Significant Residue Features Revealed By Eigenvalue Decomposition Analysis Of Blosum Matrices

Here we attempt to characterize protein evolution by residue features which dominate residue substitution in homologous proteins. Evolutionary information contained in residue substitution matrix is abstracted with the method of eigenvalue decomposition. Top eigenvectors in the eigenvalue spectrums are analyzed as function of the level of similarity, i.e. sequence identity (SI) between homologous proteins. It is found that hydrophobicity and volume are two significant residue features conserved in protein evolution. There is a transition point at SI approximate to 45%. Residue hydrophobicity is a feature governing residue substitution as SI >= 45%. Whereas below this SI level, residue volume is a dominant feature. (C) 2007 Elsevier B.V. All rights reserved.

Oxidation of cyclohexane - A significant impact of stainless steel reactor wall

The selective oxidation of cyclohexane to cyclohexanol and cyclohexanone is an important chemical process and it has been paid more attentions recently. In the present work, the stainless steel reactor wall was found to influence the selective oxidation of cyclohexane very significantly, and a quasi-crystalline Ti45Zr35Ni17Cu3 alloy with the similar compositions as the reactor wall was used as a catalyst for the cyclohexane oxidation, as expected, a higher activity was obtained with it. The present results open up a new avenue for developing new catalyst for alkane oxidation.

Single-walled carbon nanotubes binding to human telomeric i-motif DNA: significant acceleration of S1 nuclease cleavage rate

Single-walled carbon nanotubes (SWNTs) binding to human telomeric i-motif DNA can significantly accelerate S1 nuclease cleavage rate by increasing the enzyme turnover number.

A new model to estimate significant wave heights with ERS-1/2 scatterometer data

A new model is proposed to estimate the significant wave heights with ERS-1/2 scatterometer data. The results show that the relationship between wave parameters and radar backscattering cross section is similar to that between wind and the radar backscattering cross section. Therefore, the relationship between significant wave height and the radar backscattering cross section is established with a neural network algorithm, which is, if the average wave period is <= 7s, the root mean square of significant wave height retrieved from ERS-1/2 data is 0.51 m, or 0.72 m if it is >7s otherwise.