Title: The cellular modifier MOAG‐4/SERF drives amyloid formation through charge complementation
Abstract: Article7 October 2021Open Access Source DataTransparent process The cellular modifier MOAG-4/SERF drives amyloid formation through charge complementation Anita Pras Anita Pras orcid.org/0000-0003-2752-152X European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands These authors contributed equally to this work. Search for more papers by this author Bert Houben Bert Houben orcid.org/0000-0002-6750-011X VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium These authors contributed equally to this work. Search for more papers by this author Francesco A Aprile Francesco A Aprile orcid.org/0000-0002-5040-4420 Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK Search for more papers by this author Renée Seinstra Renée Seinstra orcid.org/0000-0001-5083-399X European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Rodrigo Gallardo Rodrigo Gallardo VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Leen Janssen Leen Janssen orcid.org/0000-0002-1973-304X European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Wytse Hogewerf Wytse Hogewerf European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Christian Gallrein Christian Gallrein orcid.org/0000-0002-7623-2778 Department of Molecular Physiology and Cell Biology, Leibniz Research Institute for Molecular Pharmacology im Forschungsverbund Berlin e.V. (FMP), Berlin, Germany Search for more papers by this author Matthias De Vleeschouwer Matthias De Vleeschouwer VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Alejandro Mata-Cabana Alejandro Mata-Cabana orcid.org/0000-0002-0179-2746 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Mandy Koopman Mandy Koopman orcid.org/0000-0003-1429-2078 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Esther Stroo Esther Stroo European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Minke de Vries Minke de Vries European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Samantha Louise Edwards Samantha Louise Edwards orcid.org/0000-0002-7722-5959 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Janine Kirstein Janine Kirstein orcid.org/0000-0003-4990-2497 Department of Molecular Physiology and Cell Biology, Leibniz Research Institute for Molecular Pharmacology im Forschungsverbund Berlin e.V. (FMP), Berlin, Germany Faculty of Biology & Chemistry, University of Bremen, Bremen, Germany Search for more papers by this author Michele Vendruscolo Michele Vendruscolo orcid.org/0000-0002-3616-1610 Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK Search for more papers by this author Salvatore Fabio Falsone Salvatore Fabio Falsone orcid.org/0000-0002-3724-5824 Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria Search for more papers by this author Frederic Rousseau Corresponding Author Frederic Rousseau [email protected] orcid.org/0000-0002-9189-7399 VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Joost Schymkowitz Corresponding Author Joost Schymkowitz [email protected] orcid.org/0000-0003-2020-0168 VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Ellen A A Nollen Corresponding Author Ellen A A Nollen [email protected] orcid.org/0000-0003-3740-6373 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Anita Pras Anita Pras orcid.org/0000-0003-2752-152X European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands These authors contributed equally to this work. Search for more papers by this author Bert Houben Bert Houben orcid.org/0000-0002-6750-011X VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium These authors contributed equally to this work. Search for more papers by this author Francesco A Aprile Francesco A Aprile orcid.org/0000-0002-5040-4420 Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK Search for more papers by this author Renée Seinstra Renée Seinstra orcid.org/0000-0001-5083-399X European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Rodrigo Gallardo Rodrigo Gallardo VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Leen Janssen Leen Janssen orcid.org/0000-0002-1973-304X European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Wytse Hogewerf Wytse Hogewerf European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Christian Gallrein Christian Gallrein orcid.org/0000-0002-7623-2778 Department of Molecular Physiology and Cell Biology, Leibniz Research Institute for Molecular Pharmacology im Forschungsverbund Berlin e.V. (FMP), Berlin, Germany Search for more papers by this author Matthias De Vleeschouwer Matthias De Vleeschouwer VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Alejandro Mata-Cabana Alejandro Mata-Cabana orcid.org/0000-0002-0179-2746 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Mandy Koopman Mandy Koopman orcid.org/0000-0003-1429-2078 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Esther Stroo Esther Stroo European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Minke de Vries Minke de Vries European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Samantha Louise Edwards