Kinetic behavior of Desulfovibrio gigas aldehyde oxidoreductase encapsulated in reverse micelles

Biochemical and Biophysical Research Communications 308 (2003) 73–78We report the kinetic behavior of the enzyme aldehyde oxidoreductase (AOR) from the sulfate reducing bacterium Desulfovibrio gigas (Dg) encapsulated in reverse micelles of sodium bis-(2-ethylhexyl) sulfosuccinate in isooctane using benzaldehyde, octaldehyde, and decylaldehyde as substrates. Dg AOR is a 200-kDa homodimeric protein that catalyzes the conversion of aldehydes to carboxylic acids. Ultrasedimentation analysis of Dg AOR-containing micelles showed the presence of 100-kDa molecular weight species, confirming that the Dg AOR subunits can be dissociated. UV–visible spectra of encapsulated Dg AOR are indistinguishable from the enzyme spectrum in solution, suggesting that both protein fold and metal cofactor are kept intact upon encapsulation. The catalytic constant (kcat) profile as a function of the micelle size W0 (W0 ¼ ½H2O /[AOT]) using benzaldehyde as substrate showed two bell-shaped activity peaks at W0 ¼ 20 and 26. Furthermore, enzymatic activity for octaldehyde and decylaldehyde was detected only in reverse micelles. Like for the benzaldehyde kinetics, two peaks with both similar kcat values and W0 positions were obtained. EPR studies using spin-labeled reverse micelles indicated that octaldehyde and benzaldehyde are intercalated in the micelle membrane. This suggests that, though Dg AOR is found in the cytoplasm of bacterial cells, the enzyme may catalyze the reaction of substrates incorporated into a cell membrane

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions