The seesaw between normal function and protein aggregation: How functional interactions may increase protein solubility

AbstractProtein aggregation has been studied for at least 3 decades, and many of the principles that regulate this event are relatively well understood. Here, however, we present a different perspective to explain why proteins aggregate: we argue that aggregation may occur as a side‐effect of the lack of one or more natural partners that, under physiologic conditions, would act as chaperones. This would explain why the same surfaces that have evolved for functional purposes are also those that favour aggregation. In the course of reviewing this field, we substantiate our hypothesis with three paradigmatic examples that argue for the generality of our proposal. An obvious corollary of this hypothesis is, of course, that targeting the physiological partners of a protein could be the most direct and specific approach to designing anti‐aggregation molecules. Our analysis may thus inform a different strategy for combating diseases of protein aggregation and misfolding

CatRAPID signature: identification of ribonucleoproteins and RNA-binding regions

Motivation: Recent technological advances revealed that an unexpected large number of proteins interact with transcripts even if the RNA-binding domains are not annotated. We introduce catRAPID signature to identify ribonucleoproteins based on physico-chemical features instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets

ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis.RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity.AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html.CONTACT: [email protected] INFORMATION: Supplementary data are available at Bioinformatics online

Proteome-wide observation of the phenomenon of life on the edge of solubility.

To function effectively proteins must avoid aberrant aggregation, and hence they are expected to be expressed at concentrations safely below their solubility limits. By analyzing proteome-wide mass spectrometry data of Caenorhabditis elegans, however, we show that the levels of about three-quarters of the nearly 4,000 proteins analyzed in adult animals are close to their intrinsic solubility limits, indeed exceeding them by about 10% on average. We next asked how aging and functional self-assembly influence these solubility limits. We found that despite the fact that the total quantity of proteins within the cellular environment remains approximately constant during aging, protein aggregation sharply increases between days 6 and 12 of adulthood, after the worms have reproduced, as individual proteins lose their stoichiometric balances and the cellular machinery that maintains solubility undergoes functional decline. These findings reveal that these proteins are highly prone to undergoing concentration-dependent phase separation, which on aging is rationalized in a decrease of their effective solubilities, in particular for proteins associated with translation, growth, reproduction, and the chaperone system

Phase separation drives X-chromosome inactivation: a hypothesis

The long non-coding RNA Xist induces heterochromatinization of the X chromosome by recruiting repressive protein complexes to chromatin. Here we gather evidence, from the literature and from computational analyses, showing that Xist assemblies are similar in size, shape and composition to phase-separated condensates, such as paraspeckles and stress granules. Given the progressive sequestration of Xist’s binding partners during X-chromosome inactivation, we formulate the hypothesis that Xist uses phase separation to perform its function

Small Angle X-Ray and Neutron Scattering - Its Application to Supramolecular Solutions

Contains reports on seven research projects.Argonne National LaboratoryU.S. Department of Energy DE-FG01-90ER45429National Science FoundationUniversity of Florence, ItalyExxon FellowshipNational Science Foundation Grant INT 87-508

Transcriptomic differences in MSA clinical variants

Background: Multiple system atrophy (MSA) is a rare oligodendroglial synucleinopathy of unknown etiopathogenesis including two major clinical variants with predominant parkinsonism (MSA-P) or cerebellar dysfunction (MSA-C). Objective: To identify novel disease mechanisms we performed a blood transcriptomic study investigating differential gene expression changes and biological process alterations in MSA and its clinical subtypes. Methods: We compared the transcriptome from rigorously gender and age-balanced groups of 10 probable MSA-P, 10 probable MSA-C cases, 10 controls from the Catalan MSA Registry (CMSAR), and 10 Parkinson Disease (PD) patients. Results: Gene set enrichment analyses showed prominent positive enrichment in processes related to immunity and inflammation in all groups, and a negative enrichment in cell differentiation and development of the nervous system in both MSA-P and PD, in contrast to protein translation and processing in MSA-C. Gene set enrichment analysis using expression patterns in different brain regions as a reference also showed distinct results between the different synucleinopathies. Conclusions: In line with the two major phenotypes described in the clinic, our data suggest that gene expression and biological processes might be differentially affected in MSA-P and MSA-C. Future studies using larger sample sizes are warranted to confirm these results