Structural Bioinformatics of proteins involved in oncology and in neurodegenerative diseases
The bioinformatics group at the BIO@SNS, Scuola Normale Superiore, Pisa, is focused on the data-driven discovery and exploitation of novel signal transduction mechanisms through evolutionary, structural and network bioinformatics as well as machine learning approaches.
The group is focused on the following three main areas:
- Deciphering the mechanisms of aberrant signaling of proteins like LRRK2 in Parkinson or G-protein Coupled Receptors (GPCRs) in cancer. We are primarily interested in developing new machine learning alorightms that we trained on interactomics data by using molecular properties as features. The interpretability of these models allows us to gain new insights into biological mechanisms of signal transduction. We also apply a range of bioinformatics and AI techniques, exploiting evolutionary, structural and interaction network information, to predict the function of these systems.
- Another area of interest is developing and applying bioinformatics resources to mechanistically interpret omics datasets from genomics, proteomics or metabolomics experiments in the personalized medicine field.
- We are also interested in developing strategies to rationally modulate the activity of these systems, either pharmacologically or via data-driven protein design.