Joost Schymkowitz

Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins.

We have developed a statistical mechanics algorithm, TANGO, to predict protein aggregation. TANGO is based on the physico-chemical principles of β-sheet formation, extended by the assumption that the core regions of an aggregate are fully buried. Our algorithm accurately predicts the aggregation of a data set of 179 peptides compiled from the literature as well as of a new set of 71 peptides derived from human disease-related proteins, including prion protein, lysozyme and β2-microglobulin. TANGO also correctly predicts pathogenic as well as protective mutations of the Alzheimer β-peptide, human lysozyme and transthyretin, and discriminates between β-sheet propensity and aggregation. Our results confirm the model of intermolecular β-sheet formation as a widespread underlying mechanism of protein aggregation. Furthermore, the algorithm opens the door to a fully automated, sequence-based design strategy to improve the aggregation properties of proteins of scientific or industrial interest.

Exploring the sequence determinants of amyloid structure using position-specific scoring matrices

Protein aggregation results in β-sheet–like assemblies that adopt either a variety of amorphous morphologies or ordered amyloid-like structures. These differences in structure also reflect biological differences; amyloid and amorphous β-sheet aggregates have different chaperone affinities, accumulate in different cellular locations and are degraded by different mechanisms. Further, amyloid function depends entirely on a high intrinsic degree of order. Here we experimentally explored the sequence space of amyloid hexapeptides and used the derived data to build Waltz, a web-based tool that uses a position-specific scoring matrix to determine amyloid-forming sequences. Waltz allows users to identify and better distinguish between amyloid sequences and amorphous β-sheet aggregates and allowed us to identify amyloid-forming regions in functional amyloids.

The mechanism of γ-Secretase dysfunction in familial Alzheimer disease.

The mechanisms by which mutations in the presenilins (PSEN) or the amyloid precursor protein (APP) genes cause familial Alzheimer disease (FAD) are controversial. FAD mutations increase the release of amyloid β (Aβ)42 relative to Aβ40 by an unknown, possibly gain‐of‐toxic‐function, mechanism. However, many PSEN mutations paradoxically impair γ‐secretase and ‘loss‐of‐function’ mechanisms have also been postulated. Here, we use kinetic studies to demonstrate that FAD mutations affect Aβ generation via three different mechanisms, resulting in qualitative changes in the Aβ profiles, which are not limited to Aβ42. Loss of ε‐cleavage function is not generally observed among FAD mutants. On the other hand, γ‐secretase inhibitors used in the clinic appear to block the initial ε‐cleavage step, but unexpectedly affect more selectively Notch than APP processing, while modulators act as activators of the carboxypeptidase‐like (γ) activity. Overall, we provide a coherent explanation for the effect of different FAD mutations, demonstrating the importance of qualitative rather than quantitative changes in the Aβ products, and suggest fundamental improvements for current drug development efforts.

Neurotoxicity of Alzheimer's disease Aβ peptides is induced by small changes in the Aβ42 to Aβ40 ratio

The amyloid peptides Aβ40 and Aβ42 of Alzheimers disease are thought to contribute differentially to the disease process. Although Aβ42 seems more pathogenic than Aβ40, the reason for this is not well understood. We show here that small alterations in the Aβ42:Aβ40 ratio dramatically affect the biophysical and biological properties of the Aβ mixtures reflected in their aggregation kinetics, the morphology of the resulting amyloid fibrils and synaptic function tested in vitro and in vivo. A minor increase in the Aβ42:Aβ40 ratio stabilizes toxic oligomeric species with intermediate conformations. The initial toxic impact of these Aβ species is synaptic in nature, but this can spread into the cells leading to neuronal cell death. The fact that the relative ratio of Aβ peptides is more crucial than the absolute amounts of peptides for the induction of neurotoxic conformations has important implications for anti‐amyloid therapy. Our work also suggests the dynamic nature of the equilibrium between toxic and non‐toxic intermediates.