Samantha Louise Edwards orcid.org/0000-0002-7722-5959 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Janine Kirstein Janine Kirstein orcid.org/0000-0003-4990-2497 Department of Molecular Physiology and Cell Biology, Leibniz Research Institute for Molecular Pharmacology im Forschungsverbund Berlin e.V. (FMP), Berlin, Germany Faculty of Biology & Chemistry, University of Bremen, Bremen, Germany Search for more papers by this author Michele Vendruscolo Michele Vendruscolo orcid.org/0000-0002-3616-1610 Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK Search for more papers by this author Salvatore Fabio Falsone Salvatore Fabio Falsone orcid.org/0000-0002-3724-5824 Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria Search for more papers by this author Frederic Rousseau Corresponding Author Frederic Rousseau [email protected] orcid.org/0000-0002-9189-7399 VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Joost Schymkowitz Corresponding Author Joost Schymkowitz [email protected] orcid.org/0000-0003-2020-0168 VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium Search for more papers by this author Ellen A A Nollen Corresponding Author Ellen A A Nollen e.a.a[email protected] orcid.org/0000-0003-3740-6373 European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands Search for more papers by this author Author Information Anita Pras1, Bert Houben2,3, Francesco A Aprile4,8, Renée Seinstra1, Rodrigo Gallardo2,3,9, Leen Janssen1, Wytse Hogewerf1, Christian Gallrein5, Matthias De Vleeschouwer2,3, Alejandro Mata-Cabana1, Mandy Koopman1, Esther Stroo1, Minke Vries1, Samantha Louise Edwards1, Janine Kirstein5,6, Michele Vendruscolo4, Salvatore Fabio Falsone7, Frederic Rousseau *,2,3, Joost Schymkowitz *,2,3 and Ellen A A Nollen *,1 1European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands 2VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium 3Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium 4Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK 5Department of Molecular Physiology and Cell Biology, Leibniz Research Institute for Molecular Pharmacology im Forschungsverbund Berlin e.V. (FMP), Berlin, Germany 6Faculty of Biology & Chemistry, University of Bremen, Bremen, Germany 7Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria 8Present address: Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London, UK 9Present address: Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, University of Leeds, Leeds, UK *Corresponding author. Tel: +32 16 37 25 70; E-mail: [email protected] ***Corresponding author. Tel: +32 16 37 25 70; E-mail: [email protected] ****Corresponding author. Tel: +31 50 36 17 303; E-mail: [email protected] The EMBO Journal (2021)40:e107568https://doi.org/10.15252/embj.2020107568 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract While aggregation-prone proteins are known to accelerate aging and cause age-related diseases, the cellular mechanisms that drive their cytotoxicity remain unresolved. The orthologous proteins MOAG-4, SERF1A, and SERF2 have recently been identified as cellular modifiers of such proteotoxicity. Using a peptide array screening approach on human amyloidogenic proteins, we found that SERF2 interacted with protein segments enriched in negatively charged and hydrophobic, aromatic amino acids. The absence of such segments, or the neutralization of the positive charge in SERF2, prevented these interactions and abolished the amyloid-promoting activity of SERF2. In protein aggregation models in the nematode worm Caenorhabditis elegans, protein aggregation and toxicity were suppressed by mutating the endogenous locus of MOAG-4 to neutralize charge. Our data indicate that MOAG-4 and SERF2 drive protein aggregation and toxicity by interactions with negatively charged segments in aggregation-prone proteins. Such charge interactions might accelerate primary nucleation of amyloid by initiating structural changes and by decreasing colloidal stability. Our study points at charge interactions between cellular modifiers and amyloidogenic proteins as potential targets for interventions to reduce age-related protein toxicity. Synopsis The cellular modifier MOAG/SERF2 systematically interacts with negatively charged protein segments through its positively charged N-terminus. Amyloidogenic proteins are often stabilized by their supercharged segments, and the interaction with and shielding of these charges by MOAG/SERF2 seems to drive amyloidogenic proteins towards aggregation both in vitro and in vivo. The amyloid-promoting factor SERF2 interacts with negatively charged protein segments, which is enhanced by hydrophobic and aromatic residues Absence of charge interactions abolishes the binding and amyloid-promoting effect of SERF2 Reducing charge in the evolutionarily conserved N-terminus of the SERF ortholog MOAG-4 rescues Caenorhabditis elegans aggregation models, thereby increasing lifespan Introduction Protein homeostasis declines with aging (López-Otín et al, 2013; Walther et al, 2015; Stroo et al, 2017; Klaips et al, 2018). This decline