Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.

Protein aggregation remains a major area of focus in the production of monoclonal antibodies. Improving the intrinsic properties of antibodies can improve manufacturability, attrition rates, safety, formulation, titers, immunogenicity, and solubility. Here, we explore the potential of predicting and reducing the aggregation propensity of monoclonal antibodies, based on the identification of aggregation-prone regions and their contribution to the thermodynamic stability of the protein. Although aggregation-prone regions are thought to occur in the antigen binding region to drive hydrophobic binding with antigen, we were able to rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues. The reduction in aggregation propensity was accompanied by an increase in expression titer, showing that reducing protein aggregation is beneficial throughout the development process. The data presented show that this approach can significantly reduce liabilities in novel therapeutic antibodies and proteins, leading to a more efficient path to clinical studies.

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: frederic.rousseau@switch.vib-kuleuven.be or joost.schymkowitz@switch.vib-kuleuven.be

Aggregation is a sequence-specific process, nucleated by short aggregation-prone regions (APRs) that can be exploited to induce aggregation of proteins containing the same APR. Here, we find that most APRs are unique within a proteome, but that a small minority of APRs occur in many proteins. When aggregation is nucleated in bacteria by such frequently occurring APRs, it leads to massive and lethal inclusion body formation containing a large number of proteins. Buildup of bacterial resistance against these peptides is slow. In addition, the approach is effective against drug-resistant clinical isolates ofEscherichiacoliandAcinetobacterbaumannii, reducing bacterial load in a murine bladder infection model. Our results indicate that redundant APRs are weak points of bacterial protein homeostasis and that targeting these may be an attractive antibacterial strategy.

Natural selection shapes protein solubility to physiological requirements and recombinant applications that require higher protein concentrations are often problematic. This raises the question whether the solubility of natural protein sequences can be improved. We here show an anti-correlation between the number of aggregation prone regions (APRs) in a protein sequence and its solubility, suggesting that mutational suppression of APRs provides a simple strategy to increase protein solubility. We show that mutations at specific positions within a protein structure can act as APR suppressors without affecting protein stability. These hot spots for protein solubility are both structure and sequence dependent but can be computationally predicted. We demonstrate this by reducing the aggregation of human α-galactosidase and protective antigen of Bacillus anthracis through mutation. Our results indicate that many proteins possess hot spots allowing to adapt protein solubility independently of structure and function.

Aggregation by design: Amyloid aggregation is driven by short sequences within proteins that self-assemble into characteristic amyloid structures. About 30 human proteins are implicated in amyloid-associated diseases, but many more contain short sequences that are potentially amyloidogenic. Gallardo et al. designed a peptide based on an amyloidogenic sequence in the vascular endothelial growth factor receptor VEGFR2. The peptide induced VEGFR2 to form aggregates with features characteristic of amyloids. Amyloids were toxic only in cells that required VEGFR2 activity, suggesting that the toxicity was due to loss of function of VEGFR2, rather than to inherent toxicity of the aggregates. The peptide inhibited VEGFR2-dependent tumor growth in a mouse tumor model. Science , this issue p. 10.1126/science.aah4949