Computational Analysis of Molecular Interaction Networks Underlying Change of XXXX Resistance to Selected Reverse Transcriptase Inhibitors.

@entity1522 @entity9611 @entity16332 @entity14027 @entity2858 @entity22208 @entity645 @entity5198 @entity15833 @entity791
MOTIVATION: Despite more than two decades of research, @entity645 resistance to drugs remains a serious obstacle in developing efficient @entity791 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. RESULTS: We analyzed a number of @entity2858 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: @entity22208 , @entity16332 , @entity14027 , @entity9611 , @entity15833 and @entity5198 . The networks consider interdependencies at the level of physicochemical properties of mutating @entity1522 , 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. AVAILABILITY: 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.
