Big Data and Machine Learning Take on HIV

A vaccine still isn't in sight, though a lot of progress has been made in controlling the disease by harnessing broadly neutralizing antibodies (bnAbs) that can mute many of its variations. To build a better bnAb, or create the right combination of them, researchers must find ways to counter those mutations. For that, a team from Hong Kong University of Science and Technology (HKUST), alongside collaborators from the Massachusetts Institute of Technology (MIT), have turned to big data. Using data from 20,000 sequences drawn from nearly 2,000 HIV-positive patients, they were looking to map out the virus's "spike," or the protein protrusions on the surface of its molecules that bnAbs are designed to target. The researchers sought "An accurate representation of viral fitness as a function of its protein sequences (a fitness landscape), with explicit accounting of the effects of coupling between mutations."