Viruses’ natural mutational agility has long been problematic for established therapies. Determining a therapeutic compound’s effectiveness against a mutated viral pathogen mostly entails empirical screening of the mutated virus with compounds to gauge effectiveness. This week researchers from the Barcelona Supercomputing Center and IrsiCaixa, the Catalan AIDS Research Institute, reported developing a bioinformatics method to predict the effect of each mutation on the resistance of the virus to existing HIV drugs. This is an important step forward.
The BSC-IrsiCaixa method combines HIV DNA sequencing, identification of genetic mutations, computational protein modeling and the simulation of drugs binding with the proteins of the virus. The entire bioinformatics analysis can be performed in fewer than 24 hours on relatively small-scale computing equipment. One of the main features of the system is the use of PELE, a piece of software developed at BSC to predict how drugs will interact with their targets, which has been shown to have competitive advantages over commercially available software.
BSC researcher Victor Guallar, who appears as principal investigator in the article published in the Journal of Chemical Information and Modeling (Computational Prediction of HIV-1 Resistance to Protease Inhibitors) and is the lead developer of PELE, explains that “this system is one of the first tangible steps in the area of what will eventually be personalized medicine, where treatment will be decided following genetic analysis of the causes of the disease in each patient and of which drug would be most effective in each individual case.”
In their article, researchers explain how this method has effectively predicted the resistance of the virus with genetic mutations in the HIV-1 protease, a protein which is essential for the replication of the virus, to the drugs amprenavir and darunavir. The method could easily be applied to other drugs and proteins.
“In this study we demonstrate how to connect routine clinical diagnosis of HIV-1 with structural computer modeling. This is a multidisciplinary proof of concept which overcomes the limitations of current practice when deciding antiretroviral treatment and which, in addition, allows new drugs to be designed more quickly,” adds IrsiCaixa researcher Marc Noguera-Julian, who participated in the study.
BSC has created an automatic platform, available for free via the web, on which researchers can enter a patient’s HIV-1 PR protease genomic sequence and predict the effectiveness of prescribing the drugs amprenavir and darunavir. So far these are the only predictions available, pending advances in research on the effect of HIV mutations on other proteins within the virus and interactions with other antiretroviral drugs.
Link to the article Computational Prediction of HIV-1 Resistance to Protease
Link to a related video: https://www.youtube.com/watch?v=nRDOTIoIWp0&feature=youtu.be