Increased Concentration of Polyvalent Phospholipids in the Adsorption Domain of a Charged Protein

We studied the adsorption of a charged protein onto an oppositely charged membrane, composed of mobile phospholipids of differing valence, using a statistical-thermodynamical approach. A two-block model was employed, one block corresponding to the protein-affected region on the membrane, referred to as the adsorption domain, and the other to the unaffected remainder of the membrane. We calculated the protein-induced lipid rearrangement in the adsorption domain as arising from the interplay between the electrostatic interactions in the system and the mixing entropy of the lipids. Equating the electrochemical potentials of the lipids in the two blocks yields an expression for the relations among the various lipid fractions in the adsorption domain, indicating a sensitive dependence of lipid fraction on valence. This expression is a result of the two-block picture but does not depend on further details of the protein-membrane interaction. We subsequently calculated the lipid fractions themselves using the Poisson-Boltzmann theory. We examined the dependence of lipid enrichment, i.e., the ratio between the lipid fractions inside and outside the adsorption domain, on various parameters such as ionic strength and lipid valence. Maximum enrichment was found for lipid valence of about (-3) to (-4) in physiological conditions. Our results are in qualitative agreement with recent experimental studies on the interactions between peptides having a domain of basic residues and membranes containing a small fraction of the polyvalent phosphatidylinositol 4,5-bisphosphate (PIP2). This study provides theoretical support for the suggestion that proteins adsorbed onto membranes through a cluster of basic residues may sequester PIP2 and other polyvalent lipids.Comment: 25 pages, 12 figure

Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site

Motivation: Accurate prediction of protein stability is important for understanding the molecular underpinnings of diseases and for the design of new proteins. We introduce a novel approach for the prediction of changes in protein stability that arise from a single-site amino acid substitution; the approach uses available data on mutations occurring in the same position and in other positions. Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features. Pro-Maya predicts the stability free energy difference of mutant versus wild type, denoted as ΔΔG

MuD: an interactive web server for the prediction of non-neutral substitutions using protein structural data

The discrimination between functionally neutral amino acid substitutions and non-neutral mutations, affecting protein function, is very important for our understanding of diseases. The rapidly growing amounts of experimental data enable the development of computational tools to facilitate the annotation of these substitutions. Here, we describe a Random Forests-based classifier, named Mutation Detector (MuD) that utilizes structural and sequence-derived features to assess the impact of a given substitution on the protein function. In its automatic mode, MuD is comparable to alternative tools in performance. However, the uniqueness of MuD is that user-reported protein-specific structural and functional information can be added at run-time, thereby enhancing the prediction accuracy further. The MuD server, available at http://mud.tau.ac.il, assigns a reliability score to every prediction, thus offering a useful tool for the prioritization of substitutions in proteins with an available 3D structure

The Cell Tracking Challenge: 10 years of objective benchmarking

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a signifcant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.Web of Science2071020101