- Open Access
Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
BMC Genomics volume 16, Article number: 1071 (2015)
Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities.
We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis.
In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial.
There is a growing gap between amount of proteomic data and availability of tools for their analysis . While several application programming interfaces are available to analyse computational and experimental results , a simple and intuitive interface is currently lacking or missing. Our goal is to start bridging this gap by providing algorithms for analysis of protein sets and discovery of mechanisms that regulate protein function and interactions.
The first method presented here, the multiCleverMachine (multiCM), is an extension of the cleverMachine approach (CM ) to classify multiple protein datasets using physico-chemical properties. The second algorithm, the cleverGO, is inspired by the need to simplify Gene Ontology (GO) annotation output. While GO statistics are important to characterize the functional role of proteins, their interpretation is difficult without further downstream processing [2, 4]. Current tools do not provide a unique interface that combines GO term analysis with intuitive interpretation and visualization. For instance, GOrilla  calculates GO terms enrichments, but other tools are needed to summarize the results (e.g. REVIGO ). cleverGO integrates multiple analyses in one platform and facilitates GO processing through an interactive analysis accessible via web browser.
We demonstrate the usefulness of our methods by investigating the RNA-binding abilities of S. cerevisiae chaperones and their substrates, the physico-chemical determinants of protein insolubility in S. cerevisiae, M. musculus and H. sapiens, and the relationship between aggregation and longevity in C. elegans. The purpose of our analysis is twofold: to provide examples that can be used as a reference in other studies and to shed light on the link between nucleic-acid binding abilities and protein features, such as structural disorder and aggregation, that are increasingly recognized as key factors for cellular function and homeostasis [7–9].
The multiCM accepts multiple protein sets in FASTA format. Individual sets are classified as positive or negative for binary comparison (the assignment is only needed to create two groups and does not influence the calculations). In each list, the CM screens physico-chemical properties encoded by protein sequences  to identify those that best discriminate positive and negative classes (currently supported physico-chemical properties are: nucleic acid binding propensity, membrane propensity, alpha-helix propensity, aggregation propensity, beta sheet propensity, burial propensity and hydrophobicity, but custom properties can be included, as explained in the online Tutorial). For a detailed description of CM performances, we refer to our previous publication .
In each multiCM run, the information is compiled together from individual models into a high-level overview:
The user can glean what trend is detected in the data using different physico-chemical features. The indicators collate 10 predictors for each selected feature and represent their consensus with a colour, akin to a micro-array slide (Fig. 1a). The colour of each array-spot represents differential states of enrichment for the dataset pairs and allows easy interpretation of increase, decrease or insufficient signal.
The analysis is not restricted to the consensus information only - a link to a full CM view is provided in the main panel (with details on p-value, cross-validation performances, ROC curves and other statistics). The detail view contains ID number of the CM run providing the ability to use it in creation of a cleverClassifier to study new datasets , as well as a link to perform Gene Ontology analysis using the second part of our toolkit, the cleverGO.
The cleverGO webserver provides two ways to explore data:
The first view of the cleverGO tool is a classic enrichment table. Enriched GO terms are showed along with coverage, significance and additional information such as the term depth taken from the acyclic GO graph . The enrichment employs interactive filters - users can match text in the description field, sort by significance or exclude terms based on their term depth or precision . Each GO term is linked to AmiGO .
In the second cleverGO visualisation, a force-layout is used to dynamically organize the graph depending on the strength of the connections and separate analyses are generated for biological process, molecular function and cellular component ontologies (Fig. 1b). To illustrate relationships between GO terms and to perform functional clustering, we use semantic similarity . The user can interact with the graph: hover over each node with the cursor yields information about the node, clicking it activates an information panel about the cluster the node belongs to (Fig. 1b). For each of the clusters, cleverGO shows a list of GO terms that can be individually interrogated, as well as the description of the cluster content. We also provide cluster coverage, i.e. how many of the entries in the user’s submission are annotated with GO terms found in the cluster (the list of entries is also available for the user to download). Each of the operations above is based on the current state of the graph - if the signal strength threshold is changed, the graph’s connectedness changes. If the user applies the minimal term level or precision cut-off, nodes from the graph are filtered. The same principle applies for the p-value cut-off (Bonferroni test). Making the graph behaviour dynamic significantly reduces the time needed to perform analysis - the user does not need to re-run any calculation to see the result of a parameter change.
