  Most proteins perform their biological function by interacting with one or
more molecular partners. In this respect, characterizing the features of the
molecular surface, especially in the portions where the interaction takes
place, turned out to be a crucial step in the investigation of the mechanisms
of recognition and binding between molecules. Predictive methods often rely on
extensive samplings of molecular patches with the aim to identify hot spots on
the surface. In this framework, analysis of large proteins and/or many
molecular dynamics frames is often unfeasible due to the high computational
cost. Thus, finding optimal ways to reduce the number of points to be sampled
maintaining the biological information carried by the molecular surface is
pivotal. Here, we present a new theoretical and computational algorithm with
the aim of determining a subset of surface points, appropriately selected in
space, in order to maximize the information of the overall shape of the
molecule by minimizing the number of total points. We test our procedure by
looking at the local shape of the surface through a recently developed method
based on the formalism of Zernike polynomials in two dimensions, which is able
to characterize the local shape properties of portions of molecular surfaces.
The results of this method show that a remarkably higher ability of this
algorithm to reproduce the information of the complete molecular surface
compared to uniform random sampling.
