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Advances in computational modeling of enzyme inhibition :Prediction of potency and drug resistance targeting HIV-1 reverse transcriptase

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in computational modeling of enzyme inhibition :
其他題名:	Prediction of potency and drug resistance targeting HIV-1 reverse transcriptase
作者:	Udier Blagovic, Marina.
面頁冊數:	174 p.
附註:	Director: William L. Jorgensen.
附註:	Source: Dissertation Abstracts International, Volume: 65-03, Section: B, page: 1333.
Contained By:	Dissertation Abstracts International65-03B.
標題:	Chemistry, Organic.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3125320
ISBN:	0496726102

Advances in computational modeling of enzyme inhibition :Prediction of potency and drug resistance targeting HIV-1 reverse transcriptase
Udier Blagovic, Marina.

Advances in computational modeling of enzyme inhibition :Prediction of potency and drug resistance targeting HIV-1 reverse transcriptase [electronic resource] - 174 p.

Director: William L. Jorgensen.

Thesis (Ph.D.)--Yale University, 2004.

A master regression equation for the wild type (WT) RT was shown to predict 47 experimental activities for the K103N mutant with a q 2 = 0.55 and average error of only 0.46 kcal/mol. Further analysis identified key features for binding to the K103N mutant: ligand flexibility, burial of hydrophobic surface area, and protein-ligand van der Waals interactions.

ISBN: 0496726102Subjects--Topical Terms:

193634
Chemistry, Organic.

Advances in computational modeling of enzyme inhibition :Prediction of potency and drug resistance targeting HIV-1 reverse transcriptase
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520 # $a A master regression equation for the wild type (WT) RT was shown to predict 47 experimental activities for the K103N mutant with a q 2 = 0.55 and average error of only 0.46 kcal/mol. Further analysis identified key features for binding to the K103N mutant: ligand flexibility, burial of hydrophobic surface area, and protein-ligand van der Waals interactions.
520 # $a A scoring function was obtained that described the binding for 210 diverse non-nucleoside inhibitors (NNRTIs) with HIV-1 reverse transcriptase (HIVRT). The regression developed by fitting the MC simulation results to experimental data was additionally validated on a test set. Calculated and experimental activities were successfully correlated with an r 2 of 0.6 and q2 of 0.51 using nine physically intuitive descriptors.
520 # $a The effect of the K103N mutation on the activity of efavirenz analogs was studied via MC/FEP simulations. The computed fold resistance energies support the claim that efavirenz binds to K103N in a manner similar to the WT enzyme. The improved performance of the quinazolinones against the mutant enzyme is attributed to formation of a more optimal hydrogen-bonding network between the ligands and Glu138.
520 # $a The introduction of a novel procedure for derivation of partial atomic charges enhanced the description of non-bonded interactions in non-polarizable force fields. Partial charges derived from semiempirical AM1 and PM3 methods were tested with the OPLS-AA force field through computation of free energies of hydration via free energy perturbation calculations coupled with Monte Carlo simulations (MC/FEP). Scaled CM1A and CM3A charges reproduced experimentally determined quantities with improved accuracy over more traditional and computationally expensive quantum mechanical calculations.
520 # $a The structure for the complex of TMC125 and HIV-1 RT has been determined and validated through computation of resistance profiles using MC/FEP calculations. The good quantitative agreement between the computed and experimental anti-HIV activities for TMC125, nevirapine and efavirenz with WT RT and four common mutants (L100I, K103N, Y181C, and Y188L) confirms the correctness of the predicted structure and provides insights into the improved potency of this novel NNRTI.
520 # $a This dissertation describes advancements and applications of computer simulations in force-field parameterization, scoring function development, protein-ligand structure prediction, and computation of resistance profiles.
590 $a School code: 0265.
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650 # 0 $a Biophysics, General. $3 226901
650 # 0 $a Chemistry, Pharmaceutical. $3 206042
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