Upon activation of the detail view on the multiCM output page, the user can access the Boxplotter. The Boxplotter takes the input datasets with best-performing features (passed automatically from the detail view) and shows the distribution of associated propensity scores. On top of the physico-chemical scale information, the Boxplotter matches protein IDs with protein abundance databases  to provide information on the distribution of expression values. In addition, the Boxplotter performs discrimination analysis with the data, showing p-values for the statistics and Receiver Operating Characteristic (ROC) curves.
Results and discussion
To illustrate the performances of both multiCM and cleverGO, we studied the RNA-binding abilities of S. cerevisiae chaperone substrates , the physico-chemical determinants of protein insolubility in in S. cerevisiae, M. musculus and H. sapiens , and the link between protein aggregation and longevity in C. elegans .
RNA-binding abilities of S. cerevisiae chaperone substrates
Systematic analysis of physical TAP-tag based protein-protein interactions revealed individual networks of S. cerevisiae chaperones . In agreement with experimental evidence, the multiCM predicts that Hsp90 (Hsp82)  and Hsp40 (Cwc23)  are prone to associate with RNA-binding proteins (RBPs; Fig. 1a; red dots indicate enrichment over other chaperones). By contrast, Hsp60 shows the lowest propensity to interact with RBPs, which is consistent with its main role of guiding hydrophobic proteins to fold into the native state  (Fig. 1a; green dots indicate depletion over other chaperones). Moreover, Hsp70 (Ssb1) binds directly with transcripts and is predicted to have more RBP partners than Hsp60 . AAA+ (Hsp78) shows similar pattern as Hsp70, in agreement with the fact that the two chaperones work together . As for other physico-chemical features, multiCM reports that both Hsp40 and Hsp78 associate with structurally disordered (and hydrophilic ) proteins, which is in line with previous experimental studies on prion propagation , while Hsp60, Hsp70 and Hsp90 are predicted to bind to hydrophobic proteins [3, 19]. To further investigate Hsp90 features, we performed cleverGO analysis of its substrates. Looking at the molecular function (Fig. 1b), we observe an enrichment in GO terms related to RBPs (e.g., class “RNA-binding” shows p-value < 10−5; Bonferroni test), which very well complements our predictions of physico-chemical features. Importantly, the nucleic-acid cluster is the largest in terms of dataset coverage (>40% of the substrates list; Fig. 1b).
Physico-chemical determinants of protein insolubility
A recent mass-spectrometry study investigated protein precipitates formed upon centrifugation of S. cerevisiae, M. musculus and H. sapiens cells . Two major determinants have been reported to promote insolubility: structural disorder in H. sapiens and M. musculus, which induces aberrant interactions promoting precipitation of protein complexes , and aggregation propensity  in S. cerevisiae cells, which is linked to the presence of hydrophobic residues exposed on protein surfaces . Using the multiCM approach to compare low-solubility (LS) and high-solubility (HS) proteins, we observed that H. sapiens and M. musculus have a larger fraction of structurally disordered regions in the LS group, while non-significant enrichments were found in yeast (Fig. 2a). Differently from H. sapiens and M. musculus cells, S. cerevisiae shows high intrinsic aggregation propensity (i.e., calculated in the unfolded state) for LS proteins (Fig. 2b), in agreement with analyses carried out with TANGO  and AGGRESCAN  performed in the original study . Yet, the HS group has higher burial in H. sapiens and M. musculus (Additional file 1: Figure S1A), which suggests that aggregation-prone amino acids are less abundant on surfaces when proteins are natively folded [28, 29]. In addition to discriminating LS and HS groups in S. cerevisiae (p-value = 10−11; Mann–Whitney–Wilcoxon test; Area under the ROC curve = 0.72; Fig. 2b) the aggregation propensity is also anti-proportional to protein abundance (p-value = 10−9; Mann–Whitney–Wilcoxon test; Area under the ROC curve = 0.70; Fig. 2c), which is in line with previous observations suggesting an evolutionary pressure to reduce the expression of amyloidogenic proteins [30–32]. In agreement with GO analysis performed in the experimental study , we found strong enrichment of RBPs in the LS proteins of human (e.g., class “RNA-binding” has p-value < 10−8; Bonferroni test), mouse (“RNA-binding” with p-value < 10−8) and yeast (“RNA-binding” with p-value < 9*10−8) cells, supporting the hypothesis that RNA molecules provide the scaffold for protein interactions  and (Fig. 2d, e and f).
