Molecular Docking, Quantitative Structure-activity Relationship And De Novo Molecular Design Studies Of Neuraminidase Inhibitors

Recently, neuraminidase inhibitors (NIs) are a focus of new anti-influenza virus drugs. NIs can selectively inhibit neuraminidase (NA), and prevent progeny virosome from replication and release in the host cell, thus take precautions against influenza and the alleviation symptom effectively. As an effective kind of anti-influenza drugs, there is a rising trend toward resistance of NIs due to emergence of new influenza virus. So, it is necessary to research and develop new NIs.In the dissertation, Quantitative structure-activity relationship (QSAR) and molecular docking in computer-aided drug design (CADD) were employed to systematically investigate six classes with the same skeleton and two classes with different skeletons of NIs. The main conclusions were as follows:(1) Sequence alignment of 8 NA indicated that the identical residues were 19.7%. Hydrogen bonding, hydrophobic and electrostatic interactions were important factors which had effect on NA-NIs interactions. Moreover, Arginine (118, 152, 292 and 371), Aspartate 151, Glutamate 227, Tryptophan 178 and so on were the key residues in the active site.Docking results between 8 classes of NIs and NA showed that hydrogen bonding, hydrophobic and electrostatic interactions were key factors which affected NA-NIs interactions. RMSD (root mean squared deviation) between the position calculated and that observed of crystal structure in NA were all smaller than 1.50?, and similarity (a measure of similarity between solution coordinates and reference coordinates) were from 0.72 to 0.84. Docking studies between 8 NA and 87 pyrrolidine derivatives showed that the hydrogen bonding, hydrophobic and electrostatic interactions affected on the activity of NIs. There was a significant correlation between docking score (total scores) and the experimental activities of 87 pyrrolidine derivatives, correlation coefficient r=0.443~0.692 (p<0.001).(2) 87 pyrrolidine derivatives: R2, Q2 and R2test of HQSAR and Almond models were 0.846 and 0.830, 0.670 and 0.560, 0.872 and 0.856, respectively. R2, Q2 and R2test of CoMFA and CoMSIA (common structures, receptor and pharmacophore superposition) models were from 0.588 to 0.861, from 0.437 to 0.625, and from 0.614 to 0.875, respectively. Based on CoMFA model (common structure alignment), 8 new compounds with high docking score and predicted activity were obtained by LeapFrog. (3) 36 acyl(thio)urea and thiadiazolo [2,3-a] pyrimidine derivatives: R2, Q2 and R2test of HQSAR, CoMFA and CoMSIA (based on receptor alignment) models were from 0.863 to 0.899, from 0.509 to 0.656, and from 0.558 to 0.667, respectively. At the same time, the pharmacophore model with 8 features was obtained by GALAHAD. Based on pharmacophore superposition, R2 and Q2 of CoMFA and CoMSIA models were 0.989 and 0.737, 0.470 and 0.401, respectively. In addition, R2, Q2 and R2test of HoVAIFA QSAR for 30 acyl(thio)urea derivatives were in turn 0.849, 0.724 and 0.689. 7 and 10 new compounds with high docking scores and predictive activity were obtained by LeapFrog and UNITY 3D search, respectively.(4) 40 flavonoids derivatives: R2, Q2, R2test and Q2ext of HQSAR, Almond, CoMFA and CoMSIA (common structure, receptor and pharmacophore alignment) models were from 0.872 to 0.988, from 0.420 to 0.772, from 0.214 to 0.935, and from 0.272 to 0.782, respectively. By LeapFrog and UNITY database 3D Search, 8 and 6 new compounds with high docking score and predicted activity were obtained, respectively.(5) 68 cyclohexene derivatives: R2,Q2,R2test and Q2ext of HQSAR, CoMFA and CoMSIA (pharmacophore and receptor superposition) models were from 0.847 to 0.920, from 0.413 to 0.652, from 0.526 to 0.841, and from 0.531 to 0.838, respectively. 15 and 13 new compounds with high docking score and predicted activity were obtained using LeapFrog and UNITY database 3D search, respectively.(6) 45 benzoic acid derivatives: R2,Q2 and SEE (standard error of estimate) of HQSAR, CoMFA and CoMSIA models were from 0.724 to 0.758, from 0.418 to 0.466, and from 0.418 to 0.466, respectively. 20 new compounds with high docking scores and CScore values were obtained by UNITY database 3D search and virtual screening.(7) 38 cyclopentane derivatives: R2, Q2 and SEE of HQSAR, CoMFA and CoMSIA models were from 0.783 to 0.963, from 0.256 to 0.413, and from 0.152 to 0.375, respectively. Based pharmacophore model, 12 new compounds with docking score and docking consistency values CScore were obtained through UNITY database search and virtual screening.(8) 124 NIs with different skeletons: R2, Q2, R2test and Q2ext of HQSAR, HoVAIFA, Almond, CoMFA and CoMSIA models were from 0.775 to 0.952, from 0.456 to 0.818, from 0.518 to 0.748, and from 0.516 to 0.799, repectively. 5 and 11 new compounds with high predicted activity and docking score were obtained by LeapFrog and UNITY database 3D search, respectively.(9) 41 NIs with different skeletons: R2, Q2, R2test and Q2ext of HQSAR, Alomnd, CoMFA and CoMSIA, HQAR and CoMSIA models were from 0.661 to 0.910, from 0.390 to 0.554, from 0.396 to 0.812, and from 0.300 to 0.744, respectively. Based pharmacophore model, 12 new compounds with high docking score and CScore values were obtained through UNITY database 3D search and virtual screening.(10) ADME researches indicated that one third of 127 new designed compounds had good permeation ability, high protein binding affinity, volume distribution and metabolic stability.