Lipids revert inert Aβ amyloid fibrils to neurotoxic protofibrils that affect learning in mice

Although soluble oligomeric and protofibrillar assemblies of Aβ‐amyloid peptide cause synaptotoxicity and potentially contribute to Alzheimers disease (AD), the role of mature Aβ‐fibrils in the amyloid plaques remains controversial. A widely held view in the field suggests that the fibrillization reaction proceeds ‘forward’ in a near‐irreversible manner from the monomeric Aβ peptide through toxic protofibrillar intermediates, which subsequently mature into biologically inert amyloid fibrils that are found in plaques. Here, we show that natural lipids destabilize and rapidly resolubilize mature Aβ amyloid fibers. Interestingly, the equilibrium is not reversed toward monomeric Aβ but rather toward soluble amyloid protofibrils. We characterized these ‘backward’ Aβ protofibrils generated from mature Aβ fibers and compared them with previously identified ‘forward’ Aβ protofibrils obtained from the aggregation of fresh Aβ monomers. We find that backward protofibrils are biochemically and biophysically very similar to forward protofibrils: they consist of a wide range of molecular masses, are toxic to primary neurons and cause memory impairment and tau phosphorylation in mouse. In addition, they diffuse rapidly through the brain into areas relevant to AD. Our findings imply that amyloid plaques are potentially major sources of soluble toxic Aβ‐aggregates that could readily be activated by exposure to biological lipids.

Gain of function of mutant p53 by coaggregation with multiple tumor suppressors

Many p53 missense mutations possess dominant-negative activity and oncogenic gain of function. We report that for structurally destabilized p53 mutants, these effects result from mutant-induced coaggregation of wild-type p53 and its paralogs p63 and p73, thereby also inducing a heat-shock response. Aggregation of mutant p53 resulted from self-assembly of a conserved aggregation-nucleating sequence within the hydrophobic core of the DNA-binding domain, which becomes exposed after mutation. Suppressing the aggregation propensity of this sequence by mutagenesis abrogated gain of function and restored activity of wild-type p53 and its paralogs. In the p53 germline mutation database, tumors carrying aggregation-prone p53 mutations have a significantly lower frequency of wild-type allele loss as compared to tumors harboring nonaggregating mutations, suggesting a difference in clonal selection of aggregating mutants. Overall, our study reveals a novel disease mechanism for mutant p53 gain of function and suggests that, at least in some respects, cancer could be considered an aggregation-associated disease.

PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes

We have developed a web tool, PupaSuite, for the selection of single nucleotide polymorphisms (SNPs) with potential phenotypic effect, specifically oriented to help in the design of large-scale genotyping projects. PupaSuite uses a collection of data on SNPs from heterogeneous sources and a large number of pre-calculated predictions to offer a flexible and intuitive interface for selecting an optimal set of SNPs. It improves the functionality of PupaSNP and PupasView programs and implements new facilities such as the analysis of users data to derive haplotypes with functional information. A new estimator of putative effect of polymorphisms has been included that uses evolutionary information. Also SNPeffect database predictions have been included. The PupaSuite web interface is accessible through and through .

The Unfolding Story of Three-Dimensional Domain Swapping

Three-dimensional domain swapping is the event by which a monomer exchanges part of its structure with identical monomers to form an oligomer where each subunit has a similar structure to the monomer. The accumulating number of observations of this phenomenon in crystal structures has prompted speculation as to its biological relevance. Domain swapping was originally proposed to be a mechanism for the emergence of oligomeric proteins and as a means for functional regulation, but also to be a potentially harmful process leading to misfolding and aggregation. We highlight experimental studies carried out within the last few years that have led to a much greater understanding of the mechanism of domain swapping and of the residue- and structure-specific features that facilitate the process. We discuss the potential biological implications of domain swapping in light of these findings.

A graphical interface for the FoldX forcefield

SUMMARYnA 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.nnnAVAILABILITY AND IMPLEMENTATIONnThe plugin is written in Python and is freely available for download at http://foldxyasara.switchlab.org/ and supported on Linux, MacOSX and MS Windows.

SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants

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.