87 research outputs found
Quasiclassical theory of superconductivity: a multiple interface geometry
The purpose of the paper is to suggest a new method which allows one to study
multiple coherent reflection/transmissions by partially transparent interfaces
(e.g. in multi-layer mesoscopic structures or grain boundaries in high-Tc's) in
the framework of the quasiclassical theory of superconductivity. It is argued
that typically the trajectory of the particle is a simply connected tree (no
loops) with knots, i.e. the points where interface scattering events occur and
ballistic pieces of the trajectory are mixed. A linear boundary condition for
the 2-component trajectory "wave function" which factorizes matrix (retarded)
Green's function, is formulated for an arbitrary interface, specular or
diffusive. To show the usage of the method, the current response to the vector
potential (the total superfluid density rho_s) of a SS' sandwich with the
different signs of the order parameter in S and S', is calculated. In this
model, a few percent of reflection by the SS' interface transforms the
paramagnetic response (rho_s < 0) created by the zero-energy Andreev bound
states near an ideal interface (see Fauchere et al. PRL, 82, 3336 (1999),
cond-mat/9901112), into the usual diamagnetic one (rho_s >0).Comment: Extended abstract submitted to "Electron Transport in Mesoscopic
Systems", Satellite conference to LT22, Goteborg, 12-15 August, 1999. 2 pages
Minor changes + the text height problem fixe
Structural Similarity and Classification of Protein Interaction Interfaces
Interactions between proteins play a key role in many cellular processes.
Studying protein-protein interactions that share similar interaction interfaces
may shed light on their evolution and could be helpful in elucidating the
mechanisms behind stability and dynamics of the protein complexes. When two
complexes share structurally similar subunits, the similarity of the interaction
interfaces can be found through a structural superposition of the subunits.
However, an accurate detection of similarity between the protein complexes
containing subunits of unrelated structure remains an open problem
Analysis and Prediction of Translation Rate Based on Sequence and Functional Features of the mRNA
Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5′UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale
Global Analysis of Proline-Rich Tandem Repeat Proteins Reveals Broad Phylogenetic Diversity in Plant Secretomes
Cell walls, constructed by precisely choreographed changes in the plant secretome, play critical roles in plant cell physiology and development. Along with structural polysaccharides, secreted proline-rich Tandem Repeat Proteins (TRPs) are important for cell wall function, yet the evolutionary diversity of these structural TRPs remains virtually unexplored. Using a systems-level computational approach to analyze taxonomically diverse plant sequence data, we identified 31 distinct Pro-rich TRP classes targeted for secretion. This analysis expands upon the known phylogenetic diversity of extensins, the most widely studied class of wall structural proteins, and demonstrates that extensins evolved before plant vascularization. Our results also show that most Pro-rich TRP classes have unexpectedly restricted evolutionary distributions, revealing considerable differences in plant secretome signatures that define unexplored diversity
Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks
The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age
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SARS-CoV-2-reactive T cells in healthy donors and patients with COVID-19
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the rapidly unfolding coronavirus disease 2019 (COVID-19) pandemic1,2. Clinical manifestations of COVID-19 vary, ranging from asymptomatic infection to respiratory failure. The mechanisms that determine such variable outcomes remain unresolved. Here we investigated CD4+ T cells that are reactive against the spike glycoprotein of SARS-CoV-2 in the peripheral blood of patients with COVID-19 and SARS-CoV-2-unexposed healthy donors. We detected spike-reactive CD4+ T cells not only in 83% of patients with COVID-19 but also in 35% of healthy donors. Spike-reactive CD4+ T cells in healthy donors were primarily active against C-terminal epitopes in the spike protein, which show a higher homology to spike glycoproteins of human endemic coronaviruses, compared with N-terminal epitopes. Spike-protein-reactive T cell lines generated from SARS-CoV-2-naive healthy donors responded similarly to the C-terminal region of the spike proteins of the human endemic coronaviruses 229E and OC43, as well as that of SARS-CoV-2. This results indicate that spike-protein cross-reactive T cells are present, which were probably generated during previous encounters with endemic coronaviruses. The effect of pre-existing SARS-CoV-2 cross-reactive T cells on clinical outcomes remains to be determined in larger cohorts. However, the presence of spike-protein cross-reactive T cells in a considerable fraction of the general population may affect the dynamics of the current pandemic, and has important implications for the design and analysis of upcoming trials investigating COVID-19 vaccines
A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition
On the nature of the reentrant effect in susceptibility of mesoscopic cylindrical samples
Theory of the reentrant effect in susceptibility of mesoscopic cylindrical NS
samples is proposed, which is essentially based on the properties of the
Andreev levels. The specific feature of the quantum levels of the structure is
that in a varying magnetic field (or temperature) each level periodically comes
into coincidence with the chemical potential of the metal. As a result, the
state of the system becomes strongly degenerate and the amplitude of the
paramagnetic contribution to the susceptibility increases sharply.Comment: 6 pages. "Low Temperature Physics" to be publishe
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