The intracellular Ig fold: a robust protein scaffold for the engineering of molecular recognition

Protein scaffolds that support molecular recognition have multiple applications in biotechnology. Thus, protein frames with robust structural cores but adaptable surface loops are in continued demand. Recently, notable progress has been made in the characterization of Ig domains of intracellular origin--in particular, modular components of the titin myofilament. These Ig belong to the I(intermediate)-type, are remarkably stable, highly soluble and undemanding to produce in the cytoplasm of Escherichia coli. Using the Z1 domain from titin as representative, we show that the I-Ig fold tolerates the drastic diversification of its CD loop, constituting an effective peptide display system. We examine the stability of CD-loop-grafted Z1-peptide chimeras using differential scanning fluorimetry, Fourier transform infrared spectroscopy and nuclear magnetic resonance and demonstrate that the introduction of bioreactive affinity binders in this position does not compromise the structural integrity of the domain. Further, the binding efficiency of the exogenous peptide sequences in Z1 is analyzed using pull-down assays and isothermal titration calorimetry. We show that an internally grafted, affinity FLAG tag is functional within the context of the fold, interacting with the anti-FLAG M2 antibody in solution and in affinity gel. Together, these data reveal the potential of the intracellular Ig scaffold for targeted functionalization.

Mutagenesis and computer modeling studies of a GPCR conserved residue W5.43(194) in ligand recognition and signal transduction for CB2 receptor

W5.43(194), a conserved tryptophan residue among G-protein coupled receptors (GPCRs) and cannabinoid receptors (CB), was examined in the present report for its significance in CB2 receptor ligand binding and adenylyl cyclase (AC) activity. Computer modeling postulates that this site in CB2 may be involved in the affinity of WIN55212-2 and SR144528 through aromatic contacts. In the present study, we reported that a CB2 receptor mutant, W5.43(194)Y, which had a tyrosine (Y) substitution for tryptophan (W), retained the binding affinity for CB agonist CP55940, but reduced binding affinity for CB2 agonist WIN55212-2 and inverse agonist SR144528 by 8-fold and 5-fold, respectively; the CB2 W5.43(194)F and W5.43(194)A mutations significantly affect the binding activities of CP55940, WIN55212-2 and SR144528. Furthermore, we found that agonist-mediated inhibition of the forskolin-induced cAMP production was dramatically diminished in the CB2 mutant W5.43(194)Y, whereas W5.43(194)F and W5.43(194)A mutants resulted in complete elimination of downstream signaling, suggesting that W5.43(194) was essential for the full activation of CB2. These results indicate that both aromatic interaction and hydrogen bonding are involved in ligand binding for the residue W5.43(194), and the mutations of this tryptophan site may affect the conformation of the ligand binding pocket and therefore control the active conformation of the wild type CB2 receptor. W5.43(194)Y/F/A mutations also displayed noticeable enhancement of the constitutive activation probably attributed to the receptor conformational changes resulted from the mutations.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.