results in an increased accumulation of aggregation-prone proteins, which accelerates aging and is associated with a wide range of age-related disorders, including Alzheimer's and Parkinson's diseases. Although the different proteins involved in these diseases are usually unrelated in sequence and native structure, they share the tendency to convert into ordered, cross-ß structures known as amyloid fibrils (Serpell, 2000; DeMarco & Daggett, 2004; Tuttle et al, 2016). Structural conversions early in the aggregation process play an important role in amyloid formation and the associated cellular toxicity (Kayed & Lasagna-Reeves, 2012; Kim et al, 2016; Sangwan et al, 2017). The cellular mechanisms that drive these early structural conversions, however, are poorly understood, and uncovering them is key for the development of interventions to prevent such toxic structural changes in amyloid-associated diseases. For several structurally unrelated amyloidogenic proteins, previous studies have identified proteins that enhance such structural conversions, namely modifier of aggregation-4 (MOAG-4) in the nematode worm Caenorhabditis elegans and its human orthologs small EDRK-rich factors (SERF) 1A and 2 (Van Ham et al, 2010; Falsone et al, 2012; Yoshimura et al, 2017). In vitro studies with purified proteins have shown that SERF1A preferentially promotes the aggregation of amyloidogenic proteins—including alpha-synuclein, amyloid beta, and prion protein—above the aggregation of non-amyloidogenic proteins (Falsone et al, 2012). MOAG-4/SERF1A is involved early on in the aggregation process. In the case of alpha-synuclein, MOAG-4 and SERF hinder intermolecular interactions within the protein, through electrostatic interactions (Yoshimura et al, 2017; Merle et al, 2019). This results in a more aggregation-prone conformation of alpha-synuclein, which in turn seeds the formation of amyloid fibrils that can eventually form large, insoluble aggregates (Falsone et al, 2012; Yoshimura et al, 2017; Merle et al, 2019). In addition, previous studies have shown that MOAG-4 and SERF1A act transiently and are not incorporated in amyloid fibrils themselves (Falsone et al, 2012; Yoshimura et al, 2017). The shared mechanism by which MOAG-4 and SERFs bind to and induce the structural conversion of other amyloidogenic proteins remains nevertheless unknown. We therefore use a peptide array screening approach to identify interactions between SERF2 and proteins in the hope that this would give us insight into SERF2's mechanism of action. Our findings suggest that SERF2 affects the earliest steps of the aggregation process by charge complementation on amyloidogenic proteins, thereby inducing structural conversions that accelerate fibril formation. Results SERF2 selectively binds to negatively charged peptides To determine how SERF catalyzes amyloid formation of multiple unrelated amyloidogenic proteins, we first used a peptide microarray-based approach to screen for SERF2-interacting amino acid sequences. The microarray contained 12-mer peptide fragments from 27 full-length parent proteins and four dipeptide repeat polymers. Of these proteins, 19 have been classified as amyloidogenic, though many proteins can form amyloids under the appropriate conditions (Chiti & Dobson, 2017; Benson et al, 2019) (Appendix Table S1, column A). Four of these amyloidogenic proteins, Abeta, α-synuclein, huntingtin, and prion protein, have previously been shown to functionally interact with SERF (Appendix Table S1, proteins marked in bold) (Van Ham et al, 2010; Falsone et al, 2012; Merle et al, 2019; Meyer et al, 2020). The other proteins represented on the slide included three other disease-related aggregation-prone proteins, four disease-related dipeptide repeat polymers, a protein for which amyloid formation is part of its physiological function, and four non-amyloidogenic proteins (Appendix Table S1, columns B, C, D). The full-length amino acid sequences for each of these proteins were represented by 12-mer peptides that overlapped by eight residues, hence producing a sliding window over each protein sequence (Fig 1A). The microarray contained duplicates of each peptide, randomly distributed over the array (Fig 1A and Dataset EV1). Binding of ATTO633-labeled SERF2 (UniProt identifier P84101-1, 59 amino acids) was visualized by fluorescent laser scanning and quantified based on fluorescence intensity (Dataset EV1). Peptides were classified as SERF2 binders or non-binders based on their fluorescence intensity relative to a set of glycine controls (Gly) (Fig 1B and Dataset EV1). Therefore, in each of three repeat experiments, the distribution of fluorescence intensities of the Gly control peptides was assessed (Fig 1B, upper panels). For each experiment, the cutoff was determined as the mean fluorescence of the Gly control peptides plus twice their standard deviation (red dashed lines in Fig 1B). In each separate repeat, peptides were then classified as binders when the RFU (relative fluorescence unit) values of both duplicates on the array were higher than