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\begin{document}

\title{A new method for protein characterization and classification using
geometrical features for 3D face analysis: an example of tubulin
structures}



\author[1]{Luca di Grazia}%
\author[2,2]{Maral Aminpour}%
\author[1]{Enrico Vezzetti}%
\author[3]{Vahid Rezania}%
\author[1]{Federica Marcolin}%
\author[2]{Jack Tuszynski}%
\affil[1]{Politecnico di Torino}%
\affil[2]{University of Alberta}%
\affil[3]{MacEwan University City Centre Campus}%


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  \date{March 14, 2025}


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\begin{abstract}
This paper reports on the results of research aimed to translate
biometric 3D face recognition concepts and algorithms into the field of
protein biophysics in order to precisely and rapidly classify
morphological features of protein surfaces. Both human faces and protein
surfaces are free-forms and some descriptors used in differential
geometry can be used to describe them applying the principles of feature
extraction developed for computer vision and pattern recognition. The
first part of this study focused on building the protein dataset using a
simulation tool and performing feature extraction using novel
geometrical descriptors. The second part tested the method on two
examples, first involved a classification of tubulin isotypes and the
second compared tubulin with the FtSZ protein, which is its bacterial
analogue. An additional test involved several unrelated proteins.
Different classification methodologies have been used: a classic
approach with a Support Vector Machine (SVM) classifier and an
unsupervised learning with a k-means approach. The best result was
obtained with SVM and the radial basis function (RBF) kernel. The
results are significant and competitive with the state-of-the-art
protein classification methods. This opens a new area for protein
structure analysis.%
\end{abstract}%



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