  Within bioinformatics, the textual alignment of amino acid sequences has long
dominated the determination of similarity between proteins, with all that
implies for shared structure, function and evolutionary descent. Despite the
relative success of modern-day sequence alignment algorithms, so-called
alignment-free approaches offer a complementary means of determining and
expressing similarity, with potential benefits in certain key applications,
such as regression analysis of protein structure-function studies, where
alignment-base similarity has performed poorly. Here, we offer a fresh,
statistical physics-based perspective focusing on the question of
alignment-free comparison, in the process adapting results from 'first passage
probability distribution' to summarize statistics of ensemble averaged amino
acid propensity values. In this article, we introduce and elaborate this
approach.
