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.
The paper presents an image-oriented modality to functionally describe artificially and biologically nanostructured
surfaces, which can be used for the characterization of the atom neighborhoods on the surface of proteins. The property
which is mainly analyzed in this paper is the hydrophobicity distribution on protein surface, but the distributions of
charges and mutual electrical potentials can also be considered. The actual discrete hydrophobicity distribution attached
to the atoms that form a surface atom's vicinity is replaced by an approximately equivalent hydrophobicity density
distribution, computed in a standardized octagonal pattern around each atom. These representation of hydrophobicities is
used to compute the resemblance of surface atom neighborhoods belonging to a protein, defined 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 defined 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. The purpose of this work is to create a database of selected protein surfaces that will be used
for nanotechnology research and applications purposes.
The paper presents a methodology using atom or amino acid hydrophobicities to describe the surface properties of
proteins in order to predict their interactions with other proteins and with artificial nanostructured surfaces. A
standardized pattern is built around each surface atom of the protein for a radius depending on the molecule type and
size. The atom neighborhood is characterized in terms of the hydrophobicity surface density. A clustering algorithm is
used to classify the resulting patterns and to identify the possible interactions. The methodology has been implemented in
a software package based on Java technology deployed in a Linux environment.
Nucleotide genomic signals satisfy regularities that reveal restrictions in the distribution of nucleotides and pairs of
nucleotides along DNA sequences. Structurally, a chromosome appears to be more than a plain text, by satisfying
symmetry constrains that evoke the rhythm and rhyme in poems. These regularities make it easy to identify exogenous
inserts in the genomes of prokaryotes, because such inserts obey different regularities than the background sequence. The
paper presents instances of inserts found in the genomes of Bacillus subtilis, Mycobacterium tuberculosis and other
prokaryotes. Inserts of exogenous material are frequently accompanied by complementary inserts tending to restore the
original constrains.
KEYWORDS: Resistance, Signal analysis, Pathogens, Data conversion, Digital signal processing, Signal processing, Image segmentation, Imaging spectroscopy, Current controlled current source, Polymers
As previously shown the conversion of nucleotide sequences into digital signals offers the possibility to apply signal
processing methods for the analysis of genomic data. Genomic Signal Analysis (GSA) has been used to analyze large
scale features of DNA sequences, at the scale of whole chromosomes, including both coding and non-coding regions.
The striking regularities of genomic signals reveal restrictions in the way nucleotides and pairs of nucleotides are
distributed along nucleotide sequences. Structurally, a chromosome appears to be less of a "plain text", corresponding to
certain semantic and grammar rules, but more of a "poem", satisfying additional symmetry restrictions that evoke the
"rhythm" and "rhyme". Recurrent patterns in nucleotide sequences are reflected in simple mathematical regularities
observed in genomic signals. GSA has also been used to track pathogen variability, especially concerning their resistance
to drugs. Previous work has been dedicated to the study of HIV-1, Clade F and Avian Flu. The present paper applies
GSA methodology to study Mycobacterium tuberculosis (MT) rpoB gene variability, relevant to its resistance to
antibiotics. Isolates from 50 Romanian patients have been studied both by rapid LightCycler PCR and by sequencing of a
segment of 190-250 nucleotides covering the region of interest. The variability is caused by SNPs occurring at specific
sites along the gene strand, as well as by inclusions. Because of the mentioned symmetry restrictions, the GS variations
tend to compensate. An important result is that MT can act as a vector for HIV virus, which is able to retrotranscribe its
specific genes both into human and MT genomes.
The conversion of genomic sequences into digital genomic signals offers the possibility to use powerful signal processing methods for the analysis of genomic information. The study of genomic signals reveals local and global features of chromosomes that would be difficult to identify by using only the symbolic representation used in genomic data bases. The paper presents a study of HIV variability using standard 'wet' methods of nucleotide sequence analysis, corroborated with IT techniques based on the genomic signal approach. Specifically, Independent Component Analysis is used to characterize the variability defining the F subtype HIV strains isolated in Romania.
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