the cutoff value (Fig 1B and Dataset EV1). Eventually, only peptides for which this was the case in all three experiments were classified as actual binders (Dataset EV2). Peptides with RFUs below the threshold in each experiment were defined as non-binders, and peptides for which the classification varies between repeats were classified as "ambiguous". In this way, 653 peptides were identified as binders, 2,333 as non-binders, and 291 as ambiguous. Figure 1. SERF2-interacting peptide sequences are enriched for negatively charged amino acids A. Schematic representation of the peptide microarray screen. Green color represents autofluorescence of peptides at 532 nm. B. Histograms of the fluorescence intensities of wild-type ATTO633-labeled SERF2, bound to the peptides on the microarray in each of three independent repeats. Fluorescence intensities for glycine (Gly) controls are marked in yellow (top panels). Red dashed line indicates cutoff between binders and non-binders. The numbers of binders and non-binders identified in each experiment are indicated. Peptides are only classified as binders when both instances of the duplicate have a higher RFU than the cutoff. C. Enrichment scores (ln(probability ratio)) of all amino acids in SERF2 binding (n = 653) versus SERF2 non-binding peptides (n = 2,333). Statistical significance was determined through hypergeometric testing with the Bonferroni correction for multiple comparisons. *P < 0.05, ***P < 0.001, and ****P < 0.0001. D, E. Correlation of the natural logarithm of mean wild-type SERF2 binding intensities (background-corrected per experiment) with the cumulative amino acid enrichment scores (D) or net charge (E) of the microarray peptides. Mean RFU was transformed as ln(meanRFU − min(mean RFU) + 1). Linear regression curve, R2, P-value as determined through a t-test, and number of binders, non-binders, and ambiguous peptides are indicated. F. Receiver operating characteristic (ROC) curves of binder prediction based on cumulative score (blue) or net charge (brown). ROC curve shows fractions of true-positive and false-positive predictions on y- and x-axes respectively, with increasing cutoff values. The area under the curve (AUC) is indicated for both cumulative score and net charge and constitutes a metric for the predictive power of the statistic (cumulative score or net charge). Optimal cutoffs are indicated. Download figure Download PowerPoint Next, we determined the relative abundance of each individual amino acid in the group of SERF2 binding peptides compared with non-binding peptides. For this comparison, the ratio between the abundance of each amino acid in the group of binding peptides and the abundance in the group of non-binding peptides was taken, yielding a probability ratio (Dataset EV3). We then attributed an enrichment score to each amino acid, by calculating the natural logarithm of the probability ratio (Dataset EV3). This score was positive for amino acids that were more abundant, and negative for amino acids that were depleted in the group of SERF2-binding peptides (Fig 1C and Dataset EV3). This analysis revealed a more than threefold overrepresentation of the negatively charged amino acids aspartic acid (Asp, D) and glutamic acid (Glu, E) in SERF2-bound peptides (ln ratios of 1.23 for Asp and 1.50 for Glu; Fig 1C and Dataset EV3). Conversely, when compared to their presence in non-bound peptides, the positively charged amino acids lysine (Lys, K) and arginine (Arg, R) were underrepresented in SERF2-bound peptides (ln ratios of −1.09 for Arg and −1.56 for Lys; Fig 1C and Dataset EV3). To make sure these results were not biased by our choice of (predominantly amyloidogenic) proteins represented on the microarray, we compared the distributions of several general peptide characteristics between the peptides on the microarray and all 12-mer peptides derived from the human cytoplasmic proteome (Appendix Fig S1). We found that the peptide set on the microarray nicely covers the distributions of secondary structure propensities (α-helical propensity, β-sheet propensity, and β-turn propensity), as well as hydrophobicity and net charge. Next, we assessed to what extent the cumulative amino acid enrichment scores of the peptides, calculated as the sum of enrichment scores of all amino acids composing that peptide, correlated with their measured SERF2 binding intensities (Fig 1D and Dataset EV2). As shown in Fig 1D, the cumulative enrichment scores correlate linearly to the natural logarithm of the actual binding signals (P-value < 2e-16 and R2 of 0.82). This observation indicates that a simple scoring function based solely on amino acid composition and completely disregarding position-specific effects is sufficient to predict binding of SERF2 to peptides. This therefore suggests that SERF2 interaction does not require a strict binding motif. Furthermore, given that charged residues had the most extreme scores in our scoring matrix (negatively charged amino acids scored highest and positively charged amino acids scored lowest), SERF2 binding appears