Protein aggregation and longevity
It has been observed that inhibition of the insulin growth 1 signaling pathways leads to a dramatic lifespan extension of C. elegans strains carrying mutation in the daf-2 receptor and that transcription factor hsf-1 is essential for longevity . Mass-spectrometry analysis of long-lived daf-2 and short-lived hsf-1 mutant strains revealed two major types of deposits that accumulate during aging: hsf-1 mutant proteins have high aggregation propensities, while daf-2 mutant proteins show decreased structural content . Thus, decrease in longevity can be associated with accumulation of aggregation-prone proteins, whereas lower hydrophobicity is linked to different type of deposits and significantly reduced toxicity. Using the multiCM approach to compare the insoluble fraction of hsf-1 mutant strain with wild type worm (WT), we found that proteins showing high enrichment in mass-spectrometry analysis (class HSF-1 4/4) are more aggregation-prone than those with low enrichment (class HSF-1 1/4) [Fig. 3a]. By contrast, proteins enriched in daf-2 mutant worms (DAF-2 4/4) have lower aggregation propensities than those showing low enrichment (DAF-2 1/4). In the daf-2 mutant strain (DAF-2 3/4 and DAF-2 4/4) enrichments are associated with decrease in beta-sheet content (Additional file 1: Figure S2A), while in hsf-1 mutant worms (HSF-1 3/4 and HSF-1 4/4) we observe depletion of structural disorder (Additional file 1: Figure S2B). Proteins present in the hsf-1 strain (i.e., listed in HSF-1 4/4 and not included in DAF-2 4/4) are involved in several metabolic processes (e.g., class “oxidative stress response” with p-value < 6*10−4; Bonferroni test), oxidative stress response (e.g., class “metabolic process” shows p-value < 10−7) and mitochondrial function (e.g., class “mitochondrion” with p-value < 10−7), as reported in the original study (Fig. 3c) . In addition, and in line with the work on S. cerevisiae, M. musculus and H. sapiens proteomes , we found an enrichment of RBPs (e.g., class “RNA-binding” shows p-value = 7*10−3), which reinforces the link between protein deposition and nucleic acid binding .
In this work, we introduced two innovative approaches to compare multiple protein datasets using physico-chemical properties and GO annotations: the multiCM allows feature classification and the cleverGO provides clustering through semantic relationships. We illustrated the performances of both multiCM and cleverGO using examples related to RNA-binding abilities of S. cerevisiae chaperone substrates , physico-chemical determinants of protein insolubility in S. cerevisiae, M. musculus and H. sapiens  and the link between aggregation and life-span in C. elegans . In all cases, the results are in agreement with available evidence on protein functions and interactions, providing a clear indication on the flexibility and broad applicability of our algorithms.
As shown in the examples, we are particularly interested in understanding the relationship between nucleic-acid binding ability and structural disorder and aggregation. Indeed, previous studies indicate that RNA secondary structures , especially when enriched in GC content , contribute to spatial rearrangement of disordered regions, promoting the formation of protein-RNA complexes. In agreement with these observations, it has been reported that intrinsically disordered proteins interact with RNA [8, 37], which influences protein aggregation  and, in turn, toxicity . The involvement of nucleic acid molecules in protein aggregation  is compatible with the findings discussed in our examples and provides an intriguing working hypothesis [7, 41] to study neurodegenerative events  that are characterized by aggregation  and structural disorder . As a matter of fact, previous work indicates that presence of polyanions lead to reduction of protein stability  and nucleic acids have a strong tendency to accumulate in neurofibrillary tangles and senile plaques . Recent evidence also shows that aggregation-related mutations in the RBPs Tar DNA-binding protein 43 TDP-43 and Translocated in liposarcoma protein FUS are associated with the formation of RNA granules [47, 48] that are phase separated, non-membrane-bound ribonucleoprotein aggregates [49, 50].
