Abstract Motivation: Protein aggregation is associated with a number of protein misfolding diseases and is a major concern for therapeutic proteins. Aggregation is caused by the presence of aggregation-prone regions (APRs) in the amino acid sequence of the protein. The lower the aggregation propensity of APRs and the better they are protected by native interactions within the folded structure of the protein, the more aggregation is prevented. Therefore, both the local thermodynamic stability of APRs in the native structure and their intrinsic aggregation propensity are a key parameter that needs to be optimized to prevent protein aggregation. Results: The Solubis method presented here automates the process of carefully selecting point mutations that minimize the intrinsic aggregation propensity while improving local protein stability. Availability and implementation: All information about the Solubis plugin is available at http://solubisyasara.switchlab.org/. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Summary: Accurate prediction of amyloid-forming amino acid sequences remains an important challenge. We here present an online database that provides open access to the largest set of experimentally characterized amyloid forming hexapeptides. To this end, we expanded our previous set of 280 hexapeptides used to develop the Waltz algorithm with 89 peptides from literature review and by systematic experimental characterisation of the aggregation of 720 hexapeptides by transmission electron microscopy, dye binding and Fourier transform infrared spectroscopy. This brings the total number of experimentally characterized hexapeptides in the WALTZ-DB database to 1089, of which 244 are annotated as positive for amyloid formation. Availability and implementation: The WALTZ-DB database is freely available without any registration requirement at http://waltzdb.switchlab.org. Contact: [email protected] or [email protected]

Abstract Summary: A graphical user interface for the FoldX protein design program has been developed as a plugin for the YASARA molecular graphics suite. The most prominent FoldX commands such as free energy difference upon mutagenesis and interaction energy calculations can now be run entirely via a windowed menu system and the results are immediately shown on screen. Availability and Implementation: The plugin is written in Python and is freely available for download at http://foldxyasara.switchlab.org/ and supported on Linux, MacOSX and MS Windows. Contact: [email protected]; [email protected]; [email protected]

Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides, refolding and degradation of misfolded proteins, and activation of a wide range of client proteins. The prokaryotic heat-shock protein DnaK is the E. coli representative of the ubiquitous Hsp70 family, which specializes in the binding of exposed hydrophobic regions in unfolded polypeptides. Accurate prediction of DnaK binding sites in E. coli proteins is an essential prerequisite to understand the precise function of this chaperone and the properties of its substrate proteins. In order to map DnaK binding sites in protein sequences, we have developed an algorithm that combines sequence information from peptide binding experiments and structural parameters from homology modelling. We show that this combination significantly outperforms either single approach. The final predictor had a Matthews correlation coefficient (MCC) of 0.819 when assessed over the 144 tested peptide sequences to detect true positives and true negatives. To test the robustness of the learning set, we have conducted a simulated cross-validation, where we omit sequences from the learning sets and calculate the rate of repredicting them. This resulted in a surprisingly good MCC of 0.703. The algorithm was also able to perform equally well on a blind test set of binders and non-binders, of which there was no prior knowledge in the learning sets. The algorithm is freely available at http://limbo.vib.be.