Proteins are polypeptide chains carrying out most of the basic functions of life at the molecular level.
These linear chains fold in complex 3D structures whose shape is responsible of proteins' behaviour.
Thanks to the "Genome project" millions of proteins from many organisms have been identified.
However, their folded structures and their functions are mostly unknown.
One of the main goals of bioinformatics is to build tools which can correctly predict the protein's
3D structure given its sequence of amino-acids.
- Prediction of the Disulfide Bonding State of a Protein
Cysteines are particular amino-acids that can form covalent bonds between their sulfur atoms.
These disulfide bridges impose strong constraints in protein's folding.
The aim is to predict the topology of these bridges given the primary sequence of the protein.
A prediction server developed by our group is
- Prediction of the Secondary Structure of a Protein
Reliable predictors of the secondary structure of a protein are fundamental to study its folding
and functions. I am developing tools for the prediction of secondary structure with state-of-the-art
performances. My goal is to realize a prediction server and participate to CASP6.
- Prediction of the Fine-Grained Contact Map of a Protein
Estimation of the tertiary structure of a protein is one of the main goal of bioinformatics.
A distance matrix is a bidimensional representation of a protein's 3D structure.
A contact map is an approximation of a distance matrix at a given distance cut-off.
I am developing machine learning methods for the prediction of contact maps from the primary
structures of proteins.
Genes are the instructions to build proteins written inside the DNA. Genes, through proteins, are member
of large networks of interaction whose correct functioning is responsible of life.
Microarrays are powerful methods to estimate the quantity of transcripted genes in a biological sample,
which are capable of sampling the whole genome of an organism. However, the huge quantity and the poor
quality of the data coming from microarrays demands advanced techniques for its analysis.
- Genes Selection for Cancer Classification
Data from microarrays is used to identify group of genes as indicators for the determination of cancer
in patients. Our ultimate goal is to discover misfunctioning genes involved in cancer evolution.
This work is made in collaboration with the FIRC Institute for Molecular Oncology.