The ccSOL webserver provides large-scale calculations for protein solubility using physical-chemical properties. In addition to the proteome wide identification of soluble fragments on a large scale, ccSOL omics also performs single-point mutations throughout the whole protein sequence to identify susceptible areas. Using information from coil/disorder ratios, hydrophobicity, hydrophilicity, β and α sheet propensities as a predictor of protein solubility. Training sets of over 35, 000 entries revealed an accuracy of 79%.
• Validated fast and high-throughput predictions of protein solubility
• Simultaneous in silico identification of both soluble and insoluble regions within multiple protein sequences with an accuracy of over 79%
• Prediction of aggregation propensity
• Open and online graphical interface
• Integration with chaperone and RNA data sets contribute to increased performance.
• Solubility: Accurate algorithms for the prediction of protein solubility and aggregation provide insight into the formation of disease associated aggregates in diseases such as Alzheimer.
• Aggregation: in silico prediction of soluble protein fragments provides a method of predicting the aggregate potential of the given protein. ccSOL omics discriminates (p-value < 0.05) proteins aggregating in absence of chaperones and those that fold independently (i.e., highly soluble).
• Protein folding: As ccSOL is trained on datasets encompassing over 40,000 proteins the prediction of soluble/insoluble propensities has also shown to be an accurate measure of protein folding base don a test set of chaperone and chaperone independent proteins out-performing other solubility algorithms, including SOLpro and PROSO II.