to be mainly driven by net charge. To test this, we performed a linear regression of the natural logarithm of the binding signals versus net charge (Fig 1E). This also showed a strong correlation (P-value < 2e-16), which confirmed that net charge is indeed a key driver for SERF2 binding. To rule out overfitting of the enrichment scores by basing them on the entire dataset, we recalculated the enrichment scores using just 70 percent of the data, which constitutes our training set (Appendix Fig S2A), and tested the correlations on the remaining 30 percent (Appendix Fig S2B and C), with practically identical results. In both calculation methods, the regression based solely on net charge has a lower R2 than the regression based on the cumulative enrichment score (0.717 versus 0.82, respectively, in Fig 1E) because of a stronger degree of scatter around the regression line, indicating that the cumulative enrichment is a more accurate predictor of SERF2 binding than net charge alone. To confirm this, we plotted receiver operating characteristic (ROC) curves for both predictors (cumulative score and net charge; Fig 1F). In a ROC curve, the fraction of correct binary classifications or "true-positive fraction" (SERF2 binder or non-binder) at each threshold of the predictor (either net charge alone or cumulative enrichment score) is plotted against the fraction of false classifications ("false-positive fraction"). The area under the curve (AUC) gives an indication of the performance of the predictor at the classification problem, in this case, classifying peptides into SERF2 binders or non-binders. Although both net charge and cumulative enrichment score show strong predictive power, the cumulative score outperforms net charge (AUC of 0.99 versus 0.96). These results corroborate that net charge seems a key driver of SERF2 interaction, but that likely also non-charged amino acids affect binding intensity. To next profile the SERF2 binding sites to each protein on the microarray, we mapped the binding intensities of each peptide to its corresponding position in its full-length parent protein (Fig 2). Due to the sliding window design of our microarrays, we were able to obtain a SERF2 binding profile by averaging the binding intensities of each of the three peptides that contained a particular residue (Dataset EV4). This yielded a binding profile with a resolution of four amino acids (Fig 2 and Appendix Fig S3A). To further explore the link between net charge and SERF2 binding, we similarly produced net charge profiles for all the proteins under study (Dataset EV4 and Appendix Fig S3B), this time averaging the net charges of the three peptides in which a residue is represented (Fig 2 and Appendix Fig S3B). This analysis again showed a strong association between SERF2 binding and local negative net charge. Strikingly, while both positively and negatively charged regions are present in the majority of the proteins, all of the proteins analyzed here contain at least one strong negatively charged SERF2 binding site, with the exception of one amyloid-forming protein—human islet amyloid polypeptide (hIAPP); the four dipeptide repeat polymers (poly-GR, poly-PR, poly-GA, and poly-PA); and SERF2 itself. Interestingly, despite the strong correlation observed between SERF2 binding and local net charge, we also observed strong variations in binding intensities among peptides with identical net charges (Figs 1E and 2). For example, a clear interaction between SERF2 and a negatively charged region in prion protein around position 150 was observed, while the SERF2 binding intensities were much lower for the two similarly charged, neighboring regions in this protein (Fig 2 and Appendix Fig S3A). This again indicated that besides charge, additional sequence properties contributed to the strength of the interactions. Figure 2. SERF2 binding maps to net negatively charged regions Overview of binding sites for SERF2 in proteins represented on the microarray slide. Average net charge and binding of SERF2 (see Dataset EV4 for values) in each protein are indicated. Download figure Download PowerPoint We therefore asked whether there was an overlap between SERF2 binding sites and regions predicted to drive the aggregation of their parent proteins. These regions were predicted using the Tango and Waltz algorithms, which identify aggregation-prone and amyloidogenic regions, respectively (Fernandez-Escamilla et al, 2004; Maurer-Stroh et al, 2010). However, no clear overlap could be observed (Appendix Fig S4). To assess whether the relative binding patterns observed in the microarray—in which peptides are physically fixed to the array—hold in solution, we performed microscale thermophoresis (MST) analyses with three peptides derived from alpha-synuclein, a protein previously shown to interact with SERF2, using peptides that span a range of fluorescence intensities on the microarray: the peptide with the highest fluorescence intensity (Asyn top, YEMPSEEGYQDY), one with intermediate fluorescence intensity (Asyn Med; GKNEEGAPQEGI), and the alpha-syn