Proteome-Level Interplay between Folding and Aggregation Propensities of Proteins

The cleverSuite approach for protein characterization: predictions of structural properties, solubility, chaperone requirements and RNA-binding abilities

“…We found that disordered proteins are more hydrophilic and soluble. Indeed, the coverage is 50% for hydrophobicity [corresponding to 0.7 of AUC (Eisenberg et al , 1984)], 45% for aggregation (Tartaglia and Vendruscolo, 2010) and 42% for burial (Harpaz et al , 1994). The CM achieves optimal performances by combining the scales for disorder (Sickmeier et al , 2007), hydrophobicity (Eisenberg et al , 1984), burial (Harpaz et al , 1994), aggregation (Tartaglia and Vendruscolo, 2010) and alpha-helix (Kanehisa and Tsong, 1980) (sensitivity of 0.9 and false positive rate of 0.07).…”

“…Experimental scales encoding physico-chemical properties are useful to retrieve basic information on protein sequences (Wilkins et al , 1999) and to predict features associated with protein folding (Gao et al , 2010; Tartaglia and Vendruscolo, 2010), aggregation (Fernandez-Escamilla et al , 2004; Tartaglia et al , 2008) and molecular interactions (Cirillo et al , 2013; Muppirala et al , 2011). For instance, the Zyggregator method predicts aggregation propensity using a combination of physico-chemical properties including secondary structure, solvent accessibility, hydrophobicity and polarity (Tartaglia and Vendruscolo, 2008).…”

Sequence-Based Prediction of Protein Solubility

“…[1][2][3][4][5] When proteins fail to fold, they tend to form aggregates, whose presence is associated with a variety of human conditions, including Alzheimer's and Parkinson's diseases. 1,2,6,7 Since it is becoming clear that the amino acid sequences of proteins encode not just their folding but also their aggregation, there is great interest in identifying the amino acid code responsible for the aggregation process.…”

Understanding the frustration arising from the competition between function, misfolding, and aggregation in a globular protein

“…In particular, because of the close link between misfolding and aggregation events, we evaluated the propensity of frataxin to aggregate. To investigate the competition between folding and aggregation we used the the Zyggregator method (40,41) to calculate the aggregation propensity of frataxin at the individual residue level (Fig. S6).…”

“…Folding: The three residues (L60, I70, and L81) with the highest Φvalues are highlighter in red; these residues are identified as those most important for folding (21). Aggregation propensity: The aggregation propensity was calculated at a residue level using the Zyggregator algorithm (40,41) and mapped on the structure; residues displaying a high propensity to aggregate are represented in red. nearest neighbors, the native contacts between them are defined as the number of heavy side-chain atoms within 0.65 nm in the native structure.…”