In conclusion, theoretical approaches for prediction of protein features, such as those integrated in the multiCM for prediction of structural disorder, aggregation and nucleic-acid binding ability [51–53], will be useful to provide insights into functional networks. We hope that our tools will be useful for the discovery of trends in protein datasets, complementing experimental [54, 55] and theoretical analyses [31, 56–58].
Availability and requirements
Tutorials can be accessed at http://www.tartaglialab.com/cs_multi/tutorial and http://www.tartaglialab.com/GO_analyser/tutorial. Documentation files are deposited at http://service.tartaglialab.com/static_files/algorithms/clever_machine/documentation.html.
Vizcaíno JA, Côté RG, Csordas A, Dianes JA, Fabregat A, Foster JM, et al. The Proteomics Identifications (PRIDE) database and associated tools: status in 2013. Nucl Acids Res. 2013;41:D1063–9.
Mi H, Dong Q, Muruganujan A, Gaudet P, Lewis S, Thomas PD. PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium. Nucleic Acids Res. 2010;38(Database issue):D204–10.
Klus P, Bolognesi B, Agostini F, Marchese D, Zanzoni A, Tartaglia GG. The cleverSuite Approach for Protein Characterization: Predictions of Structural Properties, Solubility, Chaperone Requirements and RNA-Binding Abilities. Bioinformatics. 2014;30(11):1601–8.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.
Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009;10:48.
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011;6:e21800.
Wolozin B. Regulated protein aggregation: stress granules and neurodegeneration. Molecular Neurodegeneration. 2012;7:56.
Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, et al. Insights into RNA Biology from an Atlas of Mammalian mRNA-Binding Proteins. Cell. 2012;149:1393–406.
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16:85–97.
Herrmann C, Bérard S, Tichit L. SimCT: a generic tool to visualize ontology-based relationships for biological objects. Bioinformatics. 2009;25:3197–8.
Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, et al. AmiGO: online access to ontology and annotation data. Bioinformatics. 2009;25:288–9.
Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics. 2010;26:976–8.
Wang M, Weiss M, Simonovic M, Haertinger G, Schrimpf SP, Hengartner MO, et al. PaxDb, a database of protein abundance averages across all three domains of life. Mol Cell Proteomics. 2012;11(8):492–500.
Gong Y, Kakihara Y, Krogan N, Greenblatt J, Emili A, Zhang Z, et al. An atlas of chaperone-protein interactions in Saccharomyces cerevisiae: implications to protein folding pathways in the cell. Mol Syst Biol. 2009;5:275.
Albu RF, Chan GT, Zhu M, Wong ETC, Taghizadeh F, Hu X, et al. A feature analysis of lower solubility proteins in three eukaryotic systems. J Proteomics. 2015;118:21–38.
Walther DM, Kasturi P, Zheng M, Pinkert S, Vecchi G, Ciryam P, et al. Widespread Proteome Remodeling and Aggregation in Aging C. elegans. Cell. 2015;161:919–32.
Sawarkar R, Sievers C, Paro R. Hsp90 globally targets paused RNA polymerase to regulate gene expression in response to environmental stimuli. Cell. 2012;149:807–18.
Ohi MD, Link AJ, Ren L, Jennings JL, McDonald WH, Gould KL. Proteomics analysis reveals stable multiprotein complexes in both fission and budding yeasts containing Myb-related Cdc5p/Cef1p, novel pre-mRNA splicing factors, and snRNAs. Mol Cell Biol. 2002;22:2011–24.
Tartaglia GG, Dobson CM, Hartl FU, Vendruscolo M. Physicochemical determinants of chaperone requirements. Journal of Molecular Biology. 2010;400:579–88.
Zimmer C, von Gabain A, Henics T. Analysis of sequence-specific binding of RNA to Hsp70 and its various homologs indicates the involvement of N- and C-terminal interactions. RNA. 2001;7:1628–37.
von Janowsky B, Major T, Knapp K, Voos W. The disaggregation activity of the mitochondrial ClpB homolog Hsp78 maintains Hsp70 function during heat stress. J Mol Biol. 2006;357:793–807.
Tartaglia GG, Caflisch A. Computational analysis of the S. cerevisiae proteome reveals the function and cellular localization of the least and most amyloidogenic proteins. Proteins. 2007;68:273–8.
Doyle SM, Genest O, Wickner S. Protein rescue from aggregates by powerful molecular chaperone machines. Nat Rev Mol Cell Biol. 2013;14:617–29.
