Dataset Information

Recognition of protein complexation based on hydrophobicity distribution.

ABSTRACT: The identification of the surface area able to generate the protein-protein complexation ligand and ion ligation is critical for the recognition of the biological function of particular proteins. The technique based on the analysis of the irregularity of hydrophobicity distribution is used as the criterion for the recognition of the interaction regions. Particularly, the exposure of hydrophobic residues on the surface of protein as well as the localization of the hydrophilic residues in the hydrophobic core is treated as potential area ready to interact with external molecules. The model based on the "fuzzy oil drop" approach treating the protein molecule as the drop of hydrophobicity concentrated in the central part of structure with the hydrophobicity close to zero on the surface according to 3-dimensional Gauss function. The comparison with the observed hydrophobicy in particular protein reveals some irregularities. These irregularities seem to represent the aim-oriented localization.

SUBMITTER: Banach M

PROVIDER: S-EPMC2828897 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Recognition of protein complexation based on hydrophobicity distribution.

Banach Mateusz M Roterman Irena I

Bioinformation 20090930 3

The identification of the surface area able to generate the protein-protein complexation ligand and ion ligation is critical for the recognition of the biological function of particular proteins. The technique based on the analysis of the irregularity of hydrophobicity distribution is used as the criterion for the recognition of the interaction regions. Particularly, the exposure of hydrophobic residues on the surface of protein as well as the localization of the hydrophilic residues in the hydr ...[more]

PMID: 20198181

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Project description:Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73.8 % ? 86.2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

Project description:Exploring the role of polymer structure for the internalization of biologically relevant cargo, specifically siRNA, is of critical importance to the development of improved delivery reagents. Herein, we report guanidinium-rich protein transduction domain mimics (PTDMs) based on a ring-opening metathesis polymerization scaffold containing tunable hydrophobic moieties that promote siRNA internalization. Structure-activity relationships using Jurkat T cells and HeLa cells were explored to determine how the length of the hydrophobic block and the hydrophobic side chain compositions of these PTDMs impacted siRNA internalization. To explore the hydrophobic block length, two different series of diblock copolymers were synthesized: one series with symmetric block lengths and one with asymmetric block lengths. At similar cationic block lengths, asymmetric and symmetric PTDMs promoted siRNA internalization in the same percentages of the cell population regardless of the hydrophobic block length; however, with 20 repeat units of cationic charge, the asymmetric block length had greater siRNA internalization, highlighting the nontrivial relationships between hydrophobicity and overall cationic charge. To further probe how the hydrophobic side chains impacted siRNA internalization, an additional series of asymmetric PTDMs was synthesized that featured a fixed hydrophobic block length of five repeat units that contained either dimethyl (dMe), methyl phenyl (MePh), or diphenyl (dPh) side chains and varied cationic block lengths. This series was further expanded to incorporate hydrophobic blocks consisting of diethyl (dEt), diisobutyl (diBu), and dicyclohexyl (dCy) based repeat units to better define the hydrophobic window for which our PTDMs had optimal activity. High-performance liquid chromatography retention times quantified the relative hydrophobicities of the noncationic building blocks. PTDMs containing the MePh, diBu, and dPh hydrophobic blocks were shown to have superior siRNA internalization capabilities compared to their more and less hydrophobic counterparts, demonstrating a critical window of relative hydrophobicity for optimal internalization. This better understanding of how hydrophobicity impacts PTDM-induced internalization efficiencies will help guide the development of future delivery reagents.

Dataset Information

Recognition of protein complexation based on hydrophobicity distribution.

Publications

Recognition of protein complexation based on hydrophobicity distribution.

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