BioComputing UP

Sito web del gruppo:

foto di gruppo Descrizione BioComputing UP

Our research concentrates in the fields of bioinformatics and computational biology, with a special emphasis on the structural and mechanistic aspects of complex systems. The research activities carried out at the BioComputing UP lab focus on two main pillars:

  • development of novel methods, tools and databases to study proteins at different levels (sequence, structure, folding, function, interactions)
  • application of methods to solve biologically relevant questions in different contexts.

Our staff is conducting cutting-edge research on structural and functional biology, large scale and genome wide analyses, genetic disease and cancer studies. The BioComputing UP is integrated in a wide international network: not only does it lead and coordinate several international consortia (NGP-net, IDPfun, REFRACT), but also it is actively involved in the ELIXIR research infrastructure, participating in the activities of the national Node, Data and Interoperability platforms, IDP User Community and Machine Learning Focus Group.



  • Silvio TOSATTO - FULL Professor
  • Giovanni MINERVINI - Assistant Professor
  • Damiano PIOVESAN - Associate Professor

Lab Member

  • Maria Cristina ASPROMONTE - Post-doctoral fellow
  • Diana BATTISTELLA - Post-doctoral fellow
  • Marco CARRARO - Post-doctoral fellow
  • Antonella FALCONIERI - Post-doctoral fellow
  • Edoardo SALLADINI - Post-doctoral fellow
  • Adel BOUHRAOUA - Research fellow
  • Francesco GREGORIS - Research fellow
  • Fatemeh KORDEVANI - Research fellow
  • Jiachen LU - Research fellow
  • Luiggi Gianpiere TENORIO KU - Research fellow
  • Martina BEVILACQUA - PhD Student
  • Giorgia Francesca CAMAGNI - PhD Student
  • Damiano CLEMENTEL - PhD Student
  • Alessio DEL CONTE - PhD Student
  • Franco PRADELLI - PhD Student
  • Federica QUAGLIA - post-doctoral fellow
  • Ivan MIČETIĆ - Research assistant

  Five recent publications

  1. Quaglia F, et al. DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation. Nucleic Acids Res. 2022 Jan 7;50(D1):D480-D487. doi: 10.1093/nar/gkab1082. PMID: 34850135; PMCID: PMC8728214.
  2. Walsh I, et al. DOME: recommendations for supervised machine learning validation in biology. Nat Methods. 2021 Oct;18(10):1122-1127. doi: 10.1038/s41592-021-01205-4. PMID: 34316068.
  3. Necci M, et al. Critical assessment of protein intrinsic disorder prediction. Nat Methods. 2021 May;18(5):472-481. doi: 10.1038/s41592-021-01117-3. PMID: 33875885; PMCID: PMC8105172.
  4. Lazar T, et al. PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins. Nucleic Acids Res. 2021 Jan 8;49(D1):D404-D411. doi: 10.1093/nar/gkaa1021. PMID: 33305318; PMCID: PMC7778965.
  5. Piovesan D, et al. MobiDB: intrinsically disordered proteins in 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D361-D367. doi: 10.1093/nar/gkaa1058. PMID: 33237329; PMCID: PMC7779018.