Computational Biology

Group leader: Elena Papaleo

The Computational Biology group (CBL) works with a multidisciplinary approach to cancer biology, combining bioinformatics of -omics data, structural biochemistry, and experimental approaches.

In our research, we start from the analysis of high-throughput cancer data from tumor samples or cellular models to define key targets and markers. We then study the selected biomolecules under the lens of structural biology to understand their mechanisms and how they are affected by cancer alterations. We also apply structural methods to understand drug-target interactions in the context of drug repurposing. We validate our predictions using cellular and in-vitro assays (such as co-immunoprecipitation, peptide arrays, NMR chemical-shift perturbations, etc.).

From the biological point of view, our current research focused on proteins of the core autophagy machinery, lysosomal pathways, and key players in mitochondrial cell-death and their regulators (kinases, transcription factors, ubiquitinating enzymes, etc.).

We have three main ongoing research lines in the group: i) the discovery of new and non-conventional Short Linear Motifs (SLiMs) belonging to the class of BH3 (apoptosis) and LIR  (autophagy) motifs and how they are regulated by post-translational modifications or altered by mutations found in cancer samples; ii) how lipid composition of different organelles can modulate the structure and function of proteins of the core autophagy machinery; iii) the application of cationic amphiphilic drugs and disulfiram in the context of drug repurposing.

Computational Biology test

Furthermore, our group is also developing and maintaining software or pipelines for bioinformatics available at our GitHub repository. In particular, we develop methods to predict allosteric changes upon interactions or mutations using graph theory (e.g., PyInteraph) and pipelines to annotate or classify the functional impact of missense mutations (e.g., MutateX).

We are supported by funds from NovoNordisk, The Danish Council for Independent Research, Carlsberg Distinguished Fellowship, Hartmann Fonden, and PRACE. We also have access to the Danish Supercomputing Center Computerome2.

Selected publications:

Fas AB, Maiani E, Sora V, Kumar M, Mashkoor M, Lambrughi M, Tiberti M; Papaleo E: The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer, Autophagy 2020; in press

Colaprico A, Olsen C, Bailey MH, Odom GJ, Terkelsen T, Silva TC, Olsen AV, Cantini L, Zinovyev A, Barillot E, Noushmehr H, Bertoli G, Castiglioni I, Cava C, Bontempi G, Chen XS, Papaleo E: Interpreting pathways to discover cancer driver genes. Nat Commun 2020;11(1):69

Terkelsen T, Krogh A, Papaleo E: CAncer bioMarker Prediction Pipeline (CAMPP) – A standardized framework for the analysis of quantitative biological data. PLoS Comput Biol 2020;16(3):e1007665

Kønig SM, Rissler V, Terklsen T, Lambrughi M, Papaleo E: Alterations of the interactome of Bcl-2 proteins in breast cancer at the transcriptional, mutational and structural level. PLoS Comput Biol 2019;15(12):e1007485

Lambrughi M, De Gioia L, Gervasio FL, Lindorff-Larsen K, Nussinov R, Urani C, Bruschi M, Papaleo E: DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions. Nucleic Acids Res 2016;44(19):9096-9109

 


 

Group leader Elena Papaleo
Research profile

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Computational Biology
Staff Members


Key Funding

Novo Nordisk Foundation

Hartmann Foundation

Carlsberg Distinguished Fellowship

The PRACE initiative


Networks

Computerome - The Danish Supercomputing Center

NAS - Nordic Autophagy Society