Babu MM, van der Lee R, de Groot NS, Gsponer J. Intrinsically disordered proteins: regulation and disease. Curr Opin Struct Biol. 2011;21:432–40.
Tartaglia GG, Vendruscolo M. The Zyggregator method for predicting protein aggregation propensities. Chem Soc Rev. 2008;37:1395–401.
Fernandez-Escamilla A-M, Rousseau F, Schymkowitz J, Serrano L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotechnol. 2004;22:1302–6.
Conchillo-Solé O, de Groot NS, Avilés FX, Vendrell J, Daura X, Ventura S. AGGRESCAN: a server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinformatics. 2007;8:65.
Tartaglia GG, Pawar AP, Campioni S, Dobson CM, Chiti F, Vendruscolo M. Prediction of aggregation-prone regions in structured proteins. J Mol Biol. 2008;380:425–36.
Bolognesi B, Tartaglia GG. Physicochemical principles of protein aggregation. Prog Mol Biol Transl Sci. 2013;117:53–72.
Tartaglia GG, Pechmann S, Dobson CM, Vendruscolo M. Life on the edge: a link between gene expression levels and aggregation rates of human proteins. Trends Biochem Sci. 2007;32:204–6.
Tartaglia GG, Vendruscolo M. Correlation between mRNA expression levels and protein aggregation propensities in subcellular localisations. Mol BioSyst. 2009;5:1873–6.
Ciryam P, Tartaglia GG, Morimoto RI, Dobson CM, Vendruscolo M. Widespread aggregation and neurodegenerative diseases are associated with supersaturated proteins. Cell Rep. 2013;5:781–90.
Tsai M-C, Manor O, Wan Y, Mosammaparast N, Wang JK, Lan F, et al. Long Noncoding RNA as Modular Scaffold of Histone Modification Complexes. Science. 2010;329:689–93.
Cirillo D, Livi CM, Agostini F, Tartaglia GG. Discovery of protein-RNA networks. Mol Biosyst. 2014;10:1632–42.
Gray DA, Woulfe J. Structural disorder and the loss of RNA homeostasis in aging and neurodegenerative disease. Front Genet. 2013;4:149.
Zanzoni A, Marchese D, Agostini F, Bolognesi B, Cirillo D, Botta-Orfila M, et al. Principles of self-organization in biological pathways: a hypothesis on the autogenous association of alpha-synuclein. Nucl Acids Res. 2013;41(22):9987–98.
Cirillo D, Marchese D, Agostini F, Livi CM, Botta-Orfila T, Tartaglia GG. Constitutive patterns of gene expression regulated by RNA-binding proteins. Genome Biol. 2014;15:R13.
Olzscha H, Schermann SM, Woerner AC, Pinkert S, Hecht MH, Tartaglia GG, et al. Amyloid-like Aggregates Sequester Numerous Metastable Proteins with Essential Cellular Functions. Cell. 2011;144:67–78.
Vavouri T, Semple JI, Garcia-Verdugo R, Lehner B. Intrinsic Protein Disorder and Interaction Promiscuity Are Widely Associated with Dosage Sensitivity. Cell. 2009;138:198–208.
Kampers T, Friedhoff P, Biernat J, Mandelkow EM, Mandelkow E. RNA stimulates aggregation of microtubule-associated protein tau into Alzheimer-like paired helical filaments. FEBS Lett. 1996;399:344–9.
Papatriantafyllou M. Protein aggregation: The secret recipe for RNA granules. Nat Rev Mol Cell Biol. 2012;13:405.
Cirillo D, Agostini F, Klus P, Marchese D, Rodriguez S, Bolognesi B, et al. Neurodegenerative diseases: Quantitative predictions of protein-RNA interactions. RNA. 2013;19:129–40.
Vendruscolo M, Tartaglia GG. Towards quantitative predictions in cell biology using chemical properties of proteins. Mol BioSyst. 2008;4:1170–5.
Porcari R, Proukakis C, Waudby CA, Bolognesi B, Mangione PP, Paton JF, et al. The H50Q Mutation Induces a 10-fold Decrease in the Solubility of α-Synuclein. J Biol Chem. 2015;290:2395–404.
