http://webclu.bio.wzw.tum.de/helixcorr/

Non-cell-autonomous proteins are incorporated into cells that form tight contacts or are invaded by bacteria, but identifying the full repertoire of transferred proteins has been a challenge. Here we introduce a quantitative proteomics approach to sort out non-cell-autonomous proteins synthesized by other cells or intracellular pathogens. Our approach combines stable-isotope labeling of amino acids in cell culture (SILAC), high-purity cell sorting and bioinformatics analysis to identify the repertoire of relevant non-cell-autonomous proteins. This 'trans-SILAC' method allowed us to discover many proteins transferred from human B to natural killer cells and to measure biosynthesis rates of Salmonella enterica proteins in infected human cells. Trans-SILAC should be a useful method to examine protein exchange between different cells of multicellular organisms or pathogen and host.

Motivation: Programs that evaluate the quality of a protein structural model are important both for validating the structure determination procedure and for guiding the model-building process. Such programs are based on properties of native structures that are generally not expected for faulty models. One such property, which is rarely used for automatic structure quality assessment, is the tendency for conserved residues to be located at the structural core and for variable residues to be located at the surface.Results: We present ConQuass, a novel quality assessment program based on the consistency between the model structure and the protein's conservation pattern. We show that it can identify problematic structural models, and that the scores it assigns to the server models in CASP8 correlate with the similarity of the models to the native structure. We also show that when the conservation information is reliable, the method's performance is comparable and complementary to that of the other single-structure quality assessment methods that participated in CASP8 and that do not use additional structural information from homologs.Availability: A perl implementation of the method, as well as the various perl and R scripts used for the analysis are available at http://bental.tau.ac.il/ConQuass/.Contact: nirb@tauex.tau.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.

Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Text-driven image editing methods usually need edit masks, struggle with edits that require significant visual changes and cannot easily keep specific details of the edited portion. In this paper we make the observation that image-generation models can be converted to image-editing models simply by fine-tuning them on a single image. We also show that initializing the stochastic sampler with a noised version of the base image before the sampling and interpolating relevant details from the base image after sampling further increase the quality of the edit operation. Combining these observations, we propose UniTune, a novel image editing method. UniTune gets as input an arbitrary image and a textual edit description, and carries out the edit while maintaining high fidelity to the input image. UniTune does not require additional inputs, like masks or sketches, and can perform multiple edits on the same image without retraining. We test our method using the Imagen model in a range of different use cases. We demonstrate that it is broadly applicable and can perform a surprisingly wide range of expressive editing operations, including those requiring significant visual changes that were previously impossible.