A new method based on neural network theory is presented to analyze and quantify the information content of far UV circular dichroism spectra. Using a backpropagation network model with a single hidden layer between input and output, it was possible to deduce five different secondary structure fractions (helix, parallel and antiparallel beta-sheet, beta-turn and random coil) with satisfactory correlations between calculated and measured secondary structure data. We demonstrate that for each wavelength interval a specific network is suitable. The remaining discrepancy between the secondary structure data from neural network prediction and crystallography may be attributed to errors in the determination of protein concentration and random noise in the CD signal, as indicated by simulations.

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Oligomerization of amyloid beta (Abeta) peptides is the decisive event in the development of Alzheimer's disease (AD), the most common neurogenerative disorder in developed countries. Recent evidence links this conformation-driven process to primary- and secondary-structure modifications of Abeta. The N and C terminus of deposited Abeta has been shown to possess conspicuous heterogeneity. While the C-terminally longer form of Abeta, i.e., Abeta (42), is considered more amyloidogenic, the role of the N-terminal modifications, e.g., truncation and glutamate cyclization accounting for the majority of the deposited peptides, is less understood. In the present study, we characterized the oligomerization and seeding capacity of pGlu-amyloid peptides using two unrelated techniques based on flow cytometry or flourescence dye binding. Under different conditions and irrespective of the C terminus of Abeta, i.e., Abeta40 or 42, pGlu-modified peptides displayed an up to 250-fold accelerated initial formation of aggregates compared to unmodified Abeta. The accelerated seed formation is accompanied by a change in the oligomerization kinetics because of N-terminal pGlu formation. Furthermore, the formation of mixed aggregates consisting of either pGlu-Abeta (3-42) or ADan or ABri and Abeta (1-42) was investigated by Abeta fluorescence labeling in flow cytometry. The results suggest that pGlu-modified peptides are potential seeding species of aggregate formation in vivo. The data presented here and the abundance of pGlu peptides in amyloidoses, such as FBD and AD, suggest pGlu-amyloid peptides as a species with biophysical characteristics that might be in particular crucial for the initiation of the disease.

Protein structure prediction is based mainly on the modeling of proteins by homology to known structures; this knowledge-based approach is the most promising method to date. Although it is used in the whole area of protein research, no general rules concerning the quality and applicability of concepts and procedures used in homology modeling have been put forward yet. Therefore, the main goal of the present work is to provide tools for the assessment of accuracy of modeling at a given level of sequence homology. A large set of known structures from different conformational and functional classes, but various degrees of homology was selected. Pairwise structure superpositions were performed. Starting with the definition of the structurally conserved regions and determination of topologically correct sequence alignments, we correlated geometrical properties with sequence homology (defined by the 250 PAM Dayhoff Matrix) and identity. It is shown that both the topological differences of the protein backbones and the relative positions of corresponding side chains diverge with decreasing sequence identity. Below 50% identity, the deviation in regions that are structurally not conserved continually increases, thus implying that with decreasing sequence identity modeling has to take into account more and more structurally diverging loop regions that are difficult to predict.

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