Hydrophobicity and charge nanoscale imaging of protein surfaces

Paul Dan Cristea; Octavian Arsene; Rodica Tuduce; Dan V. Nicolau

doi:10.1117/12.928342

2 February 2012 Hydrophobicity and charge nanoscale imaging of protein surfaces

Paul Dan Cristea, Octavian Arsene, Rodica Tuduce, Dan V. Nicolau

Proceedings Volume 8231, Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications VIII; 82310D (2012) https://doi.org/10.1117/12.928342
Event: SPIE BiOS, 2012, San Francisco, California, United States

Abstract

The paper presents an image-oriented modality to functionally describe articially and biologically nanostruc- tured surfaces, which can be used for the characterization of the atom neighborhoods on the surface of proteins. The both properties,hydrophobicity and charge distribution on protein surface, are analyzed in this paper. The actual discrete hydrophobicity and charge distribution attached to the atoms that form a surface atom's vicinity is replaced by an approximately equivalent density distribution, computed in a standardized octagonal pattern around each atom. These representations of hydrophobicities and charges are used to compute the resemblance of surface atom neighborhoods belonging to a protein, dened as the sum of the products of hydrophobicity densities of the corresponding patches (the pattern's central circles or angular sectors having the same position). The similitude and the interaction of a pair of atom neighborhoods are dened as their resemblance for parallel, respectively, anti-parallel orientations of the normals on the molecular surfaces in the points where the central atoms are located. Surface atom neighborhoods have been classied in terms of both resemblance and vector description.

Citation Download Citation

Paul Dan Cristea, Octavian Arsene, Rodica Tuduce, and Dan V. Nicolau "Hydrophobicity and charge nanoscale imaging of protein surfaces", Proc. SPIE 8231, Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications VIII, 82310D (2 February 2012); https://doi.org/10.1117/12.928342

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KEYWORDS

Chemical species

Proteins

Calibration

Molecules

Machine learning

Molecular interactions

Biological research

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