New isolated or recombinant or engineered variant beta-2-microglobulin polypeptide which is amyloidogenic under essentially physiological conditions in vitro, used e.g. to study amyloid fibrillogenesis to treat e.g. Alzheimer's disease

NOVELTY - Isolated or recombinant or engineered variant beta -2-microglobulin ( beta 2M) polypeptide (P1) which is amyloidogenic under essentially physiological conditions in vitro, is new. USE - The isolated or recombinant or engineered variant beta 2M polypeptide (P1) is useful for: forming variant beta 2M amyloid fibrils in vitro (claimed); and studying amyloid fibrillogenesis, including diagnostic and therapeutic applications which is useful for treating Alzheimer's disease, type 2 diabetes, Parkinson's disease and Huntington's disease. No biological data given. DETAILED DESCRIPTION - INDEPENDENT CLAIMS are also included for: (1) a polypeptide having at least 95% identity with the polypeptide (P1); (2) a nucleic acid molecule encoding the polypeptide (P1); (3) a recombinant vector expressing the nucleic acid molecules; (4) a host cell expressing the vector; (5) a host cell expressing the plasmid; (6) a composition comprising the polypeptide (P1); (7) a method (M1) of forming variant beta 2M amyloid fibrils in vitro, comprising: adding an isolated or recombinant or engineered variant beta 2M polypeptide (P1) to a solution under essentially physiological conditions; incubating the solution at 37 degrees C, where variant beta 2M amyloid fibrils are formed; and determining that the variant beta 2M amyloid fibrils formed specifically bind Congo red from an alkaline alcoholic solution and then show red/green birefringence when viewed under crossed polarized light; and (8) a method (M2) of testing whether a compound or composition inhibits variant beta 2M amyloid formation, comprising: adding an isolated or recombinant or engineered variant beta 2M polypeptide (P1) to a solution under essentially physiological conditions; incubating the solution at 4-37 degrees C where variant beta 2M amyloid fibrils are formed; adding the compound or composition to the variant beta 2M amyloid fibrils; and determining whether the compound or composition inhibits the formation of variant beta 2M amyloid fibrils

Proteins in solution:from X-ray scattering intensities to interaction potentials

International audienceBiological macromolecules in solution interact with each other through medium-range (from a few Angstrom to a few nm) interaction potentials. These potentials control the macromolecular distribution in solution, the macromolecular phase diagram and the crystallization process. We have previously shown that small angle X-ray scattering (SAXS) is a convenient tool to characterize the resulting potential, either attractive or repulsive, and to follow the changes induced by the crystallizing agents. In the present paper SAXS and simulation methods derived from statistical mechanics are coupled to determine the best fit potentials from the comparison of experimental and theoretical intensity curves. The currently used models in the colloid field are derived from the DLVO (Derjaguin, Landau, Verwey, Overbeek) potential where three types of interactions play a major role: hard sphere and electrostatic are repulsive, van der Waals are attractive. A combination of a short-range attractive potential and a coulombic repulsive indeed correctly accounts at low ionic strength for the phase diagram as a function of pH and salt concentration. The origin of the ion specificities at high ionic strength associated with the so-called "Hofmeister series" remain, however, unclear. The whole of the data demonstrates that the colloidal approach may be applied with success to protein crystallization. (C) 1999 Elsevier Science B.V. All rights reserved

Phase knowledge enables rational screens for protein crystallization

We show that knowledge of the phase behavior of a protein allows one to create a rational screen that increases the success rate of crystallizing challenging proteins. The strategy is based on using microfluidics to perform large numbers of protein solubility experiments across many different chemical conditions to identify reagents for crystallization experiments. Phase diagrams were generated for the identified reagents and used to design customized crystallization screens for every protein. This strategy was applied with a 75% success rate to the crystallization of 12 diverse proteins, most of which failed to crystallize when using traditional techniques. The overall diffraction success rate was 33%, about double what was achieved with conventional automation in large-scale protein structure consortia. The higher diffraction success rates are achieved by designing customized crystallization screens using the phase behavior information for each target. The identification of reagents based on an understanding of protein solubility and the use of phase diagrams in the design of individualized crystallization screens therefore promotes high crystallization rates and the production of diffraction-quality crystals