Browsing by Author "Aminpour, Maral"
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Item A nanometric probe of the local proton concentration in microtubule-based biophysical systems(2022) Kalra, Aarat P.; Eakins, Boden B.; Vagin, Sergei I.; Wang, Hui; Patel, Sahil D.; Winter, Philip; Aminpour, Maral; Lewis, John D.; Rezania, Vahid; Shankar, Karthik; Scholes, Gregory D.; Tuszynski, Jack A.; Rieger, Bernhard; Meldrum, AlkiviathesWe show a double-functional fluorescence sensing paradigm that can retrieve nanometric pH information on biological structures. We use this method to measure the extent of protonic condensation around microtubules, which are protein polymers that play many roles crucial to cell function. While microtubules are believed to have a profound impact on the local cytoplasmic pH, this has been hard to show experimentally due to the limitations of conventional sensing techniques. We show that subtle changes in the local electrochemical surroundings cause a double-functional sensor to transform its spectrum, thus allowing a direct measurement of the protonic concentration at the microtubule surface. Microtubules concentrate protons by as much as one unit on the pH scale, indicating a charge storage role within the cell via the localized ionic condensation. These results confirm the bioelectrical significance of microtubules and reveal a sensing concept that can deliver localized biochemical information on intracellular structures.Item A new method for protein characterization and classification using geometrical features for 3D face analysis: an example of tubulin structures(2021) Di Grazia, Luca; Aminpour, Maral; Vezzetti, Enrico; Rezania, Vahid; Marcolin, Federica; Tuszynski, Jack A.This article 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 analog. 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 kernel. The results are significant and competitive with the state-of-the-art protein classification methods. This leads to a new methodological direction in protein structure analysis.