Marcin Kierczak¹, Krzysztof Ginalski², Michał Dramiński³, Jacek Koronacki³, Witold Rudnicki² and Jan Komorowski^1,2

¹The Linnaeus Centre for Bioinformatics, Uppsala University BMC, Box 598, Husargatan 3, SE-751 24 Uppsala, Sweden. ²Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw University, ul.Żwirki i Wigury 93, 02-089 Warszawa, Poland. ³Institute of Computer Science, Polish Academy of Sciences, ul. J.K. Ordona 21, 01-237 Warszawa, Poland.

Abstract  

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and— more importantly—identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome.