Kierczak Marcin, Dramiński Michał, Koronacki Jacek, Komorowski Jan
The Linnaeus Centre for Bioinformatics, Uppsala University, 751-24 Uppsala, Sweden.
Bioinform Biol Insights. 2010 Dec 12;4:137-46. doi: 10.4137/BBI.S6247.
Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels.
We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering.
A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm.
尽管经过了二十多年的研究，但艾滋病毒对药物的耐药性仍然是开发有效艾滋病治疗方法的严重障碍。已经开发了几种计算方法来根据病毒蛋白（如逆转录酶（RT）或蛋白酶）的序列预测耐药水平。这些方法虽然强大且准确，但对导致耐药性/超敏感性产生的分子相互作用几乎没有提供任何见解。在这里，我们试图通过使用我们的蒙特卡罗特征选择和相互依赖性发现方法（MCFS-ID）来填补这一空白，以阐明表征耐药水平改变的病毒株的分子相互作用网络。
我们使用MCFS-ID方法分析了许多标注有耐药水平的HIV-1 RT序列。这使我们能够阐述表征对六种选定RT抑制剂（阿巴卡韦、拉米夫定、司他夫定、齐多夫定、替诺福韦和奈韦拉平）耐药性变化的相互依赖性网络。这些网络考虑了突变氨基酸物理化学性质层面的相互依赖性，例如：极性。我们将每个网络映射到RT的三维结构上，试图理解相互作用对的分子意义。发现的相互作用描述了几种已知的耐药机制，重要的是，还有一些以前未被识别的机制。我们的方法可以很容易地应用于蛋白质工程领域的一系列问题。
我们的MCFS-ID方法的便携式Java实现可供学术用户免费使用，可从以下网址获得：http://www.ipipan.eu/staff/m.draminski/software.htm。
