In an attempt to account protein flexibility and reorganizat

In an attempt to account protein flexibility and reorganization effects at the pharmacophore level the size of the excluded volume was set to increase the effective size of the binding cavity. For 3N7O complex, the generated structure-based pharmacophore model identified five functional features along with 20 excluded volume spheres, including one HBD pointed towards Ser214, one NI pointed to Lys40, and three HY centers pointed towards Tyr215, Gly216, and Leu99 amino acids, respectively. Pharmacophore model with four distinct features was generated from 1T31 complex. It composed of one HBA, one NI, one HY, and one RA with 20 excluded volume spheres. The HBA and NI features were directed to Tivantinib Gly193 and Lys192, respectively. While, RA feature of the SB_Model2 was pointed towards His57 amino acid of the active site of chymase. The pharmacophore model developed from 3SON complex also consists of four features with two HY features pointing in the direction of Gly199 and Arg200, one NI, and one RA pointing towards His45 along with 16 excluded volume spheres. The final pharmacophore model derived from 2HVX complex showed six features encompassing one HBD, two HY, two NI, and one RA with 23 excluded volume spheres. The two HY groups were pointed towards Phe191 and Gly216, and HBD pointed towards Tyr215. While, the RA feature was directed towards His57 and two NI features were pointed in the direction of Lys192 and Gly193. The comparison of above four pharmacophore models showed that hydrophobic feature was the common feature among all developed pharmacophore models. A previous study also showed that presence of hydrophobic sites for a chymase inhibitor were important for its effective binding with the key residues of the active site. Pharmacophoric features of the models were directed towards key amino acids like Tyr215, His57, Lys192, Gly193, and Ser195 which play a major role in chymase inhibition activity. Hence, these features can be considered as important chemical features to discover the novel chymase inhibitors. Common feature pharmacophore models were generated for the target protein using set of experimentally known inhibitors. With the aim of acquiring a best model, numerous common feature pharmacophore generation runs were performed by NSC305787 (hydrochloride) altering the parameters such as minimum interfeature distance values, maximum omit feature, and the permutation of pharmacophoric features. The qualitative top ten pharmacophore models were developed using identify the common features necessary to inhibit chymase.