Sedlák E, Fedunová D, Veselá V, Sedláková D, Antalík M. Polyanion hydrophobicity and protein basicity affect protein stability in protein-polyanion complexes. Biomacromolecules. 2009;10:2533–8.
Ginsberg SD, Crino PB, Lee VM, Eberwine JH, Trojanowski JQ. Sequestration of RNA in Alzheimer’s disease neurofibrillary tangles and senile plaques. Ann Neurol. 1997;41:200–9.
Bentmann E, Haass C, Dormann D. Stress granules in neurodegeneration – lessons learnt from TAR DNA binding protein of 43 kDa and fused in sarcoma. FEBS J. 2013;280:4348–70.
Baron DM, Kaushansky LJ, Ward CL, Sama RRK, Chian R-J, Boggio KJ, et al. Amyotrophic lateral sclerosis-linked FUS/TLS alters stress granule assembly and dynamics. Mol Neurodegener. 2013;8:30.
Malinovska L, Kroschwald S, Alberti S. Protein disorder, prion propensities, and self-organizing macromolecular collectives. Biochim Biophys Acta. 1834;2013:918–31.
Ramaswami M, Taylor JP, Parker R. Altered Ribostasis: RNA-Protein Granules in Degenerative Disorders. Cell. 2013;154:727–36.
Tartaglia GG, Cavalli A, Pellarin R, Caflisch A. Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences. Protein Sci. 2005;14:2723–34.
Agostini F, Vendruscolo M, Tartaglia GG. Sequence-based prediction of protein solubility. J Mol Biol. 2012;421:237–41.
Terribilini M, Lee J-H, Yan C, Jernigan RL, Honavar V, Dobbs D. Prediction of RNA binding sites in proteins from amino acid sequence. RNA. 2006;12:1450–62.
Calloni G, Chen T, Schermann SM, Chang H, Genevaux P, Agostini F, et al. DnaK Functions as a Central Hub in the E. coli Chaperone Network. Cell Reports. 2012;1:251–64.
Mossuto MF, Bolognesi B, Guixer B, Dhulesia A, Agostini F, Kumita JR, et al. Disulfide Bonds Reduce the Toxicity of the Amyloid Fibrils Formed by an Extracellular Protein. Angew Chem Int Ed Engl. 2011;50:7048–51.
Agostini F, Zanzoni A, Klus P, Marchese D, Cirillo D, Tartaglia GG. catRAPID omics: a web server for large-scale prediction of protein-RNA interactions. Bioinformatics. 2013;29:2928–30.
Livi CM, Klus P, Delli Ponti R, Tartaglia GG. catRAPID signature: identification of ribonucleoproteins and RNA-binding regions. Bioinformatics. 2015 Oct 31. pii: btv629. [Epub ahead of print].
Klus P, Cirillo D, Botta Orfila T, Tartaglia GG. Neurodegeneration and Cancer: Where the Disorder Prevails. Sci Rep. 2015 Oct 23;5:15390. doi: 10.1038/srep15390.
The authors would like to thank B. Lehner, Gianni de Fabritiis and Roderic Guigó for stimulating discussions.
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P). We also acknowledge support from AGAUR (2014 SGR 00685), the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013–2017’ (SEV-2012-0208). PK and RDP are recipients of “La Caixa” and “Severo Ochoa” studentships, respectively.
The authors declare that they have no competing interests.
PK implemented the webserver. PK and GGT designed the algorithm. CM and RDP tested the method. CM, RDP, PK and GGT wrote the manuscript. All authors read and approved the final manuscript.
Physico-chemical determinants of protein insolubility. High-solubility (HS) proteins show A) higher burial in human and mouse, in agreement with the observations reported in the original study. Figure S2. Physico-chemical of C. elegans mutant strains. A) In the hsf-1 strain, highly enriched proteins (HSF 4/4) are less structurally disordered than those poorly enriched (HSF1 1/4). B) In the daf-2 strain (long-lived), highly enriched proteins (DAF2 4/4) show lower beta-sheet propensities than those poorly enriched (DAF2 1/4), in agreement with observations reported in the original experimental study. (DOCX 412 kb)
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Klus, P., Ponti, R.D., Livi, C.M. et al. Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets. BMC Genomics 16, 1071 (2015). https://doi.org/10.1186/s12864-015-2280-z
- Protein classification
- Physico-chemical properties
- Gene ontology
- RNA-binding ability