  Mapper is a topology based data analysis method that extracts topological
features from high-dimensional data. The Mapper algorithm requires a filter
function that maps the dataset to a Euclidian space and a clustering method,
that is performed on the original dataset. This produces a graph which
represents the shape of the original data. In this work, we use Mapper to
uncover the conformational change of protein structures and we choose the
filter function from a parameterized family, based on a deep neural network
architecture. By optimizing its parameters with respect to an Energy-loss
function derived from a theoretical background, we produce a Mapper graph that
unveils the conformational pathways undertaken by the studied protein. Our
method tackles conformational pathway detection in a unsupervised manner and
therefore greatly reduces the manual and time costs necessary in this task.
