Systematic Method for the Exploration, Representation, and Classification of the Diphenylalanine Solvatomorphic Space

An
understanding of the conditions that govern the self-assembly
process of peptides is a fundamental step toward the design of new
nanostructures that possess interesting properties. In this work,
we first synthesize and explore extensively diphenylalanine (FF) self-assembling
crystals formed in different solvents (i.e., solvatomorphs) using
polarized optical microscopy and transmission electron microscopy.
Then, we develop a numerical method that allows an unambiguous classification
of the solvatomorphs through a K-means automatic clustering method.
In addition, we generate a two-dimensional (2D) representation of
the solvatomorphic space together with the clustering results via
a principal component analysis (PCA). The classification is based
on structural similarities of solvatomorphs as revealed by the analysis
of their respective infrared spectra. Among the 20 samples considered,
4 clear clusters are extracted within which the compounds show very
similar crystalline structures. The information extracted allows us
to assign many of the peaks that appear in the complex IR spectra
of the samples considered. The implementation of the overall procedure
we propose, i.e., “GAULOIS” and “REFRACT-R”,
is transferable to other types of spectra and paves the way for a
systematic, fast, and accurate classification method applicable to
various types of experimental spectroscopic data.