Thermodynamic rules for the design of high affinity HIV-1 protease inhibitors with adaptability to mutations and high selectivity towards unwanted targets

被引:51
作者
Ohtaka, H [1 ]
Muzammil, S [1 ]
Schön, A [1 ]
Velazquez-Campoy, A [1 ]
Vega, S [1 ]
Freire, E [1 ]
机构
[1] Johns Hopkins Univ, Dept Biol, Baltimore, MD 21218 USA
关键词
HIV-1; protease; drug resistance; AIDS; binding thermodynamics; enzyme inhibition; adaptive inhibitors; isothermal titration calorimetry;
D O I
10.1016/j.biocel.2004.02.021
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Protease inhibitors are key components in the chemotherapy of HIV-1 infection. However, the long term efficacy of antiretroviral therapies is hampered by issues of patient compliance often associated with the presence of severe side effects, and above all by the appearance of drug resistance. The development of new protease inhibitors with high potency, low susceptibility to mutations and minimal affinity for unwanted targets is an urgent goal. The engineering of these adaptive inhibitors requires identification of the critical determinants of affinity, adaptability, and selectivity. Analysis of the binding database for existing clinical and experimental inhibitors has allowed us to address the following questions in a quantitative fashion: (1) Is there an optimal binding affinity? Or, are the highest affinity inhibitors necessarily the best inhibitors? (2) What is the dependence of optimal affinity on adaptability and selectivity? (3) What are the determinants of adaptability to mutations associated with drug resistance? (4) How selectivity against unwanted targets can be improved? It is shown that the optimal affinity is a function of the effective target concentration and the desired adaptability and selectivity factors. Furthermore, knowledge of the enthalpic and entropic contributions to the binding affinity to the wild type provides a way of anticipating the response of an inhibitor to mutations associated with drug resistance, and therefore, a valuable guideline for optimization. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1787 / 1799
页数:13
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