Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
© Klus et al. 2015
Received: 28 May 2015
Accepted: 8 December 2015
Published: 16 December 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.
KeywordsProtein classification Physico-chemical properties Gene ontology Solubility RNA-binding ability
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 .
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 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
Protein aggregation and longevity
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.
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.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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