Lipidation of Class IV CdiA Effector Proteins Promotes Target Cell Recognition during Contact-Dependent Growth Inhibition

Contact-dependent growth inhibition (CDI) is a common form of interbacterial competition in which cells use CdiA effectors to deliver toxic proteins into their neighbors. CdiA recognizes target bacteria through specific receptor molecules on the cell surface.

Binding of Monomeric and Polymeric Alzheimers Aβ peptides to Exosomes

Exosomes are secreted by every cell in our body under both physiological and pathological conditions. They travel in the blood, CSF, and all studied biofluids. Their biological roles have been reported to include delivery of important physiological cargo between organs and cells, clearance of toxic proteins; maintenance of cellular stasis, and the propagation of disease pathology. In the case of Alzheimers disease (AD) exosomes have been shown to carry pathological proteins such as amyloid, yet the specificity of this association of amyloid and exosomes is unclear. To address this deficiency, we utilized Isothermal Titration Calorimetry (ITC) to measure the binding of amyloid to exosomes. Here we report that Ab40 and Ab42 bind to exosomes in a saturable and endothermic manner, a phenomenon not observed with the scrambled versions of either peptide. This points to this interaction being more specific than previously understood, and to amyloid associated with exosomes as an important pool of this peptide in the plasma.

Deep learning model for the prediction and classification of protein toxins across all domains of life

Toxins are widely produced by different organisms to disrupt the physiology of other organisms, and support their own existence. Their study is useful to understand protein evolution, environmental adaptation and survival competition. In-silico predictions of toxic proteins can support empirical frameworks, and help in the safety measurements needed for various industrial related processes. Some in-silico methods are slow, hard to implement or lack taxa representation in their training datasets. Here we present a deep learning model to classify protein toxins, through the use of Convolutional Neural Networks (ConvTOX). ConvTOX is able to accurately identify toxic proteins across the domains of life, with accuracies over 80% for animal and plant toxins, and over 50% for bacterial toxins. Moreover, ConvTOX is able to generalize the identification of differences among toxin types, such as neurotoxins and myotoxins, and to accurately identify structural similarities between different protein toxins. ConvTOX overcomes limitations from previous models by being able to predict toxin proteins from across all domains of life, and by not being limited to only short toxin peptides. Limitations are still clear in terms of lower accuracies for specific phylogenetic groups (such as bacterial toxins), but still this works presents itself as a one step forward for the universal use, classification and study of toxic proteins.

Pathology dynamics in healthy-toxic protein interaction and the multiscale analysis of neurodegenerative diseases

Neurodegenerative diseases are frequently associated with aggregation and propagation of toxic proteins. In particular, it is well known that along with amyloid-beta, the tau protein is also driving Alzheimer's disease. Multiscale reaction-diffusion models can assist in our better understanding of the evolution of the disease. We have modified the heterodimer model in such a way that it can now capture some of critical characteristics of this evolution such as the conversion time from healthy to toxic proteins. We have analyzed the modified model theoretically and validated the theoretical findings with numerical simulations.