Metal binding to the amyloid β‐peptide is suggested to be involved in the pathogenesis of Alzheimer's disease. We used high‐resolution NMR to study zinc binding to amyloid β‐peptide 1–40 at physiologic pH. Metal binding induces a structural change in the peptide, which is in chemical exchange on an intermediate rate, between the apo‐form and the holo‐form, with respect to the NMR timescale. This causes loss of NMR signals in the resonances affected by the binding. Heteronuclear correlation experiments, ¹⁵N‐relaxation and amide proton exchange experiments on amyloid β‐peptide 1–40 revealed that zinc binding involves the three histidines (residues 6, 13 and 14) and the N‐terminus, similar to a previously proposed copper‐binding site [Syme CD, Nadal RC, Rigby SE, Viles JH (2004) J Biol Chem279, 18169–18177]. Fluorescence experiments show that zinc shares a common binding site with copper and that the metals have similar affinities for amyloid β‐peptide. The dissociation constant K_d of zinc for the fragment amyloid β‐peptide 1–28 was measured by fluorescence, using competitive binding studies, and that for amyloid β‐peptide 1–40 was measured by NMR. Both methods gave K_d values in the micromolar range at pH 7.2 and 286 K. Zinc also has a second, weaker binding site involving residues between 23 and 28. At high metal ion concentrations, the metal‐induced aggregation should mainly have an electrostatic origin from decreased repulsion between peptides. At low metal ion concentrations, on the other hand, the metal‐induced structure of the peptide counteracts aggregation.

The goal of pE-DB (http://pedb.vib.be) is to serve as an openly accessible database for the deposition of structural ensembles of intrinsically disordered proteins (IDPs) and of denatured proteins based on nuclear magnetic resonance spectroscopy, small-angle X-ray scattering and other data measured in solution. Owing to the inherent flexibility of IDPs, solution techniques are particularly appropriate for characterizing their biophysical properties, and structural ensembles in agreement with these data provide a convenient tool for describing the underlying conformational sampling. Database entries consist of (i) primary experimental data with descriptions of the acquisition methods and algorithms used for the ensemble calculations, and (ii) the structural ensembles consistent with these data, provided as a set of models in a Protein Data Bank format. PE-DB is open for submissions from the community, and is intended as a forum for disseminating the structural ensembles and the methodologies used to generate them. While the need to represent the IDP structures is clear, methods for determining and evaluating the structural ensembles are still evolving. The availability of the pE-DB database is expected to promote the development of new modeling methods and leads to a better understanding of how function arises from disordered states.