We present here the automated structure solution pipeline "autoSHARP." It is built around the heavy-atom refinement and phasing program SHARP, the density modification program SOLOMON, and the ARP/wARP package for automated model building and refinement (using REFMAC). It allows fully automated structure solution, from merged reflection data to an initial model, without any user intervention. We describe and discuss the preparation of the user input, the data flow through the pipeline, and the various results obtained throughout the procedure.

BUSTER±TNT is a maximum-likelihood macromolecular re®nement package. BUSTER assembles the structural model, scales observed and calculated structure-factor amplitudes and computes the model likelihood, whilst TNT handles the stereochemistry and NCS restraints/constraints and shifts the atomic coordinates, B factors and occupancies. In real space, in addition to the traditional atomic and bulk-solvent models, BUSTER models the parts of the structure for which an atomic model is not yet available (`missing structure') as lowresolution probability distributions for the random positions of the missing atoms. In reciprocal space, the BUSTER structure-factor distribution in the complex plane is a twodimensional Gaussian centred around the structure factor calculated from the atomic, bulk-solvent and missing-structure models. The errors associated with these three structural components are added to compute the overall spread of the Gaussian. When the atomic model is very incomplete, modelling of the missing structure and the consistency of the BUSTER statistical model help structure building and completion because (i) the accuracy of the overall scale factors is increased, (ii) the bias affecting atomic model re®nement is reduced by accounting for some of the scattering from the missing structure, (iii) the addition of a spatial de®nition to the source of incompleteness improves on traditional Luzzati and ' A -based error models and (iv) the program can perform selective density modi®cation in the regions of unbuilt structure alone.

Recent evidence suggests that alterations in insulin/insulin-like growth factor 1 (IGF1) signaling (IIS) can increase mammalian life span. For example, in several mouse mutants, impairment of the growth hormone (GH)/IGF1 axis increases life span and also insulin sensitivity. However, the intracellular signaling route to altered mammalian aging remains unclear. We therefore measured the life span of mice lacking either insulin receptor substrate (IRS) 1 or 2, the major intracellular effectors of the IIS receptors. Our provisional results indicate that female Irs1-/- mice are long-lived. Furthermore, they displayed resistance to a range of age-sensitive markers of aging including skin, bone, immune, and motor dysfunction. These improvements in health were seen despite mild, lifelong insulin resistance. Thus, enhanced insulin sensitivity is not a prerequisite for IIS mutant longevity. Irs1-/- female mice also displayed normal anterior pituitary function, distinguishing them from long-lived somatotrophic axis mutants. In contrast, Irs2-/- mice were short-lived, whereas Irs1+/- and Irs2+/- mice of both sexes showed normal life spans. Our results therefore suggest that IRS1 signaling is an evolutionarily conserved pathway regulating mammalian life span and may be a point of intervention for therapies with the potential to delay age-related processes.