rcell2: Microscopy-Based Cytometry in R.

This article describes a method for quantifying various cellular features (e.g., volume, curvature, total and sub-cellular fluorescence localization) of individual cells from sets of microscope images, and for tracking them over time-course microscopy experiments. One purposely defocused transmission image (sometimes referred to as bright-field or BF) is used to segment the image and locate each cell. Fluorescence images (one for each of the color channels or z-stacks to be analyzed) may be acquired by conventional wide-field epifluorescence or confocal microscopy. This method uses a set of R packages called rcell2. Relative to the original release of Rcell (Bush et al., 2012), the updated version bundles, into a single software suite, the image-processing capabilities of Cell-ID, offers new data analysis tools for cytometry, and relies on the widely used data analysis and visualization tools of the statistical programming framework R. © 2023 Wiley Periodicals LLC. Basic Protocol: Extracting quantitative information from single cells Support Protocol 1: Obtaining and installing Cell-ID and R Support Protocol 2: Preparing cells for imaging.

Thousands of protein linear motif classes may still be undiscovered.

Linear motifs are short protein subsequences that mediate protein interactions. Hundreds of motif classes including thousands of motif instances are known. Our theory estimates how many motif classes remain undiscovered. As commonly done, we describe motif classes as regular expressions specifying motif length and the allowed amino acids at each motif position. We measure motif specificity for a pair of motif classes by quantifying how many motif-discriminating positions prevent a protein subsequence from matching the two classes at once. We derive theorems for the maximal number of motif classes that can simultaneously maintain a certain number of motif-discriminating positions between all pairs of classes in the motif universe, for a given amino acid alphabet. We also calculate the fraction of all protein subsequences that would belong to a motif class if all potential motif classes came into existence. Naturally occurring pairs of motif classes present most often a single motif-discriminating position. This mild specificity maximizes the potential number of coexisting motif classes, the expansion of the motif universe due to amino acid modifications and the fraction of amino acid sequences that code for a motif instance. As a result, thousands of linear motif classes may remain undiscovered.

PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins.

The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptors of the qualitative and quantitative properties of the ensembles. High quality of the data is guaranteed by a new submission process, which combines both automatic and manual evaluation steps. A team of biocurators integrate structured metadata describing the ensemble generation methodology, experimental constraints and conditions. A new search engine allows the user to build advanced queries and search all entry fields including cross-references to IDP-related resources such as DisProt, MobiDB, BMRB and SASBDB. We expect that the renewed PED will be useful for researchers interested in the atomic-level understanding of IDP function, and promote the rational, structure-based design of IDP-targeting drugs.