Controlled solvent-exchange deposition of phospholipid membranes onto solid surfaces.

Controlled deposition of lipid bilayers plays a key role in creating supported membranes for biosensing devices and biophysical cell studies. The authors adopt a solvent-exchange method in order to deposit a phospholipid bilayer on solid substrates. The basic concept of deposition is to dissolve phospholipids in isopropanol-water mixtures and to increase water content gradually. Shortly before the onset of the micelle-to-vesicle transition, a lipid bilayer nucleates at the solid surface. They investigate the bulk phase behavior and surface coverage using small angle x-ray scattering and attenuated total reflection-Fourier transform infrared spectroscopy. They find a sequence of transitions from inverted-monomeric-micellar and vesicle phases correlating with an increasing amount of lipid on the adsorption layer. Supported lipid bilayers, prepared using this approach, are homogeneous and fluid.

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies.

BACKGROUND: All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences. RESULTS: The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%. CONCLUSIONS: This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.

An efficient visualization tool for the analysis of protein mutation matrices.

BACKGROUND: It is useful to develop a tool that would effectively describe protein mutation matrices specifically geared towards the identification of mutations that produce either wanted or unwanted effects, such as an increase or decrease in affinity, or a predisposition towards misfolding. Here, we describe a tool where such mutations are efficiently identified, categorized and visualized. To categorize the mutations, amino acids in a mutation matrix are arranged according to one of three sets of physicochemical characteristics, namely hydrophilicity, size and polarizability, and charge and polarity. The magnitude and frequencies of mutations for an alignment are subsequently described using color information and scaling factors. RESULTS: To illustrate the capabilities of our approach, the technique is used to visualize and to compare mutation patterns in evolving sequences with diametrically opposite characteristics. Results show the emergence of distinct patterns not immediately discernible from the raw matrices. CONCLUSION: Our technique enables effective categorization and visualization of mutations by using specifically-arranged mutation matrices. This tool has a number of possible applications in protein engineering, notably in simplifying the identification of mutations and/or mutation trends that are associated with specific engineered protein characteristics and behavior.

A study of the structural correlates of affinity maturation: antibody affinity as a function of chemical interactions, structural plasticity and stability.

Mutations introduced in an antibody germline sequence as a result of somatic hypermutation could cause its derivatives to have an altered affinity for its target. Affinity maturation favors the selection of the antibodies which exhibit increased affinity. The mutations in 80 high affinity anti-thyroid peroxidase sequences derived from six germlines were analysed in terms of the physicochemical properties of the replacement residues, namely hydrophilicity, size and polarizability, and charge and polarity, in the context of its position and probable solvent accessibility. The effects of these substitutions were evaluated in terms of the resultant increased chemical interactivity potential of the affinity-matured antibodies relative to the germline. The results of the analysis would be useful in the rational design of antibodies and of other proteins for improved binding properties.