  Despite the recent advancements by deep learning methods such as AlphaFold2,
\textit{in silico} protein structure prediction remains a challenging problem
in biomedical research. With the rapid evolution of quantum computing, it is
natural to ask whether quantum computers can offer some meaningful benefits for
approaching this problem. Yet, identifying specific problem instances amenable
to quantum advantage, and estimating quantum resources required are equally
challenging tasks. Here, we share our perspective on how to create a framework
for systematically selecting protein structure prediction problems that are
amenable for quantum advantage, and estimate quantum resources for such
problems on a utility-scale quantum computer. As a proof-of-concept, we
validate our problem selection framework by accurately predicting the structure
of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.
