  Data science has emerged from the proliferation of digital data, coupled with
advances in algorithms, software and hardware (e.g., GPU computing).
Innovations in structural biology have been driven by similar factors, spurring
us to ask: can these two fields impact one another in deep and hitherto
unforeseen ways? We posit that the answer is yes. New biological knowledge lies
in the relationships between sequence, structure, function and disease, all of
which play out on the stage of evolution, and data science enables us to
elucidate these relationships at scale. Here, we consider the above question
from the five key pillars of data science: acquisition, engineering, analytics,
visualization and policy, with an emphasis on machine learning as the premier
analytics approach.
