We are about to release cross-platform versions of our software, please drop us a line if you want to know about it.
  • catRapid algorithm suite

  • cleverMachine and classifier

  • ccSol algorithm

  • SeAMotE algorithm

  • PAnDA algorithm

  • catGRANULE algorithm

  • CROSS algorithm

About us

Our main focus is to understand the role played by RNA molecules in protein networks. Characterizing protein-RNA associations is key to unravel the complexity and functionality of mammalian genomes and could open up therapeutic avenues for the treatment of a broad range of neurodegenerative disorders. Our research focuses on associations of coding/non-coding RNAs with proteins involved in i) transcriptional and translational regulation (e.g., X-chromosome inactivation) and ii) neurodegenerative diseases (examples include Parkinson’s a-synuclein, Alzheimer’s disease amyloid protein APP, TDP-43 and FUS).

We aim to discover the involvement of RNA molecules in regulatory networks controlling protein production. More specifically, we are interested in discovering and understanding mechanisms whose alteration lead to aberrant accumulation of proteins. We have recently observed that interaction between proteins and their cognate mRNAs (autogenous associations) induce feedback loops that are crucial in protein homeostasis.


Neurodegenerative diseases

Although neurodegenerative diseases are traditionally described as protein disorders leading to amyloidosis, very recent evidence indicates that protein-RNA associations are involved in their onset. We are interested in ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer’s, and Parkinson’s diseases. We recently focused on RNA interactions with fragile X mental retardation protein FMRP; protein sequestration caused by CGG repeats; noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; autogenous regulation of TDP-43 and FMRP; iron-mediated expression of amyloid precursor protein APP and α-synuclein; interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and disfunction.

Functional and dysfunctional ribonucleoprotein networks

RNA-binding proteins regulate a number of cellular processes, including synthesis, folding, translocation, assembly and clearance of RNAs. Recent studies have reported that an unexpectedly large number of proteins are able to interact with RNA, but the partners of many RNA-binding proteins are still uncharacterized. Our integration of in silico and ex vivo data unraveled two major types of protein–RNA interactions, with positively correlated patterns related to cell cycle control and negatively correlated patterns related to survival, growth and differentiation. Our analysis sheds light on the role of RNA-binding proteins in regulating proliferation and differentiation processes, and we provide a data exploration tool to aid future studies

Analysis of protein networks

The recent shift towards high-throughput screening is posing new challenges for the interpretation of experimental results. We designed the cleverSuite approach for large-scale characterization of protein groups. The central part of the cleverSuite is the cleverMachine (CM), an algorithm that performs statistics on protein sequences by comparing their physico-chemical propensities. The second element is called cleverClassifier and builds on top of the models generated by the CM to allow classification of new datasets. We already applied the cleverSuite to predict secondary structure properties, solubility, chaperone requirements and RNA-binding abilties. Using cross-validation and independent datasets, the cleverSuite reproduces experimental findings with great accuracy and provides models that can be used for future investigations.

Areas of Research

Autogenous interactions

Recent evidence indicates that a number of proteins are able to interact with cognate mRNAs. These autogenous associations represent important regulatory mechanisms controlling gene expression at the translational level. Using the catRAPID approach to study the propensity of proteins to bind to RNA, we investigated the occurrence of autogenous associations in the human proteome. Our methods correctly identified binding sites in well-known cases such as thymidylate synthase, tumor suppressor P53, synaptotagmin-1, serine/ariginine-rich splicing factor 2, heat shock 70 kDa, ribonucleic particle-specific U1A and ribosomal protein S13. In addition, we found that several other proteins are able to bind to their own mRNAs. A large-scale analysis of biological pathways revealed that aggregation-prone and structurally disordered proteins have the highest propensity to interact with cognate RNAs. As a tight anti-correlation exists between RNA expression levels and aggregation rates of corresponding proteins, our latest results suggest that autogenous interactions might reduce the aggregation potential of proteins by controlling expression via feedback loops.

X-chromosome inactivation

We used our algorithm catRAPID to investigate interactions of long non-coding RNAs such as Xist with a number of epigenetic modifiers as well as transcription and splicing factors including SUZ12, EZH2, YY1, SAF-A, SFRS1 and SATB. Our calculations suggest that localization and confinement of Xist are finely regulated by multiple factors acting at the interface between chromosome X and the nuclear matrix. Our results are compatible with a model in which following X-chromosome docking mediated by YY1, matrix-associated proteins SAF-A and SATB1 recruit the 5′-half of Xist and drive the translocation in cis of the Xist–PRC2 complex. This project represents the first theoretical effort to predict functional associations of long non-coding RNAs.

Large scale prediction of protein-RNA interactions

We developed a new method to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis. The algorithm computes the interaction between a molecule (protein/transcript) and the reference library (transcriptome/proteome) in each model organism. In addition to the interaction propensities, discriminative power and strength, the method allows detection of RNA-binding regions in proteins and recognition motifs in RNA molecules. The method has been validated on PAR-CLIP data and predicts associations with high significance (p-values<0.05).

Recent Blog Posts

catRAPID approach

by Gian

In the catRAPID approach, proteins and RNAs are associated with wave-packets that contain information on their binding ability.

The wave-packets ...


Notable Citations