Making glassy solids ductile at room temperature by imparting flexibility into their amorphous structure

<p>Making glasses ductile at room temperature is a daunting challenge, but has been shown to be feasible in recent years. We explain the plastic flow from the standpoint of the flexibility available in the amorphous structure: imparting flexibility into the structure facilitates bond switching needed to mediate shear transformations to carry strain. This structure–property correlation is demonstrated using molecular dynamics simulation data. The flexibility can be improved via ultrafast quench or rejuvenation. In particular, the flexibility volume parameter offers a quantitative metric to explain the flexibility and deformability, even for glasses where the commonly cited free volume is not applicable.</p> <p>This Perspective demonstrates using examples and models that it is the flexibility rather than the excess volume that can be tuned to facilitate plastic flow and ductility in glassy materials.</p

The predictive IBS results of the online webserver.

<p>The predictive IBS results of the online webserver.</p

iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids

<div><p>Cysteine S-sulfenylation is an important post-translational modification (PTM) in proteins, and provides redox regulation of protein functions. Bioinformatics and structural analyses indicated that S-sulfenylation could impact many biological and functional categories and had distinct structural features. However, major limitations for identifying cysteine S-sulfenylation were expensive and low-throughout. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor iSulf-Cys which incorporated 14 kinds of physicochemical properties of amino acids was proposed. With the 10-fold cross-validation, the value of area under the curve (AUC) was 0.7155 ± 0.0085, MCC 0.3122 ± 0.0144 on the training dataset for 20 times. iSulf-Cys also showed satisfying performance in the independent testing dataset with AUC 0.7343 and MCC 0.3315. Features which were constructed from physicochemical properties and position were carefully analyzed. Meanwhile, a user-friendly web-server for iSulf-Cys is accessible at <a href="http://app.aporc.org/iSulf-Cys/" target="_blank">http://app.aporc.org/iSulf-Cys/</a>.</p></div

The TwoSampleLogo between sulfenylation and non-sulfenylation peptides (p<0.01).

<p>The TwoSampleLogo between sulfenylation and non-sulfenylation peptides (p<0.01).</p

The 10-fold cross-validation results of three different feature constructions on the balanced training dataset.

<p>The results have been run 20 times for every feature construction by SVM algorithm with g = 0.005 and cutoff = 0.5. The values are mean ± standard variance. The results of MDD-SOH were obtained in 5-fold cross-validation.</p

The number of positive and negative peptides in training and independent test dataset.

<p>The number of positive and negative peptides in training and independent test dataset.</p

A diagram flow to illustrate the predicting procedure.

<p>A diagram flow to illustrate the predicting procedure.</p

The 10-fold cross-validation results of independent test by SVM algorithm with g = 0.005 and cutoff = 0.5.

<p>The 10-fold cross-validation results of independent test by SVM algorithm with g = 0.005 and cutoff = 0.5.</p

The number of dimensions of three feature constructions.

<p>The number of dimensions of three feature constructions.</p

Combining Suprathreshold Average Voxel Intensity and Cluster Extent with Permutation Test Framework

Poster submitted to OHBM 2005