’, composed of residues from the helix α2 and helix α3 helices and a flexible L5 loop on the surface of the motor domain. The L5 loop in KIF11 closes around the inhibitor and is open in the absence of inhibitor (Turner et al., 2001, Yan et al., 2004). For inhibitors that bind to the L5 pocket, the mechanism of inhibition is that they slow ADP release from the catalytic active site (Learman et al., 2009) and inhibit ATP-dependent directional motion (Ulaganathan et al., 2013). Other sites of inhibitor binding between helix α4 of the switch II cluster and helix α6 preceding have also been identified in the KIF11 domain. The compound had small changes in the nucleotide-binding pocket induced by ATP hydrolysis leads to the structural changes in switch II cluster and neck-linker region. It generates the molecular motion responsible for shifting two antiparallel Microtubules apart from the bipolar spindle (Cochran et al., 2005, Kwok et al., 2006). The binding site helix α4 of the switch II cluster and helix α6 had high affinity binding driven by network of aromatic interaction and hydrogen interaction with carboxylate group of Benzimadazole-8(BI8) when compare to interactions in helix α2, L5 loop and helix α3 helices binding site (Cochran et al., 2005).
In our study describes the efforts to identify the essential structural requirements to inhibit KIF11 and thereby designing novel potent inhibitors as antimitotic therapeutics. Ligand based pharmacophore modeling approaches along with virtual screening of large chemical databases for novel scaffolds, molecular docking and density functional theory studies have been successfully employed to achieve our goal.
Section snippets
Pharmacophore modeling
Pharmacophore modeling approaches is the major tool to discover new and novel scaffold molecules for the specific target. It can be done either based on the ligands or on the active/binding site of proteins. For the ligand based pharmacophore model, HypoGen algorithm which uses the activity values of the compounds in the training set to generate the best hypotheses. In our present study, HypoGen have been successfully and extensively applied to build a 3D-QSAR pharmacophore model for KIF11
Pharmacophore model generation
The pharmacophore generation is based on the conformational analysis such as best and fast method offered by DS2.5.v. In our study 21 training set molecules which were associated with their conformations are submitted to 3D QSAR Pharmacophore Generation. It produces the hypothesis by undergoes constructive, subtractive, and optimization phases respectively. The geometric fit value was calculated based on the fit function checking whether the chemical feature is mapped or not and also assure
Conclusions
Ligand based 3D pharmacophore hypotheses of KIF11 inhibitors have been successfully developed using 3D QSAR Pharmacophore Generation protocol existing in DS v.2.5. The best quantitative pharmacophore model, Hypo1 was characterized by the highest cost difference, best correlation coefficient and lowest RMSD respectively. The best Hypo1 consisted of two HBA, one HY and one RA features. Hypo1 was further validated by test set, Fischer randomization test and decoy set methods. The test set
Acknowledgement
We sincerely thankful to Dr. N. Sundaraganesan to provide Gaussian 03 software for our work.
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