MOTIVATION: Light chain amyloidosis is a disease where misfolded antibody light chains (LCs) form toxic amyloid fibrils, leading to organ damage. Although LC overproduction occurs in all cases, only certain individuals develop the disease, suggesting that specific LC sequences and properties drive amyloid formation. This process is complex, involving both protein sequence and environmental factors, but mutations that destabilize the LC fold are linked to amyloid aggregation. Despite the significance of the disease, our understanding of LC fibril formation remains limited due to the lack of extensive data and technical challenges in studying amyloid structures. To address this, a tool is needed to compare unknown LC sequences with known structures and predict which amyloids are likely to adopt new conformations, guiding experimental investigations. RESULTS: HMMSTUFF addresses this by using a Hidden Markov Model to generate similarity scores between LC sequences and existing PDB templates, eventually modeling the LC amyloid structures similar enough to known templates. HMMSTUFF on one side expands our understanding of LC amyloid fibril conformations, and on the other highlights the gaps in our current knowledge of LC structural space. AVAILABILITY AND IMPLEMENTATION: HMMSTUFF is available as pypi package and as source code at https://github.com/grogdrinker/hmmstuff.

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]

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.